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Lesley King: 
Hi. I’m Lesley King

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and I’m a part
of the Sickle Cell Unit team.

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And I will be guiding you

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through the first 
session this morning.

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I hope everybody
had a good sleep

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and is now refreshed
and ready for day two.

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Day one, we know was exciting,
lots of information.

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And I expect that day two
is going to be much of the same.

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Another packed day, so we’re
going to start 8:33 a.m.

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We’re already
a little bit late.

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And this morning, we’re going to
start off with a welcome

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from Professor
Marshall Tulloch-Reid,

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who is the director
of CAIHR, Caribbean

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Institute for Health Research.

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It is a tape, as he’s unable
to be here in person.

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So, over to IT.

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Marshall Tulloch-Reid: 
Good morning,

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ladies and gentlemen,
conference attendees.

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My apologies for not
being present at today’s meeting

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due to people’s obligation.
But once again, I’m pleased,

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on behalf of the Caribbean
Institute for Health Research,

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to welcome you to this year’s
Sickle Cell in Focus meeting.

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We at the Caribbean Institute
for Health Research

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are once again pleased to
partner with the National Heart,

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Lung, and Blood Institute of NIH
in staging this annual meeting.

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As you may already be aware,

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the Caribbean Institute
for Health Research

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is a regional institute of the
University of the West Indies,

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and that means
that it has branches in more

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than one of
the university’s campuses.

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In the case of the Caribbean
Institute for Health Research,

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we have campuses located
in Jamaica and Barbados.

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Three of those in Jamaica

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are the Tropical
Metabolism Research Unit,

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which is the oldest unit
comprising the institute;

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the Epidemiology
Research Unit;

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and, of course,
the Sickle Cell Unit,

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which is today’s focus.

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In Barbados, we have
the George Alleyne

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Chronic Disease
Research Center,

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which became part
of the institute

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a couple of years
after it was formed in 1999.

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Each unit focuses on different
aspects of health research

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or focus, of course,

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is a lot around issues
related to the health conditions

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that are of importance
to the region.

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Particularly, non communicable
diseases, their determinants,

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working on surveillance
of these conditions,

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interventions that may help
to change the natural history

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of these conditions, nutrition,

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child development research,
climate, and health,

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and then of course,
sickle cell disease.

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So, as you may already
be aware of,

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the Sickle Cell Unit, founded
by Professor Graham Serjeant,

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is now headed by
Professor Monika Parshad-Asnani.

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They have a strong tradition
of research and service

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to people living
with sickle cell disease

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in Jamaica
and around the world.

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It operates
the Sickle Cell Unit,

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one of the largest clinics
in the region,

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providing training and clinical
support to clinicians,

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not only in Jamaica
but the Caribbean.

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It’s also a part
of the CAIHR unit

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that supports the island
wide newborn screenings

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you’ve been discussing
at your meeting.

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And works with
other Caribbean islands

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to support their
screening services as well.

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We’ve been part of a number
of innovative clinical trials,

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including studies of hydroxyurea
and other curative measures

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which are being used now
to address issue

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of chronic of sickle
cell disease.

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So, members of the unit
are integrally involved

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in a lot
of translational work

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and work closely
with the ministries of health

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as well as persons living
with sickle cell disease

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to implement
a number of policies

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which are evidence based to help
to improve quality of life.

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We hope that you 
enjoy the next few days

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and the continued 
deliberations that you have.

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We wish you all the best
as you continue to exchange

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and discuss current issues

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which are critical to the care
of the patients that we manage.

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And we wish you all the best for
another successful conference.

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[applause]

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Lesley King: Great.
Thanks, Professor Tulloch-Reid.

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All right.
Still missing some people,

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but we must press on.
Okay.

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So, our next speaker
is going to speak to us

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about Improving Hydroxyurea

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Adherence: Understanding
Barriers and Enablers.

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Dr. Monika Asnani is a 
professor of Family Medicine

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and Epidemiology

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and the director
of the Sickle Cell Unit

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of the Caribbean Institute
for Health Research

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at the University
of the West Indies in Jamaica.

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Professor Asnani,
over to you.

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Monika Parshad-Asnani:
Thank you, Lesley.

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This is probably for best.

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You know you don’t want to have
to speak after Russell Ware.

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[laughter]

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Why did you agree so quickly?

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[laughter]

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Okay. Here goes.
Morning, everyone.

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Everybody had
a very good time yesterday.

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I think that’s where
we are a little short

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waking up this morning.
But here goes.

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We’re going to be speaking to
use of hydroxyurea in Jamaica,

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barriers that
we are identifying.

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I’m not sure
if there are enablers,

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but we can suggest some.
Let me try this.

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Okay. All right.

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So, Jennifer spoke
to some of this.

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And you would know that
this is where you guys all are.

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You know, right,
where you are?

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Where is this going?
Right here in Jamaica.

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So, the Caribbean region,
a tiny region here,

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44.2 million population,
with all of us together,

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which is just about .54 percent
of the world’s population.

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But yet, the incidence
of sickle cell disease

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is second highest
in the Caribbean region,

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second only to
Sub-Saharan Africa.

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And the prevalence
of the sickle trait

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is 8 to 10 percent
in most territories.

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If you look at --
just to provide some context.

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My first couple of slides,
I thought,

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I’ll provide context
to our setting,

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you know, to really understand.

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Because, you know,
that is important for use

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of any strategies that we are
using in any diseases.

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So, from the --
from some of the data,

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this is --
the next couple of slides

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are from Global Burden
of Disease data

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where sickle cell disease
numbers were presented.

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And this was published last year
in The Lancet Haematology.

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If you look at it overall,
there has been --

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and this is looking
at data from 2000

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and comparing it to 2021,
there’s been a 13.7 percent

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rise in global numbers
of the disease.

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Now we estimate about
almost half a million people

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are being born --

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half a million babies
are being born annually

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with sickle cell disease.

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And really, this increase
has been massive

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in Sub-Saharan Africa.

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And the only other region,
the only other GBD super region

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that has shown
an incline in numbers,

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is Latin America
and the Caribbean.

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All the other regions
are actually showing a decline.

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All right.
This is the next slide.

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And looking
at the birth incidence,

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the overall
global birth incidence

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is about 382 per 
100,000 live births,

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highest in Sub-Saharan Africa,

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followed by Latin America,
and the Caribbean.

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And if you look at Jamaica’s
number in the blue --

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sorry for all my colors.

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This makes -- you know,
it’s easy for me --

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I’m a little blind --
to point out things to you.

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Jamaica is probably the largest,
or one of the largest,

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or the highest incidents,
at 573 per 100,000.

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It’s actually closer
to about 650

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based on our more
precise numbers.

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These two pointers,
you know, is killing me.

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So, looking at this sickle cell
disease in Jamaica,

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10 percent sickle cell trait,
and 5 percent of the population

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has some other trait for
some other abnormal hemoglobin.

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One in 150 live births are born
with sickle cell disease,

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and we estimate
about 19,000 people in Jamaica

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are living with the disease.

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Median survival
is about 42 years,

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which is three decades

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lower than
the national life expectancy.

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Maternal mortality ratio
is about 378 deaths

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per 100,000 live births.

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And fortunate,
it’s about four times

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that in non-sickle
births in Jamaica.

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And there really hasn’t been
any change over the last two

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decades in this.
But our win is --

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Jennifer said this yesterday --
the childhood mortality.

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So, deaths under five
no different than that

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in the non-sickle
cell children with provision

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of newborn screening
and early childhood care.

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Marshall spoke to 
this quite a bit.

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So, I’ll just say
the Sickle Cell Unit

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is one of the four units
within the Caribbean

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Institute for Health Research,

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which is a department within
the University of the West

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Indies, a regional university.

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And our mission is improving
lives for persons

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living with sickle cell diseases

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through research, education,
and clinical care.

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The unit is one of the largest
comprehensive care clinics

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in the region.
We have more than 10,000 people

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registered in our database
historically.

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Now, currently, numbers 
have fallen a bit,

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definitely during the pandemic,
and thereafter.

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We have about 2,800 patients
annually who come to the clinic

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making up close to 9,000 visits,
30 to 40 visits.

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That number
is a little bit down.

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I think -- I don’t think
we see 50 most days now.

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Thirty to 40 patients
are being seen every day.

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And of course,
you would have heard many times

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over the last --
yesterday and today,

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that is staffed primarily
by primary care physicians,

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pediatricians,
and family physicians.

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Currently, we have
no hematologist on staff.

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And we do provide
expert consultations,

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not only for patients
who come to us,

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but others in the island
being managed

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by other healthcare providers,
as well as in the region.

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And the Jamaica sickle cell
cohort study -- and that’s Prof.

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Serjeant and Mrs. Searjent.
We are missing them today.

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They live right here
in Kingston, as you know.

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The Jamaica sickle cell
cohort study,

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which was led by Prof. Serjeant,
is one of the oldest birth

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cohort studies
on sickle cell disease.

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And this is just a quick map.
This is a map of Jamaica.

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And that big green bunch up here
is Kingston, where we are,

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where the Sickle Cell Unit
is located.

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And just to show each dot,

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whether it’s red or yellow
or green, is a patient,

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represents a patient
of the clinic.

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So, you can see
that these are patients.

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Our clinic does see patients
from around the island,

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which is not necessarily
a best thing.

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Our model of care is
a comprehensive, integrated,

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and continuing care,

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including provision
of social support.

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We follow the 
chronic care model

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because it’s ideal to manage
such a chronic disorder.

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And our aim is really
to provide equitable access

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to quality services

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wherever people living
with sickle cell disease

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living in Jamaica.
Okay.

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And we do engage --
to affect our aims --

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we do engage closely
with patient support groups

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as well as policy makers.

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Through our work and that
of other critical stakeholders

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in the Ministry of Health
of Jamaica’s 2013

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to 2018 National Strategic Plan
for Prevention

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and Control of 
Non-communicable Diseases,

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sickle cell disease made
its entry for the first time.

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And here the aim at that time

244
00:12:09,831 --> 00:12:11,533
was really to 
strengthen management

245
00:12:11,533 --> 00:12:12,867
of sickle cell disease

246
00:12:12,867 --> 00:12:17,739
with the main targets
being set for newborn screening

247
00:12:17,739 --> 00:12:20,308
and training
of healthcare providers.

248
00:12:22,010 --> 00:12:23,878
The Sickle Cell 
Technical Working Group,

249
00:12:23,878 --> 00:12:27,048
which has been mandated
by the chief medical officer,

250
00:12:27,048 --> 00:12:29,417
is led by
Ministry of Health and Wellness,

251
00:12:29,417 --> 00:12:32,287
but with members
from Sickle Cell

252
00:12:32,287 --> 00:12:35,256
Unit, Sickle Cell
Support Foundation of Jamaica,

253
00:12:35,256 --> 00:12:37,258
from the four regional
health authorities,

254
00:12:37,258 --> 00:12:38,860
and the National Public Health.

255
00:12:39,427 --> 00:12:42,063
These are all members of
this technical working group.

256
00:12:42,063 --> 00:12:43,631
And it leads, for now,

257
00:12:43,631 --> 00:12:46,534
especially provision
of newborn screening

258
00:12:46,534 --> 00:12:48,470
and early childhood care.

259
00:12:48,470 --> 00:12:51,005
We have had universal
newborn screening

260
00:12:51,005 --> 00:12:53,007
since 2015 in the island,

261
00:12:53,007 --> 00:12:54,676
which was quite delayed
for a country

262
00:12:54,676 --> 00:12:56,911
that where the benefits
of newborn screening

263
00:12:56,911 --> 00:12:59,781
for sickle cell disease
were first identified, you know.

264
00:13:01,349 --> 00:13:03,118
On the other hand,
the National Health Fund

265
00:13:03,118 --> 00:13:05,587
is a drug subsidy program
that covers medicines

266
00:13:05,587 --> 00:13:08,590
for 17 common
chronic diseases in Jamaica.

267
00:13:09,657 --> 00:13:10,959
And it’s a statutory body

268
00:13:10,959 --> 00:13:14,729
that has been appointed
by the MOHW in 2003.

269
00:13:14,729 --> 00:13:17,198
Sickle cell disease,
through a lot of advocacy,

270
00:13:17,765 --> 00:13:20,268
was added to this list in 2015.

271
00:13:21,603 --> 00:13:25,773
So, these are some of the areas.
The picture on the right --

272
00:13:25,773 --> 00:13:27,408
because you’ve heard
about newborn screening

273
00:13:27,408 --> 00:13:28,643
and the NHF already.

274
00:13:28,643 --> 00:13:31,479
Now this is our second edition
of our Clinical

275
00:13:31,479 --> 00:13:34,649
Care Guidelines that have been
written by Sickle Cell Unit.

276
00:13:34,649 --> 00:13:39,487
And we have copies for you
to view at our little table

277
00:13:40,221 --> 00:13:41,823
to just show you
Sickle Cell

278
00:13:41,823 --> 00:13:44,459
Unit, because most of you
have not been able to visit us.

279
00:13:44,459 --> 00:13:46,127
And I’m going to see
if next year,

280
00:13:46,127 --> 00:13:48,129
next time, if ever John,

281
00:13:48,129 --> 00:13:50,131
we end up being in Jamaica
for the conference,

282
00:13:50,131 --> 00:13:53,368
I think we should have a day
where whoever wishes to visit

283
00:13:53,368 --> 00:13:55,670
the unit
will be happy to welcome them.

284
00:13:56,571 --> 00:13:59,574
But these guidelines are used

285
00:13:59,574 --> 00:14:01,910
by the Ministry
of Health and Wellness.

286
00:14:01,910 --> 00:14:03,678
They are adopted for use
in their primary

287
00:14:03,678 --> 00:14:05,747
health care system
throughout Jamaica.

288
00:14:05,747 --> 00:14:08,416
And there are many
other countries in the region

289
00:14:08,416 --> 00:14:10,852
who employ
our Clinical Care Guidelines.

290
00:14:10,852 --> 00:14:15,256
So, how do we manage sickle cell
disease in Jamaica,

291
00:14:15,256 --> 00:14:17,492
specifically,
the treatments being used here?

292
00:14:18,226 --> 00:14:21,196
Chronic transfusion programs
are not employed.

293
00:14:22,764 --> 00:14:24,966
A large part of why that is so

294
00:14:24,966 --> 00:14:28,803
is because blood products
are limited in availability.

295
00:14:28,803 --> 00:14:32,407
We have no BMT, bone marrow
transplantation facilities

296
00:14:32,407 --> 00:14:34,242
available for
sickle cell disease in Jamaica

297
00:14:34,242 --> 00:14:36,110
or in the wider region.

298
00:14:36,110 --> 00:14:38,680
Only three through
overseas links

299
00:14:38,680 --> 00:14:41,482
that we might be able
to send patients for that.

300
00:14:41,482 --> 00:14:44,919
Gene therapies, of course,
are not available here though,

301
00:14:44,919 --> 00:14:47,755
through the trial
with Cincinnati in Punam Malik,

302
00:14:47,755 --> 00:14:51,793
the Aruvant gene therapy trial,
we were one of the major sites.

303
00:14:51,793 --> 00:14:54,762
Seven of their treated patients
came from us.

304
00:14:55,496 --> 00:14:57,899
So, we’ve had three individuals
from our unit

305
00:14:57,899 --> 00:14:59,500
who have gotten gene therapy.

306
00:15:01,736 --> 00:15:04,939
I think the oldest is --
I mean,

307
00:15:04,939 --> 00:15:09,077
the first one is seven years --
going seven years.

308
00:15:09,077 --> 00:15:12,981
She did make seven years
in July. So, doing fairly well.

309
00:15:15,250 --> 00:15:17,819
And if you look
at the sickle cell,

310
00:15:17,819 --> 00:15:20,088
other sickle cell
disease treatments,

311
00:15:20,088 --> 00:15:21,789
the disease
modifying treatments,

312
00:15:21,789 --> 00:15:25,526
the only DMT available
in Jamaica is hydroxyurea.

313
00:15:25,526 --> 00:15:27,528
Though the unit has 
participated in trials

314
00:15:27,528 --> 00:15:30,064
for both crizanlizumab
and voxelotor,

315
00:15:30,064 --> 00:15:32,000
they are not licensed
for use here.

316
00:15:32,000 --> 00:15:34,902
L-glutamine is only available
in the health stores

317
00:15:34,902 --> 00:15:36,504
as a supplement.

318
00:15:37,038 --> 00:15:39,240
The situation is 
pretty similar to this

319
00:15:39,240 --> 00:15:40,842
for the rest of the Caribbean.

320
00:15:41,342 --> 00:15:44,112
And if you just look at the
average cost of hydroxyurea

321
00:15:45,713 --> 00:15:47,015
here on the top --

322
00:15:47,015 --> 00:15:49,050
oh, this thing
keeps disappearing.

323
00:15:49,717 --> 00:15:52,620
Then with the NHF,
the National Health Fund subsidy

324
00:15:52,620 --> 00:15:57,525
for an average 25 milligram
per kilogram per day dose,

325
00:15:57,525 --> 00:15:59,861
for an average
60 kilogram adult,

326
00:15:59,861 --> 00:16:03,164
the cost would be about
equivalent of about us $20.

327
00:16:03,798 --> 00:16:06,868
And that’s really not a huge
cause. It’s pretty reasonable.

328
00:16:08,002 --> 00:16:10,471
But what’s the current
use of hydroxyurea

329
00:16:10,471 --> 00:16:15,109
at the sickle cell unit?
As of about a week ago,

330
00:16:16,544 --> 00:16:21,549
795 patients
had used hydroxyurea.

331
00:16:21,549 --> 00:16:25,620
The first case was in 2002
when we really started working

332
00:16:25,620 --> 00:16:28,389
and thinking of using
hydroxyurea in the clinic.

333
00:16:29,090 --> 00:16:31,092
Most of those were SS.

334
00:16:31,092 --> 00:16:34,028
I just want to highlight
that 24 of those have been SC.

335
00:16:34,762 --> 00:16:36,864
And the discussions yesterday
were very interesting.

336
00:16:36,864 --> 00:16:40,968
I think we really do need
to take a closer look

337
00:16:40,968 --> 00:16:44,138
at what our SCs are doing,
have been doing.

338
00:16:44,138 --> 00:16:45,473
And look at their numbers

339
00:16:45,473 --> 00:16:48,009
and see how hydroxyurea
has gone in them.

340
00:16:48,009 --> 00:16:52,313
If you look at numbers
from January 1, 2003,

341
00:16:52,313 --> 00:16:54,082
last year January 1 to now,

342
00:16:55,016 --> 00:16:58,619
we’ve had a total
of 2,589 patients

343
00:16:58,619 --> 00:17:00,088
who have visited the clinic.

344
00:17:00,088 --> 00:17:03,558
Of those,
631 are on hydroxyurea.

345
00:17:03,558 --> 00:17:05,993
That’s a good number,
24.4 percent.

346
00:17:05,993 --> 00:17:10,431
But if you look at even just
two years ago, September 2022,

347
00:17:10,431 --> 00:17:12,734
less than 10 percent
of our clinic population

348
00:17:12,734 --> 00:17:14,402
was taking hydroxyurea.

349
00:17:14,402 --> 00:17:17,672
That number was 15.7 percent
in March of last year.

350
00:17:17,672 --> 00:17:20,508
So, we are right now
at 24.4 percent.

351
00:17:20,508 --> 00:17:22,110
And this figure is very nice.

352
00:17:22,944 --> 00:17:24,879
Thanks to Lesley
for this figure.

353
00:17:24,879 --> 00:17:29,984
Where you can see
how many persons

354
00:17:29,984 --> 00:17:33,521
have started hydroxyurea
per year,

355
00:17:33,521 --> 00:17:35,089
and you can see
the sharp incline

356
00:17:35,089 --> 00:17:36,457
in the last two
to three years.

357
00:17:36,457 --> 00:17:38,426
Don’t worry about
this dip in 2024

358
00:17:38,426 --> 00:17:40,495
because we haven’t
completed the year yet.

359
00:17:40,495 --> 00:17:43,331
So, numbers are going up.

360
00:17:43,331 --> 00:17:47,902
But do remember that this is
a specialized center

361
00:17:47,902 --> 00:17:51,305
providing comprehensive care
for sickle cell disease.

362
00:17:53,841 --> 00:17:56,711
This is another set of data.

363
00:17:56,711 --> 00:17:58,679
And this is looking
at the National Health

364
00:17:58,679 --> 00:18:01,849
Fund, which, as I said earlier,
is a drug subsidy program,

365
00:18:02,683 --> 00:18:04,385
and where sickle cell was added

366
00:18:04,385 --> 00:18:06,621
to the formulary
in June of 2015.

367
00:18:06,621 --> 00:18:12,794
Data as at March of 2024
is that --

368
00:18:12,794 --> 00:18:15,163
remember, I said
that we estimate about 18

369
00:18:15,163 --> 00:18:17,031
to 19,000 persons in Jamaica

370
00:18:17,031 --> 00:18:19,066
are living
with sickle cell disease.

371
00:18:19,066 --> 00:18:22,403
Just a little over 3,000
of these individuals

372
00:18:22,403 --> 00:18:26,240
are actually enrolled on the NHF

373
00:18:26,240 --> 00:18:27,575
for their sickle cell
disease status.

374
00:18:27,575 --> 00:18:30,278
And something we recommend
that every person living

375
00:18:30,278 --> 00:18:33,080
with this chronic disorder
should be registered.

376
00:18:33,080 --> 00:18:35,616
Total claims
over a one year period

377
00:18:35,616 --> 00:18:42,323
from April ‘23 to March 2024
have been only 3,676.

378
00:18:42,323 --> 00:18:44,192
This is not hydroxyurea.

379
00:18:44,192 --> 00:18:47,929
The last bullet is on
hydroxyurea for that time period

380
00:18:48,696 --> 00:18:52,400
where total HU claimants

381
00:18:52,400 --> 00:18:55,903
have just been 292
and total hydroxyurea

382
00:18:55,903 --> 00:18:59,307
claims have just been
about 1,438.

383
00:18:59,307 --> 00:19:02,310
That’s just about five claims
per individual.

384
00:19:02,877 --> 00:19:05,279
I just wanted to say,
though that --

385
00:19:05,279 --> 00:19:09,684
remember I said 631 of our
patients are taking hydroxyurea.

386
00:19:09,684 --> 00:19:12,286
But here, it shows
the claimants as only 292.

387
00:19:12,887 --> 00:19:14,322
This just represents
prescriptions

388
00:19:14,322 --> 00:19:16,724
that are being filled
in the private pharmacies,

389
00:19:16,724 --> 00:19:19,360
because hydroxyurea can be
accessed free of cost

390
00:19:19,360 --> 00:19:22,129
in the public system.
We are awaiting that data.

391
00:19:22,129 --> 00:19:25,199
But we certainly don’t expect it
to be huge numbers,

392
00:19:25,199 --> 00:19:26,467
much bigger numbers than that.

393
00:19:26,467 --> 00:19:29,003
So, Jamaica does have
universal health care,

394
00:19:29,003 --> 00:19:30,671
which means
in the public system,

395
00:19:31,272 --> 00:19:34,208
persons living in Jamaica
can access free health care.

396
00:19:36,043 --> 00:19:38,079
But numbers are subpar still.

397
00:19:38,079 --> 00:19:40,515
So, why the under
use of hydroxyurea?

398
00:19:40,515 --> 00:19:42,817
What factors have been
identified generally --

399
00:19:42,817 --> 00:19:44,519
we’re not talking
about Jamaica here --

400
00:19:44,519 --> 00:19:46,420
leading to the under
use of hydroxyurea?

401
00:19:46,420 --> 00:19:49,724
These factors are thought to be
related to provider issues,

402
00:19:49,724 --> 00:19:53,427
patients or caregiver issues,
or system level issues,

403
00:19:53,427 --> 00:19:57,064
poor availability of the drug,
as well as widespread access.

404
00:19:57,064 --> 00:19:58,599
You know, where can you
access the drug?

405
00:19:58,599 --> 00:19:59,834
Even if it is available,

406
00:19:59,834 --> 00:20:03,471
is it available in different
parts of your country,

407
00:20:03,471 --> 00:20:05,606
you know, or just at one
or two pharmacies?

408
00:20:06,140 --> 00:20:08,376
In many LMICs,
liquid preparations,

409
00:20:08,376 --> 00:20:11,379
for example, are unavailable.
Access to laboratory

410
00:20:11,379 --> 00:20:14,048
testing that is needed
for monitoring may be limited.

411
00:20:14,682 --> 00:20:18,152
Provider and patient factors
may include low self-efficacy,

412
00:20:18,152 --> 00:20:20,021
low awareness of its benefits,

413
00:20:20,021 --> 00:20:22,690
lack of medical advice
and concerns about side effects,

414
00:20:22,690 --> 00:20:25,226
both at the provider level
as well as the patient

415
00:20:25,226 --> 00:20:27,194
and caregiver level.

416
00:20:27,194 --> 00:20:29,497
Providers might have
their own biases.

417
00:20:29,497 --> 00:20:32,166
They might perceive that
patients will not be compliant

418
00:20:32,166 --> 00:20:34,468
with daily medications
or may not be able to keep

419
00:20:34,468 --> 00:20:36,671
the more frequent visits
that are required,

420
00:20:36,671 --> 00:20:41,075
and definitely might not be
too willing to do labs.

421
00:20:43,344 --> 00:20:47,214
You know, patients want --
even when they start,

422
00:20:47,214 --> 00:20:50,384
might have poor adherence
to use of hydroxyurea.

423
00:20:50,384 --> 00:20:52,253
And you know,
that’s not a good thing.

424
00:20:52,253 --> 00:20:54,922
That’s not -- we don’t --
we want our patients

425
00:20:54,922 --> 00:20:56,924
to be taking this drug
on a daily basis.

426
00:20:57,658 --> 00:21:00,127
And this adherence issues
may be worse

427
00:21:00,127 --> 00:21:02,263
when you have complex
dosing schedules.

428
00:21:02,263 --> 00:21:05,967
So, not everywhere do you
have preparations

429
00:21:05,967 --> 00:21:10,037
that allow for easier dosing.
And what can be done?

430
00:21:10,037 --> 00:21:13,107
What might work to improve
this underutilization

431
00:21:13,107 --> 00:21:14,709
training of healthcare
providers,

432
00:21:14,709 --> 00:21:17,545
along with provision of
clear clinical care guidelines,

433
00:21:17,545 --> 00:21:20,214
which might need to be
adapted for various settings?

434
00:21:20,214 --> 00:21:21,849
You know, what works in Jamaica

435
00:21:21,849 --> 00:21:24,518
might not work in Barbados
and vice versa.

436
00:21:24,518 --> 00:21:26,554
Improving provider benefits
and attitudes

437
00:21:26,554 --> 00:21:28,789
and reducing their biases
to respond

438
00:21:28,789 --> 00:21:30,992
to some of the patient
and caregiver issues.

439
00:21:30,992 --> 00:21:33,461
Partnering with
peer support networks

440
00:21:33,461 --> 00:21:34,929
to strengthen the messages.

441
00:21:34,929 --> 00:21:36,998
Improving their ability
for shared decision

442
00:21:36,998 --> 00:21:39,100
making could all help.

443
00:21:39,100 --> 00:21:41,369
Improving access
and affordability of drugs

444
00:21:41,369 --> 00:21:44,171
and wider availability
of the drug within communities

445
00:21:44,171 --> 00:21:46,140
can also lessen
barriers to uptake.

446
00:21:46,907 --> 00:21:50,144
So, our study that we initiated
early last year

447
00:21:50,144 --> 00:21:52,480
aimed to understand barriers
to hydroxyurea

448
00:21:52,480 --> 00:21:55,316
use across Jamaica
at the provider,

449
00:21:55,316 --> 00:21:57,718
patient and caregiver
and health systems level.

450
00:21:57,718 --> 00:22:00,788
Some of that health system I
presented in the slides earlier,

451
00:22:01,422 --> 00:22:03,391
we are conducting
a mixed method study,

452
00:22:03,391 --> 00:22:05,993
including desk reviews
of key policy documents

453
00:22:05,993 --> 00:22:08,629
and in depth interviews
with key stakeholders.

454
00:22:08,629 --> 00:22:11,599
I’ll present
some initial results today.

455
00:22:13,801 --> 00:22:17,338
Starting with the parent
or caregiver arm.

456
00:22:17,338 --> 00:22:19,407
One hundred seventy
two caregivers,

457
00:22:19,407 --> 00:22:22,043
mostly mothers,
have been surveyed,

458
00:22:22,043 --> 00:22:25,579
and a subset have had in depth
interviews of the group.

459
00:22:25,579 --> 00:22:27,948
Only 36 percent
have an NHF card.

460
00:22:30,718 --> 00:22:33,254
27 percent a little
over a quarter,

461
00:22:33,254 --> 00:22:35,723
have never heard of hydroxyurea.

462
00:22:35,723 --> 00:22:39,427
57.6 percent have been advised
for their child

463
00:22:39,427 --> 00:22:41,262
to take hydroxyurea.

464
00:22:41,262 --> 00:22:44,899
Of these, about 68 percent
have started it.

465
00:22:46,667 --> 00:22:49,870
It’s good news here
that almost all

466
00:22:49,870 --> 00:22:52,440
who have started
it have continued using it.

467
00:22:53,107 --> 00:22:55,776
Barriers identified the parents
and caregivers

468
00:22:55,776 --> 00:22:59,280
have been both via the survey
and through the interviews,

469
00:22:59,280 --> 00:23:02,183
is that they need
more information on hydroxyurea.

470
00:23:02,183 --> 00:23:04,351
Almost 40 percent
have stated this.

471
00:23:04,351 --> 00:23:08,956
36 percent worried about costs
of medications and of labs.

472
00:23:08,956 --> 00:23:11,325
And over a quarter
worried about side effects

473
00:23:11,325 --> 00:23:15,930
and the increased frequency of
visits and laboratory testing.

474
00:23:17,932 --> 00:23:24,038
This table looks a little deeper
at those 68 percent

475
00:23:24,038 --> 00:23:25,272
who were actually advised

476
00:23:25,272 --> 00:23:28,709
or who remember being advised
to take hydroxyurea.

477
00:23:28,709 --> 00:23:36,517
Because our current guidelines
have our own inclusion criteria

478
00:23:36,517 --> 00:23:38,953
or criteria
for initiation of hydroxyurea.

479
00:23:38,953 --> 00:23:42,223
So, we thought taking a look
at those who had been advised

480
00:23:42,223 --> 00:23:45,359
to take it,
comparing barriers in those

481
00:23:45,359 --> 00:23:47,461
who actually began
and those who didn’t.

482
00:23:47,995 --> 00:23:51,799
Inadequate knowledge,
availability of hydroxyurea,

483
00:23:51,799 --> 00:23:55,169
and not wishing their child
to take additional medications

484
00:23:55,169 --> 00:23:57,471
were some of
the significant barriers,

485
00:23:57,471 --> 00:24:00,207
especially knowledge
if you look at those numbers.

486
00:24:02,777 --> 00:24:04,378
Moving to the adults.

487
00:24:05,079 --> 00:24:07,181
And again, I mean,
we didn’t try for this,

488
00:24:07,181 --> 00:24:10,317
but we have almost a similar
number that we surveyed.

489
00:24:10,317 --> 00:24:12,520
We have done some
in depth interviews as well.

490
00:24:12,520 --> 00:24:15,189
One hundred seventy four adults
living with sickle cell disease,

491
00:24:15,189 --> 00:24:17,291
and these are people
over the age of 18.

492
00:24:17,291 --> 00:24:18,926
73 percent who are female,

493
00:24:20,194 --> 00:24:23,397
just over a half
have an NHF card.

494
00:24:23,397 --> 00:24:26,767
So, I don’t know why Lesley
that you know more adults

495
00:24:26,767 --> 00:24:29,236
have a NHF card than kids do.

496
00:24:29,236 --> 00:24:32,773
But here, 36 percent
have never heard of hydroxyurea.

497
00:24:32,773 --> 00:24:37,444
That’s over a third.
43.7 percent have been counseled

498
00:24:37,444 --> 00:24:39,513
read the need
to take hydroxyurea.

499
00:24:39,513 --> 00:24:42,616
Again, two-thirds of those
who were counseled

500
00:24:42,616 --> 00:24:44,218
actually ever started it.

501
00:24:44,852 --> 00:24:49,156
And only half of those tanks
are currently taking it.

502
00:24:50,858 --> 00:24:53,928
Barriers identified
over a half state

503
00:24:53,928 --> 00:24:56,130
that they don’t know
enough about the drug.

504
00:24:56,130 --> 00:24:59,099
A third over a third state
cost as a barrier,

505
00:24:59,099 --> 00:25:01,869
but near 30 percent again
worry about side effects,

506
00:25:01,869 --> 00:25:03,204
such as low --

507
00:25:03,204 --> 00:25:05,773
the drug lowering their ability
to fight infections,

508
00:25:05,773 --> 00:25:08,175
damaging their children
if they are to have children,

509
00:25:08,175 --> 00:25:09,777
and cancer risks.

510
00:25:10,678 --> 00:25:13,280
Over a quarter don’t want to
have to do frequent blood tests,

511
00:25:13,280 --> 00:25:15,850
and nearly a quarter
not interested in a drug

512
00:25:15,850 --> 00:25:17,451
to take daily.

513
00:25:18,686 --> 00:25:21,689
We got to survey
54 healthcare providers to date,

514
00:25:21,689 --> 00:25:24,225
that includes 46 physicians
and eight nurses.

515
00:25:24,225 --> 00:25:25,826
Surveys in pharmacies --

516
00:25:26,660 --> 00:25:29,897
and pharmacists because
we do hope to reach out

517
00:25:29,897 --> 00:25:32,867
to pharmacists as well,
haven’t reached far yet.

518
00:25:32,867 --> 00:25:37,805
Practice sites for the HCPs
included family medicine,

519
00:25:37,805 --> 00:25:39,540
emergency room, pediatrics.

520
00:25:39,540 --> 00:25:42,276
The lowest frequency
was for hematologists.

521
00:25:42,276 --> 00:25:44,545
63 percent of these surveyed

522
00:25:44,545 --> 00:25:47,047
managed both adults
and children.

523
00:25:47,047 --> 00:25:49,250
Median years in 
practice was 15 years.

524
00:25:50,217 --> 00:25:54,221
Whereas, 16.7 percent
were very comfortable.

525
00:25:54,221 --> 00:25:56,824
Part of this group
is individuals,

526
00:25:56,824 --> 00:25:58,893
doctors and nurses who work
at the sickle cell unit.

527
00:25:58,893 --> 00:26:02,196
About 8 to 9 of these 54

528
00:26:02,196 --> 00:26:03,898
are people who work
at sickle cell units,

529
00:26:03,898 --> 00:26:06,400
16.7 percent who are
very comfortable

530
00:26:06,400 --> 00:26:07,968
managing sickle cell disease.

531
00:26:07,968 --> 00:26:11,739
Just a little under half
are just somewhat comfortable.

532
00:26:11,739 --> 00:26:15,009
20 percent are not comfortable
managing sickle cell disease.

533
00:26:15,776 --> 00:26:18,579
I thought this was an
interesting figure down below.

534
00:26:18,579 --> 00:26:20,581
Comfortable
managing acute pain.

535
00:26:20,581 --> 00:26:24,385
Just over a half, but less than
a quarter are comfortable

536
00:26:24,385 --> 00:26:27,755
managing chronic pain.

537
00:26:29,356 --> 00:26:33,060
Two-thirds of the 
surveyed individuals

538
00:26:33,060 --> 00:26:36,664
said they have no patients
taking hydroxyurea.

539
00:26:36,664 --> 00:26:39,900
And 44 percent were
uncomfortable in using it,

540
00:26:39,900 --> 00:26:44,271
with only 25, 28 percent saying
they have some level of comfort

541
00:26:44,271 --> 00:26:45,472
in using hydroxyurea.

542
00:26:45,472 --> 00:26:51,378
The criteria they identified for
use of hydroxyurea pretty low.

543
00:26:51,378 --> 00:26:54,315
Just over 30 percent
would use it for frequent pain

544
00:26:54,315 --> 00:26:56,150
being managed in hospital,

545
00:26:56,150 --> 00:26:58,552
but very few of those
would use it

546
00:26:58,552 --> 00:27:00,688
if these pain events
occurred at home.

547
00:27:00,688 --> 00:27:03,424
26 percent said yes for stroke,
said yes for stroke,

548
00:27:03,424 --> 00:27:05,359
and a quarter
for acute chest syndrome.

549
00:27:06,961 --> 00:27:10,431
Providers feel families
decline hydroxyurea

550
00:27:10,431 --> 00:27:12,399
because of worry
about side effects

551
00:27:12,399 --> 00:27:14,768
and not wanting
frequent labs or visits.

552
00:27:14,768 --> 00:27:19,540
Whereas important considerations
for them are patients,

553
00:27:19,540 --> 00:27:22,209
anticipation of side effects,
patients’ adherence,

554
00:27:22,209 --> 00:27:25,079
adherence to labs,
possibility of pregnancy.

555
00:27:26,146 --> 00:27:28,215
Over a quarter said
that they don’t have --

556
00:27:28,215 --> 00:27:30,884
they lack the 
resources to counsel.

557
00:27:30,884 --> 00:27:33,854
20 percent didn’t know
whether hydroxyurea

558
00:27:33,854 --> 00:27:36,056
could be effective
in managing pain.

559
00:27:38,325 --> 00:27:39,927
I’ll move on to the next one.

560
00:27:42,496 --> 00:27:44,098
So, what have we seen?

561
00:27:44,098 --> 00:27:47,401
We have identified various
patient and provider barriers.

562
00:27:47,401 --> 00:27:49,236
Many are similar
to other studies,

563
00:27:49,236 --> 00:27:51,805
mainly in the U.S.
and certain parts of Africa.

564
00:27:51,805 --> 00:27:53,273
They’re not a lot of studies,

565
00:27:53,273 --> 00:27:55,709
but definitely studies
from these regions.

566
00:27:55,709 --> 00:27:57,644
Barriers are greater in adults.

567
00:27:58,479 --> 00:28:00,180
Provider barriers
are also similar.

568
00:28:00,180 --> 00:28:03,417
44 percent of them
are uncomfortable prescribing.

569
00:28:04,151 --> 00:28:06,587
And they are personal biases
that we have identified

570
00:28:06,587 --> 00:28:07,821
in just even this small group

571
00:28:07,821 --> 00:28:09,590
that we have reached out
to so far.

572
00:28:10,157 --> 00:28:14,128
Striking for us also
is the low registration

573
00:28:14,128 --> 00:28:16,230
with the National Health Fund.

574
00:28:17,498 --> 00:28:19,333
What can we do?

575
00:28:19,333 --> 00:28:23,003
We clearly need to enable better
understanding of hydroxyurea

576
00:28:23,003 --> 00:28:26,373
use and efficacy
among providers and patients.

577
00:28:26,373 --> 00:28:29,777
Enhanced tools could assist
with providers,

578
00:28:29,777 --> 00:28:32,613
use of peer support networks,
social media,

579
00:28:32,613 --> 00:28:34,181
and use of patient navigators.

580
00:28:34,181 --> 00:28:35,949
We’ll hear our nurses
in clinic say,

581
00:28:35,949 --> 00:28:38,585
"You know, I’ll have
a mommy who is there --

582
00:28:38,585 --> 00:28:40,487
whose child has been
taking hydroxyurea

583
00:28:40,487 --> 00:28:42,723
speak to this one that you want
to go on hydroxide.

584
00:28:42,723 --> 00:28:44,358
That’s always a win.

585
00:28:44,358 --> 00:28:46,260
But to do it
in a more determined way

586
00:28:46,260 --> 00:28:48,328
could possibly help 
patients and caregivers

587
00:28:48,328 --> 00:28:50,264
in their decision
making process.

588
00:28:50,264 --> 00:28:54,535
Improving registration with NHF
could reduce cost burden,

589
00:28:54,535 --> 00:28:56,904
but maybe increase
visibility of access

590
00:28:56,904 --> 00:28:59,006
to this critical 
medicine as well.

591
00:29:01,175 --> 00:29:02,943
So, the study was mainly

592
00:29:02,943 --> 00:29:05,079
within the southeast 
region of the island,

593
00:29:05,079 --> 00:29:06,647
which is where
the Sickle Cell Unit is.

594
00:29:06,647 --> 00:29:08,649
We do have patients
from other parts.

595
00:29:08,649 --> 00:29:10,751
And we did reach out
to some patients

596
00:29:12,152 --> 00:29:16,523
for in the Northeast
and the western region.

597
00:29:17,124 --> 00:29:20,694
But we do still need more
robust data from other regions,

598
00:29:20,694 --> 00:29:24,398
where especially ambulatory care
sites for adults are limited.

599
00:29:24,398 --> 00:29:26,834
We also need to enable
identification of persons

600
00:29:26,834 --> 00:29:30,270
with sickle cell disease who are
eligible for hydroxyurea use,

601
00:29:30,270 --> 00:29:33,006
because, you know that could be
a targeted group

602
00:29:33,006 --> 00:29:35,342
to have them enrolled for care.

603
00:29:35,342 --> 00:29:38,278
Further work to get a deeper
understanding of these areas

604
00:29:38,278 --> 00:29:39,513
and possible solutions

605
00:29:39,513 --> 00:29:41,648
is ongoing as we look
at these results.

606
00:29:42,382 --> 00:29:45,486
So, that’s it.
This is my last slide.

607
00:29:46,854 --> 00:29:48,122
I want to end by acknowledging

608
00:29:48,122 --> 00:29:50,824
and thanking members
of the study team in the blue.

609
00:29:52,860 --> 00:29:55,562
Our partners, the Sickle Cell
Technical Working Group,

610
00:29:55,562 --> 00:29:57,664
the parish teams, and the
Ministry Of Health and Wellness

611
00:29:57,664 --> 00:29:59,700
for seed funding
for this study.

612
00:29:59,700 --> 00:30:02,002
Thanks to Lesley King
and Zachary Ramsay

613
00:30:02,002 --> 00:30:04,972
for helping with the slides
and the data in the slides,

614
00:30:04,972 --> 00:30:07,841
and Mrs. Dawson Shaw
from the National Health Fund,

615
00:30:07,841 --> 00:30:11,078
who partners well with us.
Thank you all for listening.

616
00:30:11,078 --> 00:30:15,883
[applause]

617
00:30:15,883 --> 00:30:17,417
Lesley King: Okay. Thanks
very much, Prof.

618
00:30:17,417 --> 00:30:19,686
Asnani for a great
presentation.

619
00:30:20,988 --> 00:30:26,393
Are there any questions
from the audience?

620
00:30:28,228 --> 00:30:29,830
The mic is coming.

621
00:30:30,497 --> 00:30:31,798
Female Speaker: 
Thank you.

622
00:30:31,798 --> 00:30:34,201
It went by a little
too fast for me.

623
00:30:34,201 --> 00:30:36,470
But I noticed that one
of the reasons

624
00:30:36,470 --> 00:30:40,307
why physicians
didn’t use hydroxyurea

625
00:30:40,307 --> 00:30:43,477
or didn’t recommend it was
they didn’t believe it worked.

626
00:30:43,477 --> 00:30:46,580
But I didn’t see the percent.

627
00:30:47,814 --> 00:30:51,385
Are there still physicians
who don’t think it works?

628
00:30:51,385 --> 00:30:52,920
Monika Parshad-Asnani: That was
a low percent.

629
00:30:52,920 --> 00:30:54,288
That was about 20 percent

630
00:30:54,288 --> 00:30:56,356
who weren’t sure
about this efficacy.

631
00:30:56,356 --> 00:30:58,058
So, then I don’t think
we had anybody

632
00:30:58,058 --> 00:31:00,360
who said
I don’t believe it works.

633
00:31:10,504 --> 00:31:12,573
Female Speaker: That was great.
I’m sorry [unintelligible] late.

634
00:31:12,573 --> 00:31:13,840
But what I was
going to say was,

635
00:31:13,840 --> 00:31:17,878
even when I was in the U.K.,
before I came to the U.S.,

636
00:31:18,679 --> 00:31:21,315
the [unintelligible]
hydroxyurea is pretty low.

637
00:31:21,848 --> 00:31:23,150
Even though it’s available,

638
00:31:23,150 --> 00:31:24,885
even though we 
counsel them many,

639
00:31:24,885 --> 00:31:28,855
many times, I think there was --
maybe now it has improved.

640
00:31:28,855 --> 00:31:33,026
I have to ask Daniel here, who’s
just joined the King’s College.

641
00:31:33,760 --> 00:31:36,563
Perhaps, you know,
this apprehension number

642
00:31:36,563 --> 00:31:38,732
is being, you know,

643
00:31:38,732 --> 00:31:40,634
[unintelligible]
use for cancer treatment.

644
00:31:40,634 --> 00:31:43,470
I think that has a lot to do
with it among the patients.

645
00:31:44,371 --> 00:31:47,074
Monika Parshad-Asnani: I was
happy to see in this study,

646
00:31:47,074 --> 00:31:48,809
the data that we have
to date, you know,

647
00:31:48,809 --> 00:31:52,913
that was not a huge worry.
Even in the in depth interviews,

648
00:31:52,913 --> 00:31:57,184
a few that we have done so far,
that that was not a huge worry.

649
00:31:57,184 --> 00:31:59,386
Now, this data is
on the background of,

650
00:31:59,386 --> 00:32:02,055
you know, COVID
was good in some ways.

651
00:32:02,823 --> 00:32:04,658
It allowed us to have
so many Zoom meetings.

652
00:32:04,658 --> 00:32:06,059
It taught us for the first time,

653
00:32:06,059 --> 00:32:08,195
to reach out
in really good ways.

654
00:32:08,829 --> 00:32:12,833
You know, Lesley leads
the clinical care team

655
00:32:12,833 --> 00:32:14,968
through the Office Sickle Cell
Technical Working Group.

656
00:32:14,968 --> 00:32:17,170
We have done a lot
of training sessions.

657
00:32:17,170 --> 00:32:21,208
We have done a whole echo
series on hydroxyurea use.

658
00:32:21,208 --> 00:32:23,477
So, I think the cancer
thing is low,

659
00:32:23,477 --> 00:32:25,212
but we really need
some more data.

660
00:32:26,013 --> 00:32:27,314
You know, the drug has --

661
00:32:27,314 --> 00:32:30,017
that’s why I thought it was
important to present numbers

662
00:32:30,017 --> 00:32:31,885
for what’s
the health system doing?

663
00:32:31,885 --> 00:32:34,988
Because Jamaica is passionate
about using hydroxyurea,

664
00:32:34,988 --> 00:32:36,523
but we’re not quite there.

665
00:32:36,523 --> 00:32:39,860
Even at our own unit,
I think we are not there at all.

666
00:32:39,860 --> 00:32:44,097
Only 20 something percent
of our patients are taking it,

667
00:32:45,599 --> 00:32:53,340
[inaudible]

668
00:32:53,340 --> 00:32:54,941
Yes, definitely.

669
00:32:55,542 --> 00:32:58,612
And among the women
in Jamaica and the Caribbean,

670
00:32:58,612 --> 00:33:01,214
you know, and like other parts
of the world,

671
00:33:01,882 --> 00:33:03,450
fertility is a big issue.

672
00:33:03,450 --> 00:33:05,285
Most of these young people
want to have a child.

673
00:33:05,285 --> 00:33:06,687
So, if you are not able
to say to them,

674
00:33:06,687 --> 00:33:09,790
that’s why I really want to hear
what Russell has to say next,

675
00:33:10,657 --> 00:33:12,826
that you know, you might not
be able to have kids,

676
00:33:12,826 --> 00:33:15,395
or you have to stop it.
And then, you know, pregnancy,

677
00:33:15,395 --> 00:33:18,198
we have shown in our slides --
in our numbers here,

678
00:33:18,198 --> 00:33:21,168
is really a high risk pregnancy
in our women.

679
00:33:21,168 --> 00:33:24,237
So, do they stop it
and get in trouble, et cetera?

680
00:33:24,237 --> 00:33:30,210
So, those are concerns,
but I do believe that person --

681
00:33:30,210 --> 00:33:32,813
we are not sending messages
that we need to.

682
00:33:32,813 --> 00:33:35,849
That’s what this is showing,
right? So, we need to learn.

683
00:33:35,849 --> 00:33:38,819
We need to collaborate
with communication people.

684
00:33:38,819 --> 00:33:41,988
How do we spread these messages
in a good way?

685
00:33:41,988 --> 00:33:45,092
How do we enable
shared decision making?

686
00:33:45,092 --> 00:33:46,626
Because so many of them
are saying

687
00:33:46,626 --> 00:33:48,795
that they’ve never
heard of this drug.

688
00:33:48,795 --> 00:33:51,465
And a lot of these patients
surveyed are ones

689
00:33:51,465 --> 00:33:54,201
who are coming to the unit
or have access to,

690
00:33:54,735 --> 00:33:58,171
you know, communication
or Instagram page, et cetera.

691
00:33:58,171 --> 00:34:01,274
So, we need to build
better communication channels

692
00:34:01,274 --> 00:34:05,078
to spread the information
that we need to.

693
00:34:06,513 --> 00:34:08,915
Female Speaker: Hi,
I’m over here. Great talk.

694
00:34:09,449 --> 00:34:13,820
How much of an issue is it
for moms to get childcare

695
00:34:13,820 --> 00:34:15,389
for, say, the well --

696
00:34:15,389 --> 00:34:18,592
the children that don’t have
sickle cell disease back at home

697
00:34:18,592 --> 00:34:20,560
and leave those kids
and bring their child

698
00:34:20,560 --> 00:34:22,662
with sickle cell disease
to the clinic.

699
00:34:23,864 --> 00:34:25,198
That’s one question.

700
00:34:25,198 --> 00:34:30,370
And then another question
is just, what do females

701
00:34:30,370 --> 00:34:34,508
that want to have
children do for treatment,

702
00:34:34,508 --> 00:34:36,743
for the sickle cell pain?
Do they do nothing?

703
00:34:37,577 --> 00:34:39,379
Monika Parshad-Asnani: You have
such wonderful questions.

704
00:34:40,080 --> 00:34:41,615
That’s an excellent
question about,

705
00:34:41,615 --> 00:34:43,450
what do they do
with the children?

706
00:34:43,450 --> 00:34:44,918
I wonder. Lesley,
you want to take that on?

707
00:34:44,918 --> 00:34:52,025
Mind you, our clinic
opens in in, in working hours,

708
00:34:52,025 --> 00:34:55,462
meaning 8:30 a.m., to 4:30 p.m.
So, most of them do come.

709
00:34:55,462 --> 00:34:57,697
But we have to identify
these barriers.

710
00:34:57,697 --> 00:34:59,366
We did not get that.

711
00:34:59,966 --> 00:35:02,602
We’ve only done about
25 in depth surveys,

712
00:35:02,602 --> 00:35:04,504
and we did talk
about transportation.

713
00:35:04,504 --> 00:35:07,707
This is part of a larger study
looking at access to care.

714
00:35:07,707 --> 00:35:10,177
Transportation was not
much of a barrier.

715
00:35:10,177 --> 00:35:11,711
And you know,
reaching the clinic,

716
00:35:11,711 --> 00:35:14,214
whether getting time off
from work or so.

717
00:35:14,214 --> 00:35:17,984
But Jamaica, the social system,
has a good network

718
00:35:17,984 --> 00:35:19,719
even though
most of these individuals

719
00:35:19,719 --> 00:35:21,354
we surveyed were mothers.

720
00:35:21,354 --> 00:35:24,124
But a lot of times, grandmothers
will bring children.

721
00:35:24,124 --> 00:35:25,425
They have extended family.

722
00:35:25,425 --> 00:35:29,196
But I’m just kind of guessing on
the background of what I know.

723
00:35:29,196 --> 00:35:30,497
Your second bit was --

724
00:35:30,497 --> 00:35:31,865
do you have something
to add to that, Lesley?

725
00:35:31,865 --> 00:35:33,633
Lesley is the head
of our clinical services,

726
00:35:33,633 --> 00:35:35,769
and she’s a pediatrician.
Keep going?

727
00:35:35,769 --> 00:35:38,872
[laughs] What was the second bit
you were saying?

728
00:35:38,872 --> 00:35:41,808
Right. What do women
who want to get pregnant do?

729
00:35:41,808 --> 00:35:43,910
We advise them --
they get pregnant, yes.

730
00:35:43,910 --> 00:35:45,912
And after that, they said,
"Oh, my God, you’re pregnant.

731
00:35:45,912 --> 00:35:48,048
You need to come off your
hydroxyurea if you’re on it.

732
00:35:48,048 --> 00:35:50,851
Because we still don’t
let them carry it through,

733
00:35:51,618 --> 00:35:53,286
and that’s something
we need to talk through.

734
00:35:53,286 --> 00:35:54,888
Yeah.

735
00:35:55,522 --> 00:35:57,524
Lesley King: All right.
Great, yep, I think that’s it.

736
00:35:57,524 --> 00:35:59,059
There are two questions
here in the --

737
00:35:59,059 --> 00:36:01,061
but I’ll probably
just take them quickly.

738
00:36:01,061 --> 00:36:03,363
So, there are two questions
here from Dr. Sinque

739
00:36:03,363 --> 00:36:04,865
[phonetic sp] in the chat.

740
00:36:04,865 --> 00:36:06,566
She’s asking
about the difficulty

741
00:36:06,566 --> 00:36:08,268
in getting labs done on HU

742
00:36:08,268 --> 00:36:10,704
and can you give
your minimum labs needed?

743
00:36:10,704 --> 00:36:14,107
And so, what I think is
I think Russell’s talk

744
00:36:14,107 --> 00:36:15,709
may help us with that,

745
00:36:16,376 --> 00:36:19,145
in trying to give us
a better idea

746
00:36:19,145 --> 00:36:21,314
of what are the minimum labs
that we need to do.

747
00:36:21,314 --> 00:36:23,350
Because that has always
been a barrier.

748
00:36:23,350 --> 00:36:27,120
And certainly, what formulation
of HU do you use for children?

749
00:36:27,120 --> 00:36:31,825
And we really only have tabs
available in Jamaica

750
00:36:31,825 --> 00:36:33,126
at this time.

751
00:36:33,126 --> 00:36:35,395
But a number of
our pharmacists will --

752
00:36:35,395 --> 00:36:39,566
can make it into a suspension
for our children.

753
00:36:39,566 --> 00:36:43,136
All right. Okay.
So, thanks, Prof. Asnani.

754
00:36:43,136 --> 00:36:45,572
Great talk and great
discussion so far.

755
00:36:45,572 --> 00:36:47,807
So, now we are going
to move back

756
00:36:47,807 --> 00:36:50,277
to our original
first presentation

757
00:36:51,077 --> 00:36:52,746
from Dr. Russell Ware

758
00:36:52,746 --> 00:36:56,516
on revisiting hydroxyurea
use in sickle cell disease.

759
00:36:56,516 --> 00:36:58,818
So, Russell really needs
no introduction,

760
00:36:58,818 --> 00:37:01,922
and I already introduced him.
So, over to you, Russell.

761
00:37:06,660 --> 00:37:12,399
And we’re seeing your slides now
but we haven’t heard you.

762
00:37:12,399 --> 00:37:14,301
Are you saying anything yet?

763
00:37:14,301 --> 00:37:15,802
Russell Ware: I just
got unmuted, so I --

764
00:37:15,802 --> 00:37:17,571
Lesley King: Great.
All right, go ahead.

765
00:37:17,571 --> 00:37:18,905
Russell Ware: Thank you.

766
00:37:18,905 --> 00:37:22,242
And let me offer my apologies
for not being there

767
00:37:22,242 --> 00:37:25,512
and in person due
to personal reasons.

768
00:37:26,112 --> 00:37:28,715
But I’m very happy
to have a chance to speak.

769
00:37:29,849 --> 00:37:32,085
This was the title
that I was given,

770
00:37:32,085 --> 00:37:34,321
revisiting hydroxyurea use.

771
00:37:34,854 --> 00:37:38,992
Although, based
on Professor Asnani’s data,

772
00:37:38,992 --> 00:37:41,928
maybe it should be
introducing hydroxyurea use.

773
00:37:41,928 --> 00:37:45,365
It seems like
a remarkable statistic

774
00:37:45,365 --> 00:37:49,936
that so many adults would
have never heard of hydroxyurea.

775
00:37:49,936 --> 00:37:52,639
But nevertheless, I asked
what revisiting meant --

776
00:37:53,607 --> 00:37:56,109
and if I can advance the slide.

777
00:37:56,977 --> 00:38:03,750
I’m not able to advance
so I might need some assistance.

778
00:38:08,088 --> 00:38:10,190
These three topics
were suggested.

779
00:38:10,190 --> 00:38:13,126
Talk about dosing,
talk about use in pregnancy,

780
00:38:13,126 --> 00:38:15,061
and talk about et cetera.

781
00:38:15,061 --> 00:38:19,232
So, dosing, I’ll spend
about 8 or 10 minutes

782
00:38:19,232 --> 00:38:21,868
talking about why
we think this matters.

783
00:38:21,868 --> 00:38:23,870
Certainly, if you use
too little drug,

784
00:38:24,771 --> 00:38:26,139
you won’t get a benefit,

785
00:38:26,139 --> 00:38:29,175
and that might be why patients
don’t think that it works.

786
00:38:30,043 --> 00:38:32,412
If you use too much,
you can be toxic,

787
00:38:32,412 --> 00:38:35,715
and that’s not good for anyone,
providers, families,

788
00:38:35,715 --> 00:38:41,254
or patients themselves.
We like an optimal dose.

789
00:38:41,254 --> 00:38:43,990
The second topic I’ll spend
a little time on is,

790
00:38:43,990 --> 00:38:46,960
what do we know and not know
about its use in pregnancy,

791
00:38:46,960 --> 00:38:48,561
and specifically, is it safe?

792
00:38:49,529 --> 00:38:51,665
And then in the last
five minutes,

793
00:38:51,665 --> 00:38:54,634
I decided et cetera would be
a follow on to pregnancy

794
00:38:54,634 --> 00:38:56,903
and talk about
what we know about moms

795
00:38:56,903 --> 00:38:58,505
who wish to breastfeed.

796
00:39:00,507 --> 00:39:03,743
So, dosing first.
We know a lot about dosing.

797
00:39:03,743 --> 00:39:05,645
This drug has been
around a long time.

798
00:39:06,379 --> 00:39:09,783
And here is my now
40-year timeline

799
00:39:09,783 --> 00:39:11,785
that is spilling over
the edge left

800
00:39:11,785 --> 00:39:16,556
and right, that shows
a wide array of studies

801
00:39:16,556 --> 00:39:18,558
that have been performed
over the years.

802
00:39:19,059 --> 00:39:21,861
The ones in green are phase
one two trials,

803
00:39:21,861 --> 00:39:24,497
many of them
funded by the NHLBI.

804
00:39:25,699 --> 00:39:28,068
Reddish ones
are phase three trials.

805
00:39:28,068 --> 00:39:30,570
Again, many of them
funded by NIH.

806
00:39:31,538 --> 00:39:38,712
And the purple boxes at 1997
and 2017 represent the dates

807
00:39:38,712 --> 00:39:41,247
at which
the US FDA

808
00:39:41,247 --> 00:39:44,150
approved hydroxyurea for use,
first in adults

809
00:39:44,784 --> 00:39:46,920
and then 20 years later,
in children.

810
00:39:48,988 --> 00:39:50,690
What it shows, collectively,

811
00:39:50,690 --> 00:39:53,727
despite all
the theoretical concerns

812
00:39:53,727 --> 00:39:58,698
and naysayers and hate mail,
is that the drug really is safe

813
00:39:58,698 --> 00:40:02,068
and effective in all ages,
from infancy to adulthood.

814
00:40:02,068 --> 00:40:04,137
There’s a lot of nuances,
of course.

815
00:40:05,472 --> 00:40:08,641
But I think that
the overwhelming compendium

816
00:40:08,641 --> 00:40:10,643
of evidence
supports this statement.

817
00:40:11,377 --> 00:40:13,379
And the other is
that the dose matters.

818
00:40:13,379 --> 00:40:16,583
As I said, too little,
too much is not good.

819
00:40:16,583 --> 00:40:18,585
But it’s not uncommon
to have drugs

820
00:40:18,585 --> 00:40:20,987
with what we call
a therapeutic window

821
00:40:20,987 --> 00:40:22,789
where we try to get
the right dose.

822
00:40:23,556 --> 00:40:25,825
And I’m going to use the term
occasionally, MTD,

823
00:40:25,825 --> 00:40:29,129
maximum tolerated
dose as a marker.

824
00:40:29,129 --> 00:40:32,031
Because it’s in the literature,
but it’s really a misnomer.

825
00:40:32,031 --> 00:40:35,168
We’re not trying to push
the patient to a toxic level.

826
00:40:35,735 --> 00:40:38,037
It really should be called
the optimal dose.

827
00:40:38,037 --> 00:40:40,240
It’s the one at which you get
the greatest benefits

828
00:40:40,240 --> 00:40:42,242
without any toxicity.

829
00:40:42,242 --> 00:40:44,410
And I’m going to start by
talking about a study

830
00:40:44,410 --> 00:40:49,249
long ago from 1992.
This one is -- was published in

831
00:40:49,249 --> 00:40:51,951
Blood over 30 years ago.
It was led by Sam

832
00:40:51,951 --> 00:40:55,488
Suresh, a great leader
in the use of hydroxyurea

833
00:40:55,488 --> 00:40:59,125
and also the architect
of the phase three MSH trial.

834
00:40:59,692 --> 00:41:02,796
This study is probably
the best article

835
00:41:02,796 --> 00:41:04,864
in the medical literature
on this topic

836
00:41:04,864 --> 00:41:06,199
that I’ve ever seen.

837
00:41:06,199 --> 00:41:08,968
It covers everything
about hydroxyurea,

838
00:41:08,968 --> 00:41:11,805
and I’ve listed all of
the things on the left column.

839
00:41:12,372 --> 00:41:15,708
It’s really a rich study.
It’s well written.

840
00:41:15,708 --> 00:41:19,512
It’s as if Professor
Suresh is telling a story to us.

841
00:41:20,079 --> 00:41:23,383
So, I would encourage you
to dig back into your PubMed

842
00:41:23,383 --> 00:41:25,852
and find this article
and read it,

843
00:41:25,852 --> 00:41:29,088
and you’ll learn a lot about
hydroxyurea, including dosing.

844
00:41:30,490 --> 00:41:33,626
The two stories that I’m
going to tell you about dosing.

845
00:41:33,626 --> 00:41:36,496
One represents a -- well,
they’re both prospective trials,

846
00:41:36,496 --> 00:41:38,932
but one prospectively
looked at the dosing,

847
00:41:38,932 --> 00:41:41,201
and the other one
has a way to look at it.

848
00:41:41,201 --> 00:41:43,937
So, trial number one is called
No Harm.

849
00:41:43,937 --> 00:41:46,506
This is a study
that I was fortunate to join

850
00:41:46,506 --> 00:41:48,808
with Chandy John on the left,
from Indiana,

851
00:41:48,808 --> 00:41:51,711
who’s a malaria expert;
and Robert Opoka,

852
00:41:52,846 --> 00:41:55,949
a pediatrician and physician
scientist in Uganda.

853
00:41:56,616 --> 00:41:59,652
And they were interested
in using hydroxyurea.

854
00:42:00,587 --> 00:42:01,821
Professor Opoka was

855
00:42:01,821 --> 00:42:03,356
but there was
a lot of reluctance,

856
00:42:03,356 --> 00:42:05,725
because it was in
a malaria endemic region.

857
00:42:06,459 --> 00:42:08,161
And some data
from Inserm [phonetic sp]

858
00:42:08,161 --> 00:42:11,698
had shown that while hydroxyurea
might have solitary benefits

859
00:42:11,698 --> 00:42:14,300
on sickle cell,
it also increased adhesion

860
00:42:14,300 --> 00:42:17,403
molecule expression
on endothelium,

861
00:42:17,403 --> 00:42:20,607
and these might serve as
docking proteins for malaria.

862
00:42:20,607 --> 00:42:22,208
So, it might make
your sickle cell better

863
00:42:22,208 --> 00:42:25,845
but make you more risk --
at risk for malaria severity.

864
00:42:26,613 --> 00:42:30,416
And so, we were able then --
together with the three of us,

865
00:42:30,416 --> 00:42:34,187
to create a protocol
that would look at hydroxyurea

866
00:42:34,187 --> 00:42:36,122
in the setting of
Sub-Saharan Africa,

867
00:42:36,122 --> 00:42:39,525
but the primary endpoint
would be malaria infections.

868
00:42:40,426 --> 00:42:42,962
And so, this was published
in 2017

869
00:42:42,962 --> 00:42:45,932
but it really started
back in 2013, 2014

870
00:42:45,932 --> 00:42:49,068
and it was a double
blind placebo controlled trial.

871
00:42:49,068 --> 00:42:51,971
So, you wouldn’t think that
you could do many more placebo

872
00:42:51,971 --> 00:42:54,440
controlled trials
with hydroxyurea.

873
00:42:54,440 --> 00:42:57,677
But in this case, it was for
12 months based on the fact

874
00:42:57,677 --> 00:42:59,612
that the primary endpoint
was malaria.

875
00:43:00,179 --> 00:43:02,248
And the ethics committee
agreed to this,

876
00:43:02,248 --> 00:43:05,084
or actually recommended it,
as long as with --

877
00:43:05,084 --> 00:43:08,521
there was no signal of danger
that all of the patients

878
00:43:08,521 --> 00:43:10,823
could roll over
to open label hydroxyurea.

879
00:43:10,823 --> 00:43:12,492
And that is what happened.

880
00:43:12,492 --> 00:43:15,128
Two hundred children,
very young, age one to four,

881
00:43:15,128 --> 00:43:16,329
were enrolled quickly.

882
00:43:16,329 --> 00:43:18,564
You can see the enrollment curve
on the right.

883
00:43:18,564 --> 00:43:21,401
Very brisk enrollment
over about a four month period.

884
00:43:22,869 --> 00:43:26,539
And so, the treatment
was fixed dose hydroxyurea

885
00:43:26,539 --> 00:43:29,042
of 20 milligrams
per kilogram per day.

886
00:43:29,042 --> 00:43:31,878
It showed no risk of --
no increased risk of malaria.

887
00:43:31,878 --> 00:43:34,914
If anything, it was higher
in the placebo arm.

888
00:43:34,914 --> 00:43:37,083
We had all of
the treatment benefits.

889
00:43:37,083 --> 00:43:40,219
And surprisingly, equivalent
dose limiting toxicities.

890
00:43:40,219 --> 00:43:43,022
There were plenty of DLTs,

891
00:43:43,022 --> 00:43:45,258
as we call them,
on the placebo arm,

892
00:43:45,258 --> 00:43:48,728
and the equivalent number
to the study treatment.

893
00:43:49,963 --> 00:43:51,698
So, then what we had were
children

894
00:43:51,698 --> 00:43:55,368
who had been on hydroxyurea
or placebo for 12 months.

895
00:43:55,368 --> 00:43:58,738
And per the Ethics Committee,
they all got put on open label.

896
00:43:58,738 --> 00:44:01,140
And you can see
a very simple figure here

897
00:44:01,140 --> 00:44:02,408
on the right that shows that

898
00:44:02,408 --> 00:44:05,211
although there was a difference
of about a gram,

899
00:44:05,211 --> 00:44:08,147
a gram and a half of hemoglobin
during year one,

900
00:44:08,147 --> 00:44:09,615
hydroxyurea placebo,

901
00:44:09,615 --> 00:44:12,352
this quickly rectified.
And by --

902
00:44:12,352 --> 00:44:16,055
in year two, they all had
the same higher hemoglobin.

903
00:44:16,055 --> 00:44:17,790
So, everyone was taking
their medicine,

904
00:44:17,790 --> 00:44:19,959
and this was 20 milligrams
per kilogram,

905
00:44:19,959 --> 00:44:21,661
so a reasonable dose.

906
00:44:22,328 --> 00:44:25,298
But what we realized then was
we had a unique opportunity

907
00:44:25,298 --> 00:44:27,834
to prospectively
ask the question,

908
00:44:27,834 --> 00:44:31,004
does it matter
if you go up to MTD?

909
00:44:31,004 --> 00:44:32,872
Does it matter if you stay at 20

910
00:44:32,872 --> 00:44:35,308
or go up to 30 milligrams
per kilogram?

911
00:44:35,308 --> 00:44:38,011
What would be the risks
and benefits of higher dosing?

912
00:44:39,012 --> 00:44:41,614
And our prediction was that
you would have more laboratory

913
00:44:41,614 --> 00:44:43,783
and clinical benefits at MTD,

914
00:44:43,783 --> 00:44:45,685
but you’d also have
more toxicities.

915
00:44:45,685 --> 00:44:47,120
And it would be
a shared decision

916
00:44:47,120 --> 00:44:50,890
making or a weighted discussion
of whether it was better

917
00:44:50,890 --> 00:44:54,427
to stay safe and lower
or to push the dose up.

918
00:44:56,629 --> 00:44:58,698
So, what happens surprised us.

919
00:44:58,698 --> 00:45:01,934
Here on the left, you can see --
the top left, here’s the dose.

920
00:45:01,934 --> 00:45:03,669
The fixed dose
stayed at about 20

921
00:45:03,669 --> 00:45:05,505
for the duration
of the 18 months.

922
00:45:06,172 --> 00:45:10,810
And the dose went up to 30
in the escalation arm.

923
00:45:10,810 --> 00:45:13,212
So, it did what
it was supposed to do.

924
00:45:13,880 --> 00:45:15,548
And you can see
on the lower left

925
00:45:15,548 --> 00:45:18,651
that almost all of
the clinical events

926
00:45:18,651 --> 00:45:22,588
were significantly reduced
on the higher treatment arm.

927
00:45:23,423 --> 00:45:26,259
Now this is not treatment
versus placebo.

928
00:45:26,259 --> 00:45:29,429
This is 30 milligrams
versus 20 milligrams.

929
00:45:30,263 --> 00:45:33,299
And the dose limiting
toxicities, to our surprise,

930
00:45:33,299 --> 00:45:34,901
was equal or equal.

931
00:45:35,568 --> 00:45:37,403
And so, the reason
it was only 18 months

932
00:45:37,403 --> 00:45:39,439
was that the primary
endpoint was met.

933
00:45:39,439 --> 00:45:42,408
The safety thresholds
were not crossed.

934
00:45:42,408 --> 00:45:45,078
And so, the study
was halted early by the DSMB.

935
00:45:45,678 --> 00:45:47,947
And so, this was one of
the two publications

936
00:45:47,947 --> 00:45:49,649
that I’m going
to show you

937
00:45:49,649 --> 00:45:52,118
that actually show dose
escalation mattered

938
00:45:52,652 --> 00:45:56,322
and seemed safe
and seemed worthy of doing it.

939
00:45:58,224 --> 00:46:00,560
Now the second trial
started almost the same way,

940
00:46:00,560 --> 00:46:02,361
but it was not 
placebo controlled.

941
00:46:02,361 --> 00:46:05,131
It was an open label --
is an open label called REACH.

942
00:46:05,731 --> 00:46:10,903
And this was to use hydroxy
in four countries, Angola,

943
00:46:11,537 --> 00:46:15,541
DR Congo, Uganda and Kenya,
fixed dose for six months,

944
00:46:15,541 --> 00:46:19,612
then a dose escalation with 600
children across the four sites.

945
00:46:19,612 --> 00:46:23,816
And here are the PIs of that.
And this is funded by the NHLBI.

946
00:46:24,617 --> 00:46:26,686
And in the original report,

947
00:46:26,686 --> 00:46:30,423
all of the events that were
relevant to us at the time,

948
00:46:30,423 --> 00:46:33,292
sickle events, vaso-occlusive
pain, acute chest,

949
00:46:33,292 --> 00:46:36,729
this is all compared to the
screening pre-treatment period.

950
00:46:36,729 --> 00:46:38,798
And this was also published
in New England Journal.

951
00:46:38,798 --> 00:46:41,567
And there’s Professor Tshilolo,

952
00:46:41,567 --> 00:46:44,504
who presented it at ASH
in a plenary session,

953
00:46:44,504 --> 00:46:47,773
the first African author
to ever present at ASH

954
00:46:47,773 --> 00:46:49,675
in a plenary session.

955
00:46:49,675 --> 00:46:52,345
So, what happened after that?

956
00:46:52,345 --> 00:46:54,046
Well, this children
have been on treatment

957
00:46:54,046 --> 00:46:55,781
now for about eight years.

958
00:46:55,781 --> 00:46:58,751
We have a terrific retention
over that time.

959
00:46:59,318 --> 00:47:02,021
And the other thing that we’ve
done is slowly increase the dose

960
00:47:02,021 --> 00:47:05,391
from what an initial
conservative MTD call

961
00:47:05,391 --> 00:47:07,293
to what we’ll call
an optimized dose.

962
00:47:07,293 --> 00:47:09,428
And you can see
that the dose is about 25

963
00:47:09,428 --> 00:47:11,631
to 30 across all the sites.

964
00:47:11,631 --> 00:47:15,168
And this is almost exactly
what we see in the U.S.

965
00:47:15,168 --> 00:47:17,036
other high resource settings,

966
00:47:17,036 --> 00:47:20,006
that the dose for children
is about 25 to 30,

967
00:47:20,006 --> 00:47:21,841
well tolerated at that.

968
00:47:21,841 --> 00:47:24,644
But this also gave us
the opportunity to look at

969
00:47:24,644 --> 00:47:26,612
what happened
during the fixed dose,

970
00:47:26,612 --> 00:47:28,781
what happened
during the escalation,

971
00:47:28,781 --> 00:47:31,617
and more importantly, what
happened after we reached MTD,

972
00:47:31,617 --> 00:47:33,819
and what we’ll call
the optimization.

973
00:47:33,819 --> 00:47:36,022
And there are thousands
of patient years here,

974
00:47:36,022 --> 00:47:38,391
so we really have
a good opportunity.

975
00:47:38,391 --> 00:47:41,160
And what I’m going
to show you here next

976
00:47:41,160 --> 00:47:43,563
is the comparison
between the fixed dose,

977
00:47:43,563 --> 00:47:49,035
which was about 17.5,
to this MTD optimal dose,

978
00:47:49,035 --> 00:47:50,937
which is about 27.

979
00:47:50,937 --> 00:47:53,105
So, this is analogous
to the no harm data

980
00:47:53,105 --> 00:47:54,774
that I just showed you.

981
00:47:54,774 --> 00:47:57,543
And this was just published
in The Lancet Haematology

982
00:47:57,543 --> 00:47:59,111
and shows once again,

983
00:47:59,111 --> 00:48:02,114
that although
being on any hydroxyurea

984
00:48:02,114 --> 00:48:05,685
is far better than untreated
and better than placebo,

985
00:48:05,685 --> 00:48:08,487
there is still a significant
benefit that you can get

986
00:48:09,222 --> 00:48:14,260
by increasing the dose
toward mild myelosuppression.

987
00:48:15,127 --> 00:48:17,163
And so, I would encourage you
to consider,

988
00:48:17,163 --> 00:48:18,931
if you’re going
to do the investment

989
00:48:18,931 --> 00:48:20,266
of treating your patients,

990
00:48:20,266 --> 00:48:23,135
that you try to use
an optimal dose,

991
00:48:23,135 --> 00:48:26,205
that you try to get a small
amount of myelosuppression.

992
00:48:26,205 --> 00:48:31,010
Neutrophil count of 2,000, 3,000
and this will give you

993
00:48:31,010 --> 00:48:33,379
the best bump
in hemoglobin, hemoglobin F.

994
00:48:33,379 --> 00:48:34,614
And as you can see,

995
00:48:34,614 --> 00:48:37,917
significantly better
than a lower dose.

996
00:48:37,917 --> 00:48:42,688
So, some of the reluctance
to use hydroxyurea in terms of,

997
00:48:42,688 --> 00:48:44,790
it doesn’t work,
it doesn’t help me

998
00:48:44,790 --> 00:48:49,195
is likely to be due
to not having a sufficient dose.

999
00:48:51,297 --> 00:48:54,400
Okay. Topic 2, hydroxyurea
during pregnancy.

1000
00:48:55,067 --> 00:48:59,639
So, 10 years ago, I had the
chance encounter with a woman.

1001
00:49:00,273 --> 00:49:01,974
I was in the Middle East
at a conference

1002
00:49:01,974 --> 00:49:05,411
and she was from the
Maldives archipelago off India.

1003
00:49:06,078 --> 00:49:08,447
And she told me
she has sickle thalassemia,

1004
00:49:10,049 --> 00:49:13,219
essentially beta zero
thalassemia we since learned.

1005
00:49:13,753 --> 00:49:18,591
She said, "I was pregnant
in 2005. I was told --

1006
00:49:18,591 --> 00:49:21,327
I started hydroxyurea in 2003,
became pregnant,

1007
00:49:21,327 --> 00:49:22,895
was told I had to stop.

1008
00:49:22,895 --> 00:49:25,131
I became so sick
and I miscarried.

1009
00:49:25,798 --> 00:49:28,134
So, I was determined
not to let that happen again.

1010
00:49:28,134 --> 00:49:30,169
In 2008, I was again told

1011
00:49:30,169 --> 00:49:33,139
I had to stop
at about 8 weeks, 10 weeks.

1012
00:49:33,139 --> 00:49:37,443
I was sick, almost died,
delivered in term infant.

1013
00:49:37,443 --> 00:49:39,812
What should I do
in my next pregnancy?"

1014
00:49:39,812 --> 00:49:42,415
And I said, well, there really
isn’t very good data.

1015
00:49:43,149 --> 00:49:46,285
I gave her some recommendations.
I spoke --

1016
00:49:46,285 --> 00:49:49,121
I sent some things to her OB
but said ultimately,

1017
00:49:49,121 --> 00:49:52,558
it was her decision
with her own providers.

1018
00:49:52,558 --> 00:49:54,627
And in fact,
she was probably talking

1019
00:49:54,627 --> 00:49:56,429
because she intended
to get pregnant.

1020
00:49:56,429 --> 00:49:59,965
And announced that in late 2013.

1021
00:49:59,965 --> 00:50:02,501
Once again, she was told
she had to stop.

1022
00:50:02,501 --> 00:50:04,337
Within about two to four weeks,

1023
00:50:04,337 --> 00:50:07,707
she had more crises
and decided to start again.

1024
00:50:07,707 --> 00:50:09,875
And she stayed on through
the end of the first

1025
00:50:09,875 --> 00:50:12,445
trimester --
second and third trimester.

1026
00:50:14,480 --> 00:50:16,716
So, why are we worried
about this?

1027
00:50:16,716 --> 00:50:19,952
Well, here’s what the
hydroxyurea package insert says.

1028
00:50:19,952 --> 00:50:23,122
It says there are no adequate,
well controlled studies.

1029
00:50:23,122 --> 00:50:25,358
I’ve highlighted the parts
I’m going to read to you --

1030
00:50:25,358 --> 00:50:27,326
potential harm to the fetus.

1031
00:50:27,326 --> 00:50:29,528
You should avoid
becoming pregnant.

1032
00:50:29,528 --> 00:50:32,098
And it classifies
the drug as Category D.

1033
00:50:32,098 --> 00:50:35,067
And for decades,
the FDA classifies drugs

1034
00:50:35,067 --> 00:50:37,870
according to their risk
and what’s known about it,

1035
00:50:37,870 --> 00:50:39,305
A, B, C, D and X.

1036
00:50:39,305 --> 00:50:41,941
X being the only truly
contraindicated one.

1037
00:50:42,842 --> 00:50:44,577
And then it adds that
for nursing mothers,

1038
00:50:44,577 --> 00:50:47,046
hydroxyurea is excreted
in human milk.

1039
00:50:47,680 --> 00:50:52,218
And a decision should be made
to either discontinuing nursing

1040
00:50:52,218 --> 00:50:54,620
or discontinue the drug,
taking into account

1041
00:50:54,620 --> 00:50:56,389
the importance of the drug
to the mother.

1042
00:50:56,389 --> 00:51:00,659
So, that’s an interesting little
recommendation at the end.

1043
00:51:01,527 --> 00:51:03,129
So, what is Category D?

1044
00:51:03,129 --> 00:51:04,997
Well, very similar
to what I just read you.

1045
00:51:04,997 --> 00:51:07,533
Positive evidence
of human fetal risk.

1046
00:51:07,533 --> 00:51:10,669
But the potential benefits
from the use of the drug

1047
00:51:10,669 --> 00:51:12,738
in pregnant women
may be acceptable,

1048
00:51:12,738 --> 00:51:18,544
despite its potential risks
if the drug is needed in a life

1049
00:51:18,544 --> 00:51:21,247
threatening situation
or serious disease.

1050
00:51:21,981 --> 00:51:24,049
So, this is where we should
think about hydroxyurea

1051
00:51:24,049 --> 00:51:25,284
and sickle cell.

1052
00:51:25,284 --> 00:51:27,186
Do we really think
that sickle cell

1053
00:51:27,186 --> 00:51:30,156
is a serious enough disease
that people need to be treated?

1054
00:51:31,257 --> 00:51:33,692
Curious, if only 25 percent

1055
00:51:33,692 --> 00:51:37,062
of the Jamaican sickle
cell population is on it.

1056
00:51:37,062 --> 00:51:39,932
Because without
blood transfusion support,

1057
00:51:39,932 --> 00:51:44,603
that means 75 percent of people
are not even on any disease

1058
00:51:44,603 --> 00:51:47,339
modifying or symptom
modifying therapy.

1059
00:51:47,973 --> 00:51:50,409
So, they’re basically untreated.

1060
00:51:50,409 --> 00:51:52,211
I think it is a life
threatening disease,

1061
00:51:52,211 --> 00:51:54,814
and there are data from Jamaica
that talk about this,

1062
00:51:54,814 --> 00:51:56,649
particularly in
the maternal context.

1063
00:51:56,649 --> 00:52:00,953
So, here’s a review
from Dr. Asnani, et.

1064
00:52:00,953 --> 00:52:04,924
al., talking about the maternal
risk of being pregnant

1065
00:52:04,924 --> 00:52:06,192
and in Jamaica.

1066
00:52:06,192 --> 00:52:10,529
And the maternal mortality ratio
was about 10 times higher

1067
00:52:10,529 --> 00:52:14,099
than in the rest of the island.
And then she also mentioned

1068
00:52:14,099 --> 00:52:16,035
Professor Serjeant’s
cohort study.

1069
00:52:16,035 --> 00:52:17,870
This was published
a few years back.

1070
00:52:18,471 --> 00:52:20,840
Again, a large number
of pregnancies

1071
00:52:20,840 --> 00:52:22,641
and a large number
of women,

1072
00:52:22,641 --> 00:52:26,479
this would, of course, have
no hydroxyurea or transfusion,

1073
00:52:26,479 --> 00:52:30,115
just the risk of being
pregnant on the island.

1074
00:52:30,115 --> 00:52:33,085
You can see more abortions,
fewer live births,

1075
00:52:33,085 --> 00:52:34,820
higher risk
of retained placenta.

1076
00:52:34,820 --> 00:52:37,523
And actually,
five of the 71 women died.

1077
00:52:37,523 --> 00:52:42,828
So, much higher mortality rate
than the Sickle Cell Unit one.

1078
00:52:42,828 --> 00:52:46,765
So, sickle cell and pregnancy
are high risk in Jamaica,

1079
00:52:46,765 --> 00:52:48,501
but it’s also high risk
around the world.

1080
00:52:48,501 --> 00:52:52,571
This is a meta-analysis
that was published in Blood 2015

1081
00:52:52,571 --> 00:52:55,641
that said, "Overall, six times
the mortality ratio,

1082
00:52:55,641 --> 00:52:59,178
but also a lot of morbidity
for the mother and the baby."

1083
00:52:59,979 --> 00:53:02,882
And a U.S. study that
was published last year

1084
00:53:03,782 --> 00:53:06,585
showed that there was
a much higher mortality risk,

1085
00:53:06,585 --> 00:53:07,786
even in the U.S.

1086
00:53:07,786 --> 00:53:12,157
So -- and also morbidity
with stroke and thromboembolism.

1087
00:53:12,157 --> 00:53:13,993
So, pregnancy is high risk.

1088
00:53:13,993 --> 00:53:17,129
Pregnancy in sickle cell
is very high risk.

1089
00:53:17,129 --> 00:53:19,999
And so, we should consider
this a serious disease.

1090
00:53:20,733 --> 00:53:21,967
So, why are we worried?

1091
00:53:21,967 --> 00:53:24,303
Well, there are studies
in the toxicology

1092
00:53:24,303 --> 00:53:27,006
that hydroxyurea
can harm fetuses.

1093
00:53:27,006 --> 00:53:29,341
Here’s one in the rat fetus
using 200

1094
00:53:29,341 --> 00:53:31,744
to 1,000 milligrams
per kilogram.

1095
00:53:32,344 --> 00:53:35,314
Now that’s a very high dose.
And of course, mice and rats

1096
00:53:35,314 --> 00:53:37,783
don’t metabolize hydroxyurea
the same way.

1097
00:53:37,783 --> 00:53:41,153
But at that dose,
it was teratogenic.

1098
00:53:41,153 --> 00:53:43,689
But the mice
and the rats were dead

1099
00:53:43,689 --> 00:53:45,491
because they had
aplastic marrow.

1100
00:53:45,491 --> 00:53:49,094
So, they were clearly treated
with very, very high doses.

1101
00:53:50,563 --> 00:53:53,866
Here’s another study
that used up to 150 milligrams.

1102
00:53:53,866 --> 00:53:55,968
This was done with guidance

1103
00:53:55,968 --> 00:53:59,238
by the U.S. Environmental
Protection Group.

1104
00:53:59,238 --> 00:54:01,073
And they put in their discussion

1105
00:54:01,073 --> 00:54:04,043
that strategies are needed
in the animal literature

1106
00:54:04,043 --> 00:54:06,679
to establish clinically
relevant exposures.

1107
00:54:08,514 --> 00:54:12,117
And I was part of an invited
review a few years ago

1108
00:54:12,117 --> 00:54:14,253
with a bona fide card

1109
00:54:14,253 --> 00:54:18,090
carrying environmental
toxicologist Steve Dertinger.

1110
00:54:18,090 --> 00:54:20,826
And we reviewed all of the data
that was available.

1111
00:54:20,826 --> 00:54:23,596
And there, I think,
an interesting findings

1112
00:54:23,596 --> 00:54:26,765
about what hydroxyurea does
and what it doesn’t do based

1113
00:54:26,765 --> 00:54:31,437
on a large amount of preclinical
animal and human data.

1114
00:54:32,838 --> 00:54:36,642
All right. The EPA also has
this sort of infamous statement

1115
00:54:36,642 --> 00:54:39,144
that toxicity,
you document it in animals,

1116
00:54:39,144 --> 00:54:41,280
but safety, you need
to try it in humans.

1117
00:54:41,280 --> 00:54:42,815
So, what’s the experience?

1118
00:54:42,815 --> 00:54:46,185
Well, the MSH trial
followed 300 participants,

1119
00:54:46,185 --> 00:54:50,422
150 females,
and reported 94 pregnancies.

1120
00:54:50,422 --> 00:54:53,559
And there’s a summary, some of
the people in the audience

1121
00:54:53,559 --> 00:54:56,962
probably are part of this,
said that exposure of the fetus

1122
00:54:56,962 --> 00:55:00,265
to hydroxyurea does not
cause teratogenic changes.

1123
00:55:01,166 --> 00:55:02,668
A large study from Europe,

1124
00:55:02,668 --> 00:55:07,172
the ESCORT-HU looks
at almost 2,000 patients,

1125
00:55:07,172 --> 00:55:10,976
about 100 women
with pregnancy and rights.

1126
00:55:10,976 --> 00:55:14,580
Pregnancy outcomes after
hydroxyurea exposure in patients

1127
00:55:14,580 --> 00:55:17,583
are rather reassuring
when compared to the risks

1128
00:55:17,583 --> 00:55:19,852
of maternal
and perinatal complications

1129
00:55:19,852 --> 00:55:22,354
related to sickle cell
disease itself.

1130
00:55:22,354 --> 00:55:25,057
So, again, it goes back
to that phraseology

1131
00:55:25,057 --> 00:55:27,526
in the package insert

1132
00:55:27,526 --> 00:55:30,963
that referred to the seriousness
of the disease itself

1133
00:55:30,963 --> 00:55:33,832
versus the potential risks
of the treatment.

1134
00:55:33,832 --> 00:55:38,170
However, there was
a more recent article in 2022

1135
00:55:38,170 --> 00:55:40,072
that talked --
it was a self-report.

1136
00:55:40,072 --> 00:55:43,208
So, probably a little less
in terms of documentation.

1137
00:55:43,208 --> 00:55:47,279
Again, several of the authors
are probably at this audience

1138
00:55:47,279 --> 00:55:50,149
that said for mothers
who were taking hydroxyurea

1139
00:55:50,149 --> 00:55:51,450
when they got pregnant,

1140
00:55:51,450 --> 00:55:53,118
that didn’t seem
to cause a problem.

1141
00:55:53,118 --> 00:55:57,122
But if they kept
going during pregnancy,

1142
00:55:57,122 --> 00:55:59,458
then they had a higher rate
of miscarriage.

1143
00:55:59,458 --> 00:56:03,429
So, it’s possible that there are
some risks to take it during.

1144
00:56:03,996 --> 00:56:07,166
Now, interestingly, ASH has
a pocket handbook about it.

1145
00:56:07,166 --> 00:56:11,704
I don’t know where this
bullet point comes from.

1146
00:56:11,704 --> 00:56:14,373
I’ve asked the people who wrote
it, they don’t remember.

1147
00:56:14,373 --> 00:56:18,310
But it said some women choose
to stop taking hydroxyurea

1148
00:56:18,310 --> 00:56:19,611
early in their pregnancy

1149
00:56:19,611 --> 00:56:22,214
and then started again
in the third trimester.

1150
00:56:22,214 --> 00:56:24,083
Presumably to help
their own health,

1151
00:56:24,083 --> 00:56:26,351
maybe improve blood flow
for the baby,

1152
00:56:26,351 --> 00:56:29,588
maybe to improve birth weight
and so on.

1153
00:56:29,588 --> 00:56:31,190
So, best is what’s out there.

1154
00:56:32,691 --> 00:56:34,760
So, how are we going to do it?

1155
00:56:34,760 --> 00:56:37,529
Monika said we need more data,
and we absolutely do.

1156
00:56:37,529 --> 00:56:39,431
And there are two
clinical trials

1157
00:56:39,431 --> 00:56:41,800
that we’ve been associated
with in the Caribbean,

1158
00:56:41,800 --> 00:56:43,869
one in Dominican Republic,
called Sacred,

1159
00:56:43,869 --> 00:56:45,771
one in Jamaica Extend.

1160
00:56:46,305 --> 00:56:48,507
And this took youngsters
who were on --

1161
00:56:49,308 --> 00:56:52,377
who benefited from hydroxyurea
for stroke prevention.

1162
00:56:52,377 --> 00:56:54,780
And they’ve been treated
for more than five years,

1163
00:56:54,780 --> 00:56:56,782
some up to almost 10 years.

1164
00:56:56,782 --> 00:57:02,855
And they’re now older teens
entering child-bearing age,

1165
00:57:02,855 --> 00:57:05,958
and we can now ask the questions
about long term risks.

1166
00:57:05,958 --> 00:57:08,227
And so, they --
all of these youngsters

1167
00:57:08,227 --> 00:57:09,495
are going to enroll

1168
00:57:09,495 --> 00:57:11,864
in a surveillance study
known as SAFE,

1169
00:57:11,864 --> 00:57:15,267
which is a serial assessment
of fertility experiences.

1170
00:57:15,267 --> 00:57:17,369
And we’re going to do
quarterly visits

1171
00:57:17,369 --> 00:57:20,439
as they’re on their medication
and ask questions

1172
00:57:20,439 --> 00:57:23,542
and gather data about
their fertility potential

1173
00:57:23,542 --> 00:57:25,144
and reproductive outcomes.

1174
00:57:25,811 --> 00:57:28,747
We don’t know whether we’ll
get many or any pregnancies.

1175
00:57:28,747 --> 00:57:30,349
We’re certainly
not advocating yet.

1176
00:57:30,349 --> 00:57:31,917
But while
they’re on hydroxyurea,

1177
00:57:31,917 --> 00:57:34,186
we do want to capture
that information.

1178
00:57:34,186 --> 00:57:37,256
And so, we’re grateful
to both of these cohorts

1179
00:57:37,256 --> 00:57:39,358
and their leaders
to enroll the patients.

1180
00:57:40,959 --> 00:57:43,462
All right. Last quick topic,
the et cetera is --

1181
00:57:43,462 --> 00:57:45,531
well, if you get
through your pregnancy

1182
00:57:45,531 --> 00:57:47,733
and you wish to breastfeed,
what about that?

1183
00:57:48,400 --> 00:57:51,703
Well, why does the package
insert say that it’s unsafe?

1184
00:57:51,703 --> 00:57:55,908
It references this article.
So, you pull this article.

1185
00:57:55,908 --> 00:57:59,978
You find out that it is exactly
one patient, a case report.

1186
00:58:00,512 --> 00:58:04,183
And it’s a 29 year old Woman
with chronic leukemia.

1187
00:58:04,183 --> 00:58:06,852
She was taking hydroxyurea
three times a day.

1188
00:58:07,419 --> 00:58:10,756
She had breast milk collected
two hours after the last dose.

1189
00:58:10,756 --> 00:58:12,658
It was detected in the milk.

1190
00:58:12,658 --> 00:58:15,828
And the conclusion was,
hydroxyurea does enter the milk.

1191
00:58:15,828 --> 00:58:19,598
So, women should not lactate
cut and dried.

1192
00:58:20,566 --> 00:58:23,602
But as you know,
mothers with sickle cell anemia

1193
00:58:23,602 --> 00:58:25,504
really wouldn’t take
the drug that way.

1194
00:58:25,504 --> 00:58:27,639
They would take it once a day,

1195
00:58:27,639 --> 00:58:30,108
a higher dose
with a shorter half-life.

1196
00:58:30,108 --> 00:58:32,578
Most of it should be gone
from the serum.

1197
00:58:32,578 --> 00:58:34,646
Most of it is excreted
in the urine.

1198
00:58:35,347 --> 00:58:37,850
Does it go into the breast milk?
Does it accumulate?

1199
00:58:37,850 --> 00:58:40,552
Does it go in? Does it go out?
Really wasn’t known.

1200
00:58:41,220 --> 00:58:43,989
So, with the help of our
clinical pharmacology group

1201
00:58:43,989 --> 00:58:45,224
at Cincinnati Children’s,

1202
00:58:45,224 --> 00:58:50,629
we designed a prospective trial
to study in lactating women

1203
00:58:50,629 --> 00:58:53,498
whether hydroxyurea
goes into the blood

1204
00:58:53,498 --> 00:58:55,167
and the urine
and the breast milk.

1205
00:58:55,167 --> 00:58:59,438
And can we measure it, and can
we create population profiles?

1206
00:58:59,938 --> 00:59:01,373
And I was pretty skeptical

1207
00:59:01,373 --> 00:59:03,775
that lactating women
would sign up for this,

1208
00:59:03,775 --> 00:59:06,478
but I was very wrong.
Women were very eager.

1209
00:59:06,478 --> 00:59:07,980
Most of them
didn’t have sickle cell.

1210
00:59:07,980 --> 00:59:10,515
We did allow a few
who were pregnant

1211
00:59:10,515 --> 00:59:14,620
to lactating women with
sickle cell to join at the end.

1212
00:59:14,620 --> 00:59:17,356
But these were normal,
healthy women.

1213
00:59:18,290 --> 00:59:20,359
And so, what we found
was that, in fact,

1214
00:59:20,359 --> 00:59:21,660
it does go into the milk.

1215
00:59:21,660 --> 00:59:24,763
You can see, here’s four
examples of plasma levels

1216
00:59:24,763 --> 00:59:26,498
as well as milk levels.

1217
00:59:26,498 --> 00:59:29,301
There’s a very easy passage,
if you will,

1218
00:59:29,301 --> 00:59:31,270
from plasma into milk.

1219
00:59:31,270 --> 00:59:32,771
But then it’s two way street.

1220
00:59:32,771 --> 00:59:36,041
So, if you wait a few minutes,
it actually drops in the milk.

1221
00:59:36,041 --> 00:59:39,378
And because it sort of
equilibrates with the plasma,

1222
00:59:39,378 --> 00:59:42,214
so you get this initial amount
that’s in the milk.

1223
00:59:42,214 --> 00:59:45,017
But as time goes by,
and that time is in minutes,

1224
00:59:45,017 --> 00:59:49,388
not hours, as the drug
gets excreted from the plasma,

1225
00:59:49,388 --> 00:59:50,889
the equilibration goes back,

1226
00:59:50,889 --> 00:59:52,824
and the drug actually
leaves the milk.

1227
00:59:53,492 --> 00:59:55,961
So, it does not accumulate,
not like urine,

1228
00:59:55,961 --> 00:59:57,229
where it can’t go backward.

1229
00:59:57,229 --> 00:59:59,164
And so, at the lower right,
you can see that

1230
00:59:59,164 --> 01:00:02,434
after a single thousand
milligram hydroxyurea dose.

1231
01:00:02,968 --> 01:00:06,271
A total of 2.2 milligrams
would be transferred.

1232
01:00:06,271 --> 01:00:09,441
But this is if you count
and add up all the milk

1233
01:00:09,441 --> 01:00:13,245
that was like at 30 minutes
and 60 minutes and 90 minutes.

1234
01:00:13,245 --> 01:00:15,380
But that’s not
the way women breastfeed.

1235
01:00:15,380 --> 01:00:17,950
If you were to do it
at three hours, six hours,

1236
01:00:17,950 --> 01:00:20,485
nine hours, it’s about
half that one milligram.

1237
01:00:21,219 --> 01:00:25,757
So, 1 milligram out of 1000.
Is that safe? Is it not safe?

1238
01:00:25,757 --> 01:00:27,726
So, WHO gives some guidance

1239
01:00:27,726 --> 01:00:29,761
called the relative
infant dosage.

1240
01:00:29,761 --> 01:00:33,632
And it’s the fraction of the
dose in milligrams per kilogram

1241
01:00:33,632 --> 01:00:35,367
relative to what
the mother’s dose.

1242
01:00:35,367 --> 01:00:38,704
And if the relative infant dose
is less than 10 percent,

1243
01:00:38,704 --> 01:00:40,439
most medications
are quite safe.

1244
01:00:41,406 --> 01:00:43,976
And so, for hydroxy,
it’s 3.4 percent

1245
01:00:43,976 --> 01:00:46,812
and that’s if you fed every
30 minutes after your dose.

1246
01:00:46,812 --> 01:00:48,680
If you wait three hours,
it’s half that.

1247
01:00:48,680 --> 01:00:51,383
So, about 1 to 1.5 percent.

1248
01:00:51,383 --> 01:00:54,086
And so, based on these
WHO recommendations,

1249
01:00:54,086 --> 01:00:56,488
you would conclude
that hydroxyurea is not

1250
01:00:56,488 --> 01:00:59,458
contraindicated
because it’s in the safe levels.

1251
01:00:59,458 --> 01:01:01,326
Again, based on whether
you think

1252
01:01:01,326 --> 01:01:03,128
the drug is important
for that mother

1253
01:01:03,128 --> 01:01:06,999
to have versus having untreated
sickle cell disease.

1254
01:01:07,866 --> 01:01:12,371
So, maybe it’s time to not
ignore the theoretical risks

1255
01:01:12,371 --> 01:01:15,240
but use the human data
for safety and incorporate them

1256
01:01:15,240 --> 01:01:17,042
into our clinical decision
making.

1257
01:01:17,542 --> 01:01:21,980
Here’s the mom who delivered
her hydroxy treated child,

1258
01:01:21,980 --> 01:01:25,050
who is now 10 years old and
the smartest child in her class.

1259
01:01:25,050 --> 01:01:29,121
And this mother has always
offered herself as a guidepost

1260
01:01:29,121 --> 01:01:32,324
and likes to talk to women
who need to make their choices.

1261
01:01:32,324 --> 01:01:35,994
And so, with that, I’ll end
and happy to take any questions.

1262
01:01:35,994 --> 01:01:37,596
Thank you.

1263
01:01:41,833 --> 01:01:43,435
Lesley King: Thanks, Russell.

1264
01:01:44,002 --> 01:01:46,838
Okay.
So, another great presentation.

1265
01:01:46,838 --> 01:01:51,109
Are there any questions
from the floor?

1266
01:01:52,811 --> 01:01:56,415
Okay. So, Prof.

1267
01:01:56,415 --> 01:01:58,517
Knight-Madden has
a question here for you.

1268
01:02:00,052 --> 01:02:02,054
Jennifer Knight-Madden: Hello.

1269
01:02:02,054 --> 01:02:03,355
Russell Ware: Hello.

1270
01:02:03,355 --> 01:02:04,589
Jennifer Knight-Madden: Okay.

1271
01:02:04,589 --> 01:02:08,093
Hi. Hey, Russell.
Great talk as usual. Question.

1272
01:02:08,093 --> 01:02:12,030
So, the study that’s going
on in Dom Rep and Jamaica,

1273
01:02:13,298 --> 01:02:16,701
what will happen when the --
if the girls get pregnant?

1274
01:02:17,803 --> 01:02:20,505
What advice would they be given
at that point in time?

1275
01:02:21,740 --> 01:02:23,341
Russell Ware: It’s a
great question.

1276
01:02:23,341 --> 01:02:25,343
Remember, this is
a surveillance study.

1277
01:02:25,343 --> 01:02:30,482
So, there’s no guidance
about even birth control

1278
01:02:30,482 --> 01:02:31,850
or sexual activity.

1279
01:02:31,850 --> 01:02:33,952
This is handled
by the physicians,

1280
01:02:34,519 --> 01:02:36,621
which I think
in this transition age,

1281
01:02:36,621 --> 01:02:39,291
is probably some combination
of the pediatric

1282
01:02:39,291 --> 01:02:40,559
and the adult group.

1283
01:02:40,559 --> 01:02:42,861
Angela Rankine-Mullings,
I think, is there.

1284
01:02:42,861 --> 01:02:45,764
And maybe Professor Reid
can answer more about that.

1285
01:02:47,132 --> 01:02:48,834
The information
would be captured.

1286
01:02:49,434 --> 01:02:51,870
And I don’t know
if they would insist

1287
01:02:51,870 --> 01:02:54,406
that the young lady
come off treatment

1288
01:02:54,406 --> 01:02:57,642
and whether they would restart
it later or use transfusions.

1289
01:02:57,642 --> 01:02:59,211
I don’t know what the practice

1290
01:02:59,211 --> 01:03:02,514
is now of pregnant women
in Jamaica.

1291
01:03:02,514 --> 01:03:03,915
Jennifer Knight-Madden: So,
it would be based

1292
01:03:03,915 --> 01:03:06,051
on the standard of care

1293
01:03:06,051 --> 01:03:08,320
in the clinic
is what you’re saying.

1294
01:03:08,320 --> 01:03:09,554
Russell Ware: That’s right.

1295
01:03:09,554 --> 01:03:13,058
Jennifer Knight-Madden: Okay.
Thank you.

1296
01:03:15,193 --> 01:03:16,795
Lesley King: Go ahead first.

1297
01:03:18,296 --> 01:03:23,435
Okay. So, one, and then
you [unintelligible]. Go ahead.

1298
01:03:23,435 --> 01:03:24,636
Cheryl Stucky: Hi, Dr. Ware.

1299
01:03:24,636 --> 01:03:26,972
This is Cheryl Stucky.
Great presentation.

1300
01:03:27,706 --> 01:03:30,342
I work on animal models
of sickle cell disease

1301
01:03:30,342 --> 01:03:31,643
using the mouse models.

1302
01:03:31,643 --> 01:03:34,479
And I was wondering
if it would be beneficial

1303
01:03:34,479 --> 01:03:38,416
to do a mouse study
where we use the dose

1304
01:03:38,416 --> 01:03:42,554
equivalent of your higher
30 milligrams per kilogram dose

1305
01:03:42,554 --> 01:03:46,691
in the mice and sickle mice
and control mice.

1306
01:03:48,059 --> 01:03:50,195
Do long term treatment
with the dams,

1307
01:03:50,195 --> 01:03:51,429
then let them get pregnant.

1308
01:03:51,429 --> 01:03:56,434
Then thoroughly analyze the pups
for any sort of deficits

1309
01:03:57,302 --> 01:04:00,405
and the breast milk
as well see how they develop

1310
01:04:00,405 --> 01:04:04,042
and let them also then
have pups themselves.

1311
01:04:04,042 --> 01:04:05,644
Would that be
a valuable study?

1312
01:04:06,378 --> 01:04:07,712
Russell Ware: I think so.

1313
01:04:07,712 --> 01:04:10,715
And I’d be happy to talk
with you further about it.

1314
01:04:10,715 --> 01:04:14,586
I think the mouse model
is an imperfect version

1315
01:04:14,586 --> 01:04:16,087
for humans in part

1316
01:04:16,087 --> 01:04:21,193
because, remember,
any affected pups

1317
01:04:21,193 --> 01:04:22,961
are going to have
sickle disease

1318
01:04:22,961 --> 01:04:25,230
all through
their gestation, right?

1319
01:04:25,230 --> 01:04:26,898
There is no fetal.

1320
01:04:26,898 --> 01:04:29,367
I don’t know, maybe
your mouse model is different.

1321
01:04:30,402 --> 01:04:32,837
But yes, there needs
to be some animal data.

1322
01:04:33,371 --> 01:04:35,006
We also need the human data.

1323
01:04:35,006 --> 01:04:37,209
We need to do surveillance
and reporting.

1324
01:04:37,809 --> 01:04:40,645
And I’m pleased to say
that our colleagues

1325
01:04:40,645 --> 01:04:43,148
in Caribbean
part of the [unintelligible]

1326
01:04:43,148 --> 01:04:47,719
network have some examples.
Maybe 10 or 15 women

1327
01:04:47,719 --> 01:04:52,791
who are receiving hydroxy
exposure during pregnancy.

1328
01:04:53,391 --> 01:04:55,193
Professor Joe [unintelligible]

1329
01:04:55,193 --> 01:04:57,696
is in the process
of writing that up.

1330
01:04:57,696 --> 01:04:59,698
So, we’ll get
more information over time.

1331
01:04:59,698 --> 01:05:01,733
Cheryl Stucky: That’s great.
Thank you so much.

1332
01:05:01,733 --> 01:05:03,401
Lesley King: Prof.
Thein has a question. Go ahead.

1333
01:05:03,401 --> 01:05:04,603
Swee Lay Thein: Hi, Russell.

1334
01:05:04,603 --> 01:05:07,172
Sorry to not be able
to see you in person.

1335
01:05:07,172 --> 01:05:08,773
It’s Swee Lay here.

1336
01:05:08,773 --> 01:05:11,610
I think it was great
that you did this study

1337
01:05:11,610 --> 01:05:15,280
on the lactating, you know,
where they’re safe to lactate.

1338
01:05:15,280 --> 01:05:18,516
But you know, many mothers,
they collect their milk

1339
01:05:19,417 --> 01:05:24,589
because they store them in the
fridge for communion time.

1340
01:05:24,589 --> 01:05:28,159
So, given what you’ve done
and from the studies,

1341
01:05:28,159 --> 01:05:32,931
when do you think is the best
time window to collect the milk?

1342
01:05:34,299 --> 01:05:35,567
Russell Ware: That one’s
pretty --

1343
01:05:35,567 --> 01:05:38,436
has a clear answer, Swee Lay.
Thank you for the question.

1344
01:05:39,037 --> 01:05:45,043
The mother should finish
breastfeeding infants

1345
01:05:45,043 --> 01:05:46,645
and then take the medicine.

1346
01:05:46,645 --> 01:05:48,947
So, then for the next
two or three hours,

1347
01:05:48,947 --> 01:05:52,150
there’s no milk transfer
to the baby.

1348
01:05:52,751 --> 01:05:56,488
And then there’s a process
called pump and dump.

1349
01:05:56,488 --> 01:05:59,024
If you’re worried about
that first three hour feeding,

1350
01:05:59,024 --> 01:06:01,126
then you would just
throw that one away.

1351
01:06:01,126 --> 01:06:05,330
And then pump again at six hours
or feed again at six hours.

1352
01:06:05,330 --> 01:06:07,832
So, as long as you’re a few
hours out from that first one,

1353
01:06:07,832 --> 01:06:10,735
the amount of hydroxy
in the milk is really miniscule.

1354
01:06:11,936 --> 01:06:13,238
Swee Lay Thein: So, if it’s not
convenient,

1355
01:06:13,238 --> 01:06:14,739
they can actually
collect the milk

1356
01:06:14,739 --> 01:06:16,074
and feed it the baby
a bit later.

1357
01:06:16,074 --> 01:06:17,309
Russell Ware: Oh, sure.

1358
01:06:17,309 --> 01:06:19,311
That’s what breastfeeding women
do all the time.

1359
01:06:19,311 --> 01:06:23,181
They pump and freeze
for months.

1360
01:06:24,849 --> 01:06:27,852
Lesley King: All right.
Okay. Great. Russell, thanks.

1361
01:06:28,586 --> 01:06:30,288
Another great talk as usual,

1362
01:06:30,288 --> 01:06:34,392
and I’m sure we’ll see you here
in Jamaica sometime soon.

1363
01:06:34,392 --> 01:06:35,694
All right.

1364
01:06:35,694 --> 01:06:37,062
Russell Ware: I’m sorry
for the inconvenience.

1365
01:06:37,062 --> 01:06:38,296
Lesley King: Not a problem.
Take care.

1366
01:06:38,296 --> 01:06:40,298
Happy that you were able
to participate.

1367
01:06:41,266 --> 01:06:44,336
Okay. So, we are maybe about --
running about five minutes late.

1368
01:06:44,336 --> 01:06:47,172
So, we’re going to go on now
to our third speaker,

1369
01:06:47,172 --> 01:06:49,674
who is Dr. Luisanna Sanchez.

1370
01:06:49,674 --> 01:06:53,078
And she is a pediatric
hematologist-oncologist

1371
01:06:53,078 --> 01:06:56,247
at the Indiana Hemophilia
& Thrombosis Center.

1372
01:06:56,247 --> 01:06:59,751
And she’s going to speak to us
on clinical and hematopoietic

1373
01:06:59,751 --> 01:07:03,021
profiles associated
with sustained response.

1374
01:07:19,237 --> 01:07:20,872
Luisanna Sanchez: Good morning,
everyone.

1375
01:07:20,872 --> 01:07:22,240
My name is Luisanna Sanchez.

1376
01:07:22,240 --> 01:07:24,442
And I’m a pediatric hematologist
and sickle cell doctor

1377
01:07:24,442 --> 01:07:26,978
at the Indiana Hemophilia &
Thrombosis Center.

1378
01:07:26,978 --> 01:07:29,547
I thank the organizers
for the invitation here today.

1379
01:07:29,547 --> 01:07:31,149
This is my second time
in Kingston.

1380
01:07:31,149 --> 01:07:33,518
And I can’t believe that it was
almost 10 years ago

1381
01:07:33,518 --> 01:07:36,221
when I was here
presenting at the 2016

1382
01:07:36,221 --> 01:07:37,455
[unintelligible]

1383
01:07:37,455 --> 01:07:39,824
Conference when I was
only a medical student

1384
01:07:39,824 --> 01:07:42,694
hoping to become
a hematologist one day.

1385
01:07:42,694 --> 01:07:45,530
So, it’s a privilege to be here
today as a junior faculty,

1386
01:07:45,530 --> 01:07:47,232
sharing the room
with so many people

1387
01:07:47,232 --> 01:07:48,967
who are an inspiration for me

1388
01:07:48,967 --> 01:07:51,269
and having an inspiration
so far in my career.

1389
01:07:52,437 --> 01:07:54,038
Today, I would like to talk
to you

1390
01:07:54,038 --> 01:07:57,542
about the research study
that I participated on

1391
01:07:57,542 --> 01:08:00,145
when I was a fellow
at Texas Children’s Hospital.

1392
01:08:00,145 --> 01:08:01,980
It’s titled Clinical
and Hematopoietic

1393
01:08:01,980 --> 01:08:04,582
Profiles Associated with
a Sustained Hydroxyl Response

1394
01:08:04,582 --> 01:08:06,484
for Patients
with Sickle Cell Disease.

1395
01:08:09,721 --> 01:08:15,093
I do not have any disclaimers,
but after going to talk after

1396
01:08:15,093 --> 01:08:18,062
Dr. Russell Ware
and Dr. Monika Asnani,

1397
01:08:18,062 --> 01:08:20,832
I would like to say
that I love hydroxyurea.

1398
01:08:20,832 --> 01:08:22,600
[laughter]

1399
01:08:22,600 --> 01:08:25,069
And despite what you’re going
to hear in this presentation,

1400
01:08:25,069 --> 01:08:27,205
I’m a true believer
in hydroxyurea.

1401
01:08:27,806 --> 01:08:29,307
Especially because I was born

1402
01:08:29,307 --> 01:08:30,708
and raised
in Dominican Republic,

1403
01:08:30,708 --> 01:08:32,977
where we share
some of the limitations

1404
01:08:33,745 --> 01:08:36,981
and access barriers that were
discussed by Monika earlier on.

1405
01:08:37,749 --> 01:08:40,351
And now that
I have been practicing

1406
01:08:40,351 --> 01:08:43,788
and to have my own group
of sickle cell patients,

1407
01:08:43,788 --> 01:08:46,724
I’ve seen how children
who were studied on hydroxyurea

1408
01:08:46,724 --> 01:08:51,029
when they were infants,
and now are 11, 12 years old,

1409
01:08:51,029 --> 01:08:53,398
they have never experienced
a sickle cell crisis

1410
01:08:53,398 --> 01:08:55,767
in their lives,
and that’s something amazing.

1411
01:08:56,367 --> 01:08:59,037
And so, I do believe
that hydroxyurea improves

1412
01:08:59,037 --> 01:09:01,539
the lives of individuals
with sickle cell disease.

1413
01:09:02,173 --> 01:09:04,342
But now that hydroxyurea
has been around

1414
01:09:04,342 --> 01:09:06,811
for over two decades,
we’re seeing some heterogeneity

1415
01:09:06,811 --> 01:09:09,147
in long term response
to the therapy.

1416
01:09:09,147 --> 01:09:10,648
Patients may experience
a decline

1417
01:09:10,648 --> 01:09:13,284
on their F levels over time,
despite being compliant.

1418
01:09:14,118 --> 01:09:16,421
Some authors have reported
that sickle cell --

1419
01:09:16,988 --> 01:09:18,456
or individuals
with sickle cell disease

1420
01:09:18,456 --> 01:09:21,960
have well documented changes
in their bone marrows,

1421
01:09:21,960 --> 01:09:24,429
specifically when talking
about hematopoietic

1422
01:09:24,429 --> 01:09:26,798
stem and parenteral cells
or HSPCs.

1423
01:09:27,765 --> 01:09:29,367
So, we wonder in this context

1424
01:09:29,367 --> 01:09:31,803
if perhaps the impact
of hydroxyurea

1425
01:09:31,803 --> 01:09:35,306
in the HSPCs of pediatric
patients during treatment

1426
01:09:35,306 --> 01:09:37,475
could at least
be partially responsible

1427
01:09:37,475 --> 01:09:39,878
for the declining F
that we’re seeing over time.

1428
01:09:41,546 --> 01:09:43,715
To answer this question,
we first wanted to determine

1429
01:09:43,715 --> 01:09:47,252
if this was a true phenomenon
and what was the clinical impact

1430
01:09:47,252 --> 01:09:49,888
of declining hydroxyl response
in pediatric patients.

1431
01:09:50,455 --> 01:09:52,457
And second, we wanted
to characterize

1432
01:09:52,457 --> 01:09:55,460
the HSPC profiles associated
with hydroxyl response.

1433
01:09:57,328 --> 01:10:00,231
We started by screening a large
cohort of pediatric patients

1434
01:10:00,231 --> 01:10:05,069
seen in Texas Children’s
Hospital from 2011 to 2021.

1435
01:10:05,904 --> 01:10:10,008
Individuals who were SS or HbS
beta-thalassemia genotypes,

1436
01:10:10,542 --> 01:10:12,610
had these genotypes
and were on hydroxyurea

1437
01:10:12,610 --> 01:10:16,014
for more than five years
were eligible for the study.

1438
01:10:16,814 --> 01:10:20,151
We used mean corpuscular volumes
and absolute neutrophil counts,

1439
01:10:20,151 --> 01:10:23,288
as well as number
of hydroxyurea prescriptions

1440
01:10:23,288 --> 01:10:26,457
and outpatient notes,
has surrogates of compliance.

1441
01:10:27,091 --> 01:10:31,062
If an individual was felt
to be eligible for the study,

1442
01:10:31,062 --> 01:10:36,701
then we use the hemoglobin F
to be able to --

1443
01:10:36,701 --> 01:10:40,238
has a response to hydroxyurea
to categorize them

1444
01:10:40,238 --> 01:10:42,040
in one of two subgroups.

1445
01:10:42,040 --> 01:10:43,908
First the sustained
response group,

1446
01:10:43,908 --> 01:10:45,610
if after starting hydroxyurea,

1447
01:10:45,610 --> 01:10:49,180
they had an initial F induction
of more than 20 percent

1448
01:10:49,180 --> 01:10:50,615
that remained
above 20 percent

1449
01:10:50,615 --> 01:10:52,784
throughout their treatment
history.

1450
01:10:52,784 --> 01:10:55,853
The second option was to be part
of the decreased response group

1451
01:10:55,853 --> 01:10:58,289
when they had an appropriate
initial response

1452
01:10:58,289 --> 01:11:00,959
but then decreased to less
than 10 percent over time.

1453
01:11:01,759 --> 01:11:03,728
We proceeded to collect
clinical and laboratory

1454
01:11:03,728 --> 01:11:06,698
data longitudinally
for each individual patient

1455
01:11:06,698 --> 01:11:08,766
over the course
of 10 years of treatment.

1456
01:11:09,334 --> 01:11:11,769
Now, in the present time
when they came to clinic,

1457
01:11:11,769 --> 01:11:14,739
we collected whole blood samples
from these patients

1458
01:11:14,739 --> 01:11:16,240
that sent to our lab

1459
01:11:16,240 --> 01:11:18,843
to isolate peripheral blood
mononuclear cells.

1460
01:11:19,677 --> 01:11:23,314
We identified HSPC populations
using flow cytometry.

1461
01:11:23,314 --> 01:11:24,983
And then a subset
of the samples

1462
01:11:24,983 --> 01:11:27,151
was done for single cell
RNA sequencing.

1463
01:11:28,920 --> 01:11:31,089
Two hundred eight children
with SS or HbS

1464
01:11:31,089 --> 01:11:33,591
beta-thalassemia were included
in the study.

1465
01:11:33,591 --> 01:11:37,195
71 percent of them were part
of the sustained response group,

1466
01:11:37,195 --> 01:11:40,098
and 29 percent was --
were categorized as having

1467
01:11:40,098 --> 01:11:41,899
a decreased response
to hydroxyurea.

1468
01:11:42,600 --> 01:11:44,636
As you can see in the table,
their demographic

1469
01:11:44,636 --> 01:11:46,938
characteristics
were very homogeneous.

1470
01:11:47,672 --> 01:11:49,574
And the only thing
to highlight here

1471
01:11:49,574 --> 01:11:52,310
is that the median age
of start of hydroxyurea

1472
01:11:52,310 --> 01:11:53,745
in the sustained
response group

1473
01:11:53,745 --> 01:11:56,214
was four years old
compared to two years old

1474
01:11:56,214 --> 01:11:57,815
in the decreased response group.

1475
01:11:59,384 --> 01:12:01,819
In the interest of time,
we’re going to focus mainly

1476
01:12:01,819 --> 01:12:03,521
on the F values
of these patients.

1477
01:12:04,188 --> 01:12:05,456
As you can see in green,

1478
01:12:05,456 --> 01:12:07,258
patients with
a sustained response,

1479
01:12:07,825 --> 01:12:11,429
their baseline F levels
were about 26 percent

1480
01:12:11,429 --> 01:12:14,632
at the start of treatment
and remain above 20 percent

1481
01:12:14,632 --> 01:12:16,367
throughout their
treatment history.

1482
01:12:16,968 --> 01:12:19,270
In red, we’re seeing
those patients categorized

1483
01:12:19,270 --> 01:12:21,272
as having
a decreased response.

1484
01:12:21,272 --> 01:12:26,144
They started as well with high
F values, above 24 percent.

1485
01:12:26,911 --> 01:12:28,613
But these values
decreased to less

1486
01:12:28,613 --> 01:12:30,214
than 10 percent of our time.

1487
01:12:31,149 --> 01:12:33,785
When we looked at the mean
corpuscular volumes

1488
01:12:33,785 --> 01:12:36,654
and absolute counts
of these patients,

1489
01:12:36,654 --> 01:12:39,157
we’re seeing that they share
the macrocytosis

1490
01:12:39,157 --> 01:12:42,760
and myelosuppression at least
for the first eight years.

1491
01:12:42,760 --> 01:12:44,362
They were very similar.

1492
01:12:45,763 --> 01:12:50,234
When we subdivided
or when we adjusted for age,

1493
01:12:50,234 --> 01:12:54,205
their F values, something
very interesting happened.

1494
01:12:54,205 --> 01:12:57,709
Here we see
in the right upper corner

1495
01:12:57,709 --> 01:13:00,144
the patients who had
a sustained response.

1496
01:13:01,045 --> 01:13:04,415
In dark green, you can see
that individuals

1497
01:13:04,415 --> 01:13:08,419
that were at the time
of the transitional cut off

1498
01:13:08,419 --> 01:13:09,887
when we were analyzing data,

1499
01:13:09,887 --> 01:13:12,056
they were between
15 and 21 years old.

1500
01:13:12,623 --> 01:13:15,693
And they had been on hydroxyurea
for 10 years at least.

1501
01:13:15,693 --> 01:13:18,896
So, they were started
when they were already older.

1502
01:13:18,896 --> 01:13:22,266
And so, we were thinking
that if someone was started

1503
01:13:22,266 --> 01:13:23,868
on hydroxyurea
when they were older,

1504
01:13:23,868 --> 01:13:28,473
perhaps their F response
was not going to be as robust.

1505
01:13:28,473 --> 01:13:30,241
But we saw that actually,

1506
01:13:30,241 --> 01:13:34,345
these patients maintain levels
up of 20 percent over 10 years.

1507
01:13:35,513 --> 01:13:37,682
The opposite in the right
lower corner,

1508
01:13:37,682 --> 01:13:40,918
when you have a decreased
response group in children

1509
01:13:40,918 --> 01:13:44,155
who were started earlier
on on hydroxyurea,

1510
01:13:44,756 --> 01:13:47,558
even those that were
at the present time ages

1511
01:13:47,558 --> 01:13:49,627
between five and nine years old,

1512
01:13:49,627 --> 01:13:53,898
had a decreased response
and were less than 20 percent.

1513
01:13:55,900 --> 01:13:58,536
In this slide, we’re presenting
the clinical outcomes

1514
01:13:58,536 --> 01:13:59,971
of age-matched patients.

1515
01:13:59,971 --> 01:14:02,173
Age-matched patients
between the two groups.

1516
01:14:02,740 --> 01:14:04,909
We’re seeing that
between the groups,

1517
01:14:04,909 --> 01:14:07,278
they had similar number
of office visits.

1518
01:14:07,979 --> 01:14:11,449
But the sustained response group
had a higher incidence --

1519
01:14:12,283 --> 01:14:14,619
sorry, a decreased incidence
in sickle cell events

1520
01:14:14,619 --> 01:14:18,790
requiring hospitalizations,
transfusions, and procedures.

1521
01:14:18,790 --> 01:14:21,926
This data is expressed in number
of events per 100 patient use.

1522
01:14:22,894 --> 01:14:25,630
So, this was speaking
to the protective --

1523
01:14:26,864 --> 01:14:28,733
to the protection
of fetal hemoglobin

1524
01:14:28,733 --> 01:14:30,334
in the sustained
response group.

1525
01:14:31,769 --> 01:14:36,707
At this point, we were thinking,
well, having patients

1526
01:14:36,707 --> 01:14:38,943
that are experiencing
a decreased response

1527
01:14:38,943 --> 01:14:40,912
to hydroxyurea
is a true phenomenon.

1528
01:14:41,479 --> 01:14:44,749
And second, this is leading
to significant

1529
01:14:44,749 --> 01:14:47,018
clinical repercussions
in our patients.

1530
01:14:47,018 --> 01:14:49,687
So, we decided to take
a look at their cells.

1531
01:14:49,687 --> 01:14:52,456
But before doing that,
let’s just go through

1532
01:14:52,456 --> 01:14:54,992
this figure
as a reminder for all of us

1533
01:14:55,693 --> 01:14:58,529
that we have been
talking about HSPCs.

1534
01:14:58,529 --> 01:15:01,065
This is essentially
this four subgroups here

1535
01:15:01,065 --> 01:15:03,000
that we are seeing
on the far right

1536
01:15:03,568 --> 01:15:06,304
when we’re seeing stem cells,
multiple temporary cells,

1537
01:15:06,304 --> 01:15:08,840
committed progenitors
and recursive cells.

1538
01:15:09,574 --> 01:15:13,077
But all of them start from this
small group of cells

1539
01:15:13,077 --> 01:15:15,079
in the top middle
of the screen.

1540
01:15:15,079 --> 01:15:17,148
They’re called hematopoietic
stem cells.

1541
01:15:17,882 --> 01:15:21,052
These are a subgroup of cells
that are subdivided

1542
01:15:21,052 --> 01:15:24,088
in long term HSCs
and short term HSCs.

1543
01:15:24,822 --> 01:15:27,825
Their main characteristic
is that they maintain

1544
01:15:27,825 --> 01:15:33,064
their ability to self-renewal,
or self-regenerate over time.

1545
01:15:33,898 --> 01:15:37,869
The short term HSCs
then eventually generate

1546
01:15:37,869 --> 01:15:39,604
the multiple temporary
inter cells.

1547
01:15:40,504 --> 01:15:42,974
They will eventually
go down the path

1548
01:15:43,474 --> 01:15:45,476
and lose
their generation capacity

1549
01:15:45,476 --> 01:15:49,380
and give rise to all the cells
in the body,

1550
01:15:49,380 --> 01:15:51,082
including erythroid cells.

1551
01:15:54,585 --> 01:15:58,222
We use specific antibodies
to target cell

1552
01:15:58,222 --> 01:16:00,324
surface markers in the cells

1553
01:16:00,324 --> 01:16:02,760
and to be able to characterize
each compartment

1554
01:16:02,760 --> 01:16:04,362
that we saw
in the prior slide.

1555
01:16:06,597 --> 01:16:09,867
We found out that patients
with a sustained response

1556
01:16:09,867 --> 01:16:13,671
here in green had a higher
number of multipotent cells

1557
01:16:13,671 --> 01:16:15,439
and hematopoietic stem cells.

1558
01:16:15,439 --> 01:16:17,375
While the decreased
response group

1559
01:16:17,375 --> 01:16:21,746
had a higher number of
multipotent progenitor cells

1560
01:16:21,746 --> 01:16:24,949
in general and megakaryocyte
erythroid progenitor cells.

1561
01:16:27,051 --> 01:16:29,287
So, what does this mean?
Let’s go back to the figure.

1562
01:16:29,287 --> 01:16:30,688
And essentially,
what we’re saying

1563
01:16:30,688 --> 01:16:32,857
is that the sustained
response group

1564
01:16:32,857 --> 01:16:34,392
had a higher number of cells

1565
01:16:34,392 --> 01:16:36,794
that maintained
their regeneration capacity,

1566
01:16:37,328 --> 01:16:40,464
while the decreased response
group had more cells

1567
01:16:40,464 --> 01:16:43,034
that already committed
to become erythroid cells.

1568
01:16:45,169 --> 01:16:48,439
Now, let’s talk about
single cell RNA sequencing.

1569
01:16:48,439 --> 01:16:51,609
We randomly selected
four different patients,

1570
01:16:52,143 --> 01:16:55,713
two from each group,
sustained response group

1571
01:16:56,280 --> 01:16:58,282
and decreased response group.

1572
01:16:59,417 --> 01:17:03,587
Here, each patient had been
on hydroxyurea

1573
01:17:03,587 --> 01:17:08,392
for at least five years and had
similar hydroxyurea doses

1574
01:17:08,392 --> 01:17:10,861
by the time
that the sample was collected.

1575
01:17:10,861 --> 01:17:17,168
As you can see, these patients
had their F level values

1576
01:17:17,168 --> 01:17:19,904
that were expected for the group
that they belong to.

1577
01:17:19,904 --> 01:17:23,607
For example,
patient sample number

1578
01:17:23,607 --> 01:17:27,712
three had an F value
of 10.5 percent.

1579
01:17:28,512 --> 01:17:31,449
Across the four samples there
or [unintelligible]

1580
01:17:31,449 --> 01:17:33,484
microcytosis
and myelosuppression.

1581
01:17:35,653 --> 01:17:40,157
We proceeded to select
C 34+ Lin-

1582
01:17:40,157 --> 01:17:46,530
cells that were selected to
flow cytometry and cell sorting.

1583
01:17:47,631 --> 01:17:50,201
And then use
10 x genomics technology

1584
01:17:50,201 --> 01:17:51,469
to perform single cell

1585
01:17:51,469 --> 01:17:53,904
sequencing of approximately
10,000 cells

1586
01:17:53,904 --> 01:17:55,606
for these four
different patients.

1587
01:17:56,907 --> 01:17:58,709
On the right, you can see
the clustering

1588
01:17:58,709 --> 01:18:02,146
of these different cells.
Each dot represents a cell,

1589
01:18:02,146 --> 01:18:05,549
and each cluster or color
represents cells

1590
01:18:05,549 --> 01:18:09,086
that are related to each other
by their gene expression.

1591
01:18:10,321 --> 01:18:13,290
We found that this cluster
right here in the middle

1592
01:18:13,290 --> 01:18:14,892
seems to have cells
that are more

1593
01:18:14,892 --> 01:18:18,229
HMC like or
hematopoietic multipotentials.

1594
01:18:19,063 --> 01:18:21,799
And that actually
30 percent of this cluster

1595
01:18:21,799 --> 01:18:24,535
belonged to the sustained
response group,

1596
01:18:24,535 --> 01:18:26,504
while only 3.5 percent
of them

1597
01:18:26,504 --> 01:18:28,606
were part of
the decreased response group.

1598
01:18:29,707 --> 01:18:32,510
On the other hand, we saw
a cluster of cells right here

1599
01:18:32,510 --> 01:18:35,546
in the right lower corner
with a C34+ --

1600
01:18:36,280 --> 01:18:38,716
sorry, C33+ myeloid signature.

1601
01:18:40,317 --> 01:18:44,088
We know that this is
a cell server antigen

1602
01:18:44,088 --> 01:18:47,258
that is absent from multipotent
hematopoietic stem cells.

1603
01:18:48,359 --> 01:18:52,129
We found that only patients with
a decreased response group --

1604
01:18:52,129 --> 01:18:54,532
in the decreased response
group had these cells.

1605
01:18:57,268 --> 01:19:04,008
In terms of gene expression,
when we look at the results,

1606
01:19:04,008 --> 01:19:07,578
we found that nearly 200 genes
with over two full gene

1607
01:19:07,578 --> 01:19:09,280
expression
between the two groups.

1608
01:19:10,081 --> 01:19:11,315
72 percent --

1609
01:19:11,315 --> 01:19:15,686
72 of these genes were enriched
in the decreased response group.

1610
01:19:16,420 --> 01:19:18,355
The most interesting aspect
of all of this

1611
01:19:18,355 --> 01:19:22,893
is that 13 of the 72 genes
were in the NF Kappa B1 pathway.

1612
01:19:24,662 --> 01:19:27,131
When we look at the expression
of the NF Kappa B

1613
01:19:27,131 --> 01:19:30,267
across the cluster profiling
on our single cell,

1614
01:19:30,267 --> 01:19:32,303
similar to the slide
before on the right,

1615
01:19:32,837 --> 01:19:35,673
we see how the highest
expression is identified

1616
01:19:35,673 --> 01:19:37,741
in patients
who had a decreased response.

1617
01:19:39,176 --> 01:19:43,581
NF Kappa B is a pathway known
to promote differentiation

1618
01:19:43,581 --> 01:19:46,617
and loss of cell renewal
in response to inflammation.

1619
01:19:47,751 --> 01:19:50,454
We think that this is maybe
part of the difference

1620
01:19:50,454 --> 01:19:53,290
why we’re seeing patients
lose a response --

1621
01:19:53,290 --> 01:19:54,525
or like lose their ability

1622
01:19:54,525 --> 01:19:56,360
to response of hydroxyurea
over time.

1623
01:19:58,462 --> 01:20:01,632
So, in summary, we found that
almost 30 percent of children

1624
01:20:01,632 --> 01:20:04,034
with sickle cell disease
who are taking hydroxyurea

1625
01:20:04,034 --> 01:20:06,403
eventually will have
a decreased response

1626
01:20:06,403 --> 01:20:08,305
based on their F levels.

1627
01:20:08,305 --> 01:20:11,175
We saw both by flow cytometry
and single cell

1628
01:20:11,175 --> 01:20:13,711
RNA sequencing of HSPCs,

1629
01:20:13,711 --> 01:20:15,579
that patients with
a decreased response

1630
01:20:15,579 --> 01:20:18,015
will have a lower
amount of cells

1631
01:20:18,015 --> 01:20:20,151
that are hematopoietic,
multipotent cells.

1632
01:20:20,718 --> 01:20:24,488
And had more cells
that are C33+ myeloid

1633
01:20:24,488 --> 01:20:27,057
that were committed
to become erythroid cells.

1634
01:20:29,593 --> 01:20:33,898
I do want to highlight
that we saw again in this study,

1635
01:20:33,898 --> 01:20:36,700
just like many other studies
in the 40 years

1636
01:20:36,700 --> 01:20:39,603
that hydroxyurea has been around
to protect our slides,

1637
01:20:40,171 --> 01:20:43,707
that F values
or F levels are protected.

1638
01:20:44,208 --> 01:20:46,977
Hydroxyurea is protective
of patients.

1639
01:20:47,578 --> 01:20:50,014
And I do want to acknowledge
that it’s possible

1640
01:20:50,014 --> 01:20:52,216
that increasing hydroxyurea
doses for these patients

1641
01:20:52,216 --> 01:20:54,919
may restore
some of this F induction.

1642
01:20:55,653 --> 01:20:58,789
But at the same time,
our findings are suggesting

1643
01:20:58,789 --> 01:21:02,259
that premature aging of
the hematopoietic system

1644
01:21:02,259 --> 01:21:05,396
plays an important role in
long term hydroxyurea response.

1645
01:21:06,463 --> 01:21:09,200
So, where are we going
to go next?

1646
01:21:09,800 --> 01:21:11,368
One of the important
questions is,

1647
01:21:11,368 --> 01:21:13,204
is this the chicken or the egg?

1648
01:21:13,204 --> 01:21:16,540
So, are these changes
that we’re seeing related

1649
01:21:16,540 --> 01:21:20,077
to an individual susceptibility
to lose fetal induction,

1650
01:21:20,744 --> 01:21:22,980
or are we seeing this
as a consequence

1651
01:21:22,980 --> 01:21:26,183
of fetal of hydroxyurea
related effects?

1652
01:21:27,284 --> 01:21:29,486
So, we would like to add
more patient samples

1653
01:21:29,486 --> 01:21:32,489
to our flow cytometry
experiments,

1654
01:21:32,489 --> 01:21:34,959
as well as single cell
RNA sequencing.

1655
01:21:34,959 --> 01:21:38,562
We would like to add a control
samples for patients who are --

1656
01:21:38,562 --> 01:21:41,565
who have sickle cell disease
and who are not on hydroxyurea.

1657
01:21:42,633 --> 01:21:46,337
And we would like to know more
about how age

1658
01:21:46,337 --> 01:21:47,605
is playing a role here,

1659
01:21:47,605 --> 01:21:53,410
and how we can use this data
to hopefully,

1660
01:21:53,410 --> 01:21:56,814
patients who are known to have
a decreased response phenotype,

1661
01:21:57,348 --> 01:21:59,583
we can’t offer
them personalized

1662
01:21:59,583 --> 01:22:01,785
strategies to enhance
F induction.

1663
01:22:02,319 --> 01:22:06,090
And perhaps facilitate early
selection of alternative

1664
01:22:06,090 --> 01:22:07,825
treatments
for sickle cell disease.

1665
01:22:10,628 --> 01:22:13,697
Thank you very much to the NHLBI

1666
01:22:13,697 --> 01:22:17,601
and Sickle Cell in Focus
Conference organizers,

1667
01:22:17,601 --> 01:22:19,570
to the Texas
Children’s Hematology Center,

1668
01:22:19,570 --> 01:22:21,939
Flanagan Lab,
our collaborators

1669
01:22:21,939 --> 01:22:24,041
and, of course,
the participants that --

1670
01:22:24,742 --> 01:22:26,810
patients that participated
in the study.

1671
01:22:26,810 --> 01:22:28,946
I would like to also
thank the financial support

1672
01:22:28,946 --> 01:22:30,614
of the American Society
of Hematology

1673
01:22:30,614 --> 01:22:32,983
and National Brain
Disorders Foundation.

1674
01:22:32,983 --> 01:22:34,451
Thank you very much.

1675
01:22:34,451 --> 01:22:39,323
[applause]

1676
01:22:39,323 --> 01:22:42,559
Lesley King: Okay.
Great presentation, Dr. Sanchez.

1677
01:22:42,559 --> 01:22:45,062
And I see a number
of hands here already.

1678
01:22:46,297 --> 01:22:47,598
I think this is
quite interesting,

1679
01:22:47,598 --> 01:22:48,832
what she’s talking about.

1680
01:22:48,832 --> 01:22:51,769
Because just doing a quick
review of some of our patients,

1681
01:22:51,769 --> 01:22:53,904
this is something that
I’ve actually noticed,

1682
01:22:53,904 --> 01:22:57,341
is that we do get
that initial response.

1683
01:22:57,341 --> 01:23:01,478
But that sustained response,
we see the F levels dropping.

1684
01:23:01,478 --> 01:23:03,614
And the question is,
you know, why?

1685
01:23:04,114 --> 01:23:07,484
Well, okay.
So, we’re going to start off.

1686
01:23:07,484 --> 01:23:09,520
Male Speaker: That was
fantastic.

1687
01:23:09,520 --> 01:23:11,155
Thanks for sharing
the RNA seq data

1688
01:23:11,155 --> 01:23:15,459
which is really provocative.
So, I wondered,

1689
01:23:16,960 --> 01:23:19,430
did you do a heat map
looking at the inflammasome?

1690
01:23:19,430 --> 01:23:22,433
And were there more than
NF Kappa B

1691
01:23:23,167 --> 01:23:25,469
elevated in the second part
of that was --

1692
01:23:25,469 --> 01:23:27,104
I think it’d be
really interesting

1693
01:23:27,104 --> 01:23:29,740
to look at the same study

1694
01:23:29,740 --> 01:23:32,209
and those getting
regular transfusions to see

1695
01:23:32,209 --> 01:23:35,779
if the signature
can be changed,

1696
01:23:35,779 --> 01:23:37,948
particularly in
the non-responding group.

1697
01:23:39,783 --> 01:23:41,618
Luisanna Sanchez: Thank you
very much for the question.

1698
01:23:42,186 --> 01:23:46,190
We are still analyzing
our RNA sequencing data.

1699
01:23:46,190 --> 01:23:51,295
And we have not explored
other pathways yet.

1700
01:23:51,829 --> 01:23:53,697
But it’s certainly something
that we would like

1701
01:23:53,697 --> 01:23:55,632
to add as a next step.

1702
01:23:56,200 --> 01:24:01,305
And we hope to add
within those control groups,

1703
01:24:01,305 --> 01:24:04,041
patients were also
on chronic transfusions,

1704
01:24:04,041 --> 01:24:07,878
who were on purpose,
did not include in the study.

1705
01:24:07,878 --> 01:24:10,447
Thinking that made
that a confounding factor.

1706
01:24:10,447 --> 01:24:12,750
But indeed, it’s something that
we’re going to start looking at.

1707
01:24:12,750 --> 01:24:14,485
Thank you.

1708
01:24:14,485 --> 01:24:16,587
Deepika Darbari: Great
presentation and great study.

1709
01:24:16,587 --> 01:24:19,757
And this is such a big problem
for many of our patients,

1710
01:24:19,757 --> 01:24:23,193
as we see, as they grow
and response is not as much.

1711
01:24:23,193 --> 01:24:26,130
How do you determine the
compliance in these patients?

1712
01:24:26,130 --> 01:24:28,465
Because many times
we just blame, you know,

1713
01:24:28,465 --> 01:24:30,734
maybe it’s they’re not
taking the medication.

1714
01:24:30,734 --> 01:24:32,336
Luisanna Sanchez: Great
question, Dr. Darbari.

1715
01:24:33,370 --> 01:24:38,509
So, this is, in part,
a retrospective review study

1716
01:24:38,509 --> 01:24:42,679
with a prospective arm
in the sense

1717
01:24:42,679 --> 01:24:46,450
of we reviewed
thousands of charts.

1718
01:24:46,450 --> 01:24:50,454
And took into consideration
that MCV values

1719
01:24:50,454 --> 01:24:52,456
and absolute interval counts,

1720
01:24:52,456 --> 01:24:54,658
as well as counting
the prescriptions.

1721
01:24:55,559 --> 01:24:57,828
That in their outpatient notes,

1722
01:24:57,828 --> 01:25:01,832
where providers will typically
write in their assessments

1723
01:25:01,832 --> 01:25:04,201
if they thought there was
a compliance issue.

1724
01:25:04,201 --> 01:25:07,337
So, that’s why we only select
the patients

1725
01:25:07,337 --> 01:25:12,276
who we felt met criteria
being compliant in both arms.

1726
01:25:13,043 --> 01:25:16,480
Those 200 patients are part
of like 2,000 patients

1727
01:25:16,480 --> 01:25:20,017
that we looked at,
that we felt were compliant

1728
01:25:20,017 --> 01:25:23,287
and had their F values
pertaining to each category.

1729
01:25:24,054 --> 01:25:26,457
But it’s certainly something
that I would like

1730
01:25:26,457 --> 01:25:29,226
to acknowledge,
that even if we push them

1731
01:25:29,226 --> 01:25:32,763
a little bit more
with their hydroxyurea,

1732
01:25:32,763 --> 01:25:37,067
having higher doses,
reaching to optimal doses,

1733
01:25:37,067 --> 01:25:39,870
I think that we could see
a little bit more of F,

1734
01:25:39,870 --> 01:25:42,206
especially in the decrease
response group.

1735
01:25:42,206 --> 01:25:45,876
But I do not think that
the findings

1736
01:25:45,876 --> 01:25:48,011
that we’re seeing
in the RNA sequencing

1737
01:25:48,011 --> 01:25:49,680
are going to change
significantly.

1738
01:25:49,680 --> 01:25:51,815
So, I do think
this is a true phenomenon

1739
01:25:51,815 --> 01:25:55,919
that many of us have experienced
and looked at over time,

1740
01:25:55,919 --> 01:25:57,287
and particularly
in our patients.

1741
01:25:57,287 --> 01:26:01,058
And perhaps, we have not
investigated deep enough yet.

1742
01:26:02,860 --> 01:26:04,127
Male Speaker: Really interesting
talk.

1743
01:26:04,127 --> 01:26:06,563
Do you -- very nice work.
Do you think that --

1744
01:26:06,563 --> 01:26:08,899
or is there any way to look back
at the data originally?

1745
01:26:08,899 --> 01:26:10,968
I don’t know if you did RNA
seq at baseline

1746
01:26:10,968 --> 01:26:13,170
to see if there are signals
or predictors

1747
01:26:13,170 --> 01:26:16,340
for who might continue
to respond or who might not.

1748
01:26:16,340 --> 01:26:17,975
And that might
help us understand

1749
01:26:17,975 --> 01:26:19,476
who should be on hydroxyurea
long term

1750
01:26:19,476 --> 01:26:21,278
and who maybe --
who shouldn’t.

1751
01:26:21,278 --> 01:26:22,513
Luisanna Sanchez: That
is amazing --

1752
01:26:22,513 --> 01:26:24,314
an amazing question.
We did not.

1753
01:26:25,249 --> 01:26:27,117
This was part of
my fellowship project

1754
01:26:27,885 --> 01:26:30,687
founded by ASH and National
Bleeding Disorders Foundation.

1755
01:26:30,687 --> 01:26:33,924
So, we were limited
in the amount of resources

1756
01:26:33,924 --> 01:26:35,826
that we could use
for RNA sequencing.

1757
01:26:35,826 --> 01:26:38,529
But now we’re going
to add more patients.

1758
01:26:39,029 --> 01:26:42,533
And one of the main criteria
was to be on hydroxyurea

1759
01:26:42,533 --> 01:26:45,869
for at least five years
before drawing the sample.

1760
01:26:46,570 --> 01:26:48,672
So, I wonder if we can
do that in patients

1761
01:26:48,672 --> 01:26:50,874
who are nine months of age.

1762
01:26:50,874 --> 01:26:53,110
And we can just --
at least have a few patients

1763
01:26:53,110 --> 01:26:55,379
that have never been
on hydroxyurea in that way.

1764
01:26:55,379 --> 01:26:56,980
Male Speaker: Yeah.
Really nice work.

1765
01:26:59,149 --> 01:27:00,384
Female Speaker: Fantastic study.

1766
01:27:00,384 --> 01:27:02,386
I’m so glad you accepted
the invitation

1767
01:27:02,386 --> 01:27:03,854
to come and talk here.

1768
01:27:03,854 --> 01:27:05,822
Because when I saw
your ASH presentation,

1769
01:27:05,822 --> 01:27:07,424
I said,
"I want to know more."

1770
01:27:08,025 --> 01:27:11,395
And this is a problem
that I treat adults, okay.

1771
01:27:11,929 --> 01:27:13,530
And when I see adults,

1772
01:27:13,530 --> 01:27:15,799
they become so sensitive
to the dose.

1773
01:27:16,633 --> 01:27:18,669
300 milligrams,
and they already hit,

1774
01:27:19,202 --> 01:27:21,972
you know, very low
ANC [phonetic sp].

1775
01:27:21,972 --> 01:27:23,807
And the other thing,
which I couldn’t explain

1776
01:27:23,807 --> 01:27:27,578
is when we restart them,
they no longer response.

1777
01:27:28,178 --> 01:27:31,582
So, I think all your future
directions to look at,

1778
01:27:31,582 --> 01:27:33,584
I think, is probably
premature aging

1779
01:27:33,584 --> 01:27:35,218
and the hematopoietic
stem cells.

1780
01:27:35,886 --> 01:27:39,756
But I really look forward
to your RNA seq data

1781
01:27:39,756 --> 01:27:44,161
and more to discuss with you.
But one thing I noticed, though,

1782
01:27:44,161 --> 01:27:47,064
even in those
with sustained response,

1783
01:27:47,064 --> 01:27:49,800
you use less than
10 percent [unintelligible].

1784
01:27:49,800 --> 01:27:52,436
But you look at those
with sustained response,

1785
01:27:52,436 --> 01:27:55,305
that was slowly declining.
Right?

1786
01:27:55,305 --> 01:27:58,508
From 30 down to like
just above 20.

1787
01:27:59,543 --> 01:28:03,280
So, maybe you observe
over another 10 years,

1788
01:28:03,280 --> 01:28:04,514
you will go down.

1789
01:28:04,514 --> 01:28:05,749
Luisanna Sanchez: That is true.
Yes.

1790
01:28:05,749 --> 01:28:07,084
Thank you very much,
[unintelligible].

1791
01:28:07,084 --> 01:28:09,019
Lesley King: All right.
One final question.

1792
01:28:09,019 --> 01:28:11,755
Male Speaker: Yes. Also
really impressed by these data.

1793
01:28:11,755 --> 01:28:17,794
I’m wondering how growth curves
factor into dosing over time.

1794
01:28:17,794 --> 01:28:20,797
Because you know
the sustained response

1795
01:28:20,797 --> 01:28:24,201
and the non-sustained response
were in different age groups.

1796
01:28:24,201 --> 01:28:27,671
So, I wonder how tightly
controlled the dosing

1797
01:28:27,671 --> 01:28:32,342
was per kg over the growth
of those individuals,

1798
01:28:32,342 --> 01:28:35,112
and whether
the non-sustained response

1799
01:28:35,112 --> 01:28:39,016
reflected a lower milligram
per kilogram dosing

1800
01:28:39,016 --> 01:28:42,586
because of not keeping up
with their growth.

1801
01:28:43,120 --> 01:28:44,321
Have you looked at that?

1802
01:28:44,321 --> 01:28:46,556
Luisanna Sanchez: Yes.
We use --

1803
01:28:46,556 --> 01:28:51,128
we make sure to write down
the milligrams of hydroxyurea

1804
01:28:51,128 --> 01:28:54,665
that each patient was on
throughout that 10 year period.

1805
01:28:55,699 --> 01:28:59,536
And that’s why I made
that acknowledgement at the end,

1806
01:28:59,536 --> 01:29:01,204
that there is some work
to do

1807
01:29:01,204 --> 01:29:03,173
still to push them
a little bit higher.

1808
01:29:03,674 --> 01:29:05,175
So, I do think, in part,

1809
01:29:05,175 --> 01:29:08,011
some of that decrease
could be related to that.

1810
01:29:08,011 --> 01:29:11,415
But regardless the changes,
that wouldn’t change necessarily

1811
01:29:11,415 --> 01:29:15,519
what we saw on flow cytometry
and RNA sequencing.

1812
01:29:15,519 --> 01:29:17,921
But I do think
that that’s a component.

1813
01:29:17,921 --> 01:29:19,222
Thank you.

1814
01:29:19,222 --> 01:29:20,424
Lesley King: All right.

1815
01:29:20,424 --> 01:29:22,025
[applause]
Luisanna Sanchez: Thanks.

1816
01:29:23,627 --> 01:29:24,828
Lesley King: Okay, great.

1817
01:29:24,828 --> 01:29:27,964
And certainly, look forward
to some future work

1818
01:29:27,964 --> 01:29:29,933
coming out from you,
Dr. Sanchez.

1819
01:29:29,933 --> 01:29:33,637
All right. So, we’re doing not
too badly on time right now.

1820
01:29:33,637 --> 01:29:37,240
Our last presentation
for this session

1821
01:29:38,108 --> 01:29:40,143
will be by Dr. Lauren Merz

1822
01:29:40,143 --> 01:29:43,880
on reexamining the utility
of absolute neutrophil count

1823
01:29:43,880 --> 01:29:47,718
as MTD in the context
of Duffy status.

1824
01:29:47,718 --> 01:29:51,755
Dr. Merz is currently a fellow
in hematology-oncology

1825
01:29:51,755 --> 01:29:53,924
at Dana-Farber Cancer
Institute

1826
01:29:54,524 --> 01:30:00,363
[unintelligible]
Brigham. Okay. Welcome.

1827
01:30:00,931 --> 01:30:06,336
[applause]

1828
01:30:06,336 --> 01:30:08,505
Lauren Merz: Hi, everyone.
It’s so great to be here.

1829
01:30:08,505 --> 01:30:12,709
Thank you for the invitation.
It’s a huge honor to be here

1830
01:30:12,709 --> 01:30:14,678
among such giants
in sickle cell.

1831
01:30:14,678 --> 01:30:16,480
So, thank you
for having me.

1832
01:30:16,480 --> 01:30:19,549
So, we’re going to talk about
the Duffy null phenotype today.

1833
01:30:19,549 --> 01:30:21,118
We’re first going to start
with looking at

1834
01:30:21,118 --> 01:30:24,554
how the Duffy null phenotype
impacts healthy people

1835
01:30:24,554 --> 01:30:25,889
and what we can expect.

1836
01:30:25,889 --> 01:30:28,358
We’re then going to look at the
impact of sickle cell disease

1837
01:30:28,358 --> 01:30:32,262
and ANC, and then how Duffy
null status plays into that.

1838
01:30:32,262 --> 01:30:34,531
We’re also then going
to examine what we know about

1839
01:30:34,531 --> 01:30:37,834
Duffy null status
and hydroxyurea MTD.

1840
01:30:37,834 --> 01:30:39,736
And then we’re going
to spend time with --

1841
01:30:39,736 --> 01:30:41,671
talking about gaps in knowledge
and next steps,

1842
01:30:41,671 --> 01:30:43,106
which there are many.

1843
01:30:43,106 --> 01:30:44,975
And there is lots of work
to be done.

1844
01:30:46,209 --> 01:30:48,378
So, we have to start
with neutrophil development.

1845
01:30:48,378 --> 01:30:50,413
So, our neutrophils mature
in the bone marrow.

1846
01:30:50,413 --> 01:30:52,883
It takes about 7
to 10 days to complete.

1847
01:30:52,883 --> 01:30:55,385
And then once we have
mature neutrophils,

1848
01:30:55,385 --> 01:30:56,820
these circulate
throughout the body

1849
01:30:56,820 --> 01:30:58,421
in places
where they’re most needed.

1850
01:30:58,421 --> 01:31:00,090
I think one of the important
things to emphasize

1851
01:31:00,090 --> 01:31:03,126
is that only 1 to 5 percent
of mature neutrophils

1852
01:31:03,126 --> 01:31:06,096
are circulating in the periphery
at any one time.

1853
01:31:06,096 --> 01:31:09,299
And they only remain there
for 3 to 24 hours.

1854
01:31:09,299 --> 01:31:12,068
The other 95 percent
of our mature Neutrophils

1855
01:31:12,068 --> 01:31:14,137
are in other reservoirs,
like the bone marrow,

1856
01:31:14,137 --> 01:31:16,606
the liver, the lungs,
and the spleen.

1857
01:31:17,307 --> 01:31:20,610
So, this means that when we
obtain our CBC with differential

1858
01:31:20,610 --> 01:31:22,779
and we look at
the absolute neutrophil count,

1859
01:31:22,779 --> 01:31:25,182
what we’re seeing is
a very brief snapshot

1860
01:31:25,182 --> 01:31:27,184
that can fluctuate
throughout time.

1861
01:31:27,184 --> 01:31:29,319
And we’re also looking
at an imperfect proxy

1862
01:31:29,319 --> 01:31:31,955
for the value
that really matters in health

1863
01:31:31,955 --> 01:31:34,891
and disease of total body
neutrophil counts.

1864
01:31:35,959 --> 01:31:38,695
When we look at
how we define normal,

1865
01:31:38,695 --> 01:31:41,064
which is what we often look at
with our reference intervals,

1866
01:31:41,064 --> 01:31:43,366
there’s a lot
of variation worldwide.

1867
01:31:43,366 --> 01:31:44,868
So, when we look at the African
continent,

1868
01:31:44,868 --> 01:31:47,070
we see that normal,
absolute nature fill counts

1869
01:31:47,070 --> 01:31:51,174
are anywhere from 500
to 5,400 in Togo,

1870
01:31:51,174 --> 01:31:53,910
900 to 3,800 in Uganda,

1871
01:31:53,910 --> 01:31:58,014
or 950 to 7,600
in the United Arab Emirates.

1872
01:31:58,949 --> 01:32:00,483
We also have a lot of variation

1873
01:32:00,483 --> 01:32:02,285
in normal ANC
in the United States.

1874
01:32:02,285 --> 01:32:05,422
So, as mentioned, I work at the
Dana-Farber Cancer Institute,

1875
01:32:05,422 --> 01:32:06,957
as well as Brigham
Women’s Hospital,

1876
01:32:06,957 --> 01:32:09,226
which are physically
connected institutions

1877
01:32:09,226 --> 01:32:11,027
that are across the street
from each other,

1878
01:32:11,027 --> 01:32:14,164
and yet there are dramatically
different reference intervals

1879
01:32:14,164 --> 01:32:16,266
that are used
by each of these institutions.

1880
01:32:16,266 --> 01:32:19,236
But Dana-Farber have a lower
limit of normal of 2,200

1881
01:32:19,236 --> 01:32:21,338
compared to Brigham Women
of 1,900.

1882
01:32:21,972 --> 01:32:24,541
When we look at large data
sets within the United States

1883
01:32:24,541 --> 01:32:25,909
that are broken down
by race,

1884
01:32:25,909 --> 01:32:28,578
we do see that there
are differences in normal

1885
01:32:28,578 --> 01:32:30,413
ANC among healthy people.

1886
01:32:30,413 --> 01:32:33,183
Where people who self-identify
as Black or African American

1887
01:32:33,183 --> 01:32:35,518
have cells that are about
700 cells

1888
01:32:35,518 --> 01:32:38,521
lower than people
who identify as white race.

1889
01:32:38,521 --> 01:32:41,191
And when we look at that
threshold of neutropenia,

1890
01:32:41,191 --> 01:32:44,828
which we’ve defined for decades
as an ANC of 1,500,

1891
01:32:44,828 --> 01:32:48,732
we see that 4.5 percent
of healthy black people

1892
01:32:48,732 --> 01:32:51,167
will be called neutropenic,

1893
01:32:51,167 --> 01:32:53,003
will be told that
they have a disease,

1894
01:32:53,003 --> 01:32:55,639
compared to only .79 percent
of white people.

1895
01:32:56,606 --> 01:32:59,409
So, I have to emphasize here
that race is a social construct.

1896
01:32:59,409 --> 01:33:02,379
It’s not a biologic fact,
and there is much more

1897
01:33:02,379 --> 01:33:05,548
genetic variation
within race than between race.

1898
01:33:05,548 --> 01:33:07,150
But we still have to ask
the question

1899
01:33:07,150 --> 01:33:10,120
of why are there observed
differences in ANC

1900
01:33:10,120 --> 01:33:11,955
between white
and black populations?

1901
01:33:12,889 --> 01:33:15,058
And surprisingly,
given the title of my talk,

1902
01:33:15,058 --> 01:33:17,060
we got the answer
in the early 2000s

1903
01:33:17,060 --> 01:33:18,795
with the Duffy null phenotype.

1904
01:33:18,795 --> 01:33:21,831
This is a T to C substitution
on chromosome 1

1905
01:33:21,831 --> 01:33:24,267
on one of the gene transcription
initiation sites.

1906
01:33:24,267 --> 01:33:27,003
And the homozygous polymorphism
C/C results

1907
01:33:27,003 --> 01:33:28,872
in the lack of the ACKR1

1908
01:33:28,872 --> 01:33:31,007
or DARC expression
on erythrocytes.

1909
01:33:31,007 --> 01:33:33,610
So, this is the Duffy
null phenotype.

1910
01:33:33,610 --> 01:33:35,679
We still see ACKR1
in other cells,

1911
01:33:35,679 --> 01:33:37,948
especially endothelial cells
and lymphoblasts.

1912
01:33:38,515 --> 01:33:40,183
We think that the Duffy --

1913
01:33:40,183 --> 01:33:43,787
the lack of the Duffy protein
was protective against malaria.

1914
01:33:43,787 --> 01:33:46,122
You take away
one of the docking proteins

1915
01:33:46,122 --> 01:33:49,359
for the malaria parasite to bind
to and enter the red blood cell.

1916
01:33:49,926 --> 01:33:53,830
When we account for the Duffy
null phenotype,

1917
01:33:53,830 --> 01:33:56,499
we no longer see any differences

1918
01:33:56,499 --> 01:33:58,935
in absolute neutrophil
counts by race.

1919
01:33:58,935 --> 01:34:00,670
So, we have
a complete explanation

1920
01:34:00,670 --> 01:34:02,472
for the differences
in absolute neutrophil

1921
01:34:02,472 --> 01:34:06,042
count with the Duffy
null phenotype alone.

1922
01:34:07,510 --> 01:34:09,279
Unsurprisingly, we see
the frequency

1923
01:34:09,279 --> 01:34:12,048
of the Duffy null phenotype
where it be most beneficial

1924
01:34:12,048 --> 01:34:14,617
and most protective
against plasmodium vivax.

1925
01:34:14,617 --> 01:34:19,556
So, in West Africa, we see 80
to 100 percent of the population

1926
01:34:19,556 --> 01:34:20,957
that will be Duffy null.

1927
01:34:20,957 --> 01:34:22,692
This is backed up
by some other recent studies,

1928
01:34:22,692 --> 01:34:23,960
and things like Clozapine,

1929
01:34:23,960 --> 01:34:25,462
where, when you actually
tested it

1930
01:34:25,462 --> 01:34:28,965
was near 100 percent
Duffy null phenotype.

1931
01:34:28,965 --> 01:34:31,735
In the Arabian Peninsula,
we see about 50 to 80 percent

1932
01:34:31,735 --> 01:34:33,670
of the population
that’ll be Duffy null.

1933
01:34:33,670 --> 01:34:35,739
And then in the United States
and the Caribbean,

1934
01:34:35,739 --> 01:34:39,175
among people who self-identify
as Black or African American,

1935
01:34:39,175 --> 01:34:41,411
we see about two
in every three people

1936
01:34:41,411 --> 01:34:43,380
who will have the Duffy
null phenotype.

1937
01:34:44,047 --> 01:34:47,117
So, I’ve been talking a lot
about a red blood cell antigen,

1938
01:34:47,117 --> 01:34:49,185
but how does
this impact neutrophils?

1939
01:34:49,819 --> 01:34:52,389
The short answer is
we don’t really know for sure.

1940
01:34:52,389 --> 01:34:55,425
We have some ideas
that we’ve seen in mouse models.

1941
01:34:55,425 --> 01:34:57,694
We also have some theories
based on what we know

1942
01:34:57,694 --> 01:35:00,830
about cytokine regulation,
but we don’t really know.

1943
01:35:00,830 --> 01:35:04,234
And the one of the models that
I think is the most compelling

1944
01:35:04,234 --> 01:35:06,870
is the one that was put down
by [unintelligible]

1945
01:35:06,870 --> 01:35:10,006
here, where there is ACKR1
mediated interactions

1946
01:35:10,006 --> 01:35:12,575
with hematopoietic
stem cells in the bone marrow.

1947
01:35:12,575 --> 01:35:15,111
And when we take away
that ACKR1 expressions,

1948
01:35:15,111 --> 01:35:18,081
we have neutrophils
that have altered markers

1949
01:35:18,081 --> 01:35:21,117
and an increased propensity
to leave the circulation.

1950
01:35:21,117 --> 01:35:23,686
These scientists are very clever
and tagged the neutrophils

1951
01:35:23,686 --> 01:35:25,188
and looked at all
those reservoirs,

1952
01:35:25,188 --> 01:35:27,057
like liver, lung, spleen.

1953
01:35:27,057 --> 01:35:30,060
And really we only saw
that the neutrophils

1954
01:35:30,060 --> 01:35:33,596
that lacked ACKR1 expression
went to the spleen.

1955
01:35:33,596 --> 01:35:35,865
And so, this emphasizes
and tells us

1956
01:35:35,865 --> 01:35:39,869
that total body
neutrophil count is unchanged,

1957
01:35:39,869 --> 01:35:42,138
but localization is altered.

1958
01:35:43,506 --> 01:35:47,744
So, that leaves us with what is
a normal neutrophil count.

1959
01:35:47,744 --> 01:35:49,646
I apologize that
I’m an adult hematologist,

1960
01:35:49,646 --> 01:35:51,448
so we’ve mostly looked
at this in adults.

1961
01:35:51,448 --> 01:35:54,551
But we do have pediatric data
that is in the works right now

1962
01:35:54,551 --> 01:35:57,821
to look at this as well.
But we looked at black patients

1963
01:35:57,821 --> 01:35:59,923
who are coming for
primary care in Boston,

1964
01:35:59,923 --> 01:36:02,459
and we asked permission
to add on Duffy status

1965
01:36:02,459 --> 01:36:04,727
as well as
an absolute neutrophil count.

1966
01:36:04,727 --> 01:36:06,996
And we found that patients
who are Duffy null,

1967
01:36:06,996 --> 01:36:08,364
which are seen in the blue --

1968
01:36:08,364 --> 01:36:13,002
on the graph had a median
ANC of 2,820

1969
01:36:13,002 --> 01:36:16,940
compared to 5,000 which we found
in the Duffy non- null patients.

1970
01:36:16,940 --> 01:36:18,341
There was
no significant difference

1971
01:36:18,341 --> 01:36:19,609
in our institutional reference

1972
01:36:19,609 --> 01:36:22,712
ranges between Duffy
non-null and institutional,

1973
01:36:22,712 --> 01:36:24,614
but there was
a significant difference

1974
01:36:24,614 --> 01:36:26,416
in our Duffy
null population.

1975
01:36:26,416 --> 01:36:28,751
And I specifically like to call
out that 10 percent

1976
01:36:28,751 --> 01:36:31,588
of these healthy primary
care patients

1977
01:36:31,588 --> 01:36:32,822
would have been
called neutropenic

1978
01:36:32,822 --> 01:36:35,258
because they had
an ANC of less than 1,500.

1979
01:36:36,126 --> 01:36:38,027
We then expanded this
to try to think

1980
01:36:38,027 --> 01:36:40,930
about what a normal
reference interval should be.

1981
01:36:40,930 --> 01:36:43,867
And our data found that there
are reference intervals

1982
01:36:43,867 --> 01:36:46,736
better at a lower limit
of normal of 12,000 which again,

1983
01:36:46,736 --> 01:36:50,373
is a closer replication of what
we see in the African continent

1984
01:36:50,373 --> 01:36:53,209
than what we’re used to seeing
here in the United States.

1985
01:36:54,277 --> 01:36:55,912
The last thing I really want
to talk about

1986
01:36:55,912 --> 01:36:57,347
before we get into sickle cell

1987
01:36:57,347 --> 01:37:00,750
is thinking about the impact of
the Duffy antigen and cytokines.

1988
01:37:00,750 --> 01:37:02,485
If you’ve heard of the Duffy
antigen before,

1989
01:37:02,485 --> 01:37:05,522
you’ve probably heard about it
in the context of neutrophils.

1990
01:37:05,522 --> 01:37:08,591
But there’s a lot of really
unexplored data

1991
01:37:08,591 --> 01:37:09,859
or interesting things

1992
01:37:09,859 --> 01:37:12,262
to think about with
cytokine regulation as well.

1993
01:37:12,896 --> 01:37:15,498
The Duffy antigen is
a pretty promiscuous receptor.

1994
01:37:15,498 --> 01:37:17,033
It’s a decoy receptor.

1995
01:37:17,033 --> 01:37:20,069
There is a lot of CXC
and CC class chemokines

1996
01:37:20,069 --> 01:37:21,304
that it regulates,

1997
01:37:21,304 --> 01:37:23,306
and it seems to have
a really important impact

1998
01:37:23,306 --> 01:37:26,142
in cytokine circulation
and leukocyte trafficking.

1999
01:37:26,142 --> 01:37:29,245
And when you look at the adult
oncology literature,

2000
01:37:29,245 --> 01:37:31,247
there’s also some
very interesting suggestions

2001
01:37:31,247 --> 01:37:34,984
about things like tumor control
and angiogenesis

2002
01:37:34,984 --> 01:37:37,453
in people who are Duffy
null versus Duffy non null.

2003
01:37:37,453 --> 01:37:40,156
So, a really untapped
and under explored area

2004
01:37:40,156 --> 01:37:41,491
that we’ll get back to as well

2005
01:37:41,491 --> 01:37:43,393
in the context
of sickle cell disease.

2006
01:37:44,160 --> 01:37:46,196
So, let’s think about
how we should apply this

2007
01:37:46,196 --> 01:37:49,399
to sickle cell disease,
and specifically hydroxyurea.

2008
01:37:50,633 --> 01:37:53,036
I will just say
that we don’t know much,

2009
01:37:53,036 --> 01:37:55,505
but we’ll kind of review
what we do know.

2010
01:37:55,505 --> 01:37:57,874
So, first, let’s think about
the impact of the Duffy

2011
01:37:57,874 --> 01:37:59,075
null genotype

2012
01:37:59,075 --> 01:38:01,477
on absolute neutrophil
count in white blood cells.

2013
01:38:01,477 --> 01:38:03,313
There was a study
that looked at HUSTLE,

2014
01:38:03,313 --> 01:38:04,514
SWiTCH and TWiTCH.

2015
01:38:04,514 --> 01:38:06,317
HUSTLE is more of an
observational

2016
01:38:06,317 --> 01:38:07,483
study on hydroxyurea.

2017
01:38:07,483 --> 01:38:10,553
SWiTCH and TWiTCH, we’re looking
at stroke prevention.

2018
01:38:10,553 --> 01:38:14,123
So, sicker patients
that were hemoglobin SS

2019
01:38:14,123 --> 01:38:16,092
as part of the criteria.

2020
01:38:16,092 --> 01:38:18,861
They reported that
the mean white blood cells

2021
01:38:18,861 --> 01:38:20,697
were different by Duffy status.

2022
01:38:20,697 --> 01:38:23,733
With those who were Duffy
null of 13.5 compared to Duffy

2023
01:38:23,733 --> 01:38:28,104
non null of 14.6,
they also looked at mean ANC.

2024
01:38:28,104 --> 01:38:31,074
And there was also a significant
difference in what mean

2025
01:38:31,074 --> 01:38:34,978
ANC was by the cohort
with Duffy null being 72,000

2026
01:38:34,978 --> 01:38:38,181
and Duffy non null being 8,500.
And this results

2027
01:38:38,181 --> 01:38:41,017
in a mean difference
about 1.1 for white blood cells

2028
01:38:41,017 --> 01:38:43,586
and 1,300 for our absolute
neutrophil counts.

2029
01:38:44,454 --> 01:38:46,422
There are a couple more recent
pieces of data.

2030
01:38:46,422 --> 01:38:48,658
It’s very exciting
that has just come out as well.

2031
01:38:48,658 --> 01:38:51,160
This is a retrospective cohort
from Nationwide Children’s

2032
01:38:51,160 --> 01:38:52,428
that we’ll look at first.

2033
01:38:52,428 --> 01:38:55,365
They had 187 patients
with sickle cell disease.

2034
01:38:55,365 --> 01:38:57,767
This was all genotypes
that were included.

2035
01:38:57,767 --> 01:39:02,238
They reported 78.6 percent of
their population is Duffy null.

2036
01:39:02,238 --> 01:39:03,473
And they did not

2037
01:39:03,473 --> 01:39:07,143
reach statistical
significance in baseline ANC.

2038
01:39:07,143 --> 01:39:10,980
But they got very close
and reported that baseline ANC.

2039
01:39:10,980 --> 01:39:13,783
This is at the one year
visit before hydroxyurea

2040
01:39:13,783 --> 01:39:16,986
was a meeting of 2.9
in your Duffy null cohort,

2041
01:39:16,986 --> 01:39:20,556
compared to 4.1
in your Duffy non-null cohort.

2042
01:39:20,556 --> 01:39:22,358
St Jude actually,
around the same time,

2043
01:39:22,358 --> 01:39:25,261
put out some similar data.
This is only an abstract form,

2044
01:39:25,261 --> 01:39:26,696
so it’s a little bit
more bare bones

2045
01:39:26,696 --> 01:39:28,531
than our
Nationwide Children’s data.

2046
01:39:28,531 --> 01:39:31,034
But they had 250 patients
with sickle cell disease

2047
01:39:31,034 --> 01:39:32,969
of which 72 percent
were Duffy null.

2048
01:39:33,569 --> 01:39:35,905
And they found similarly that
in our Duffy

2049
01:39:35,905 --> 01:39:39,008
null patient population,
there was a median --

2050
01:39:39,008 --> 01:39:44,414
or mean ANC of 3,800
and non-null of 4,700.

2051
01:39:44,414 --> 01:39:46,249
They did reach statistical
significance,

2052
01:39:46,249 --> 01:39:48,851
which is different
than Nationwide Children’s.

2053
01:39:48,851 --> 01:39:51,154
And so, from this
in our pediatric patients

2054
01:39:51,154 --> 01:39:52,488
with sickle cell disease,

2055
01:39:52,488 --> 01:39:54,657
we can say that there is
a difference in baseline

2056
01:39:54,657 --> 01:39:56,459
ANC by Duffy status.

2057
01:39:57,327 --> 01:39:59,929
We have a lot less on this
and the adult population.

2058
01:39:59,929 --> 01:40:02,699
There was one study
that came out in 2023

2059
01:40:02,699 --> 01:40:04,067
that that looked at it --

2060
01:40:04,067 --> 01:40:05,435
this in a slightly
different lens,

2061
01:40:05,435 --> 01:40:07,270
but I think it’s very clever.

2062
01:40:07,270 --> 01:40:11,441
They looked at how variants
and absolute neutrophil count

2063
01:40:11,441 --> 01:40:14,077
could be contributed
or attributed

2064
01:40:14,077 --> 01:40:17,480
to different genomic variants,
including sickle cell.

2065
01:40:17,480 --> 01:40:19,415
And so, they use
three large adult cohorts,

2066
01:40:19,415 --> 01:40:23,686
the CSSCD, GEN-MOD, Mondor/Lyon
which has over 2,000 patients.

2067
01:40:23,686 --> 01:40:24,987
And then healthy controls

2068
01:40:24,987 --> 01:40:27,390
were obtained
from BioMe in the UK Biobank.

2069
01:40:28,191 --> 01:40:30,660
They found in our non-sickle
cell disease individuals --

2070
01:40:30,660 --> 01:40:31,928
so, again,

2071
01:40:31,928 --> 01:40:34,964
like healthy people kind of
living their everyday life,

2072
01:40:34,964 --> 01:40:38,568
that variation in neutrophil
count can be explained

2073
01:40:38,568 --> 01:40:42,939
about 18 to 24 percent
by the Duffy null phenotype.

2074
01:40:42,939 --> 01:40:44,173
This was associated
with a mean --

2075
01:40:44,173 --> 01:40:46,209
white blood cell
mean reduction of 1.9

2076
01:40:46,209 --> 01:40:48,945
and an ANC mean
reduction of 1,600.

2077
01:40:49,512 --> 01:40:52,215
In contrast, our patients
with sickle cell disease,

2078
01:40:52,215 --> 01:40:55,218
there was a weak
or non-significant impact

2079
01:40:55,218 --> 01:40:58,154
of the Duffy null phenotype
on ANC variants.

2080
01:40:58,154 --> 01:41:00,723
There was, you know, some slight
variation with white blood cell

2081
01:41:00,723 --> 01:41:05,661
mean reduction of .76 and an ANC
mean reduction of 840.

2082
01:41:05,661 --> 01:41:09,198
But a significantly lower impact
of the Duffy null genotype

2083
01:41:09,198 --> 01:41:11,167
on what we can expect

2084
01:41:11,167 --> 01:41:13,536
in variation
of absolute neutrophil counts.

2085
01:41:13,536 --> 01:41:15,571
This is a great example
of epistasis,

2086
01:41:15,571 --> 01:41:18,441
which is when the expression
of one gene like Duffy null

2087
01:41:18,441 --> 01:41:20,910
is modified by
the expression of another gene

2088
01:41:20,910 --> 01:41:22,879
like sickle cell disease.

2089
01:41:22,879 --> 01:41:25,348
I thought this graphic
explained things pretty well.

2090
01:41:25,348 --> 01:41:26,749
So, the top two lines,

2091
01:41:26,749 --> 01:41:29,652
the red and pink are patients
with sickle cell disease.

2092
01:41:29,652 --> 01:41:31,921
The pink line is those
who are Duffy null.

2093
01:41:31,921 --> 01:41:34,157
So, we are seeing that
there is a difference

2094
01:41:34,157 --> 01:41:37,560
in absolute neutrophil
count by Duffy status.

2095
01:41:37,560 --> 01:41:39,829
But the lines are
a lot closer together

2096
01:41:39,829 --> 01:41:42,064
than our bottom two lines,
which are healthy,

2097
01:41:42,064 --> 01:41:44,700
non-sickle cell disease patients
by Duffy status.

2098
01:41:44,700 --> 01:41:47,904
Where the gray on the bottom
is our Duffy null patients

2099
01:41:47,904 --> 01:41:51,707
and the blue on the top
is the Duffy non-null.

2100
01:41:51,707 --> 01:41:53,042
The authors, actually,
from this data,

2101
01:41:53,042 --> 01:41:55,878
found that Duffy null
has a twofold weaker effect

2102
01:41:55,878 --> 01:41:58,347
on absolute neutrophil counts
in sickle cell patients

2103
01:41:58,347 --> 01:42:00,416
than in non-sickle cell
disease patients.

2104
01:42:01,250 --> 01:42:03,619
We also did look at this
in sickle cell trait,

2105
01:42:03,619 --> 01:42:06,055
and did not find
any impact like this.

2106
01:42:06,055 --> 01:42:08,257
And so, we can comfortably
put our sickle cell

2107
01:42:08,257 --> 01:42:11,961
trait patients in the healthy,
non-sickle cell disease bucket,

2108
01:42:11,961 --> 01:42:14,130
and there doesn’t seem to be
an intermediary

2109
01:42:14,130 --> 01:42:16,799
or any interaction
between those two things.

2110
01:42:18,301 --> 01:42:19,602
So, why is this?

2111
01:42:19,602 --> 01:42:24,307
Why are we seeing that there’s
less impact of Duffy on ANC

2112
01:42:24,307 --> 01:42:26,142
and our patients
with sickle cell disease?

2113
01:42:26,142 --> 01:42:29,479
There are two hypotheses
that I think could be playing --

2114
01:42:29,479 --> 01:42:30,746
could be at play here,

2115
01:42:30,746 --> 01:42:33,082
and I think it might be
a little bit of both.

2116
01:42:33,082 --> 01:42:35,885
The first is the reality
is a splenic atrophy.

2117
01:42:35,885 --> 01:42:38,821
This reduces the reservoir
that we hypothesize is

2118
01:42:38,821 --> 01:42:41,057
where most of
the Neutrophils are localized.

2119
01:42:41,057 --> 01:42:42,692
So, when you take away
that reservoir,

2120
01:42:42,692 --> 01:42:44,360
you’re going to have to have
the neutrophils

2121
01:42:44,360 --> 01:42:46,696
circulate other places,
like the periphery.

2122
01:42:46,696 --> 01:42:48,998
And so, that might be one of the
reasons we’re not really seeing

2123
01:42:48,998 --> 01:42:51,267
that the difference is
a smaller effect size.

2124
01:42:52,068 --> 01:42:54,370
The second potential thing
is the cytokine theory.

2125
01:42:54,370 --> 01:42:57,940
Again, we know in patients
who are healthy and Duffy null

2126
01:42:57,940 --> 01:43:00,576
that if they’re sick,
if they are given steroids,

2127
01:43:00,576 --> 01:43:03,746
if they’re given any sort of
other inflammatory impetus,

2128
01:43:03,746 --> 01:43:06,616
you have neutrophils come out
into the periphery.

2129
01:43:06,616 --> 01:43:09,085
And it starts to mirror
what we see in Duffy

2130
01:43:09,085 --> 01:43:10,820
non null people
with sickle cell.

2131
01:43:10,820 --> 01:43:13,189
It’s a very pro
inflammatory disease,

2132
01:43:13,189 --> 01:43:16,559
and so you may just have these
elevated levels of cytokines

2133
01:43:16,559 --> 01:43:18,227
and inflammation all the time.

2134
01:43:18,227 --> 01:43:20,062
And so, mitigates
a lot of the effect

2135
01:43:20,062 --> 01:43:21,297
that we would otherwise see

2136
01:43:21,297 --> 01:43:23,633
with the Duffy null phenotype
in healthy people.

2137
01:43:23,633 --> 01:43:25,768
But the long and short of it,
so we don’t really know.

2138
01:43:25,768 --> 01:43:27,470
And this is something too that
we should work on

2139
01:43:27,470 --> 01:43:29,071
from a mechanistic standpoint.

2140
01:43:30,072 --> 01:43:32,708
So, let’s pivot to how this
might impact

2141
01:43:32,708 --> 01:43:34,210
things like hydroxyurea.

2142
01:43:34,210 --> 01:43:36,612
It was amazing to hear
these talks beforehand

2143
01:43:36,612 --> 01:43:39,916
that really emphasized
how important things like MTD

2144
01:43:39,916 --> 01:43:43,185
is for having good outcomes
in sickle cell.

2145
01:43:43,185 --> 01:43:45,922
And I think there’s a lot of
questions in this space as well

2146
01:43:45,922 --> 01:43:48,124
when you when
you consider Duffy status.

2147
01:43:48,124 --> 01:43:49,325
So, as a quick review,

2148
01:43:49,325 --> 01:43:51,527
in our phase 1 and 2
trials for hydroxyurea,

2149
01:43:51,527 --> 01:43:53,863
which was done in like
the late 80s, early 90s.

2150
01:43:54,363 --> 01:43:56,399
Initially, toxicity criteria
were defined

2151
01:43:56,399 --> 01:43:58,601
as an ANC of less than 3,000.

2152
01:43:58,601 --> 01:44:00,136
And then given that
so many people

2153
01:44:00,136 --> 01:44:02,939
were hitting that threshold,
it was reduced to 2000.

2154
01:44:02,939 --> 01:44:04,674
Unsurprisingly, the authors
reported the most

2155
01:44:04,674 --> 01:44:09,045
common toxicity of
was neutropenia at 73 percent.

2156
01:44:09,045 --> 01:44:11,447
However, they did emphasize
that there was no difference

2157
01:44:11,447 --> 01:44:15,718
or no increase in the number of
infections or unusual infections

2158
01:44:15,718 --> 01:44:18,421
while they were getting ready
to understand hydroxyurea

2159
01:44:18,421 --> 01:44:20,389
for these patients.

2160
01:44:20,389 --> 01:44:24,260
So, when you look at guidelines
on how to do MTD escalation,

2161
01:44:24,260 --> 01:44:25,494
most of them will say,

2162
01:44:25,494 --> 01:44:27,496
"Oh, you can increase
your hydroxyurea

2163
01:44:27,496 --> 01:44:30,132
and ANC of 1,500 to 3,000

2164
01:44:30,132 --> 01:44:32,268
by doing polls
of what we do in Boston."

2165
01:44:32,268 --> 01:44:33,869
And then also talking
to some of you all,

2166
01:44:33,869 --> 01:44:37,206
most high volume sickle
cell centers will use 1,500

2167
01:44:37,206 --> 01:44:38,841
if not massage
that a little bit lower.

2168
01:44:38,841 --> 01:44:41,110
Because patient looks good
and let’s keep going up.

2169
01:44:41,110 --> 01:44:44,146
We don’t want to reduce it,
which is dramatically different

2170
01:44:44,146 --> 01:44:46,349
than what is actually
in the package insert.

2171
01:44:46,349 --> 01:44:50,086
So, the package insert says that
acceptable ranges and increasing

2172
01:44:50,086 --> 01:44:53,489
really should only happen
when ANC is greater than 2,500

2173
01:44:53,489 --> 01:44:57,560
and you do not increase the dose
when the ANC is less than 2,000.

2174
01:44:57,560 --> 01:45:01,063
Which raises some questions
on what is done by experts

2175
01:45:01,063 --> 01:45:04,166
in high volume centers versus
what is maybe done with people

2176
01:45:04,166 --> 01:45:06,969
who have one
to five sickle cell patients

2177
01:45:06,969 --> 01:45:08,704
and aren’t that familiar
with hydroxyurea

2178
01:45:08,704 --> 01:45:10,606
within the United States
or worldwide.

2179
01:45:12,274 --> 01:45:16,245
So, there’s been a few small,
single center studies

2180
01:45:16,245 --> 01:45:18,648
that looked
at Duffy null and hydroxyurea.

2181
01:45:18,648 --> 01:45:20,149
The first two studies
I’m going to mention

2182
01:45:20,149 --> 01:45:21,851
look at just prescriptions.

2183
01:45:21,851 --> 01:45:25,021
So, this first one was in adults
with SS at Duke and UNC

2184
01:45:25,021 --> 01:45:28,491
in the early 2000s
and they found that 73.4 percent

2185
01:45:28,491 --> 01:45:30,393
of sickle cell patients
were Duffy null.

2186
01:45:30,393 --> 01:45:32,028
And they did find a difference

2187
01:45:32,028 --> 01:45:36,365
in initiation of hydroxyurea
by Duffy status.

2188
01:45:36,365 --> 01:45:39,001
They didn’t really go into that
anymore, by what was going on,

2189
01:45:39,001 --> 01:45:40,403
if it was neutrophils,
infections,

2190
01:45:40,403 --> 01:45:42,004
but they just reported this.

2191
01:45:42,805 --> 01:45:44,940
Again, with the Nationwide
Children’s paper

2192
01:45:44,940 --> 01:45:47,877
that just was released,
they did not find any difference

2193
01:45:47,877 --> 01:45:50,479
in initiation of hydroxyurea
by Duffy status.

2194
01:45:50,479 --> 01:45:52,882
And they also looked at
what was happening a year

2195
01:45:52,882 --> 01:45:55,084
after hydroxyurea initiation,

2196
01:45:55,084 --> 01:45:59,055
and they found also
no difference in hydroxyurea

2197
01:45:59,055 --> 01:46:01,290
at one year after initiation.

2198
01:46:02,425 --> 01:46:04,393
The Nationwide Children’s
also looked a little bit deeper

2199
01:46:04,393 --> 01:46:06,796
at things like
at hydroxyurea initiation.

2200
01:46:06,796 --> 01:46:08,397
What was going on
with the neutrophils?

2201
01:46:08,397 --> 01:46:09,999
What were the prescribed
HU doses?

2202
01:46:09,999 --> 01:46:12,168
And really did not find
any difference

2203
01:46:12,168 --> 01:46:13,669
by these two parameters.

2204
01:46:13,669 --> 01:46:16,505
And then similarly, at one year
of hydroxyurea therapy,

2205
01:46:17,139 --> 01:46:19,709
they did not find any difference
in median ANC

2206
01:46:19,709 --> 01:46:23,145
or prescribed hydroxyurea
doses by Duffy status.

2207
01:46:23,145 --> 01:46:24,847
Although I will point out
that meeting

2208
01:46:24,847 --> 01:46:27,616
ANC seems to be opening up
a little bit

2209
01:46:27,616 --> 01:46:29,552
when you compared
to an initiation.

2210
01:46:29,552 --> 01:46:33,289
But again, it’s hard to know
if that’s a small sample size

2211
01:46:33,289 --> 01:46:35,257
or if there might be
something there or not.

2212
01:46:35,257 --> 01:46:37,626
And so, from this,
we can say that Duffy status

2213
01:46:37,626 --> 01:46:39,328
was not associated
with differences

2214
01:46:39,328 --> 01:46:42,531
in mean hydroxyurea doses
or differences in ANC

2215
01:46:42,531 --> 01:46:44,200
while prescribed hydroxyurea.

2216
01:46:44,200 --> 01:46:46,469
In the HUSTLE,
SWiTCH and TWiTCH,

2217
01:46:46,469 --> 01:46:48,571
we actually get
some similar information.

2218
01:46:48,571 --> 01:46:51,607
They also found 71 percent of
their patients were Duffy null.

2219
01:46:52,174 --> 01:46:54,276
And they found
that higher hydroxyurea

2220
01:46:54,276 --> 01:46:55,511
MTD was associated

2221
01:46:55,511 --> 01:46:59,648
with higher pre-treatment
white blood cells and ANC

2222
01:46:59,648 --> 01:47:01,817
but was not associated
with Duffy status.

2223
01:47:01,817 --> 01:47:03,719
So, again, it was kind of
where you were at

2224
01:47:03,719 --> 01:47:04,920
when you first started,

2225
01:47:04,920 --> 01:47:07,857
rather than Duffy status alone,
that seemed to drive it.

2226
01:47:07,857 --> 01:47:09,558
Again, these two things
are interconnected,

2227
01:47:09,558 --> 01:47:11,694
but it seems like
the stats magic

2228
01:47:11,694 --> 01:47:14,430
that a lot
of our biostatisticians

2229
01:47:14,430 --> 01:47:16,198
can do kind of emphasize
that it seemed

2230
01:47:16,198 --> 01:47:19,535
to be the baseline neutrophil
and white blood cell count,

2231
01:47:19,535 --> 01:47:22,037
rather than Duffy status,
that was the big driver.

2232
01:47:22,738 --> 01:47:25,007
St. Jude found something
very similar, that there was

2233
01:47:25,007 --> 01:47:28,544
no significant difference
in MTD by Duffy status.

2234
01:47:28,544 --> 01:47:31,380
And Duffy status
did impact time to MTD,

2235
01:47:31,380 --> 01:47:34,884
but again, only indirectly,
via baseline ANC.

2236
01:47:35,751 --> 01:47:37,920
They did have this interesting
extra bit

2237
01:47:37,920 --> 01:47:39,755
where they said
that people with Duffy

2238
01:47:39,755 --> 01:47:43,826
null had more frequent episodes
of an ANC of less than 1,500.

2239
01:47:43,826 --> 01:47:47,530
Again, our artificial
kind of random threshold

2240
01:47:47,530 --> 01:47:50,733
that we’ve been using for
decades to think about toxicity.

2241
01:47:51,667 --> 01:47:54,670
And they did not report
anything about infection

2242
01:47:54,670 --> 01:47:55,938
or hospitalization

2243
01:47:55,938 --> 01:47:58,908
or other myelosuppression things
that would actually matter.

2244
01:47:58,908 --> 01:48:01,043
Just really,
did they cross that threshold?

2245
01:48:01,043 --> 01:48:04,547
That we’ve drawn a line
in the sand yes or no of 1,500.

2246
01:48:05,181 --> 01:48:08,117
They found that people
with Duffy null were 1.2 versus

2247
01:48:08,117 --> 01:48:09,952
.5 in Duffy non-null.

2248
01:48:09,952 --> 01:48:12,154
And so, again, from this,
we can say that Duffy status

2249
01:48:12,154 --> 01:48:14,490
was not associated
with differences in mean

2250
01:48:14,490 --> 01:48:17,159
MTD in pediatric patients
with sickle cell disease.

2251
01:48:18,294 --> 01:48:20,763
So, we’re going to last bit
is think

2252
01:48:20,763 --> 01:48:24,433
about how Duffy might impact
sickle cell disease outcomes.

2253
01:48:24,433 --> 01:48:25,801
Again, there’s a lot
of mysteries

2254
01:48:25,801 --> 01:48:28,504
around some of the cytokine
homeostasis and milieu.

2255
01:48:28,504 --> 01:48:31,240
And so, some people have tried
to look at that to see

2256
01:48:31,240 --> 01:48:32,741
if there’s anything there.

2257
01:48:32,741 --> 01:48:34,844
I will warn you,
the data is quite mixed,

2258
01:48:34,844 --> 01:48:36,812
but we’ll at least report
what we know.

2259
01:48:37,646 --> 01:48:39,548
First, there was some basic
science literature

2260
01:48:39,548 --> 01:48:41,350
that first kind
of prompted this too.

2261
01:48:41,350 --> 01:48:46,055
That irreversible sickle RBCs
had a 17 fold higher amount

2262
01:48:46,055 --> 01:48:50,726
of sickling in Duffy null RBCs
compared to Duffy non-null RBCs.

2263
01:48:50,726 --> 01:48:52,595
And so, then from this,
there’s a lot of questions

2264
01:48:52,595 --> 01:48:55,364
on what’s going on
with outcomes in humans.

2265
01:48:55,364 --> 01:48:59,168
So, in the adult literature,
there was one study from the UK

2266
01:48:59,168 --> 01:49:01,937
that found that there were
no differences in complications

2267
01:49:01,937 --> 01:49:03,606
except for leg ulcers.

2268
01:49:03,606 --> 01:49:05,274
But again, those numbers
are really small.

2269
01:49:05,274 --> 01:49:07,576
We’re saying, you know, 5
out of 15 and 25,

2270
01:49:07,576 --> 01:49:10,412
out of 133 with a P value of 04.

2271
01:49:10,980 --> 01:49:13,816
Another study in the USA found
no difference

2272
01:49:13,816 --> 01:49:15,951
in the complications,
including leg ulcers.

2273
01:49:15,951 --> 01:49:17,887
But they did find
that there were some differences

2274
01:49:17,887 --> 01:49:20,289
in proteinuria
that was higher among those

2275
01:49:20,289 --> 01:49:23,025
that were Duffy
null compared to Duffy non-null.

2276
01:49:23,025 --> 01:49:24,860
And then a final study
from the French West Indies

2277
01:49:24,860 --> 01:49:26,328
found that there were
no differences

2278
01:49:26,328 --> 01:49:28,097
in any complications whatsoever.

2279
01:49:28,697 --> 01:49:30,399
Hard to know
what to do with that.

2280
01:49:31,033 --> 01:49:33,068
Pediatrics, similar things,
St. Jude

2281
01:49:33,068 --> 01:49:35,404
looked at the frequencies
of vaso-occlusive crises

2282
01:49:35,404 --> 01:49:36,639
or acute chest syndrome.

2283
01:49:36,639 --> 01:49:40,242
Within two years, hydroxy
initiation found no differences.

2284
01:49:41,210 --> 01:49:42,511
There was another -- the HUSTLE,

2285
01:49:42,511 --> 01:49:45,014
TWiTCH and SWiTCH
studies did find

2286
01:49:45,014 --> 01:49:46,916
that Duffy positive status

2287
01:49:46,916 --> 01:49:48,984
seemed to be protected against
Albuminuria [phonetic sp].

2288
01:49:48,984 --> 01:49:52,755
Again, reminiscent of what
the USA study and adults found,

2289
01:49:53,289 --> 01:49:55,824
but no other complications
or associations.

2290
01:49:55,824 --> 01:49:58,494
And there was a study
that was released literally less

2291
01:49:58,494 --> 01:50:00,429
than a week ago
on [unintelligible]

2292
01:50:00,429 --> 01:50:03,399
that also looked at
complications by Duffy status,

2293
01:50:03,399 --> 01:50:05,167
as well as a couple
other things,

2294
01:50:05,167 --> 01:50:08,304
And found that there was
no association with Duffy status

2295
01:50:08,304 --> 01:50:10,105
with three plus pain crises,

2296
01:50:10,105 --> 01:50:12,441
stroke priapism
or acute chest syndrome.

2297
01:50:13,275 --> 01:50:15,878
So, from this, there’s no clear
or consistent differences

2298
01:50:15,878 --> 01:50:18,314
in sickle cell disease
complications by Duffy status

2299
01:50:18,314 --> 01:50:19,915
but more work to be done.

2300
01:50:20,816 --> 01:50:25,688
So, what do we know?
We know that age, genotype,

2301
01:50:25,688 --> 01:50:27,823
disease severity,
hydroxyurea use,

2302
01:50:27,823 --> 01:50:31,327
Duffy status, all impact
absolute neutrophil count

2303
01:50:31,327 --> 01:50:34,797
in sickle cell disease.
We know that sickle cell disease

2304
01:50:34,797 --> 01:50:37,733
has an epistatic effect
on the Duffy null genotype.

2305
01:50:37,733 --> 01:50:38,934
Baseline

2306
01:50:38,934 --> 01:50:42,104
ANC is higher in all patients
with sickle cell disease.

2307
01:50:42,104 --> 01:50:45,140
And although you do see
a separation by Duffy status,

2308
01:50:45,140 --> 01:50:48,577
the effect size is much smaller
than what we see in our healthy

2309
01:50:48,577 --> 01:50:50,646
non-sickle cell
disease patients.

2310
01:50:51,480 --> 01:50:53,482
We know that 70
to 80 percent of patients

2311
01:50:53,482 --> 01:50:56,285
with sickle cell disease
have the Duffy null phenotype.

2312
01:50:56,285 --> 01:50:58,687
If you remember, by people
who just identify as Black

2313
01:50:58,687 --> 01:50:59,989
or African American,

2314
01:50:59,989 --> 01:51:03,492
that number was closer to two
and three in the United States.

2315
01:51:03,492 --> 01:51:07,229
So, we see a higher prevalence
of the Duffy null phenotype

2316
01:51:07,229 --> 01:51:08,897
in those
with sickle cell disease.

2317
01:51:08,897 --> 01:51:10,899
This also means
that all those trials

2318
01:51:10,899 --> 01:51:13,502
that have been referred
to included patients

2319
01:51:13,502 --> 01:51:14,803
with the Duffy null phenotype.

2320
01:51:14,803 --> 01:51:17,573
So, trials and medications
were approved

2321
01:51:17,573 --> 01:51:19,575
and built
with these populations.

2322
01:51:19,575 --> 01:51:21,377
This is very different
from what we’re struggling with

2323
01:51:21,377 --> 01:51:23,846
in cancer clinical trials
and other clinical trials.

2324
01:51:23,846 --> 01:51:25,948
Right now, where we have very
low black recruitment

2325
01:51:25,948 --> 01:51:27,249
and enrollment,

2326
01:51:27,249 --> 01:51:29,218
which means that we likely
have very low

2327
01:51:29,218 --> 01:51:31,820
Duffy null phenotype
representation as well.

2328
01:51:31,820 --> 01:51:33,989
So, that’s reassuring
that we’ve been inclusive

2329
01:51:33,989 --> 01:51:35,257
of these patients.

2330
01:51:35,257 --> 01:51:38,794
There are still questions of why
are we choosing an ANC of 1,500?

2331
01:51:38,794 --> 01:51:41,163
It’s an arbitrary number,
and should we be using a number

2332
01:51:41,163 --> 01:51:44,700
that’s a little bit different
for MTD or toxicity thresholds.

2333
01:51:46,035 --> 01:51:47,636
Duffy status does not seem
to impact

2334
01:51:47,636 --> 01:51:49,905
hydroxyurea prescription
in pediatrics;

2335
01:51:49,905 --> 01:51:53,809
does not seem to impact
MTD in pediatric patients;

2336
01:51:53,809 --> 01:51:55,878
and there’s no clear impact
of Duffy status

2337
01:51:55,878 --> 01:51:57,546
on sickle cell
disease complications,

2338
01:51:57,546 --> 01:51:59,715
except maybe some proteinuria
or leg ulcers

2339
01:51:59,715 --> 01:52:03,252
that should be explored.
So, where are our gaps?

2340
01:52:03,252 --> 01:52:06,021
We really need to look at adults
with sickle cell disease

2341
01:52:06,021 --> 01:52:09,224
and look at prescription
and MTD by Duffy status.

2342
01:52:09,224 --> 01:52:10,559
Again, we have one study,

2343
01:52:10,559 --> 01:52:12,895
single center
from the early 2000s

2344
01:52:12,895 --> 01:52:15,831
that suggests that maybe there’s
lower initiation of hydroxyurea.

2345
01:52:15,831 --> 01:52:18,434
But a lot of work
to be done in that space.

2346
01:52:18,434 --> 01:52:20,269
We need to confirm
some of these different --

2347
01:52:20,269 --> 01:52:22,671
that there’s no differences
in hydria use of MTD

2348
01:52:22,671 --> 01:52:25,974
by Duffy status in pediatrics
and larger data sets.

2349
01:52:25,974 --> 01:52:28,577
And specifically, I want to look
at those lower center,

2350
01:52:28,577 --> 01:52:30,979
low volume sickle
cell disease centers.

2351
01:52:30,979 --> 01:52:33,649
I’m not worried about St Jude
and Nationwide Children’s,

2352
01:52:33,649 --> 01:52:35,751
but I am worried about
the centers that, you know,

2353
01:52:35,751 --> 01:52:38,153
maybe it’s a general
hematologist that only sees,

2354
01:52:38,153 --> 01:52:41,623
you know, a few patients with
sickle cell disease every year.

2355
01:52:41,623 --> 01:52:43,258
Are they following
the package insert

2356
01:52:43,258 --> 01:52:45,127
and is that resulting
any disparities

2357
01:52:45,127 --> 01:52:46,728
or are we still doing okay?

2358
01:52:47,529 --> 01:52:49,431
We need to better
understand mechanisms

2359
01:52:49,431 --> 01:52:52,101
of elevated white blood cell
count in ANC,

2360
01:52:52,101 --> 01:52:54,236
in adverse events
in sickle cell disease.

2361
01:52:54,236 --> 01:52:55,737
There’s a lot of literature
that shows

2362
01:52:55,737 --> 01:52:58,707
that high white blood cells,
high ANC, bad,

2363
01:52:58,707 --> 01:53:01,610
but it doesn’t seem like again,
we know the chicken or the egg.

2364
01:53:01,610 --> 01:53:05,347
Is it that elevated ANC
is a marker of severe disease,

2365
01:53:05,347 --> 01:53:07,816
or is it a cause
of inferior outcomes,

2366
01:53:07,816 --> 01:53:09,251
or maybe it’s
a little bit of both?

2367
01:53:09,251 --> 01:53:10,953
And that’s an important
question to answer.

2368
01:53:10,953 --> 01:53:13,388
And factoring
in Duffy status to that

2369
01:53:13,388 --> 01:53:15,958
is also going to be
an important question as well.

2370
01:53:15,958 --> 01:53:17,926
And then finally, we need
to assess for differences

2371
01:53:17,926 --> 01:53:20,028
in sickle cell disease
complications in adults

2372
01:53:20,028 --> 01:53:21,997
and pediatrics
by Duffy status.

2373
01:53:21,997 --> 01:53:25,801
And again, keeping in mind both
the impact on neutrophil levels

2374
01:53:25,801 --> 01:53:27,769
as well as some of these
cytokine regulations

2375
01:53:27,769 --> 01:53:29,071
and homeostasis,

2376
01:53:29,071 --> 01:53:31,373
how is it impacting
things like response

2377
01:53:31,373 --> 01:53:33,909
to transplantation
or response

2378
01:53:33,909 --> 01:53:36,645
to some of the complications
or response to hydria?

2379
01:53:36,645 --> 01:53:38,247
So, a lot of questions here,

2380
01:53:38,814 --> 01:53:40,916
and a lot of richness
to be uncovered.

2381
01:53:40,916 --> 01:53:43,018
So, with that, I thank you
for your attention,

2382
01:53:43,018 --> 01:53:45,053
and I’m looking forward
to any questions.

2383
01:53:49,491 --> 01:53:58,767
Female Speaker: Dr. Merz,
that was a great presentation.

2384
01:53:59,368 --> 01:54:03,505
You know, I heard you and ASH.
[laughs]

2385
01:54:03,505 --> 01:54:08,310
I want to know more about
this Duffy and hydroxyurea

2386
01:54:08,310 --> 01:54:09,545
[unintelligible].

2387
01:54:09,545 --> 01:54:15,017
So, I concur with you
about your last three summary

2388
01:54:15,017 --> 01:54:16,585
about pediatrics.

2389
01:54:16,585 --> 01:54:19,588
The adult study,
which you mentioned in the U.K.

2390
01:54:19,588 --> 01:54:20,956
was done by our group.

2391
01:54:20,956 --> 01:54:22,191
Lauren Merz: Oh, I didn’t know.

2392
01:54:22,191 --> 01:54:23,792
Female Speaker: We found,
in fact,

2393
01:54:24,326 --> 01:54:27,229
that when patients
with sickle cell disease

2394
01:54:27,229 --> 01:54:28,997
go into sickle crisis,

2395
01:54:28,997 --> 01:54:31,200
they can mobilize
a white cell count,

2396
01:54:31,200 --> 01:54:33,468
just as those
who are deafly known now.

2397
01:54:34,736 --> 01:54:37,406
And the late ulcers
incidence was not high,

2398
01:54:37,406 --> 01:54:39,975
but it did take longer
for the late ulcers to heal.

2399
01:54:40,709 --> 01:54:43,779
But it didn’t seem to impact
sickle complications.

2400
01:54:43,779 --> 01:54:45,080
Lauren Merz: Yeah.

2401
01:54:45,080 --> 01:54:48,417
Female Speaker: So, then,
when I came here,

2402
01:54:48,417 --> 01:54:51,086
we just recently did
a retrospective audit

2403
01:54:51,653 --> 01:54:53,322
of our NIH patients.

2404
01:54:53,989 --> 01:54:58,493
The incidence of Duffy now
is similar

2405
01:54:58,493 --> 01:55:02,798
to the African
American population.

2406
01:55:03,665 --> 01:55:06,635
It did not impact the MTD dose

2407
01:55:06,635 --> 01:55:08,870
in those who are
taking hydroxyurea.

2408
01:55:08,870 --> 01:55:12,241
But you look at the reverse
when you look at MTD dose,

2409
01:55:12,241 --> 01:55:17,246
the MT come with the same.
So, I think you know,

2410
01:55:17,246 --> 01:55:19,948
as you pointed out
in your very first slide,

2411
01:55:19,948 --> 01:55:22,818
that most of the neutrophils
stay in the bone marrow.

2412
01:55:22,818 --> 01:55:26,622
And they can mobilize the
neutrophils when it’s needed.

2413
01:55:26,622 --> 01:55:29,091
But I also can confirm
that when we look

2414
01:55:30,025 --> 01:55:33,328
at the sickle cell populations
now

2415
01:55:33,328 --> 01:55:37,933
and non-now and the difference
is much narrower

2416
01:55:38,567 --> 01:55:40,802
than those
without sickle cell disease.

2417
01:55:41,770 --> 01:55:44,339
And I think maybe it’s
the background

2418
01:55:44,339 --> 01:55:47,576
inflammation
that’s mobilizing the --

2419
01:55:47,576 --> 01:55:48,777
Lauren Merz: No, I very much --
great.

2420
01:55:48,777 --> 01:55:50,445
I’m looking forward
to seeing that data

2421
01:55:50,445 --> 01:55:51,713
out of your group as well.

2422
01:55:51,713 --> 01:55:53,048
Female Speaker: I want
to discuss more with you.

2423
01:55:53,048 --> 01:55:53,815
Thank you so much.

2424
01:55:53,815 --> 01:55:55,050
Lauren Merz: That would be
great.

2425
01:55:55,050 --> 01:55:56,318
And, you know,
I think we see that

2426
01:55:56,318 --> 01:55:58,220
in non-sickle cell
disease patients as well,

2427
01:55:58,220 --> 01:55:59,955
that there’s been
a lot of studies, you know.

2428
01:55:59,955 --> 01:56:02,724
Back when we were calling
it benign ethnic neutropenia.

2429
01:56:02,724 --> 01:56:04,926
We would, you know,
do things like give steroids.

2430
01:56:04,926 --> 01:56:07,129
And we saw great mobilization,
which, again,

2431
01:56:07,129 --> 01:56:09,264
was reassuring
that no infections,

2432
01:56:09,264 --> 01:56:11,066
no issues with bone marrow,

2433
01:56:11,967 --> 01:56:13,468
no issues with
neutrophil function.

2434
01:56:13,468 --> 01:56:15,070
It just seems to be
where they live.

2435
01:56:15,070 --> 01:56:17,172
And then what we’re measuring
in the blood.

2436
01:56:17,172 --> 01:56:19,908
Female Speaker: One question.
In your very first slide,

2437
01:56:19,908 --> 01:56:22,878
you did show
that the Caucasians,

2438
01:56:22,878 --> 01:56:24,579
there was
a very small percentage.

2439
01:56:25,847 --> 01:56:28,750
I mean, you know,
there’s a big racial mixture

2440
01:56:29,284 --> 01:56:30,952
in the American population.

2441
01:56:30,952 --> 01:56:33,622
So, could you actually
check these Caucasians?

2442
01:56:33,622 --> 01:56:36,758
Whether they do have the they
will actually definitely so

2443
01:56:36,758 --> 01:56:39,361
Lauren Merz: So, in that data,
where it showed that,

2444
01:56:39,361 --> 01:56:40,796
you know, there was by race

2445
01:56:40,796 --> 01:56:44,166
who had an ANC of less than 1500
that was from NHANES,

2446
01:56:44,166 --> 01:56:47,169
which is like one of our big
observational studies

2447
01:56:47,169 --> 01:56:48,437
that we get in the States.

2448
01:56:48,437 --> 01:56:51,306
And they really
didn’t have granular data.

2449
01:56:51,306 --> 01:56:52,841
They don’t have genomic issues.

2450
01:56:52,841 --> 01:56:55,344
So, I use that as a suggestion
that, you know,

2451
01:56:55,344 --> 01:56:57,879
the Black patients
mostly probably had

2452
01:56:57,879 --> 01:56:59,281
the Duffy null phenotype,

2453
01:56:59,281 --> 01:57:00,949
and you don’t see this much
in white patients.

2454
01:57:00,949 --> 01:57:03,518
The one thing I will say,
and one of the many reasons

2455
01:57:03,518 --> 01:57:05,721
that it’s important to move away
from race based medicine

2456
01:57:05,721 --> 01:57:08,490
and towards biologic facts
like Duffy,

2457
01:57:08,490 --> 01:57:10,492
is that a lot of people
of Middle Eastern descent

2458
01:57:10,492 --> 01:57:13,729
will identify as white.
It was actually only this year

2459
01:57:13,729 --> 01:57:15,997
that the Middle Eastern North
African designation

2460
01:57:15,997 --> 01:57:17,199
was added to census.

2461
01:57:17,199 --> 01:57:19,735
So, in the past and a lot
of our like hospital registries,

2462
01:57:19,735 --> 01:57:21,136
you’re going to have
Middle Eastern patients

2463
01:57:21,136 --> 01:57:22,571
that will identify as white.

2464
01:57:22,571 --> 01:57:25,540
They also have, you know, over
50 percent will have Duffy null.

2465
01:57:25,540 --> 01:57:27,209
And so, you may have
that as well.

2466
01:57:27,209 --> 01:57:30,212
And then, of course, there’s
other natural variations in ANC.

2467
01:57:30,212 --> 01:57:32,114
Those with
autoimmune neutropenia.

2468
01:57:32,114 --> 01:57:34,416
You name it, like other things
going in there as well.

2469
01:57:34,416 --> 01:57:37,219
But it’s really important
to recognize normal,

2470
01:57:37,219 --> 01:57:40,989
predictable changes by,
you know, a biologic variant,

2471
01:57:40,989 --> 01:57:43,358
which is one of our
big crusades right now.

2472
01:57:45,627 --> 01:57:46,928
Francine Baker: Hi.

2473
01:57:46,928 --> 01:57:50,232
I’m Francine Baker
from the University of Maryland.

2474
01:57:50,932 --> 01:57:52,367
That was a very great talk.

2475
01:57:52,367 --> 01:57:55,137
And I’m just really
curious specifically,

2476
01:57:55,137 --> 01:58:00,876
as it relates to penicillin
prophylaxis in your results.

2477
01:58:00,876 --> 01:58:04,413
Because just whether or not
that might have an impact

2478
01:58:04,413 --> 01:58:07,048
on any of the data
that you presented,

2479
01:58:07,048 --> 01:58:10,385
and were the patients
on penicillin --

2480
01:58:10,986 --> 01:58:12,621
was everybody on penicillin?

2481
01:58:12,621 --> 01:58:18,326
And what was the use
of penicillin in the data?

2482
01:58:18,960 --> 01:58:20,495
Lauren Merz: That’s a
great question.

2483
01:58:20,495 --> 01:58:23,498
My memory of the papers
is that none of them reported

2484
01:58:23,498 --> 01:58:25,233
if the people are using
penicillin or not.

2485
01:58:25,233 --> 01:58:26,968
I will have to go back
and triple check that.

2486
01:58:26,968 --> 01:58:28,570
But I don’t remember
that being there

2487
01:58:28,570 --> 01:58:31,773
because I was looking for things
like, did you report infections?

2488
01:58:31,773 --> 01:58:34,943
Did you report prophylaxis?
Was prophylaxis different?

2489
01:58:34,943 --> 01:58:38,213
And so, that’s not something
that I saw reported

2490
01:58:38,213 --> 01:58:40,582
in the data that’s available.
But an interesting question,

2491
01:58:40,582 --> 01:58:43,485
do you mean it from like
infection prevention

2492
01:58:43,485 --> 01:58:45,220
or more does it have
an impact on the ANC?

2493
01:58:45,220 --> 01:58:46,455
Francine Baker: Yeah.

2494
01:58:46,455 --> 01:58:47,823
Would it have an impact
on the ANC?

2495
01:58:47,823 --> 01:58:49,291
Lauren Merz: My understanding
is that penicillin

2496
01:58:49,291 --> 01:58:51,660
shouldn’t have
a significant impact

2497
01:58:51,660 --> 01:58:53,161
on absolute
neutrophil counts.

2498
01:58:53,161 --> 01:58:56,331
And so, you know,
it wasn’t reported,

2499
01:58:56,331 --> 01:58:57,899
and it’s not one
that that I thought about.

2500
01:58:57,899 --> 01:58:59,701
But again, one of the other
many studies

2501
01:58:59,701 --> 01:59:01,336
that we probably
should look at

2502
01:59:01,336 --> 01:59:03,939
and have a better explanation
and a better grasp of.

2503
01:59:04,606 --> 01:59:06,541
Male Speaker: Real quick
question, really nice talk.

2504
01:59:06,541 --> 01:59:09,878
Do you -- the mechanism
looking at splenectomy --

2505
01:59:09,878 --> 01:59:11,746
or, sorry, looking at
[unintelligible] is interesting.

2506
01:59:11,746 --> 01:59:12,981
I’m curious
if you’ve looked at,

2507
01:59:12,981 --> 01:59:15,684
or people have looked at Duffy
null in splenectomized patients

2508
01:59:15,684 --> 01:59:18,386
or compared it in more mild
forms of sickle cell disease,

2509
01:59:18,386 --> 01:59:20,755
like S beta plus, right,
and looked this over time.

2510
01:59:20,755 --> 01:59:22,357
It’s probably not,
but I’m curious.

2511
01:59:22,357 --> 01:59:24,759
Lauren Merz: I’m actually trying
to do that study right now.

2512
01:59:25,293 --> 01:59:26,962
It’s very hard
to find a good data

2513
01:59:26,962 --> 01:59:28,497
set of people
who have had a splenectomy.

2514
01:59:28,497 --> 01:59:30,465
And I’m trying to pick
splenectomy for trauma,

2515
01:59:30,465 --> 01:59:32,033
not splenectomy for like,
you know,

2516
01:59:32,033 --> 01:59:34,903
ITP, or some other sort of like,
lymphoma, things like that.

2517
01:59:34,903 --> 01:59:36,738
Not many people
get splenectomies,

2518
01:59:36,738 --> 01:59:39,374
especially that’s, like,
reported in like, a UK Biobank.

2519
01:59:39,374 --> 01:59:41,977
All of us, I tried to work with,
like, the trauma surgeons.

2520
01:59:41,977 --> 01:59:43,745
But they really only care
about the first 30 days.

2521
01:59:43,745 --> 01:59:45,947
And I want to see,
you know, ANC before,

2522
01:59:45,947 --> 01:59:48,517
and then, like, a year after,
when it’s regulated.

2523
01:59:48,517 --> 01:59:49,818
And even if I do have that,

2524
01:59:49,818 --> 01:59:51,353
it’s very hard to find
the genomic data.

2525
01:59:51,353 --> 01:59:52,621
So, if anyone’s aware of a data

2526
01:59:52,621 --> 01:59:54,055
set that has all those
components, that would be great.

2527
01:59:54,055 --> 01:59:55,390
So, I think that’s
an important question.

2528
01:59:55,390 --> 01:59:56,625
Male Speaker: It might be
interesting

2529
01:59:56,625 --> 01:59:58,727
looking for older Thalassemia
patients could be a population.

2530
01:59:58,727 --> 02:00:00,428
Lauren Merz: The problem is
my understanding too

2531
02:00:00,428 --> 02:00:02,163
is thalassemia patients
have alterations

2532
02:00:02,163 --> 02:00:03,732
in their absolute
integral count as well.

2533
02:00:03,732 --> 02:00:06,401
So, again, isn’t like
the perfect cohort,

2534
02:00:06,401 --> 02:00:08,103
but we’ve got
to start somewhere.

2535
02:00:08,103 --> 02:00:09,437
And it’s a question that,
again,

2536
02:00:09,437 --> 02:00:12,908
can get at the mechanistic
aspect from the human side,

2537
02:00:12,908 --> 02:00:14,509
and I’m looking to all
the basic scientists

2538
02:00:14,509 --> 02:00:16,678
to help me with
the more like mouse

2539
02:00:16,678 --> 02:00:19,247
and basic science aspect of it.
So, we can bring it to humans.

2540
02:00:19,247 --> 02:00:20,715
Male Speaker: The VA probably
have a good database

2541
02:00:20,715 --> 02:00:22,317
on this too,
for splenectomy patients.

2542
02:00:22,317 --> 02:00:23,552
Lauren Merz: That is our best
lead so far,

2543
02:00:23,552 --> 02:00:24,886
and it’s just cracking
into that system [laughs]

2544
02:00:24,886 --> 02:00:26,154
--
Male Speaker: Yeah.

2545
02:00:26,154 --> 02:00:27,389
Lauren Merz: --
which we’re working on.

2546
02:00:27,389 --> 02:00:31,993
Lesley King: All right.
I’m going to --

2547
02:00:31,993 --> 02:00:34,029
we’re right on time right now,

2548
02:00:35,230 --> 02:00:38,033
and we’re going to --
it’s 10:40 a.m.

2549
02:00:38,033 --> 02:00:40,068
So, we’re already
five minutes running late.

2550
02:00:40,068 --> 02:00:42,604
So, I’m going to ask anybody
who has any questions,

2551
02:00:43,605 --> 02:00:45,473
Doctor Merz is here.

2552
02:00:45,473 --> 02:00:48,643
So, you can just please
have a one-on-one with her,

2553
02:00:48,643 --> 02:00:51,913
and so that we can try and stick
to time as much as possible.

2554
02:00:51,913 --> 02:00:53,348
Because we have
another packed session.

2555
02:00:53,348 --> 02:00:54,616
[applause]

2556
02:00:54,616 --> 02:00:56,818
All right?
So, thank you, everybody.

2557
02:00:56,818 --> 02:00:59,965
We have a quick 10-minute
break 

2558
02:00:59,965 --> 02:01:00,966
Angela Rankine-Mullings: 
Session two focuses

2559
02:01:00,966 --> 02:01:02,734
on the ongoing challenges

2560
02:01:02,734 --> 02:01:05,003
in the management
of sickle cell disease.

2561
02:01:06,004 --> 02:01:10,141
And our first speaker,
Dr. Lydia Pecker,

2562
02:01:11,076 --> 02:01:12,911
will be joining us remotely,

2563
02:01:13,578 --> 02:01:16,815
and she will be presenting
on fertility and pregnancy.

2564
02:01:18,116 --> 02:01:22,387
Dr. Pecker is an associate
professor of medicine

2565
02:01:22,387 --> 02:01:24,656
at Johns Hopkins University,

2566
02:01:25,724 --> 02:01:30,362
and we will now listen
to her presentation.

2567
02:01:31,229 --> 02:01:33,765
Please go ahead, Dr. Pecker,
and welcome.

2568
02:01:33,765 --> 02:01:35,600
Help me welcome her.

2569
02:01:35,600 --> 02:01:37,936
[applause]

2570
02:01:37,936 --> 02:01:39,537
Thank you.

2571
02:01:52,417 --> 02:01:54,052
Lydia Pecker: Hey, in Jamaica.
I’m from Brooklyn,

2572
02:01:54,052 --> 02:01:56,788
which, as many of you know,
is a colony of Jamaica,

2573
02:01:56,788 --> 02:02:00,058
and I’ve always wanted to eat
callaloo in the real place.

2574
02:02:00,058 --> 02:02:01,793
So, Samir [phonetic sp].
I hope to join you there.

2575
02:02:01,793 --> 02:02:03,028
And it’s so good to see you.

2576
02:02:03,028 --> 02:02:05,463
I have so many friends
in the audience today.

2577
02:02:05,463 --> 02:02:06,731
It’s a pleasure to talk to you

2578
02:02:06,731 --> 02:02:09,901
a little bit about fertility
preservation and pregnancy

2579
02:02:09,901 --> 02:02:12,404
in this exciting year
for sickle cell disease.

2580
02:02:12,404 --> 02:02:15,674
These are my disclosures,
some consultancies,

2581
02:02:15,674 --> 02:02:18,276
research funding and work
with both

2582
02:02:18,276 --> 02:02:20,679
the Sickle Cell Reproductive
Health Education Directive

2583
02:02:20,679 --> 02:02:23,515
as well as the Medical Advisory
Committee for the Foundation

2584
02:02:23,515 --> 02:02:25,450
for Women & Girls
with Blood Disorders.

2585
02:02:26,851 --> 02:02:30,622
So, hopefully, this picture
isn’t totally novel to anybody.

2586
02:02:30,622 --> 02:02:33,758
We know that we are fortunate
to live in an era where children

2587
02:02:33,758 --> 02:02:36,161
with sickle cell disease
are growing up,

2588
02:02:36,161 --> 02:02:38,763
and that this presents
a whole set of reproductive

2589
02:02:38,763 --> 02:02:40,598
health challenges
for our community.

2590
02:02:41,266 --> 02:02:43,868
This includes risks for
infertility and complications

2591
02:02:43,868 --> 02:02:46,738
with fertility preservation,
which we’ll talk about today.

2592
02:02:46,738 --> 02:02:49,774
It includes high-risk pregnancy
that we’ll talk about today.

2593
02:02:49,774 --> 02:02:51,376
And of course,
what gets sandwiched in

2594
02:02:51,376 --> 02:02:52,911
between these two realities

2595
02:02:52,911 --> 02:02:55,980
is a very complicated
preconception milieu.

2596
02:02:58,717 --> 02:03:02,487
Before I get started on the
main body of this talk,

2597
02:03:02,487 --> 02:03:03,788
I just wanted to take
a deep breath

2598
02:03:03,788 --> 02:03:05,924
and say, you know, we’re living
through this unbelievable year

2599
02:03:05,924 --> 02:03:07,225
where suddenly,
gene therapy

2600
02:03:07,225 --> 02:03:10,028
is an FDA-approved
intervention for our patients.

2601
02:03:10,028 --> 02:03:14,099
And yet I like to think
of gene therapy

2602
02:03:14,099 --> 02:03:16,101
in a set
of genetic technologies

2603
02:03:16,101 --> 02:03:18,036
that are now available
for our patients,

2604
02:03:18,036 --> 02:03:20,338
all of which either
are explicitly reproductive

2605
02:03:20,338 --> 02:03:22,207
or have reproductive
implications,

2606
02:03:22,774 --> 02:03:25,377
and all of which
offer lessons to us

2607
02:03:25,377 --> 02:03:27,846
as we think about how we
are going to ensure access

2608
02:03:27,846 --> 02:03:29,914
to gene therapy
to our patient community.

2609
02:03:30,548 --> 02:03:32,384
We now have in vitro
fertilization

2610
02:03:32,384 --> 02:03:34,919
with preimplantation
genetic testing,

2611
02:03:34,919 --> 02:03:37,589
which has been around
since 1999,

2612
02:03:37,589 --> 02:03:40,558
and about which we have
one cohort study of patients

2613
02:03:41,693 --> 02:03:43,328
of about 60 patients.

2614
02:03:43,328 --> 02:03:45,964
We now have cell-free DNA
testing of fetuses

2615
02:03:45,964 --> 02:03:48,166
to identify those
with sickle cell disease,

2616
02:03:48,166 --> 02:03:50,468
and it’s in clinical use
at some centers.

2617
02:03:50,468 --> 02:03:53,004
Yet the American College
of Obstetrics and Gynecology

2618
02:03:53,004 --> 02:03:55,507
is not yet completely
endorsing the test,

2619
02:03:55,507 --> 02:03:57,675
even though it eliminates
the need for paternal

2620
02:03:57,675 --> 02:04:00,612
testing and amniocentesis,
both of which pace --

2621
02:04:00,612 --> 02:04:03,948
place significant burdens
on pregnant people.

2622
02:04:03,948 --> 02:04:05,617
And finally, as we’re all
familiar with,

2623
02:04:05,617 --> 02:04:10,155
gene therapy at the moment
presents a risk for infertility

2624
02:04:10,155 --> 02:04:11,756
due to the use
of alkylating agents

2625
02:04:11,756 --> 02:04:13,291
in the preparative regimen.

2626
02:04:13,291 --> 02:04:15,827
And you know, something that
has gotten my attention

2627
02:04:15,827 --> 02:04:17,295
is that women
are overrepresented

2628
02:04:17,295 --> 02:04:19,931
in sickle cell clinics,
constituting somewhere

2629
02:04:19,931 --> 02:04:23,168
between 60 and 70 percent
of clinic populations,

2630
02:04:23,168 --> 02:04:25,837
but only equally represented
in the transplant data,

2631
02:04:25,837 --> 02:04:27,939
which means that people
may be getting lost

2632
02:04:27,939 --> 02:04:29,474
between the clinic
and transplant

2633
02:04:29,474 --> 02:04:31,843
for reasons
that are not fully elucidated

2634
02:04:31,843 --> 02:04:33,178
but that perhaps
have something to do

2635
02:04:33,178 --> 02:04:35,213
with access to
fertility preservation.

2636
02:04:36,047 --> 02:04:39,017
The other big idea I wanted to
present you all or new knowledge

2637
02:04:39,017 --> 02:04:40,618
that I wanted to present
to you all today

2638
02:04:40,618 --> 02:04:42,053
is to just make sure
everybody knows

2639
02:04:42,053 --> 02:04:43,488
that the Center
for Disease Control

2640
02:04:43,488 --> 02:04:47,125
and Prevention did
release very recently,

2641
02:04:47,125 --> 02:04:49,427
updates to the contraceptive
recommendations

2642
02:04:49,427 --> 02:04:51,496
for individuals
with sickle cell disease,

2643
02:04:51,496 --> 02:04:54,899
and they put --
they made the contraindication

2644
02:04:54,899 --> 02:04:58,102
to estrogen-containing
contraception much more firm

2645
02:04:58,102 --> 02:05:00,672
than it had previously
been in recognition

2646
02:05:00,672 --> 02:05:01,873
of the thrombotic risks

2647
02:05:01,873 --> 02:05:03,908
associated with
sickle cell disease

2648
02:05:03,908 --> 02:05:05,944
and the thrombotic risks
associated with

2649
02:05:05,944 --> 02:05:08,046
the use of estrogen-containing
contraception.

2650
02:05:08,046 --> 02:05:10,381
Again, that’s a change
from the previous set

2651
02:05:10,381 --> 02:05:12,183
of recommendations
from the CDC.

2652
02:05:12,684 --> 02:05:15,587
And with that, I’ll get into
the main body of my talk.

2653
02:05:16,254 --> 02:05:19,924
So, I’ve been thinking about
the ovaries for some years now,

2654
02:05:19,924 --> 02:05:22,927
and mechanisms of ovarian injury
and sickle cell disease

2655
02:05:22,927 --> 02:05:25,330
seem not so distinct
from mechanisms of injury

2656
02:05:25,330 --> 02:05:27,799
and every other end organ
in sickle cell disease.

2657
02:05:28,333 --> 02:05:31,169
The ovaries are vulnerable
to hemolysis,

2658
02:05:31,169 --> 02:05:34,038
anemia, hypoxic-ischemic injury,
inflammation,

2659
02:05:34,038 --> 02:05:37,308
hypercoagulability,
reactive oxygen species,

2660
02:05:37,308 --> 02:05:40,512
and the overall hypermetabolic
state of this disease.

2661
02:05:40,512 --> 02:05:42,580
And that,
combined with the fact

2662
02:05:42,580 --> 02:05:45,617
that this disease is a disease
of accelerated aging,

2663
02:05:45,617 --> 02:05:48,386
what we’re seeing is accelerated
decline in egg quality --

2664
02:05:48,386 --> 02:05:49,654
quantity, sorry,

2665
02:05:49,654 --> 02:05:52,023
and I’ll show you
a picture of that in a minute.

2666
02:05:52,023 --> 02:05:56,327
Many questions
about this fact endure,

2667
02:05:56,327 --> 02:05:57,629
and among them are,

2668
02:05:57,629 --> 02:06:00,098
what are chronic therapies
for sickle cell disease

2669
02:06:00,098 --> 02:06:03,468
due to the ovaries,
as well as what the curative

2670
02:06:03,468 --> 02:06:05,603
and transformational
therapies do at present?

2671
02:06:05,603 --> 02:06:08,139
Whereas what differences
or modifications

2672
02:06:08,139 --> 02:06:11,042
in our preparative regimens
can do to change the risk?

2673
02:06:12,744 --> 02:06:15,647
The data so far
is limited in terms

2674
02:06:15,647 --> 02:06:17,448
of what the effects
of hydroxyurea

2675
02:06:17,448 --> 02:06:18,917
or chronic transfusions are.

2676
02:06:18,917 --> 02:06:21,519
And I’m actually not going to go
very deeply into that today,

2677
02:06:21,519 --> 02:06:25,323
except to say that people for
whom hydroxyurea is indicated,

2678
02:06:25,323 --> 02:06:26,624
appear to have some risk;

2679
02:06:26,624 --> 02:06:28,259
and people on chronic
transfusions,

2680
02:06:28,259 --> 02:06:29,861
that risk is not very clear.

2681
02:06:31,029 --> 02:06:32,997
We looked at anti-müllerian
hormone,

2682
02:06:32,997 --> 02:06:36,034
which is a serum marker that
I didn’t hear a lot in my --

2683
02:06:36,034 --> 02:06:38,102
talked about a lot
in my hematology fellowship,

2684
02:06:38,102 --> 02:06:39,337
but that may be a marker,

2685
02:06:39,337 --> 02:06:40,972
kind of like how we think
about creatinine

2686
02:06:40,972 --> 02:06:42,540
and other measures
of renal function,

2687
02:06:42,540 --> 02:06:45,310
or AST and ALT
for renal hepatic function,

2688
02:06:45,310 --> 02:06:46,978
that we need to start
becoming familiar with

2689
02:06:46,978 --> 02:06:50,615
as a marker of what’s happening
in the ovaries of our patients.

2690
02:06:50,615 --> 02:06:52,684
We looked at anti-müllerian
hormone levels,

2691
02:06:52,684 --> 02:06:54,986
which again is a marker
of egg supply,

2692
02:06:54,986 --> 02:06:58,256
in the multicenter study
of hydroxyurea follow-up cohort.

2693
02:06:58,256 --> 02:07:00,058
And this included 98 subjects,

2694
02:07:00,058 --> 02:07:02,327
all of whom had
hemoglobin SS disease.

2695
02:07:03,027 --> 02:07:05,530
What you can see in this graph
is, on the y axis,

2696
02:07:05,530 --> 02:07:08,299
anti-müllerian hormone levels
with a marker

2697
02:07:08,299 --> 02:07:10,134
at the measure of 1.1,

2698
02:07:10,134 --> 02:07:13,004
which is where diminished
ovarian reserve is defined.

2699
02:07:13,004 --> 02:07:16,708
Diminished ovarian reserve
is a risk factor for infertility

2700
02:07:16,708 --> 02:07:19,744
and most importantly,
is a risk factor for poorer

2701
02:07:19,744 --> 02:07:21,779
outcomes
with fertility preservation

2702
02:07:21,779 --> 02:07:24,816
and in vitro fertilization.
And as we move to an era

2703
02:07:24,816 --> 02:07:26,718
where we are harvesting eggs
in our patients

2704
02:07:26,718 --> 02:07:29,253
before they go to transplant
and gene therapy,

2705
02:07:29,253 --> 02:07:31,789
this issue of when to harvest
in terms of

2706
02:07:31,789 --> 02:07:35,259
when the egg supply is optimal,
becomes of increasing concern.

2707
02:07:35,893 --> 02:07:37,862
I hope what you can see
on the x axis

2708
02:07:37,862 --> 02:07:39,764
is buckets of age in years,

2709
02:07:39,764 --> 02:07:43,001
and the gray bars are the median
anti-müllerian hormone values

2710
02:07:43,001 --> 02:07:44,369
that we found in our cohort.

2711
02:07:44,369 --> 02:07:46,771
Whereas the black bars
are the median values

2712
02:07:47,438 --> 02:07:49,240
in the assay that we used.

2713
02:07:49,240 --> 02:07:52,110
At each measure in the
reproductively relevant years,

2714
02:07:52,110 --> 02:07:54,045
which I suppose you could debate
exactly what that means,

2715
02:07:54,045 --> 02:07:55,780
but let’s say
before age 46,

2716
02:07:55,780 --> 02:07:59,550
I hope you can see that
AMH levels in the individuals

2717
02:07:59,550 --> 02:08:00,818
with sickle cell tested

2718
02:08:00,818 --> 02:08:03,788
are lower than
in the standard measure.

2719
02:08:03,788 --> 02:08:06,691
And that measure
becomes meaningful,

2720
02:08:07,191 --> 02:08:09,127
sitting at diminished
ovarian reserve

2721
02:08:09,727 --> 02:08:12,130
for people between ages
25 and 30,

2722
02:08:12,997 --> 02:08:15,366
which is sooner than
in the general population.

2723
02:08:15,933 --> 02:08:18,736
So, as we’ve thought about
this data,

2724
02:08:19,470 --> 02:08:21,673
we know that diminished
ovarian reserve

2725
02:08:21,673 --> 02:08:24,609
and risks for diminished ovarian
reserves is an indication

2726
02:08:24,609 --> 02:08:27,245
to offer fertility preservation
for -- to people.

2727
02:08:27,245 --> 02:08:29,147
And yet, we do not
yet know exactly,

2728
02:08:30,048 --> 02:08:32,016
in people who are on
chronic therapies,

2729
02:08:32,717 --> 02:08:35,219
when and how to offer
these therapies.

2730
02:08:35,219 --> 02:08:38,556
But the indications
are reasonably clear,

2731
02:08:38,556 --> 02:08:40,558
at least for those
who are being exposed

2732
02:08:40,558 --> 02:08:41,959
to transplant and gene therapy,

2733
02:08:41,959 --> 02:08:43,728
anybody exposed
to an alkylating agent

2734
02:08:43,728 --> 02:08:45,096
who hasn’t completed their --

2735
02:08:45,096 --> 02:08:48,733
building their family should be
offered fertility preservation.

2736
02:08:48,733 --> 02:08:50,468
And then anybody with
sickle cell disease

2737
02:08:50,468 --> 02:08:53,037
and an indication
for hydroxyurea

2738
02:08:53,037 --> 02:08:55,006
should at least be having
this conversation,

2739
02:08:55,006 --> 02:08:56,641
and particularly
in those of our patients

2740
02:08:56,641 --> 02:08:57,875
who are just thriving --

2741
02:08:57,875 --> 02:08:59,544
and we have more
of those every day --

2742
02:08:59,544 --> 02:09:01,212
and who are going
to graduate school

2743
02:09:01,212 --> 02:09:03,981
and doing other things
to pursue educational goals.

2744
02:09:03,981 --> 02:09:06,150
Just like women in medicine
who are at increased

2745
02:09:06,150 --> 02:09:08,186
risk for infertility,
we know that our patients

2746
02:09:08,186 --> 02:09:10,488
who are pursuing
educational aspirations

2747
02:09:10,488 --> 02:09:12,724
are at increased risk
for infertility.

2748
02:09:13,291 --> 02:09:14,659
One thing I wanted to say
about this data

2749
02:09:14,659 --> 02:09:16,627
that I didn’t quite say
is that at the bottom,

2750
02:09:16,627 --> 02:09:18,563
there’s a whole bunch
of citations.

2751
02:09:18,563 --> 02:09:20,198
And that’s --
I put them all down there,

2752
02:09:20,198 --> 02:09:22,967
even though this is my graph,
because these findings are --

2753
02:09:22,967 --> 02:09:24,302
can no longer be understood

2754
02:09:24,302 --> 02:09:25,970
to be an idiosyncrasy
of the women

2755
02:09:25,970 --> 02:09:28,806
who participated in the
follow-up study of the MSH,

2756
02:09:28,806 --> 02:09:31,976
all of whom or most of whom
were exposed to hydroxyurea

2757
02:09:31,976 --> 02:09:33,711
around the world really.

2758
02:09:33,711 --> 02:09:36,347
We are getting data
on a hemoglobin SS disease

2759
02:09:36,347 --> 02:09:39,016
in particular that identifies
this same pattern

2760
02:09:39,016 --> 02:09:40,918
of accelerated decline
in egg supply.

2761
02:09:40,918 --> 02:09:43,755
So, we have data from France
and from Nigeria

2762
02:09:43,755 --> 02:09:46,557
and from the U.K.
and even from Atlanta, Georgia,

2763
02:09:46,557 --> 02:09:48,392
and it all tells us
the same story.

2764
02:09:50,428 --> 02:09:52,363
So -- sorry --
to cut back to this idea of,

2765
02:09:52,363 --> 02:09:54,098
what do we do with this data?

2766
02:09:54,098 --> 02:09:55,500
When are we going to be
offering people?

2767
02:09:55,500 --> 02:09:58,636
How are we going to be talking
about fertility preservation

2768
02:09:58,636 --> 02:09:59,904
in our community?

2769
02:09:59,904 --> 02:10:01,405
I’m going to talk
a little more about this,

2770
02:10:01,405 --> 02:10:04,008
but I think it’s a really
thoughtful challenge.

2771
02:10:04,008 --> 02:10:06,144
I don’t think we have
the right answer yet.

2772
02:10:06,144 --> 02:10:07,712
But if people are on
chronic therapies,

2773
02:10:07,712 --> 02:10:10,548
we probably can be waiting
until after puberty.

2774
02:10:12,784 --> 02:10:15,620
But for people who are
particularly anxious and --

2775
02:10:15,620 --> 02:10:17,522
probably could
be offered sooner.

2776
02:10:17,522 --> 02:10:18,823
And one of the reasons for that

2777
02:10:18,823 --> 02:10:20,658
is that we have
two standard approaches

2778
02:10:20,658 --> 02:10:22,927
to fertility preservation now,

2779
02:10:23,494 --> 02:10:25,797
and those are ovarian
tissue cryopreservation

2780
02:10:25,797 --> 02:10:27,999
and egg
or embryo cryopreservation.

2781
02:10:28,633 --> 02:10:30,935
And I’m going to spend the rest
of the talk talking about --

2782
02:10:30,935 --> 02:10:32,937
a little bit about
what those two things are

2783
02:10:32,937 --> 02:10:35,306
because we’re doing it more
and more in our patients.

2784
02:10:35,306 --> 02:10:38,142
And again, we -- you know,
we learn a little bit about

2785
02:10:38,142 --> 02:10:41,179
how to manage
surgical conditions

2786
02:10:41,179 --> 02:10:43,381
in sickle cell disease
during fellowship.

2787
02:10:43,381 --> 02:10:46,384
But these are relatively
new procedures for our patients,

2788
02:10:46,384 --> 02:10:48,352
and we need to know a little bit
about them to make sure

2789
02:10:48,352 --> 02:10:50,922
that we can help accompany
people safely through them

2790
02:10:50,922 --> 02:10:52,390
and do a little bit
of counseling

2791
02:10:52,390 --> 02:10:54,225
that hopefully will be
meaningful to patients

2792
02:10:54,225 --> 02:10:55,726
and their families.

2793
02:10:55,726 --> 02:10:58,930
So, ovarian tissue
cryopreservation, what is it?

2794
02:10:58,930 --> 02:11:01,799
It’s laparoscopic surgery
in which you can either remove

2795
02:11:01,799 --> 02:11:04,635
an entire ovary
or sections of an ovary.

2796
02:11:04,635 --> 02:11:06,604
It takes about 60 minutes.

2797
02:11:06,604 --> 02:11:08,873
It’s usually done
under general anesthesia.

2798
02:11:08,873 --> 02:11:10,174
And because in our patients,

2799
02:11:10,174 --> 02:11:13,444
the most common indication
for this is before transplant,

2800
02:11:13,444 --> 02:11:15,046
it’s often done in coordination

2801
02:11:15,046 --> 02:11:16,981
with line placement
and other things,

2802
02:11:16,981 --> 02:11:20,184
so that our patients don’t have
to be exposed to two anesthesia.

2803
02:11:20,918 --> 02:11:22,353
While this has been a standard
of care

2804
02:11:22,353 --> 02:11:24,355
in the United States
for a few years,

2805
02:11:24,355 --> 02:11:26,123
it has been a care
standard in France

2806
02:11:26,123 --> 02:11:27,658
for a much longer
period of time.

2807
02:11:27,658 --> 02:11:28,893
And for those reasons,

2808
02:11:28,893 --> 02:11:30,561
we’re beginning
to see data from France

2809
02:11:30,561 --> 02:11:32,897
that we have not yet generated
in this country.

2810
02:11:33,497 --> 02:11:36,334
It is a fertility
preservation option

2811
02:11:36,334 --> 02:11:40,137
for both pre-pubescent girls
and women who menstruate.

2812
02:11:40,137 --> 02:11:43,074
And the hypothesis, at least
in offering this to adults,

2813
02:11:43,074 --> 02:11:46,510
is that it is less risky
and more successful.

2814
02:11:46,510 --> 02:11:48,546
But that’s really
an untested hypothesis

2815
02:11:48,546 --> 02:11:50,147
in terms of pregnancy
outcomes.

2816
02:11:50,748 --> 02:11:53,417
And even whether
ovarian tissue helps --

2817
02:11:54,252 --> 02:11:56,087
meaningfully helps over time

2818
02:11:56,087 --> 02:11:58,756
to treat premature
ovarian insufficiency

2819
02:11:58,756 --> 02:12:01,058
with hormone production
provided by the ovaries

2820
02:12:01,058 --> 02:12:02,894
is not yet totally clear.

2821
02:12:03,828 --> 02:12:06,898
And usually, if you have
ovarian tissue cryopreserved,

2822
02:12:06,898 --> 02:12:09,500
you will ultimately need
IVF for pregnancy

2823
02:12:09,500 --> 02:12:11,002
if you experience infertility

2824
02:12:11,002 --> 02:12:14,238
and wish to use that
ovarian tissue to get pregnant.

2825
02:12:15,373 --> 02:12:16,807
There’s a risk
with reimplantation

2826
02:12:16,807 --> 02:12:18,943
of tissue of infarction
and graft failure.

2827
02:12:19,777 --> 02:12:23,714
And of course, if there are
previous exposures to radiation

2828
02:12:23,714 --> 02:12:26,817
or sickle cell-related damage
to the end organ,

2829
02:12:27,618 --> 02:12:29,887
it’s not fixed through
these procedures.

2830
02:12:31,923 --> 02:12:34,158
So, you know, how do we manage
these patients?

2831
02:12:34,158 --> 02:12:37,028
Well, we can manage them
per our sickle cell routines,

2832
02:12:37,028 --> 02:12:39,430
making sure that our patients
either have a hemoglobin of 10

2833
02:12:39,430 --> 02:12:42,967
or an S fraction less than 30
percent going into anesthesia.

2834
02:12:42,967 --> 02:12:45,303
Usually, again, because
of the timing of these --

2835
02:12:45,303 --> 02:12:48,105
the nature and the timing
of these interventions,

2836
02:12:48,673 --> 02:12:51,342
exchange is done associated
with line placement

2837
02:12:51,342 --> 02:12:53,044
and before conditioning
for cure.

2838
02:12:54,312 --> 02:12:56,747
We can make people sure
they’re well hydrated,

2839
02:12:56,747 --> 02:12:59,583
first case, all these things
we do to protect our patients,

2840
02:12:59,583 --> 02:13:01,485
make sure that people
are having laparoscopic

2841
02:13:01,485 --> 02:13:04,288
abdominal incisions,
receive incentive spirometers,

2842
02:13:04,288 --> 02:13:06,257
and make sure that
individualized pain plans

2843
02:13:06,257 --> 02:13:08,926
are available to surgical
treating teams

2844
02:13:08,926 --> 02:13:10,962
to direct post-operative
pain management.

2845
02:13:11,929 --> 02:13:14,966
And you know, often, we hope
that people will be reimplanted,

2846
02:13:14,966 --> 02:13:17,735
post-cure, post-transformational
therapy.

2847
02:13:17,735 --> 02:13:21,672
And so, the prep for that
will be happily nonexistent.

2848
02:13:24,375 --> 02:13:26,444
The other standard
of care option for --

2849
02:13:27,812 --> 02:13:29,013
a bridge too far.

2850
02:13:29,013 --> 02:13:31,615
Now, we’ve really gone too far.
Sorry, folks.

2851
02:13:32,783 --> 02:13:36,754
The other standard or option
for preserving fertility

2852
02:13:36,754 --> 02:13:39,957
in people who menstruate
is ovarian stimulation.

2853
02:13:40,624 --> 02:13:43,427
And the purpose of this
is to stimulate the ovaries

2854
02:13:43,427 --> 02:13:46,397
using hormones
to make many mature eggs,

2855
02:13:46,397 --> 02:13:49,467
which are then retrieved,
harvested,

2856
02:13:49,467 --> 02:13:53,971
and frozen or comingled
with sperm to make embryos.

2857
02:13:53,971 --> 02:13:55,940
And the embryos can be frozen.

2858
02:13:56,974 --> 02:13:58,743
This is much more of a process.

2859
02:13:58,743 --> 02:14:02,913
It’s a 10-to-14-day process.
It’s timed with menses.

2860
02:14:02,913 --> 02:14:06,350
It requires many trips
to the clinic,

2861
02:14:06,350 --> 02:14:08,686
and the harvest is done
under light anesthesia

2862
02:14:08,686 --> 02:14:12,556
and only takes about 20 minutes.
It’s done across the uterus.

2863
02:14:12,556 --> 02:14:14,325
So, there’s no
abdominal incision,

2864
02:14:14,325 --> 02:14:16,360
which is elegant
for our patients.

2865
02:14:16,360 --> 02:14:18,696
Often these procedures
are performed

2866
02:14:18,696 --> 02:14:22,400
in outpatient surgery centers,
so not in the main hospital.

2867
02:14:22,400 --> 02:14:24,268
And of course,
that has some implications

2868
02:14:24,268 --> 02:14:26,437
for our more medically
vulnerable patients.

2869
02:14:27,405 --> 02:14:29,940
This, again, is a standard of
care for people who menstruate.

2870
02:14:29,940 --> 02:14:32,276
You have to be pubescent
to get this treatment.

2871
02:14:32,877 --> 02:14:35,379
And as I said,
AMH predicts egg harvest

2872
02:14:35,379 --> 02:14:37,014
and hyperstimulation risk,

2873
02:14:37,014 --> 02:14:38,649
and we’ll talk about that
in a minute.

2874
02:14:38,649 --> 02:14:41,485
This procedure has many
sickle cell landmines.

2875
02:14:41,485 --> 02:14:43,087
And because I think we are
going to continue

2876
02:14:43,087 --> 02:14:45,356
to increase use of this
procedure in our patients,

2877
02:14:45,356 --> 02:14:46,857
I’ve chosen to spend
some time

2878
02:14:46,857 --> 02:14:49,593
thinking about those landmines
in the next few slides.

2879
02:14:49,593 --> 02:14:50,795
There are other --

2880
02:14:50,795 --> 02:14:53,064
another problem
besides the landmines

2881
02:14:53,064 --> 02:14:56,233
of going through the procedure
is that for those with low AMH,

2882
02:14:56,233 --> 02:14:58,969
they may need multiple
cycles of stimulation,

2883
02:14:58,969 --> 02:15:01,439
which increases the cost,
among other things,

2884
02:15:02,173 --> 02:15:03,974
to get enough eggs
to be considered

2885
02:15:03,974 --> 02:15:06,977
a successful fertility
preservation outcome.

2886
02:15:06,977 --> 02:15:09,914
With egg harvest, as opposed
to ovarian tissue harvest,

2887
02:15:09,914 --> 02:15:12,416
there’s no potential for
resumption of hormonal function

2888
02:15:12,416 --> 02:15:15,319
if you lose that as a
consequence of the prep regimen.

2889
02:15:15,319 --> 02:15:18,756
And again, this does not fix
previous exposures of the eggs

2890
02:15:18,756 --> 02:15:21,625
to toxic
or potentially toxic things

2891
02:15:22,426 --> 02:15:24,028
before they were harvested.

2892
02:15:26,097 --> 02:15:28,065
So, this is what the ovaries
look like

2893
02:15:28,065 --> 02:15:29,700
during hyperstimulation.

2894
02:15:29,700 --> 02:15:31,402
And you know, a picture
is worth a thousand words.

2895
02:15:31,402 --> 02:15:32,670
So, first of all,
these are pictures

2896
02:15:32,670 --> 02:15:35,539
across 14 days of stimulation,

2897
02:15:35,539 --> 02:15:37,441
and each one
of these ultrasounds

2898
02:15:37,441 --> 02:15:38,709
is a visit to the clinic.

2899
02:15:38,709 --> 02:15:41,846
So, there’s one side effect
of going through this process,

2900
02:15:41,846 --> 02:15:43,848
but another is that the ovary
is really enlarged

2901
02:15:43,848 --> 02:15:45,649
and get quite a lot
of dominant follicles,

2902
02:15:45,649 --> 02:15:47,518
which you can see
on this far right axis.

2903
02:15:47,518 --> 02:15:49,720
And we’ve cared for people
with a very unique

2904
02:15:49,720 --> 02:15:52,156
sensitivity to discomfort
and pain.

2905
02:15:52,156 --> 02:15:54,558
And so, I think really being
able to hold this image

2906
02:15:54,558 --> 02:15:56,227
in my mind
when I talk to people about

2907
02:15:56,227 --> 02:15:59,964
what it’s like to go through
ovarian stimulation is helpful.

2908
02:15:59,964 --> 02:16:03,234
Because the ovaries are large.
They -- people can feel them.

2909
02:16:03,234 --> 02:16:05,603
They feel out of place.
They feel strange.

2910
02:16:05,603 --> 02:16:07,204
And helping people
anticipate that,

2911
02:16:07,204 --> 02:16:09,406
just as we help people
anticipate the discomfort

2912
02:16:09,406 --> 02:16:11,575
of an enlarged uterus
in pregnancy,

2913
02:16:11,575 --> 02:16:13,644
can be really helpful
for helping people define

2914
02:16:13,644 --> 02:16:16,680
what’s normal
with this unusual experience.

2915
02:16:18,482 --> 02:16:20,851
Another challenge with
ovarian stimulation

2916
02:16:20,851 --> 02:16:24,221
is that it is, by design,
a hyperestrogenemic state.

2917
02:16:24,221 --> 02:16:27,191
This graph just shows you
what happens to estradiol levels

2918
02:16:27,191 --> 02:16:30,361
across the 10 days
of stimulation of the ovaries

2919
02:16:30,361 --> 02:16:32,029
for an egg harvest.

2920
02:16:32,029 --> 02:16:33,497
You know,
and the purpose of that

2921
02:16:33,497 --> 02:16:35,399
is to suppress the LH surge.

2922
02:16:36,400 --> 02:16:40,137
And then you ultimately
trigger the mature eggs

2923
02:16:40,704 --> 02:16:42,306
to harvest them to mature.

2924
02:16:43,107 --> 02:16:46,677
And that trigger can be
associated with a lot of risk.

2925
02:16:47,578 --> 02:16:50,681
And so, the VTE risk here
is two to three times

2926
02:16:50,681 --> 02:16:52,383
higher than
in spontaneous pregnancy

2927
02:16:52,383 --> 02:16:54,051
and folks without
sickle cell disease.

2928
02:16:54,051 --> 02:16:55,686
We know our patients
with sickle cell disease

2929
02:16:55,686 --> 02:16:58,255
who are pregnant have
an astronomic risk of the clot.

2930
02:16:58,255 --> 02:17:00,958
So, the thrombotic risk here
for our patients

2931
02:17:00,958 --> 02:17:02,560
is likely not trivial.

2932
02:17:03,460 --> 02:17:08,098
After we had a few adverse
outcomes at Hopkins

2933
02:17:08,098 --> 02:17:11,569
about five or six years ago,
we sat down with our REI doctors

2934
02:17:12,236 --> 02:17:16,507
and thought together about how
we could learn from each other

2935
02:17:16,507 --> 02:17:17,908
about how to take
excellent care

2936
02:17:17,908 --> 02:17:19,343
of our patients
going through this.

2937
02:17:19,343 --> 02:17:22,613
And this is the protocol
that we came up with

2938
02:17:22,613 --> 02:17:25,182
and that we’ve been using
since we developed it.

2939
02:17:26,383 --> 02:17:27,851
And I just wanted
to talk you through it.

2940
02:17:27,851 --> 02:17:30,688
So, people who are referred
to fertility preservation,

2941
02:17:30,688 --> 02:17:33,090
we think first about
their sickle cell genotype,

2942
02:17:33,757 --> 02:17:36,727
whether they have SS or Sβ0
thalassemia versus SC

2943
02:17:36,727 --> 02:17:38,796
and Sβ+ thalassemia.

2944
02:17:38,796 --> 02:17:42,933
In general, we coordinate
a red cell exchange for those

2945
02:17:42,933 --> 02:17:45,069
with SS
and Sβ0 thalassemia

2946
02:17:45,069 --> 02:17:47,972
to target an S fraction
less than 30 percent,

2947
02:17:47,972 --> 02:17:50,274
and on a case-by-case basis,
make the decision

2948
02:17:50,274 --> 02:17:53,377
of whether to do that
for those with other genotypes.

2949
02:17:53,377 --> 02:17:55,646
We consider the need
for anticoagulation,

2950
02:17:55,646 --> 02:17:58,048
given the thrombotic risks
described,

2951
02:17:58,048 --> 02:18:00,551
and initiate stimulation.

2952
02:18:00,551 --> 02:18:03,687
We meet with our anesthesiology
colleagues

2953
02:18:04,255 --> 02:18:07,725
and think about the timing
of retrieval and availability

2954
02:18:07,725 --> 02:18:09,326
of teams to help.

2955
02:18:12,062 --> 02:18:14,732
And I actually can’t see
the bottom of the slide.

2956
02:18:14,732 --> 02:18:16,000
And then we think about --

2957
02:18:16,000 --> 02:18:17,801
I just want to make sure
I get --

2958
02:18:17,801 --> 02:18:19,136
lead you along here [laughs]

2959
02:18:19,136 --> 02:18:22,539
-- post-procedural pain control
and incentive spirometry,

2960
02:18:22,539 --> 02:18:24,742
which the REI teams
and anesthesia teams

2961
02:18:24,742 --> 02:18:27,378
involved in this care
don’t always have awareness of.

2962
02:18:27,378 --> 02:18:28,712
And again, they are off site.

2963
02:18:28,712 --> 02:18:30,581
So, our sickle cell team
can’t just pop in

2964
02:18:30,581 --> 02:18:32,616
and make sure
these things are happening.

2965
02:18:34,084 --> 02:18:36,487
We also, you know,
defer to our expert teams

2966
02:18:36,487 --> 02:18:37,888
for different
dimensions of this.

2967
02:18:37,888 --> 02:18:40,391
The pre-procedure assessments
of ovarian reserve,

2968
02:18:41,158 --> 02:18:44,228
which informs stimulation
and predict the stim response,

2969
02:18:44,228 --> 02:18:48,766
are really the purview
of our expert REI colleagues.

2970
02:18:48,766 --> 02:18:52,069
And we talk to them a lot about
that before we get started.

2971
02:18:52,069 --> 02:18:55,439
These are the places where
we’ve identified

2972
02:18:55,439 --> 02:18:56,940
real potential pitfalls

2973
02:18:56,940 --> 02:18:59,209
in all of this
for our patient population.

2974
02:19:00,110 --> 02:19:02,513
The first is that we make
an active decision

2975
02:19:02,513 --> 02:19:05,716
with our patients about whether
to start or stop hydroxyurea.

2976
02:19:06,417 --> 02:19:08,052
And the reason we make it
with our patients

2977
02:19:08,052 --> 02:19:10,087
is because there’s not
a right answer.

2978
02:19:10,087 --> 02:19:12,523
For people who I can
stop hydroxyurea in

2979
02:19:12,523 --> 02:19:15,459
and give three months of chronic
transfusions before doing this,

2980
02:19:15,459 --> 02:19:17,461
I do,
but there’s not a right answer.

2981
02:19:17,461 --> 02:19:20,464
And if a patient doesn’t want
to do that, we don’t have to.

2982
02:19:20,464 --> 02:19:24,368
This is -- unlike in cancer,
this is rarely an emergency.

2983
02:19:24,368 --> 02:19:25,736
So, you really do have some time

2984
02:19:25,736 --> 02:19:27,838
or should have some time
to plan this out.

2985
02:19:28,572 --> 02:19:29,840
We engage, as I mentioned,

2986
02:19:29,840 --> 02:19:32,476
in care coordination
between ourselves, REI,

2987
02:19:32,476 --> 02:19:34,845
and anesthesia.
We decide together.

2988
02:19:34,845 --> 02:19:38,248
Some people empirically use
VTE prophylaxis in this setting.

2989
02:19:38,248 --> 02:19:40,417
We don’t.
We decide with --

2990
02:19:40,417 --> 02:19:43,220
on each patient,
based on their own risk.

2991
02:19:43,787 --> 02:19:45,422
One thing we identified early on

2992
02:19:45,422 --> 02:19:47,291
is that our anesthesia
colleagues love

2993
02:19:47,291 --> 02:19:48,559
giving dexamethasone

2994
02:19:48,559 --> 02:19:51,161
as an antiemetic agent
after this procedure,

2995
02:19:51,829 --> 02:19:54,098
and we know that dexamethasone
is associated

2996
02:19:54,098 --> 02:19:55,399
with painful crises

2997
02:19:55,399 --> 02:19:57,901
and even mortality
in this patient population.

2998
02:19:57,901 --> 02:20:02,573
And so, we always talk
about alternatives to steroids,

2999
02:20:02,573 --> 02:20:04,775
except to save life
in this setting.

3000
02:20:05,609 --> 02:20:09,012
And then finally, everybody’s
at some degree of risk

3001
02:20:09,012 --> 02:20:11,548
for ovarian
hyperstimulation syndrome.

3002
02:20:11,548 --> 02:20:13,183
And I have my patients do --

3003
02:20:13,183 --> 02:20:16,787
monitor daily weights for that
for about 10 to 14 days

3004
02:20:16,787 --> 02:20:18,856
after their stim,
which is the risk period.

3005
02:20:18,856 --> 02:20:20,491
The reason I have them
monitor daily weights

3006
02:20:20,491 --> 02:20:22,893
is because this is
a capillary leak syndrome.

3007
02:20:22,893 --> 02:20:25,462
And my hypothesis is that
if they are going to

3008
02:20:25,462 --> 02:20:28,098
run into trouble with this
by having an abdominal ileus

3009
02:20:28,098 --> 02:20:31,068
or any other more
severe complication,

3010
02:20:31,068 --> 02:20:33,570
that I may see a first sign
of that in their weight

3011
02:20:33,570 --> 02:20:37,474
going up as the capillary
leak syndrome is initiated,

3012
02:20:37,474 --> 02:20:40,477
and the water retention starts,
of this syndrome.

3013
02:20:41,612 --> 02:20:43,981
Just a little bit about
ovarian hyperstimulation.

3014
02:20:43,981 --> 02:20:45,983
So, it’s capillary
leak syndrome.

3015
02:20:45,983 --> 02:20:47,284
It’s a spectrum disorder.

3016
02:20:47,284 --> 02:20:49,353
So, it ranges from mild
to critical.

3017
02:20:49,353 --> 02:20:51,422
And I hope that if you look
at this list of things,

3018
02:20:51,422 --> 02:20:53,524
you can see why none of them,
not the mild stuff,

3019
02:20:53,524 --> 02:20:54,792
and not the critical stuff,

3020
02:20:54,792 --> 02:20:57,494
appeals to me in individuals
with sickle cell disease.

3021
02:20:58,362 --> 02:21:00,998
I don’t really want my patients
having mild nausea,

3022
02:21:00,998 --> 02:21:02,366
vomiting, diarrhea.

3023
02:21:02,366 --> 02:21:05,068
I don’t want them
having ascites, abdominal pain,

3024
02:21:05,068 --> 02:21:07,337
and urea
or any kind of renal injury,

3025
02:21:07,337 --> 02:21:10,941
no clots, none of those things.
And so,

3026
02:21:10,941 --> 02:21:14,411
we are really attentive to this
in our patient population,

3027
02:21:14,411 --> 02:21:17,281
who, again, are, on average,
at lower risk of this

3028
02:21:18,582 --> 02:21:21,018
because they have
lower AMH on average.

3029
02:21:23,620 --> 02:21:25,956
Another thing, you know,
all of these patients,

3030
02:21:25,956 --> 02:21:27,257
everybody who has
an indication --

3031
02:21:27,257 --> 02:21:28,459
most, not everybody.

3032
02:21:28,459 --> 02:21:31,028
Many people who have indications
to go to transplant

3033
02:21:31,028 --> 02:21:33,030
and gene therapy
have an indication

3034
02:21:33,030 --> 02:21:35,499
that is associated
with their experience of pain.

3035
02:21:36,233 --> 02:21:38,402
And as the late,
great James Earl Jones

3036
02:21:38,402 --> 02:21:39,736
told us in Field of Dreams,

3037
02:21:39,736 --> 02:21:42,406
you know, one of the constants
throughout the years

3038
02:21:42,406 --> 02:21:43,941
in this country
is sickle cell pain,

3039
02:21:43,941 --> 02:21:45,309
and we always have
to think about

3040
02:21:45,309 --> 02:21:47,177
how what we’re doing
to our patients

3041
02:21:47,177 --> 02:21:48,512
could cause them
to have pain.

3042
02:21:48,512 --> 02:21:51,114
And so, throughout this process
and one of the reasons

3043
02:21:51,114 --> 02:21:53,650
that I use red cell
exchange up front

3044
02:21:53,650 --> 02:21:54,952
is because I’m hyperaware

3045
02:21:54,952 --> 02:21:57,354
that a painful crisis
during stimulation

3046
02:21:57,354 --> 02:21:59,790
or during retrieval
could really ruin outcomes

3047
02:21:59,790 --> 02:22:03,227
for a very high-stakes
and expensive, frankly,

3048
02:22:03,227 --> 02:22:05,295
intervention for so many
of our patients.

3049
02:22:05,295 --> 02:22:07,731
And so, being --
alerting our care teams

3050
02:22:07,731 --> 02:22:10,167
about how to manage pain
and doing everything we can

3051
02:22:10,167 --> 02:22:12,836
to not have
to abort a stem cycle

3052
02:22:12,836 --> 02:22:14,972
due to a pain crisis
is really paramount.

3053
02:22:16,273 --> 02:22:18,909
So, some questions we don’t
really know the answer to,

3054
02:22:18,909 --> 02:22:22,045
should we use a washout
period for hydroxyurea?

3055
02:22:22,045 --> 02:22:24,581
How many eggs do you need
to be enough to -- you know.

3056
02:22:24,581 --> 02:22:27,584
What does your insurance package
for getting pregnant look like?

3057
02:22:27,584 --> 02:22:28,886
What about the outcome?

3058
02:22:28,886 --> 02:22:30,754
So, we, you know,
we care about pregnancy.

3059
02:22:30,754 --> 02:22:33,657
We care about restoration
of ovarian hormone function

3060
02:22:33,657 --> 02:22:36,493
for those who have
ovarian tissue cryopreservation.

3061
02:22:36,493 --> 02:22:38,328
And you know, it’s very
difficult to compare people

3062
02:22:38,328 --> 02:22:40,397
with sickle cell disease
to other standard groups

3063
02:22:40,397 --> 02:22:43,634
that receive these interventions
because our patients have unique

3064
02:22:43,634 --> 02:22:46,570
risks and unique exposures
and prognostic factors.

3065
02:22:48,171 --> 02:22:49,773
When we think about cost,
right,

3066
02:22:49,773 --> 02:22:52,576
keeping tissue and gametes
frozen has an annual cost.

3067
02:22:52,576 --> 02:22:55,546
It’s not just the upfront costs
of these procedures.

3068
02:22:55,546 --> 02:22:58,248
And these costs are not
usually covered by insurance.

3069
02:22:58,248 --> 02:23:00,884
And then IVF for pregnancy
is required

3070
02:23:00,884 --> 02:23:03,987
and may not be covered by
insurance in the United States,

3071
02:23:03,987 --> 02:23:07,190
at least even as we see
the march of expanding access

3072
02:23:07,190 --> 02:23:10,894
to fertility preservation,
at least in some of our states.

3073
02:23:10,894 --> 02:23:12,829
We do not see necessarily,
coming with that,

3074
02:23:12,829 --> 02:23:15,332
an increased access
to in vitro fertilization.

3075
02:23:17,100 --> 02:23:19,870
So, for people going
through transplant,

3076
02:23:19,870 --> 02:23:22,806
we have a clear indication --
often, when I give this talk,

3077
02:23:23,807 --> 02:23:25,976
I don’t say what I think
about this topic.

3078
02:23:25,976 --> 02:23:27,911
And so, I finally
sort of worked it out,

3079
02:23:27,911 --> 02:23:30,647
and I wanted to provide
a little anticipatory guidance.

3080
02:23:30,647 --> 02:23:33,917
So, over time, I’ve heard a lot
from people about, "Okay. Great.

3081
02:23:33,917 --> 02:23:35,118
Well, what are we
supposed to say

3082
02:23:35,118 --> 02:23:36,520
to people about hydroxyurea?"

3083
02:23:36,520 --> 02:23:39,256
Because this is
a real community concern.

3084
02:23:39,256 --> 02:23:41,825
And also, we don’t want
to put people off hydroxyurea.

3085
02:23:41,825 --> 02:23:44,027
Because as --
and as we’ve just heard today,

3086
02:23:44,628 --> 02:23:47,664
it’s an extraordinary therapy
for our -- for the disease.

3087
02:23:48,298 --> 02:23:50,233
So, these are sort of the four
principles

3088
02:23:50,233 --> 02:23:52,169
of talking about hydroxyurea
and fertility

3089
02:23:52,169 --> 02:23:55,872
that I’ve come up with that
I use in my own clinic,

3090
02:23:56,640 --> 02:23:58,609
and that I hope
might be useful to you.

3091
02:23:59,242 --> 02:24:01,678
The first is to invite
discussion of this topic.

3092
02:24:01,678 --> 02:24:03,347
Avoiding it isn’t
really helping anybody.

3093
02:24:03,347 --> 02:24:06,049
Families are thinking about it.
Patients are thinking about it.

3094
02:24:06,049 --> 02:24:07,718
And you can gage
people’s level of interest.

3095
02:24:07,718 --> 02:24:10,187
Some people don’t really -- it’s
not their most important thing.

3096
02:24:10,187 --> 02:24:12,756
And for other people,
it’s a really big deal.

3097
02:24:12,756 --> 02:24:14,891
Assisted reproductive
technologies, broadly,

3098
02:24:14,891 --> 02:24:16,326
are important
to our community.

3099
02:24:16,326 --> 02:24:18,929
It’s not just fertility
preservation and infertility,

3100
02:24:18,929 --> 02:24:22,532
but also the potential to have
a parent of a child

3101
02:24:22,532 --> 02:24:25,335
with sickle cell disease
conceive a matched sibling donor

3102
02:24:25,335 --> 02:24:28,739
by doing HLA
typing on embryos.

3103
02:24:28,739 --> 02:24:31,308
It’s the opportunity
to use PGT-M for parents

3104
02:24:31,308 --> 02:24:33,610
who are interested,
who have affected children,

3105
02:24:33,610 --> 02:24:36,747
and those are people who are
in our sickle cell clinics.

3106
02:24:36,747 --> 02:24:38,048
And then it also --

3107
02:24:38,048 --> 02:24:40,083
as you start to have
these conversations with people,

3108
02:24:40,083 --> 02:24:43,654
it helps understand what their
individual barriers are to care.

3109
02:24:43,654 --> 02:24:45,789
The second thing is this is
multi-disciplinary care.

3110
02:24:45,789 --> 02:24:47,557
I can’t do this alone.

3111
02:24:47,557 --> 02:24:49,626
And so, there’s all kinds
of people

3112
02:24:49,626 --> 02:24:51,528
that can be your allies
in this work,

3113
02:24:51,528 --> 02:24:53,397
genetic counselors,
adolescent medicine,

3114
02:24:53,397 --> 02:24:56,800
GYN, endocrine, MFM,
the transplanters,

3115
02:24:56,800 --> 02:24:59,202
our psychology services,
our advocacy groups.

3116
02:24:59,202 --> 02:25:01,004
Everybody’s got to work
together on this stuff.

3117
02:25:01,004 --> 02:25:03,206
This is --
these are family affairs.

3118
02:25:03,206 --> 02:25:05,242
The third thing is,
we can claim solid ground.

3119
02:25:05,242 --> 02:25:07,477
And I think people --
we can really do this.

3120
02:25:07,477 --> 02:25:09,446
You know, first of all,
we need to affirm

3121
02:25:09,446 --> 02:25:11,481
that sickle cell disease
confirms --

3122
02:25:11,481 --> 02:25:14,084
confers infertility risks
and that those --

3123
02:25:14,084 --> 02:25:16,219
the risks factors
are multifactorial.

3124
02:25:16,753 --> 02:25:18,889
We can’t equivocate
about the impact

3125
02:25:18,889 --> 02:25:21,158
of hydroxyurea on this disease.

3126
02:25:21,158 --> 02:25:24,161
We see that in treated cohorts,
that the treated children

3127
02:25:24,161 --> 02:25:26,697
just behave totally differently
in terms of their hospital use,

3128
02:25:26,697 --> 02:25:29,099
their complications
than the untreated children.

3129
02:25:29,099 --> 02:25:30,600
Everybody deserves
a childhood

3130
02:25:30,600 --> 02:25:33,437
in which they can play
and learn and grow.

3131
02:25:33,937 --> 02:25:35,605
And hydroxyurea enables that.

3132
02:25:36,506 --> 02:25:38,442
We can tell families that
entering adulthood

3133
02:25:38,442 --> 02:25:40,911
with the least accumulated
morbidity is critical,

3134
02:25:40,911 --> 02:25:42,946
especially in places
like the United States,

3135
02:25:42,946 --> 02:25:45,115
where we lack
sufficient adult experts

3136
02:25:45,115 --> 02:25:47,084
to care for people
with this disease.

3137
02:25:47,084 --> 02:25:48,518
We can affirm that many women

3138
02:25:48,518 --> 02:25:50,020
with sickle cell
become pregnant,

3139
02:25:50,020 --> 02:25:52,122
and we can know
the definition of infertility.

3140
02:25:52,122 --> 02:25:54,057
So, we can screen
and monitor people

3141
02:25:54,057 --> 02:25:56,426
for when they meet criteria
for the diagnosis.

3142
02:25:56,426 --> 02:25:59,029
And finally, we can affirm that
fertility preservation exists

3143
02:25:59,029 --> 02:26:00,964
as a standard of care
for those at risk

3144
02:26:01,898 --> 02:26:04,401
and work to do
what we can to ensure access.

3145
02:26:04,968 --> 02:26:08,105
Finally, we can partner
with our families to acknowledge

3146
02:26:08,105 --> 02:26:10,373
how much uncertainty
there is in this space.

3147
02:26:11,775 --> 02:26:13,877
We know that
there’s dynamic activity.

3148
02:26:14,778 --> 02:26:16,813
You know, Dr. Tisdale,
I saw at the back of your head.

3149
02:26:16,813 --> 02:26:18,415
Dr. Tisdale, hi.

3150
02:26:19,382 --> 02:26:20,817
We know that people
are working on

3151
02:26:20,817 --> 02:26:23,720
making prep regimens
that are less gonadotoxic.

3152
02:26:23,720 --> 02:26:26,056
We know that novel chronic
therapies are coming our way.

3153
02:26:26,056 --> 02:26:27,424
We don’t know
what 10 years from now

3154
02:26:27,424 --> 02:26:29,359
is going to look like
for treatment for sickle cell

3155
02:26:29,359 --> 02:26:31,027
in the United States
and Jamaica.

3156
02:26:31,695 --> 02:26:34,264
This is also a dynamic place
for healthcare policy

3157
02:26:34,264 --> 02:26:36,032
and access to ART.

3158
02:26:36,032 --> 02:26:39,002
And finally, it can be a dynamic
place for families who --

3159
02:26:39,002 --> 02:26:40,403
especially in the United States

3160
02:26:40,403 --> 02:26:42,105
where we have
a shocking geography

3161
02:26:42,105 --> 02:26:44,641
of restricted reproductive
health care access,

3162
02:26:44,641 --> 02:26:48,111
families may choose to choose
jobs, living locations,

3163
02:26:48,111 --> 02:26:52,149
or to dedicate their savings
in ways that allows them

3164
02:26:52,149 --> 02:26:54,851
to make priorities
about their life and care.

3165
02:26:54,851 --> 02:26:56,153
And this is such a big deal

3166
02:26:56,153 --> 02:26:59,956
because it just costs
an incredible amount of money.

3167
02:27:01,758 --> 02:27:03,927
I did want to let you know that
Sickle Cell Reproductive

3168
02:27:03,927 --> 02:27:05,195
Health Education Directive,

3169
02:27:05,195 --> 02:27:06,663
in partnership with
Be The Match --

3170
02:27:06,663 --> 02:27:07,898
and thanks to
Be The Match --

3171
02:27:07,898 --> 02:27:10,400
has issued over 30 grants
to preserve fertility

3172
02:27:10,400 --> 02:27:12,736
in young people
with sickle cell disease

3173
02:27:12,736 --> 02:27:15,772
in an attempt to help mimic some
of the efforts of cancer groups

3174
02:27:15,772 --> 02:27:19,242
and others to help access
this fertility preservation care

3175
02:27:19,242 --> 02:27:21,178
that is needed for
so many of our patients.

3176
02:27:21,178 --> 02:27:23,780
And the website for how to apply
for those is there.

3177
02:27:25,148 --> 02:27:28,618
All right. Well, that concludes
my fertility preservation piece.

3178
02:27:28,618 --> 02:27:30,353
I’m going to talk a little bit
about pregnancies,

3179
02:27:30,353 --> 02:27:33,690
starting with the existence
of these CDC fact sheets

3180
02:27:33,690 --> 02:27:36,359
for patients.
They’re in English and Spanish,

3181
02:27:36,359 --> 02:27:38,161
and they include
preconception care,

3182
02:27:38,161 --> 02:27:39,830
prenatal care,
and postpartum care

3183
02:27:39,830 --> 02:27:42,165
and can help direct care
for individuals

3184
02:27:42,165 --> 02:27:44,501
with sickle cell disease
considering pregnancy.

3185
02:27:45,168 --> 02:27:47,170
It’s a difficult time
for maternal-fetal medicine

3186
02:27:47,170 --> 02:27:48,405
in the United States

3187
02:27:48,405 --> 02:27:50,907
and for sickle cell disease
as a consequence.

3188
02:27:50,907 --> 02:27:53,543
I hope you can see in these maps
the distribution

3189
02:27:53,543 --> 02:27:56,813
in the upper left column of
people with sickle cell disease

3190
02:27:56,813 --> 02:27:59,549
and in the lower left column,
the distribution of care.

3191
02:28:00,417 --> 02:28:01,952
If you can,
in your mind’s eye,

3192
02:28:01,952 --> 02:28:04,855
overlap that map with
the maternity care access.

3193
02:28:04,855 --> 02:28:06,389
And this is certainly
a shifting terrain

3194
02:28:06,389 --> 02:28:08,391
as hospitals in the South
continue to close

3195
02:28:08,391 --> 02:28:10,160
and restrict access to care.

3196
02:28:10,160 --> 02:28:12,229
But you can see why there
are parts of this country

3197
02:28:12,229 --> 02:28:13,496
where there are many,

3198
02:28:13,496 --> 02:28:15,098
many people
with sickle cell disease

3199
02:28:15,098 --> 02:28:16,366
who may be having trouble

3200
02:28:16,366 --> 02:28:18,602
accessing the obstetric care
that they need,

3201
02:28:18,602 --> 02:28:20,270
just by looking
at these maps.

3202
02:28:21,671 --> 02:28:23,273
Well, why does this matter?

3203
02:28:23,273 --> 02:28:26,243
Because we now have data
from California data sets,

3204
02:28:26,243 --> 02:28:27,477
from Jamaican data sets,

3205
02:28:27,477 --> 02:28:30,347
and from national
U.S. data sets,

3206
02:28:30,347 --> 02:28:33,650
that over the last 20
to 30 years, there --

3207
02:28:33,650 --> 02:28:37,187
the morbidity and mortality
of sickle cell disease pregnancy

3208
02:28:37,187 --> 02:28:38,588
has not changed.

3209
02:28:38,588 --> 02:28:40,690
And I think that we as
a community can step up

3210
02:28:40,690 --> 02:28:42,726
and start moving
the needle on this now,

3211
02:28:44,427 --> 02:28:48,365
This is the data that my
then-student, Dr. Macy Early --

3212
02:28:48,365 --> 02:28:50,333
she’s now a resident at
the University of Pennsylvania

3213
02:28:50,333 --> 02:28:51,534
in internal medicine

3214
02:28:51,534 --> 02:28:53,536
and a future
sickle cell doctor of America --

3215
02:28:53,536 --> 02:28:56,673
made from the National
Inpatient Sample.

3216
02:28:56,673 --> 02:28:57,874
And this is --
these are measures

3217
02:28:57,874 --> 02:29:01,611
of the severe maternal morbidity
index from the CDC,

3218
02:29:01,611 --> 02:29:04,281
which are markers of severe
obstetric outcomes

3219
02:29:04,281 --> 02:29:06,316
that present a risk for death.

3220
02:29:06,316 --> 02:29:10,654
Of interest, the SMM index does
include vaso-occlusive crisis.

3221
02:29:10,654 --> 02:29:12,222
But we didn’t measure it
in this study

3222
02:29:12,222 --> 02:29:14,357
because it was
a comparison study,

3223
02:29:14,357 --> 02:29:15,892
and nobody else
but people with sickle

3224
02:29:15,892 --> 02:29:19,329
should be getting vaso-occlusion
crises during pregnancy.

3225
02:29:20,130 --> 02:29:23,333
What this graph shows,
in the orange and yellow plots

3226
02:29:23,333 --> 02:29:24,768
are people
with sickle cell disease

3227
02:29:24,768 --> 02:29:27,537
who were coded as having
Black race, people with --

3228
02:29:28,371 --> 02:29:33,043
then the purple and blue plots
are people coded with Black race

3229
02:29:33,043 --> 02:29:34,477
but without sickle cell disease.

3230
02:29:34,477 --> 02:29:36,780
And the line one is people
coded as White

3231
02:29:36,780 --> 02:29:38,181
without sickle cell disease.

3232
02:29:38,181 --> 02:29:39,916
And so, I hope you can see

3233
02:29:39,916 --> 02:29:42,752
that the severe maternal
morbidity of this disease

3234
02:29:42,752 --> 02:29:45,221
and sickle is greater
than all these groups.

3235
02:29:45,221 --> 02:29:48,591
And, you know,
mortality is 26-fold higher than

3236
02:29:48,591 --> 02:29:50,827
the general population
and 10-fold higher than

3237
02:29:50,827 --> 02:29:53,330
Black women
in the United States.

3238
02:29:55,699 --> 02:29:59,970
We also see concerning outcomes
for fetuses and children

3239
02:29:59,970 --> 02:30:01,671
born to women
with sickle cell disease,

3240
02:30:01,671 --> 02:30:03,740
including IUGR,
which is a risk factor

3241
02:30:03,740 --> 02:30:05,875
for adverse neurodevelopmental
outcomes,

3242
02:30:05,875 --> 02:30:07,711
fetal demise,
and preterm delivery.

3243
02:30:09,779 --> 02:30:11,481
In other work that Dr. Early did
with me,

3244
02:30:11,481 --> 02:30:12,782
she identified

3245
02:30:12,782 --> 02:30:15,585
that approximately
30 percent of adverse pregnancy

3246
02:30:15,585 --> 02:30:17,354
outcomes for individuals
with sickle cell disease

3247
02:30:17,354 --> 02:30:18,621
in the United States

3248
02:30:18,621 --> 02:30:20,690
may be attributable
to disparate treatment

3249
02:30:20,690 --> 02:30:23,560
due to race, but that means
70 percent is attributable

3250
02:30:23,560 --> 02:30:25,161
to dimensions of this disease,

3251
02:30:25,161 --> 02:30:27,397
and I’m going to talk
about that next.

3252
02:30:27,397 --> 02:30:29,566
Behind this, you can see
some of the statistics

3253
02:30:29,566 --> 02:30:34,070
on racial disparities in care
and also some reporting

3254
02:30:34,070 --> 02:30:37,007
on how restricted access
to reproductive health care

3255
02:30:37,007 --> 02:30:39,042
is affecting the sickle cell
disease community

3256
02:30:39,042 --> 02:30:40,643
in the South in particular.

3257
02:30:41,511 --> 02:30:43,179
So, what do we have to treat
sickle cell disease

3258
02:30:43,179 --> 02:30:44,381
in pregnancy?

3259
02:30:44,381 --> 02:30:47,150
Well, in the United States,
we have blood transfusions.

3260
02:30:47,150 --> 02:30:49,185
We have prenatal vitamins.
We have aspirin.

3261
02:30:49,185 --> 02:30:50,653
We have low
molecular weight heparin.

3262
02:30:50,653 --> 02:30:52,589
And we have antibiotics.

3263
02:30:52,589 --> 02:30:54,991
Some of you may be wondering,
what about hydroxyurea?

3264
02:30:54,991 --> 02:30:57,427
And we just don’t have
the data yet to support that.

3265
02:30:57,427 --> 02:30:59,062
Whereas we have quite
a lot of data

3266
02:30:59,062 --> 02:31:02,198
on the use of blood transfusions
in the United States,

3267
02:31:02,198 --> 02:31:04,467
and we certainly have access
to a safe blood supply.

3268
02:31:04,467 --> 02:31:06,736
So, I’m going to talk mostly
about that today.

3269
02:31:08,471 --> 02:31:09,906
The first thing
I want to think about

3270
02:31:09,906 --> 02:31:12,175
is just why we aren’t
using transfusions,

3271
02:31:12,175 --> 02:31:14,911
and I assume it’s because we’re
afraid of alloimmunization.

3272
02:31:14,911 --> 02:31:17,747
So, let’s think about the risk
of alloimmunization in pregnant

3273
02:31:17,747 --> 02:31:18,982
people with sickle cell disease.

3274
02:31:18,982 --> 02:31:20,617
Well, okay,
we’re all afraid of delayed

3275
02:31:20,617 --> 02:31:23,520
hemolytic transfusion reactions.
That is just a terrifying thing

3276
02:31:23,520 --> 02:31:24,988
that happens to people
with sickle cell.

3277
02:31:24,988 --> 02:31:28,324
And yet, we know that there
are ways to modify that risk.

3278
02:31:28,324 --> 02:31:30,727
We don’t really have great data
that supports the idea

3279
02:31:30,727 --> 02:31:33,396
that hemolytic disease of
the newborn and fetus is that --

3280
02:31:33,396 --> 02:31:35,732
is an increased risk
in this patient population,

3281
02:31:35,732 --> 02:31:37,400
and it’s possible
that modulations

3282
02:31:37,400 --> 02:31:39,769
in the maternal immune system

3283
02:31:39,769 --> 02:31:41,438
actually change
the risk of this.

3284
02:31:42,072 --> 02:31:44,174
Nevertheless, alloimmunization
risks are real.

3285
02:31:44,174 --> 02:31:46,843
They’re not trivial
for our patient population.

3286
02:31:46,843 --> 02:31:49,012
And we need to deploy
the highest quality blood

3287
02:31:49,012 --> 02:31:51,714
banking approaches we can,
to help reduce those risks.

3288
02:31:52,449 --> 02:31:55,585
What we see in some cohorts
of pregnant people

3289
02:31:55,585 --> 02:31:57,620
in which this has been studied,

3290
02:31:57,620 --> 02:31:59,456
is that alloimmunization
doesn’t really differ

3291
02:31:59,456 --> 02:32:01,658
if you put people on
prophylactic transfusion

3292
02:32:01,658 --> 02:32:03,093
in pregnancy or not.

3293
02:32:03,093 --> 02:32:05,628
So, people who are treated
with monthly transfusions

3294
02:32:05,628 --> 02:32:06,896
throughout their pregnancy,

3295
02:32:06,896 --> 02:32:09,099
compared to people
who are allowed to --

3296
02:32:09,099 --> 02:32:11,334
who are not treated and are
only offered transfusions

3297
02:32:11,334 --> 02:32:14,537
when they either have a crisis
or have severe maternal anemia,

3298
02:32:15,605 --> 02:32:18,241
they get alloimmunization
at about the same rates.

3299
02:32:18,241 --> 02:32:20,243
We also know that about
50 percent of women

3300
02:32:20,243 --> 02:32:23,046
with sickle cell disease receive
a transfusion in pregnancy

3301
02:32:23,046 --> 02:32:24,714
or at least 50 percent.

3302
02:32:24,714 --> 02:32:26,616
And so, we’re also
balancing the risk

3303
02:32:26,616 --> 02:32:28,551
of an unanticipated transfusion,

3304
02:32:28,551 --> 02:32:30,120
potentially not
at our own center,

3305
02:32:30,120 --> 02:32:31,988
where we have experts on care,

3306
02:32:31,988 --> 02:32:34,991
versus controlled prophylactic
transfusions at our own center,

3307
02:32:34,991 --> 02:32:37,861
where we have expert --
access to expert care,

3308
02:32:38,928 --> 02:32:40,797
where it’s in
a controlled setting,

3309
02:32:40,797 --> 02:32:42,765
and where people
are not having crises,

3310
02:32:42,765 --> 02:32:44,634
which, of course,
are an independent risk factor

3311
02:32:44,634 --> 02:32:46,236
for alloimmunization.

3312
02:32:47,871 --> 02:32:49,239
There’s a lot of talk
about the need

3313
02:32:49,239 --> 02:32:51,074
for more studies
of transfusion

3314
02:32:51,074 --> 02:32:52,609
and sickle cell
disease pregnancy,

3315
02:32:52,609 --> 02:32:54,911
and I think that there’s
a lot of interesting questions

3316
02:32:54,911 --> 02:32:57,013
that I hope
we’ll answer through those.

3317
02:32:57,013 --> 02:33:00,850
However, we also have
a large meta-analysis from 2015

3318
02:33:00,850 --> 02:33:02,819
that shows fairly
remarkable results,

3319
02:33:03,586 --> 02:33:05,855
and subsequent studies
continue to affirm

3320
02:33:05,855 --> 02:33:07,557
many of these findings.

3321
02:33:07,557 --> 02:33:12,328
In particular, the 2015 blood
study by Malinowski et al.,

3322
02:33:12,328 --> 02:33:13,530
identified, you know --

3323
02:33:13,530 --> 02:33:15,999
and again, you can see
across these columns,

3324
02:33:15,999 --> 02:33:18,768
the pregnancy complications,
the number of studies involved,

3325
02:33:18,768 --> 02:33:20,937
the total number of pregnant
sickle cell individuals,

3326
02:33:20,937 --> 02:33:22,705
and the odds of the outcome.

3327
02:33:23,606 --> 02:33:28,244
And so, what they showed is that
that overall mortality

3328
02:33:28,778 --> 02:33:30,980
was decreased by transfusion.

3329
02:33:30,980 --> 02:33:33,683
Vaso-occlusive pain crises
were significantly decreased.

3330
02:33:33,683 --> 02:33:36,052
Pulmonary complications
significantly decreased.

3331
02:33:36,052 --> 02:33:38,188
Pulmonary embolize
significantly decreased.

3332
02:33:39,222 --> 02:33:41,524
Perinatal mortality,
neonatal death,

3333
02:33:41,524 --> 02:33:43,393
and preterm birth,
all decreased.

3334
02:33:43,393 --> 02:33:45,595
And yet the authors themselves,
who I adore,

3335
02:33:46,529 --> 02:33:50,300
did not see fit to say that
this data is sufficient enough

3336
02:33:50,300 --> 02:33:52,202
to start treating people
with SS disease

3337
02:33:52,202 --> 02:33:54,270
and Sβ0 thalassemia

3338
02:33:54,270 --> 02:33:56,372
with transfusion therapy
in pregnancy.

3339
02:33:57,574 --> 02:34:01,244
And I just wonder about that,
given the state of things.

3340
02:34:01,844 --> 02:34:05,114
In the obstetric literature,
we’re clear that maternal anemia

3341
02:34:05,114 --> 02:34:07,617
is a risk factor
for adverse pregnancy outcomes,

3342
02:34:07,617 --> 02:34:09,552
and most individuals
with sickle cell disease

3343
02:34:09,552 --> 02:34:11,054
meet this criterion.

3344
02:34:11,054 --> 02:34:14,257
Of course, the risk factor
of anemia in general,

3345
02:34:14,257 --> 02:34:15,458
in sickle cell disease

3346
02:34:15,458 --> 02:34:17,894
is the basis of
FDA approval for voxelotor,

3347
02:34:18,828 --> 02:34:21,030
and maternal anemia
is a recognized risk factor

3348
02:34:21,030 --> 02:34:22,498
for many things.

3349
02:34:22,498 --> 02:34:25,435
Dr. Early did a second study in
the National Inpatient Sample,

3350
02:34:25,435 --> 02:34:27,437
and you can see Figure 2 here.

3351
02:34:27,437 --> 02:34:31,007
We compared the women
with sickle cell disease --

3352
02:34:31,007 --> 02:34:34,510
who we theorized
to all have maternal anemia

3353
02:34:34,510 --> 02:34:36,579
because almost everybody
with sickle cell disease

3354
02:34:36,579 --> 02:34:38,181
has a hemoglobin
less than 10 --

3355
02:34:38,848 --> 02:34:42,118
to people who had a nutritional
anemia during pregnancy.

3356
02:34:42,118 --> 02:34:43,820
And we again looked at the --

3357
02:34:44,387 --> 02:34:47,056
at various severe
maternal outcomes.

3358
02:34:47,056 --> 02:34:49,259
And I hope that you can see that
on the one hand,

3359
02:34:49,259 --> 02:34:51,861
there are a set
of adverse outcomes

3360
02:34:51,861 --> 02:34:53,930
in which the risk ratios
are similar,

3361
02:34:54,497 --> 02:34:57,767
pre-eclampsia, preterm delivery,
hypertensive disorders,

3362
02:34:57,767 --> 02:35:01,170
C section, and then when
you get to some of these

3363
02:35:01,170 --> 02:35:03,206
really more profoundly
terrible outcomes,

3364
02:35:03,206 --> 02:35:05,341
and also the
thromboembolic pathophysiology

3365
02:35:05,341 --> 02:35:06,609
of sickle cell disease

3366
02:35:06,609 --> 02:35:09,479
really starts to come out
on the far right side

3367
02:35:10,079 --> 02:35:11,648
at the top of this graph.

3368
02:35:11,648 --> 02:35:13,116
So, this was a bit
of a thought experiment.

3369
02:35:13,116 --> 02:35:14,350
But, you know, obviously,

3370
02:35:14,350 --> 02:35:15,985
transfusions treat
maternal anemia.

3371
02:35:15,985 --> 02:35:18,955
It’s a risk factor for autism
and other things in children.

3372
02:35:20,490 --> 02:35:22,592
There has been a randomized
control trial

3373
02:35:22,592 --> 02:35:24,994
of prophylactic transfusions
for sickle cell disease.

3374
02:35:24,994 --> 02:35:29,299
It was completed by Dr. Koshy
and many others,

3375
02:35:29,932 --> 02:35:33,336
in the late ‘80s.
And I was impressed not long ago

3376
02:35:33,336 --> 02:35:34,771
to read this letter
to the editor

3377
02:35:34,771 --> 02:35:36,272
in response
to that publication.

3378
02:35:36,272 --> 02:35:38,608
Just to summarize briefly
that publication for you,

3379
02:35:38,608 --> 02:35:41,377
the randomized controlled trial
of prophylactic transfusions

3380
02:35:41,377 --> 02:35:44,147
for sickle showed
significant reductions in pain

3381
02:35:44,147 --> 02:35:47,517
and maternal complications
of sickle cell disease,

3382
02:35:47,517 --> 02:35:50,486
but it did not reduce
poor perinatal outcomes.

3383
02:35:50,486 --> 02:35:54,023
And so, the study was deemed
sort of indeterminate

3384
02:35:54,023 --> 02:35:56,459
in terms of how we should
use transfusion therapy

3385
02:35:56,459 --> 02:35:59,262
and pregnancy for women
with sickle cell disease.

3386
02:35:59,262 --> 02:36:02,899
And yet transfusion expert
Morrison and transfusion --

3387
02:36:02,899 --> 02:36:06,969
and obstetrician Morrison from
the University of Mississippi,

3388
02:36:06,969 --> 02:36:09,038
wrote a letter in response
to this publication,

3389
02:36:09,038 --> 02:36:10,239
and I think that they --

3390
02:36:10,239 --> 02:36:13,176
their objections
to the conclusions are notable.

3391
02:36:14,544 --> 02:36:16,679
They wrote, you know,
"This paper doesn’t reflect

3392
02:36:16,679 --> 02:36:18,815
our experience
over the past nine years."

3393
02:36:18,815 --> 02:36:21,517
And again, this is in the ‘80s.
"Over the past nine years,

3394
02:36:21,517 --> 02:36:24,387
we’ve delivered 69 pregnant
people with sickle cell anemia,

3395
02:36:24,387 --> 02:36:26,189
at the University
of Mississippi --"

3396
02:36:26,189 --> 02:36:28,725
in that southern part
of that map I showed you.

3397
02:36:28,725 --> 02:36:30,927
All have received
prophylactic transfusions,

3398
02:36:30,927 --> 02:36:34,197
and there have been 68
live births and one abortion.

3399
02:36:34,197 --> 02:36:36,866
The authors conclude that
the omission of prophylactic

3400
02:36:36,866 --> 02:36:39,902
transfusions during pregnancy
does no harm to a mother

3401
02:36:39,902 --> 02:36:41,938
with sickle cell anemia
or to her baby.

3402
02:36:42,572 --> 02:36:45,541
But we think that conclusion
far overreaches the data.

3403
02:36:46,075 --> 02:36:47,744
And I worry that many
of the conclusions

3404
02:36:47,744 --> 02:36:52,348
that we continue to draw
about studies of transfusion

3405
02:36:52,348 --> 02:36:55,385
and sickle cell pregnancy
continue to overreach that data.

3406
02:36:55,952 --> 02:36:57,954
It’s also interesting, you know,
as we think about

3407
02:36:57,954 --> 02:37:00,590
how people’s minds
change across their careers.

3408
02:37:00,590 --> 02:37:02,458
Perhaps mine
will change someday too.

3409
02:37:03,025 --> 02:37:06,996
One of these Morrisons
was somebody who, in just two --

3410
02:37:06,996 --> 02:37:09,198
in the 10 to 15 years
before he wrote this,

3411
02:37:09,198 --> 02:37:11,134
had been writing that people
with sickle cell disease

3412
02:37:11,134 --> 02:37:13,136
should never become
pregnant at all.

3413
02:37:13,136 --> 02:37:15,938
And so, we see how opinions
and treatments

3414
02:37:15,938 --> 02:37:18,775
really can inform the kind
of clinical care that we provide

3415
02:37:18,775 --> 02:37:21,944
and the kinds of expectations
we have for our patients.

3416
02:37:21,944 --> 02:37:23,813
You know, one of the things
that really impresses me

3417
02:37:23,813 --> 02:37:26,449
about the randomized control
trial of transfusions

3418
02:37:26,449 --> 02:37:29,318
and the meta-analysis
is that they all show

3419
02:37:29,318 --> 02:37:32,955
that transfusions in
sickle pregnancy reduces pain.

3420
02:37:32,955 --> 02:37:35,858
And I wonder why reducing pain
is an inadequate endpoint

3421
02:37:35,858 --> 02:37:37,627
for pregnant people
with sickle cell disease.

3422
02:37:37,627 --> 02:37:39,829
We know that pain occurs in 50
to 80 percent

3423
02:37:39,829 --> 02:37:42,799
of sickle cell pregnancies,
and we know that reducing pain

3424
02:37:42,799 --> 02:37:44,567
is a completely
standard endpoint

3425
02:37:44,567 --> 02:37:45,968
for most of our trials.
Right?

3426
02:37:45,968 --> 02:37:48,971
Hydroxyurea, L-glutamine,
Crizanlizumab, and gene therapy,

3427
02:37:48,971 --> 02:37:52,308
all of these things
we do to prevent pain,

3428
02:37:52,308 --> 02:37:55,011
and yet we are not
universally offering transfusion

3429
02:37:55,011 --> 02:37:56,946
to a vulnerable
patient population

3430
02:37:56,946 --> 02:37:58,681
where we’ve made no progress

3431
02:37:58,681 --> 02:38:01,184
in improving outcomes
in many decades,

3432
02:38:01,184 --> 02:38:04,220
even though they have a clear
indication for pain prevention.

3433
02:38:05,388 --> 02:38:09,225
Downstream
challenges of people with pain

3434
02:38:09,225 --> 02:38:10,593
and sickle cell
disease pregnancy

3435
02:38:10,593 --> 02:38:12,762
are not horribly different
than everybody else.

3436
02:38:12,762 --> 02:38:14,330
It’s just that
you also have a fetus

3437
02:38:14,330 --> 02:38:15,731
that’s exposed
to these things.

3438
02:38:15,731 --> 02:38:18,234
So, we have toxic stress,
not just to the patient,

3439
02:38:18,234 --> 02:38:21,471
but also to the fetus.
We have opioid exposure,

3440
02:38:21,471 --> 02:38:23,806
not just to the patient,
but to the fetus.

3441
02:38:23,806 --> 02:38:28,444
And then we have infection,
clots, as needed, transfusion,

3442
02:38:28,444 --> 02:38:30,079
and other things
downstream of that.

3443
02:38:30,079 --> 02:38:33,015
All of these things are in those
severe maternal morbidity

3444
02:38:33,015 --> 02:38:35,718
indexes as being fantastically
increased in people

3445
02:38:35,718 --> 02:38:37,687
with sickle cell disease
and pregnancy.

3446
02:38:37,687 --> 02:38:39,322
And if we can keep people
out of the hospital,

3447
02:38:39,322 --> 02:38:41,390
that may really be
part of the puzzle.

3448
02:38:43,726 --> 02:38:46,329
So, I hope we can apply
this clinical logic

3449
02:38:46,329 --> 02:38:47,964
and end some of our inertia.

3450
02:38:47,964 --> 02:38:49,632
The American Society
of Hematology

3451
02:38:49,632 --> 02:38:51,667
identifies transfusion
as a low-risk

3452
02:38:51,667 --> 02:38:54,270
intervention in pregnancy --
in sickle pregnancy

3453
02:38:54,270 --> 02:38:56,506
and potentially
profoundly beneficial.

3454
02:38:56,506 --> 02:38:58,140
We don’t even know
what it would look like

3455
02:38:58,140 --> 02:38:59,876
if we started
giving people transfusions

3456
02:38:59,876 --> 02:39:02,445
in the preconception period
and really help to optimize

3457
02:39:02,445 --> 02:39:05,248
placental development
from the get-go.

3458
02:39:05,248 --> 02:39:06,749
And we don’t really
have great data

3459
02:39:06,749 --> 02:39:09,352
on what it looks like for people
who get treatment for nine --

3460
02:39:09,352 --> 02:39:10,686
a full nine months of therapy.

3461
02:39:10,686 --> 02:39:12,522
Most of the trials
start transfusion

3462
02:39:12,522 --> 02:39:15,291
somewhere in the second
trimester of pregnancy.

3463
02:39:15,291 --> 02:39:16,659
There are calls in
the literature

3464
02:39:16,659 --> 02:39:19,195
for randomized control
of chronic transfusions,

3465
02:39:19,195 --> 02:39:20,830
which suggests
that our community thinks,

3466
02:39:20,830 --> 02:39:22,064
or some of us think,

3467
02:39:22,064 --> 02:39:24,233
there’s equipoise for
a randomized controlled trial.

3468
02:39:24,233 --> 02:39:26,235
I think that’s
an interesting idea.

3469
02:39:27,370 --> 02:39:29,205
And so, if that’s true,
then first of all,

3470
02:39:29,205 --> 02:39:32,041
we can do transfusion offerings

3471
02:39:32,041 --> 02:39:33,609
for our pregnant patients
at the present

3472
02:39:33,609 --> 02:39:35,177
with a shared
decision-making model,

3473
02:39:35,177 --> 02:39:37,947
acknowledging that that’s --
that we think there’s equipoise.

3474
02:39:37,947 --> 02:39:39,615
But the second thing is that
I think we need

3475
02:39:39,615 --> 02:39:40,950
to have
a really rigorous debate

3476
02:39:40,950 --> 02:39:43,519
about what the primary endpoints
of that trial for the --

3477
02:39:43,519 --> 02:39:46,088
should be, whether pain
is an adequate endpoint

3478
02:39:46,088 --> 02:39:47,823
for pregnant people
with sickle cell disease

3479
02:39:47,823 --> 02:39:49,992
and that should be enough
to change management,

3480
02:39:49,992 --> 02:39:53,596
or whether we should be thinking
about perinatal outcomes

3481
02:39:53,596 --> 02:39:57,400
and maternal outcomes
that would define who to treat.

3482
02:39:57,400 --> 02:39:58,634
Angela Rankine-Mullings: Sorry.
Dr. Pecker.

3483
02:39:58,634 --> 02:39:59,835
Lydia Pecker: This is the Who?
When? How?

3484
02:39:59,835 --> 02:40:01,137
slide, and I’m not
going into that now.

3485
02:40:01,137 --> 02:40:02,371
Angela Rankine-Mullings: Out
of time.

3486
02:40:02,371 --> 02:40:03,706
40: 03
And

3487
02:40:03,706 --> 02:40:05,942
I’ll conclude
by saying, you know, we just --

3488
02:40:05,942 --> 02:40:07,810
we have to move the needle
on survival

3489
02:40:07,810 --> 02:40:11,280
for adults with this disease.
And so, every little bit counts.

3490
02:40:11,280 --> 02:40:15,017
I think incorporating patient
priorities like fertility

3491
02:40:15,017 --> 02:40:17,787
and providing high-quality
maternal mental health care

3492
02:40:17,787 --> 02:40:19,388
is one of the ways
we’re going to start

3493
02:40:19,388 --> 02:40:20,790
moving the needle
in the next decade.

3494
02:40:20,790 --> 02:40:22,758
And I’m really hopeful
for all of the work

3495
02:40:22,758 --> 02:40:24,694
that you do to move
the needle as well.

3496
02:40:25,227 --> 02:40:26,729
Thanks so much.

3497
02:40:26,729 --> 02:40:28,331
Angela Rankine-Mullings:
Thank you, Dr. Pecker.

3498
02:40:31,067 --> 02:40:36,138
We actually ran out of time.
I see no questions in the chat.

3499
02:40:38,240 --> 02:40:40,776
We have -- probably we can have
just one question,

3500
02:40:40,776 --> 02:40:43,679
please, quick.
Thank you.

3501
02:40:45,648 --> 02:40:46,916
Male Speaker: Hi.

3502
02:40:46,916 --> 02:40:50,119
I had -- it was a fascinating
talk, and I was just --

3503
02:40:50,119 --> 02:40:56,225
you mentioned something
concerning anemia and autism.

3504
02:40:56,959 --> 02:40:59,795
And I’m wondering,
in sickle cell patients,

3505
02:41:00,763 --> 02:41:04,133
do you see
a higher rate of autism,

3506
02:41:04,667 --> 02:41:08,371
and if you can actually
reduce the autism

3507
02:41:08,371 --> 02:41:11,140
by doing transfusions
during the pregnancy?

3508
02:41:12,475 --> 02:41:13,709
Lydia Pecker: To my knowledge --

3509
02:41:13,709 --> 02:41:15,244
thanks for that great
question --

3510
02:41:15,244 --> 02:41:16,479
to my knowledge,

3511
02:41:16,479 --> 02:41:20,016
we have done the only study
looking at that question.

3512
02:41:21,817 --> 02:41:24,620
And that study was in
the Boston Birth Cohort,

3513
02:41:24,620 --> 02:41:27,123
where we did a propensity
match score of children

3514
02:41:27,123 --> 02:41:28,958
born to women
with sickle cell disease,

3515
02:41:28,958 --> 02:41:34,830
and we compared them to race
and ethnicity match controls.

3516
02:41:35,798 --> 02:41:37,900
In that study,
there were differences

3517
02:41:37,900 --> 02:41:41,637
in neurodevelopmental outcomes
between children born

3518
02:41:41,637 --> 02:41:43,105
to women
with sickle cell disease

3519
02:41:43,105 --> 02:41:44,907
and those in
the general population.

3520
02:41:45,441 --> 02:41:48,811
It’s important, though,
that not just maternal anemia

3521
02:41:48,811 --> 02:41:50,513
is a neurodevelopmental
risk factor --

3522
02:41:50,513 --> 02:41:52,448
yeah, there is a terrible echo.
I’m sorry.

3523
02:41:52,448 --> 02:41:54,417
It’s a little distracting
to speak.

3524
02:41:58,354 --> 02:42:01,157
It’s important to know that
it’s not just maternal anemia

3525
02:42:01,157 --> 02:42:03,759
that’s a risk factor
for adverse neurodevelopmental

3526
02:42:03,759 --> 02:42:05,261
outcomes in children

3527
02:42:05,261 --> 02:42:07,530
born to women
with sickle cell disease.

3528
02:42:07,530 --> 02:42:09,632
Opioid exposure
is a risk factor

3529
02:42:09,632 --> 02:42:11,767
for adverse
neurodevelopmental outcomes.

3530
02:42:11,767 --> 02:42:13,436
Having a parent with
a chronic disease

3531
02:42:13,436 --> 02:42:14,804
is also a risk factor

3532
02:42:14,804 --> 02:42:16,806
for adverse
neurodevelopmental outcomes.

3533
02:42:17,306 --> 02:42:19,208
Being born
with low birth weight,

3534
02:42:19,208 --> 02:42:21,410
small for gestational age,
and preterm,

3535
02:42:21,410 --> 02:42:24,046
these are all
neurodevelopmental risk factors,

3536
02:42:24,046 --> 02:42:27,416
and there are indications
that transfusion therapy

3537
02:42:27,416 --> 02:42:29,151
could help address
many of these things.

3538
02:42:29,151 --> 02:42:31,120
Male Speaker: Thank you so much.

3539
02:42:31,120 --> 02:42:32,421
Angela Rankine-Mullings:
Thank you, Dr. Pecker.

3540
02:42:32,421 --> 02:42:36,125
Thank you for
a wonderful presentation,

3541
02:42:36,125 --> 02:42:37,426
[applause]

3542
02:42:37,426 --> 02:42:40,696
And now, we will go on
to our second speaker,

3543
02:42:41,297 --> 02:42:43,332
Dr. Thomas d’Humières,

3544
02:42:44,066 --> 02:42:47,036
who is a cardiologist
and physiologist

3545
02:42:47,036 --> 02:42:51,273
working at the Henri Mondor
Teaching Hospital.

3546
02:42:51,273 --> 02:42:53,375
Thank you so much.
Let’s welcome him.

3547
02:42:53,375 --> 02:42:54,877
[applause]

3548
02:42:54,877 --> 02:42:56,112
Thomas d’Humières: Perfectly
said.

3549
02:42:56,112 --> 02:42:57,346
Angela Rankine-Mullings: I said
it. Thank you.

3550
02:42:57,346 --> 02:42:59,749
Thomas d’Humières: Very good.
Okay.

3551
02:42:59,749 --> 02:43:02,918
So, I just have
to figure this out.

3552
02:43:02,918 --> 02:43:09,625
Okay. Oh, the other one. Okay.

3553
02:43:09,625 --> 02:43:14,330
Nice. Okay. It’s a good thing.
Okay. Hi, everyone.

3554
02:43:14,330 --> 02:43:16,532
Thank you for the invitation
once again.

3555
02:43:16,532 --> 02:43:18,801
I’m very happy to talk
about a subject

3556
02:43:19,502 --> 02:43:21,303
that has been
a bit forgotten

3557
02:43:21,303 --> 02:43:23,272
in the field
of sickle cell disease,

3558
02:43:23,272 --> 02:43:25,174
which is
cardiovascular abnormalities.

3559
02:43:25,174 --> 02:43:27,977
And I’ve been working
on this topic for six years now.

3560
02:43:28,544 --> 02:43:32,214
And so, I had a lot to say.
So, I had to choose.

3561
02:43:32,882 --> 02:43:36,886
So, I will be more than happy
to talk more about this later,

3562
02:43:36,886 --> 02:43:40,790
because I can --
for hours actually.

3563
02:43:40,790 --> 02:43:42,391
No, the other one.

3564
02:43:42,391 --> 02:43:44,193
[laughter]

3565
02:43:44,193 --> 02:43:45,795
So, everybody said it.

3566
02:43:46,629 --> 02:43:49,932
The SCD population
is living longer,

3567
02:43:49,932 --> 02:43:54,737
which is really a good thing.
But we are seeing --

3568
02:43:54,737 --> 02:43:56,739
we are facing more
and more cardiovascular disease,

3569
02:43:56,739 --> 02:43:59,308
which is now the first
cause of mortality

3570
02:43:59,308 --> 02:44:01,210
in the adult population.

3571
02:44:01,210 --> 02:44:03,379
So, we have to understand it.
We have to face it.

3572
02:44:03,379 --> 02:44:06,215
And we have to somehow
try to treat it.

3573
02:44:08,284 --> 02:44:10,519
I’m not going back to all
the pathophysiology

3574
02:44:10,519 --> 02:44:11,787
of sickle cell disease

3575
02:44:11,787 --> 02:44:14,390
because you’re better
than I am in this field.

3576
02:44:14,957 --> 02:44:18,561
But it is true that
every patient is unique

3577
02:44:18,561 --> 02:44:23,165
and is actually in a spectrum
of very high hemolysis

3578
02:44:23,165 --> 02:44:25,201
to very low hemolysis,

3579
02:44:25,201 --> 02:44:28,003
and the complications
are very, very different.

3580
02:44:28,003 --> 02:44:30,139
And we know
from epidemiological studies

3581
02:44:30,139 --> 02:44:33,342
that the more hemolytic
they are,

3582
02:44:33,342 --> 02:44:35,744
the more cardiovascular
disease they have.

3583
02:44:35,744 --> 02:44:42,651
So, the exact pathophysiology
between is yet to be understood

3584
02:44:42,651 --> 02:44:47,356
but mostly, probably because
of the heme release,

3585
02:44:47,356 --> 02:44:49,892
the chronic vascular disease,
free hemoglobin release,

3586
02:44:49,892 --> 02:44:51,594
NO scavenging,
and everything linked

3587
02:44:51,594 --> 02:44:54,296
to hemolytic complication.

3588
02:44:54,296 --> 02:44:57,700
So, every --
all the vascular complications

3589
02:44:57,700 --> 02:45:01,937
are tied to this
hemolytic phenotype.

3590
02:45:03,439 --> 02:45:06,876
And this is something
that I wanted to start with.

3591
02:45:08,010 --> 02:45:11,847
Sickle cell population don’t
have normal echocardiography

3592
02:45:12,848 --> 02:45:15,417
according to a
non-SCD population

3593
02:45:16,051 --> 02:45:17,486
normality distribution.

3594
02:45:17,486 --> 02:45:20,189
And this is something that
really has to be understood.

3595
02:45:20,189 --> 02:45:24,360
Because I’ve seen a lot of
conclusions of echocardiography

3596
02:45:24,360 --> 02:45:29,865
saying dilated cardiomyopathy.
It’s not cardiomyopathy.

3597
02:45:29,865 --> 02:45:33,802
If this is adapted, if I’m doing
some echocardiography

3598
02:45:33,802 --> 02:45:37,339
in all elite athletes
who are doing more

3599
02:45:37,339 --> 02:45:40,876
than 20 hours
of sports per week,

3600
02:45:40,876 --> 02:45:43,279
they all have dilated
cardiomyopathy.

3601
02:45:43,846 --> 02:45:47,483
If I’m saying this,
their career will end.

3602
02:45:47,483 --> 02:45:52,688
So, we have to properly address
what we are saying.

3603
02:45:52,688 --> 02:45:57,927
So, they -- we are not
in the same frame of thinking.

3604
02:45:57,927 --> 02:45:59,528
So, we have to rethink.

3605
02:46:00,496 --> 02:46:03,299
What is the normal value
among the SCD population?

3606
02:46:03,999 --> 02:46:07,336
And we know that there is
an adaptation that is normal.

3607
02:46:07,336 --> 02:46:10,773
Because they are --
they have strong anemia,

3608
02:46:11,573 --> 02:46:13,709
strong cardiac output
since they were born.

3609
02:46:14,610 --> 02:46:18,781
At some point,
it shifted towards pathology.

3610
02:46:18,781 --> 02:46:20,916
And this is the tricky part.

3611
02:46:20,916 --> 02:46:24,820
It’s when can we say
that we have cardiomyopathy?

3612
02:46:24,820 --> 02:46:26,622
And this is very difficult
actually.

3613
02:46:27,256 --> 02:46:29,591
Because we all know
that all these patients

3614
02:46:29,591 --> 02:46:30,826
have cardiac output.

3615
02:46:30,826 --> 02:46:32,828
It’s normal
because there is anemia.

3616
02:46:32,828 --> 02:46:34,897
They have low systemic
arterial pressure.

3617
02:46:36,932 --> 02:46:40,002
It was too --
not strong enough.

3618
02:46:40,002 --> 02:46:44,940
I was hearing a lot of my voice,
so more voice for me.

3619
02:46:45,507 --> 02:46:48,110
[laughter]

3620
02:46:48,110 --> 02:46:50,179
There is tachycardia.
It’s actually normal

3621
02:46:50,179 --> 02:46:52,982
because there’s a lack
of oxygen delivery.

3622
02:46:53,749 --> 02:46:57,653
And they all have dilated --
more or less, dilated heart.

3623
02:46:58,620 --> 02:47:04,560
All cavities are often dilated,
and it’s not a problem, per se.

3624
02:47:05,394 --> 02:47:10,666
Then we go through problems
when there is a remodeling

3625
02:47:10,666 --> 02:47:13,769
of left ventricle
by fibrosis development.

3626
02:47:13,769 --> 02:47:16,005
The left ventricle
becomes stiffer.

3627
02:47:17,239 --> 02:47:20,442
The vascular resistance
is beginning to rise,

3628
02:47:21,176 --> 02:47:25,280
and the coronary
microcirculation is not adapted

3629
02:47:27,116 --> 02:47:30,886
to face this --
yeah, these changes.

3630
02:47:30,886 --> 02:47:34,023
And how can we put
the difference

3631
02:47:34,023 --> 02:47:35,858
between adaptation
and pathology?

3632
02:47:35,858 --> 02:47:38,027
This is something
very difficult.

3633
02:47:38,694 --> 02:47:42,765
So, of course,
all of you know, the most --

3634
02:47:44,133 --> 02:47:46,535
yeah, the most known
complication of --

3635
02:47:46,535 --> 02:47:49,304
cardiovascular complication
of sickle cell disease,

3636
02:47:49,304 --> 02:47:53,008
pulmonary hypertension,
20 to 30 percent of mortality.

3637
02:47:53,008 --> 02:47:58,247
It is a real prognostic factor
for sickle cell disease.

3638
02:47:59,048 --> 02:48:01,316
But pulmonary hypertension
is not a disease.

3639
02:48:01,316 --> 02:48:04,353
It’s a consequence,
just a pressure consequence.

3640
02:48:05,687 --> 02:48:08,390
It’s just means
that there is high pressure.

3641
02:48:08,390 --> 02:48:11,126
Actually, I’m doing this right?
Yeah.

3642
02:48:11,126 --> 02:48:13,195
There is high pressure here,

3643
02:48:13,195 --> 02:48:15,230
just in front
of the right ventricle.

3644
02:48:15,798 --> 02:48:19,968
Okay. Once we said that,
we said, actually, nothing.

3645
02:48:21,737 --> 02:48:24,239
Where does it come from?
That’s the actual question.

3646
02:48:24,239 --> 02:48:28,744
Is it because there are several
embolic thrombotic problems

3647
02:48:28,744 --> 02:48:29,978
repeating,

3648
02:48:29,978 --> 02:48:32,714
cutting some vascular --
pulmonary vascular bed?

3649
02:48:33,315 --> 02:48:36,385
Maybe. Is it because there is
a pulmonary vascular disease

3650
02:48:36,385 --> 02:48:38,153
with increased resistance,

3651
02:48:38,153 --> 02:48:39,855
such as we can see
in scleroderma?

3652
02:48:40,389 --> 02:48:42,825
Or is there a systemic
inflammatory disease?

3653
02:48:43,358 --> 02:48:47,029
Or is it because
the left heart is sick,

3654
02:48:47,029 --> 02:48:50,365
and so this is
post-capillary hypertension?

3655
02:48:50,899 --> 02:48:55,938
So, this is really important
to understand that it’s --

3656
02:48:57,840 --> 02:48:59,608
these are three
different diseases.

3657
02:49:00,409 --> 02:49:02,644
And when we look
at the literature,

3658
02:49:03,378 --> 02:49:06,014
there are three
major publications

3659
02:49:07,583 --> 02:49:10,552
that explored with
right heart catheterization,

3660
02:49:11,253 --> 02:49:13,021
sickle cell disease patient.

3661
02:49:13,021 --> 02:49:15,324
They all have about
the same conclusion.

3662
02:49:15,924 --> 02:49:18,260
We -- they observed
about 10 percent

3663
02:49:18,260 --> 02:49:22,898
of true pulmonary hypertension
in sickle cell disease,

3664
02:49:23,699 --> 02:49:25,701
just knowing that,
at the time,

3665
02:49:27,236 --> 02:49:30,606
the definition was
25 millimeters of mercury

3666
02:49:30,606 --> 02:49:33,175
to define the hypertension.
And now, it’s 20.

3667
02:49:33,175 --> 02:49:35,611
So, if we look back
to all these studies,

3668
02:49:35,611 --> 02:49:39,314
maybe we underdiagnosed
some patients.

3669
02:49:40,249 --> 02:49:44,953
But what was most disturbing
is half was precapillary --

3670
02:49:44,953 --> 02:49:48,790
classified as precapillary,
so pulmonary vascular disease;

3671
02:49:48,790 --> 02:49:51,093
half was classified
as postcapillary,

3672
02:49:51,093 --> 02:49:52,728
so left heart disease.

3673
02:49:53,529 --> 02:49:55,998
And when we think about
the disease pathophysiology,

3674
02:49:55,998 --> 02:49:57,799
the systemic disease
pathophysiology,

3675
02:49:57,799 --> 02:50:00,969
it is a bit puzzling to think
that half of the patient

3676
02:50:00,969 --> 02:50:04,206
will have only vascular
disease in the lung,

3677
02:50:04,206 --> 02:50:06,975
and the other part only
systemic vascular disease

3678
02:50:06,975 --> 02:50:08,177
but not the lung.

3679
02:50:08,177 --> 02:50:14,316
So, this is not very, yeah,
convincing for me.

3680
02:50:15,584 --> 02:50:18,053
So, just a quick example --
and I’m going --

3681
02:50:18,053 --> 02:50:19,254
not going into detail

3682
02:50:19,254 --> 02:50:23,458
because this is actually
what we see on a daily basis.

3683
02:50:24,660 --> 02:50:27,196
These are the results
from a patient here

3684
02:50:28,030 --> 02:50:30,165
of a right
heart catheterization.

3685
02:50:30,165 --> 02:50:31,400
I will explain a bit.

3686
02:50:31,400 --> 02:50:34,102
But you have the cardiac output,
10 liters per minute.

3687
02:50:34,102 --> 02:50:38,207
It is high. You have
the pulmonary pressure, 29.

3688
02:50:38,207 --> 02:50:39,741
This is high.

3689
02:50:39,741 --> 02:50:43,979
You have the left pressure --
the left heart pressure, 12.

3690
02:50:43,979 --> 02:50:47,182
Okay. The vascular --
pulmonary vascular resistance,

3691
02:50:47,182 --> 02:50:49,184
1.9 Wood units, okay.

3692
02:50:49,885 --> 02:50:52,454
And volemia,
right atrial pressure.

3693
02:50:53,021 --> 02:50:57,359
And if you look at the most
recent guidelines,

3694
02:50:57,359 --> 02:51:03,265
2022, well, okay,
there is hypertension

3695
02:51:03,265 --> 02:51:05,467
because it’s above 20.

3696
02:51:05,467 --> 02:51:10,105
But it’s not from the left heart
disease because it’s below 50.

3697
02:51:10,105 --> 02:51:14,710
It’s not from vascular lung
disease because it’s below 2.

3698
02:51:15,611 --> 02:51:22,084
So, what is it actually?
And this is -- for a long time,

3699
02:51:22,084 --> 02:51:26,989
it has been called high cardiac
output hypertension,

3700
02:51:26,989 --> 02:51:28,757
which is nonsense.

3701
02:51:28,757 --> 02:51:32,861
And actually,
you really have to put,

3702
02:51:33,996 --> 02:51:40,535
once again, the specificity of
this population in this context.

3703
02:51:41,203 --> 02:51:44,006
All this classification
was validated

3704
02:51:44,006 --> 02:51:47,042
on very broad
spectrum of patients,

3705
02:51:47,042 --> 02:51:49,011
not sickle cell patients.

3706
02:51:49,011 --> 02:51:56,952
If you have 1.9 Wood
units of vascular resistance

3707
02:51:56,952 --> 02:52:02,924
with 7.2 grams of hemoglobin,
what would it be with 12?

3708
02:52:04,660 --> 02:52:09,097
It would be a lot higher
because they are --

3709
02:52:09,765 --> 02:52:11,967
there is anemia
and high cardiac output.

3710
02:52:11,967 --> 02:52:15,804
So, vascular resistance is low,
naturally low.

3711
02:52:15,804 --> 02:52:18,807
So, 1.9 is relatively
very high.

3712
02:52:19,775 --> 02:52:24,646
So, this is something
that is really not normal.

3713
02:52:24,646 --> 02:52:28,350
And then -- it’s a mechanism
you have to train,

3714
02:52:28,350 --> 02:52:32,120
but you see that the pressure
in the left cavities

3715
02:52:32,120 --> 02:52:34,589
is not that far from the limit.

3716
02:52:35,557 --> 02:52:40,529
But the patient is,
like, very dehydrated.

3717
02:52:40,529 --> 02:52:46,201
So, what happens if we give him,
like 2050 cc of saline?

3718
02:52:47,369 --> 02:52:50,205
You will see that you will have
a linear augmentation

3719
02:52:50,205 --> 02:52:51,707
of right atrial pressure

3720
02:52:51,707 --> 02:52:55,277
and exponential augmentation
of left pressure.

3721
02:52:55,277 --> 02:53:00,082
And so, you have actually,
this non-classified patient

3722
02:53:00,082 --> 02:53:04,252
is actually a typical example
of mixed pre-

3723
02:53:04,252 --> 02:53:08,690
and post-capillary patient
and is very severe, actually,

3724
02:53:09,257 --> 02:53:12,661
but it’s just --
it doesn’t fit in any framework.

3725
02:53:14,629 --> 02:53:20,001
This one. So, we have a good,
non-invasive assessment

3726
02:53:20,001 --> 02:53:22,537
for pulmonary pressure.
Because, as you can see,

3727
02:53:22,537 --> 02:53:25,440
it leads to major
prognostic impacts.

3728
02:53:26,875 --> 02:53:29,478
The famous tricuspid
regurgitation velocity.

3729
02:53:29,478 --> 02:53:32,414
So, it’s quite an easy
echocardiography parameter

3730
02:53:32,414 --> 02:53:34,483
to assess as you can see
on the image.

3731
02:53:34,483 --> 02:53:37,819
You just take the max
velocity of a leak.

3732
02:53:38,987 --> 02:53:41,490
Just to know, for the record,

3733
02:53:41,490 --> 02:53:45,927
20 to 25 patients have no leak,
or we underestimated.

3734
02:53:45,927 --> 02:53:49,131
So, this is something
that has to be accounted for.

3735
02:53:49,131 --> 02:53:52,300
And we know that above
2.9 meter per second,

3736
02:53:52,300 --> 02:53:55,537
there is a high probability
of true pulmonary hypertension.

3737
02:53:56,405 --> 02:53:59,775
And this is a criterion
that is most commonly used

3738
02:53:59,775 --> 02:54:03,578
to assess cardiac outcome
or inclusion/exclusion criteria

3739
02:54:03,578 --> 02:54:09,684
in clinical trial or safety.
This is the only parameter.

3740
02:54:10,352 --> 02:54:12,053
And I’m not saying
that it is a bad one,

3741
02:54:12,053 --> 02:54:14,189
because it is actually
a very good one,

3742
02:54:14,756 --> 02:54:17,826
but my point is
it might be a late one.

3743
02:54:21,797 --> 02:54:26,468
So, if you are with me,
you’re seeing that a lot

3744
02:54:26,468 --> 02:54:29,237
is coming from
left heart failure actually.

3745
02:54:29,237 --> 02:54:31,540
There is a lot
of left heart failure.

3746
02:54:31,540 --> 02:54:33,308
Because when we say

3747
02:54:33,308 --> 02:54:36,845
post-capillary
pulmonary hypertension,

3748
02:54:36,845 --> 02:54:39,147
we are actually saying
left heart failure.

3749
02:54:39,147 --> 02:54:41,049
There’s exactly
the same thing.

3750
02:54:41,049 --> 02:54:43,251
So, in cardiology,
it’s always quite simple.

3751
02:54:44,152 --> 02:54:46,588
Why is there
left heart failure?

3752
02:54:46,588 --> 02:54:50,492
Is it systolic,
a contraction function problem?

3753
02:54:50,492 --> 02:54:53,495
No. In sickle cell disease,
it’s really, really marginal.

3754
02:54:54,029 --> 02:54:58,567
So, is it a feeling problem,
a stiffness problem? Yes.

3755
02:55:01,369 --> 02:55:04,372
Fifteen to 20 percent
of diastolic dysfunction

3756
02:55:04,372 --> 02:55:06,541
has been reported
in sickle cell patients

3757
02:55:06,541 --> 02:55:09,044
with a strong association
with mortality.

3758
02:55:09,044 --> 02:55:14,015
But once again, we had
this discussion with Vandana,

3759
02:55:14,015 --> 02:55:18,053
who worked a lot
and brought a lot to this field,

3760
02:55:18,920 --> 02:55:20,489
and we agree on the point

3761
02:55:20,489 --> 02:55:24,960
that we accept
that diastolic dysfunction

3762
02:55:24,960 --> 02:55:27,629
is responsible
for this heart failure.

3763
02:55:28,563 --> 02:55:32,067
But all the studies
applied the criteria

3764
02:55:32,067 --> 02:55:35,704
and the classification validated
in non-SCD population.

3765
02:55:36,505 --> 02:55:39,841
So, what are we really
measuring here?

3766
02:55:39,841 --> 02:55:43,612
It’s -- yeah, it’s
quite limited actually.

3767
02:55:43,612 --> 02:55:46,548
So, we all agree that
diastolic dysfunction

3768
02:55:46,548 --> 02:55:49,918
that we cannot assess
is associated with problems,

3769
02:55:50,652 --> 02:55:52,687
but we should go further.

3770
02:55:54,089 --> 02:55:55,657
So, when we see
diastolic dysfunction,

3771
02:55:55,657 --> 02:55:58,660
stiffness increases.
Why?

3772
02:55:59,728 --> 02:56:02,998
Is it iron overload,
as it has been suggested

3773
02:56:02,998 --> 02:56:05,100
as the reason for a while?

3774
02:56:05,100 --> 02:56:10,005
No. All MRI studies
are quite unanimous,

3775
02:56:10,005 --> 02:56:11,506
and actually, I have --

3776
02:56:11,506 --> 02:56:13,842
I haven’t any patients
with iron overload.

3777
02:56:14,676 --> 02:56:17,345
But they always found
some fibrotic component

3778
02:56:18,113 --> 02:56:20,348
with some microcirculatory
involvement.

3779
02:56:20,348 --> 02:56:23,685
But with MRI,
this is something to be nuanced.

3780
02:56:25,453 --> 02:56:30,792
This is the -- an interesting
study from Blood from 2018

3781
02:56:30,792 --> 02:56:33,061
that showed that
sickle cell patient

3782
02:56:35,597 --> 02:56:39,868
versus a control patient had
an extracellular volume in MRI,

3783
02:56:39,868 --> 02:56:41,670
which is the volume
between cells,

3784
02:56:41,670 --> 02:56:43,705
basically fibrosis or edema,

3785
02:56:44,306 --> 02:56:46,942
that was very increased
in sickle cell disease

3786
02:56:46,942 --> 02:56:48,643
and that the T1,

3787
02:56:48,643 --> 02:56:54,215
which is more likely fibrosis
than edema, was also higher.

3788
02:56:54,215 --> 02:56:57,152
And interestingly, they linked
this extracellular volume

3789
02:56:57,152 --> 02:57:00,989
to both diastolic function
measurement on echocardiography

3790
02:57:00,989 --> 02:57:04,492
and cardiac biomarkers,
NT-proBNP.

3791
02:57:04,492 --> 02:57:06,461
The correlation
is really not perfect,

3792
02:57:06,461 --> 02:57:09,397
but this is --
the story makes sense, actually.

3793
02:57:09,397 --> 02:57:10,999
So, this is good.

3794
02:57:11,566 --> 02:57:13,902
So, we have this -- yeah,

3795
02:57:13,902 --> 02:57:17,105
this understanding
that chronic hemolysis is linked

3796
02:57:17,105 --> 02:57:19,808
to endothelial dysfunction,
microcirculation ischemia,

3797
02:57:19,808 --> 02:57:21,443
vascular resistance elevation;

3798
02:57:21,443 --> 02:57:23,578
all of this leading
to left ventricular

3799
02:57:23,578 --> 02:57:27,849
remodeling with fibrosis
and diastolic dysfunction,

3800
02:57:27,849 --> 02:57:31,553
leading ultimately
to pulmonary hypertension,

3801
02:57:31,553 --> 02:57:33,488
post-capillary
pulmonary hypertension.

3802
02:57:33,488 --> 02:57:38,593
And this is a unique phenotype
in heart disease

3803
02:57:38,593 --> 02:57:42,197
of heart failure
with a high cardiac output,

3804
02:57:42,197 --> 02:57:46,434
with a preserved left ejection
fraction in a young population.

3805
02:57:46,434 --> 02:57:49,938
This is something you will only
find in sickle cell patients.

3806
02:57:52,407 --> 02:57:55,377
So, if diastolic function,
impairment seem central,

3807
02:57:55,377 --> 02:57:57,278
diastolic function
must be understood.

3808
02:57:57,278 --> 02:57:58,880
I’m just looking at the time.

3809
02:58:00,248 --> 02:58:02,217
So, there is a toolbox
that I just wanted to give you

3810
02:58:02,217 --> 02:58:04,953
to just better understand
diastolic cardiomyopathy marker

3811
02:58:04,953 --> 02:58:06,421
and evolution
using echocardiography.

3812
02:58:06,421 --> 02:58:13,128
Very simply, but we always use
the same four index.

3813
02:58:13,128 --> 02:58:16,331
The first one is a very old one,
not fancy at all,

3814
02:58:16,331 --> 02:58:18,700
but the best one,
in my opinion,

3815
02:58:19,200 --> 02:58:21,770
is something we call
E prime wave.

3816
02:58:21,770 --> 02:58:27,242
It’s just the velocity
with which the mitral annulus

3817
02:58:27,242 --> 02:58:29,110
is moving during diastole.

3818
02:58:29,944 --> 02:58:34,649
If it’s fast, it’s good.
If it’s slow, it’s stiff.

3819
02:58:35,817 --> 02:58:37,318
Kind of straightforward
thinking,

3820
02:58:37,318 --> 02:58:38,920
but it’s actually kind of good.

3821
02:58:39,621 --> 02:58:42,524
And then if there is
a diastolic dysfunction,

3822
02:58:42,524 --> 02:58:45,860
so an increase in stiffness,

3823
02:58:45,860 --> 02:58:49,397
this can lead to an increase
in pressure behind,

3824
02:58:49,397 --> 02:58:55,603
in the left atrium.
So, we will see here --

3825
02:58:56,471 --> 02:59:00,542
yeah, we will see
a more dilated left atrium

3826
02:59:00,542 --> 02:59:02,210
because of the increase
in pressure.

3827
02:59:02,210 --> 02:59:05,680
And ultimately, this will lead
to an increase

3828
02:59:05,680 --> 02:59:08,183
in pulmonary pressure
with the TRV.

3829
02:59:08,917 --> 02:59:15,657
And this is a one-way disease,
not the other way.

3830
02:59:16,191 --> 02:59:18,059
So, this is very important
to understand.

3831
02:59:18,059 --> 02:59:21,896
Because what if we can be --

3832
02:59:22,764 --> 02:59:28,269
we can stratify the risks
sooner than later actually?

3833
02:59:28,269 --> 02:59:29,871
That’s my point.

3834
02:59:30,739 --> 02:59:34,409
And you can see --
just to show I’m not lying,

3835
02:59:34,409 --> 02:59:38,012
but this is the European
guidelines to evaluate

3836
02:59:38,012 --> 02:59:40,181
diastolic function
in the normal,

3837
02:59:40,181 --> 02:59:42,217
non-sickle population.

3838
02:59:42,217 --> 02:59:46,321
And they are using the E prime,
the TR velocity,

3839
02:59:46,321 --> 02:59:51,593
the LA volume, with cutoffs,
very defined cutoffs.

3840
02:59:53,795 --> 02:59:56,164
So, the first thing I wanted
to try

3841
02:59:56,164 --> 03:00:03,605
is I had the chance
to analyze big data

3842
03:00:03,605 --> 03:00:06,841
set of sickle cell patients
and [unintelligible]

3843
03:00:06,841 --> 03:00:08,176
adult sickle cell patients,

3844
03:00:08,176 --> 03:00:11,946
and very importantly,
very non-severe,

3845
03:00:11,946 --> 03:00:15,049
without any organ damage,
at steady state.

3846
03:00:15,583 --> 03:00:19,687
And the idea was to just --
each dot here is a patient,

3847
03:00:19,687 --> 03:00:24,025
and each vector is
an echocardiography feature,

3848
03:00:24,025 --> 03:00:29,764
just to try to see, how can we
reorganize among these patients?

3849
03:00:30,431 --> 03:00:33,301
And what’s interesting
in that three clusters we saw,

3850
03:00:33,301 --> 03:00:35,703
we could find four,
five, six.

3851
03:00:35,703 --> 03:00:39,374
It’s not that --
the question is not here.

3852
03:00:40,108 --> 03:00:44,345
The interesting point is that
the more remodeled

3853
03:00:44,345 --> 03:00:49,317
hypertrophied, the more --
the less the prime was.

3854
03:00:49,317 --> 03:00:51,186
So, the more remodeled
the heart was,

3855
03:00:52,053 --> 03:00:57,058
and depending on the age,
the worse the survival was,

3856
03:00:57,759 --> 03:00:59,861
most from cardiological
complication.

3857
03:00:59,861 --> 03:01:01,663
And when we look
at those patients,

3858
03:01:02,363 --> 03:01:05,733
they had more hemolytic anemia.

3859
03:01:05,733 --> 03:01:09,938
So, we actually find
the pathophysiology,

3860
03:01:09,938 --> 03:01:12,240
but backwards, starting
from echocardiography.

3861
03:01:12,240 --> 03:01:13,842
So, that’s actually
reassuring.

3862
03:01:15,710 --> 03:01:18,980
But what is most important,
in my opinion,

3863
03:01:18,980 --> 03:01:23,718
is that when you see all that
I’ve been telling you about --

3864
03:01:23,718 --> 03:01:27,055
and we’re not going to
spend time on those numbers,

3865
03:01:27,055 --> 03:01:31,359
but if you try to classify
this patient

3866
03:01:31,359 --> 03:01:34,963
according to the usual
classifications,

3867
03:01:36,931 --> 03:01:40,568
they are all normal
in terms of compliance.

3868
03:01:40,568 --> 03:01:44,672
They’re all normal
in terms of feeling pressure.

3869
03:01:44,672 --> 03:01:48,176
And they are all abnormal
in terms of left atrial volume.

3870
03:01:49,477 --> 03:01:53,348
So, it is not adapted at all.
That’s my point.

3871
03:01:53,915 --> 03:01:58,419
You cannot apply
what’s on the left on the screen

3872
03:01:58,419 --> 03:02:03,658
to the right
that the best patients have --

3873
03:02:03,658 --> 03:02:05,426
still criteria
of diastolic dysfunction.

3874
03:02:05,426 --> 03:02:07,362
So, this is not possible.

3875
03:02:07,362 --> 03:02:11,532
Okay. So -- okay.
I have to be very quick.

3876
03:02:13,067 --> 03:02:21,309
The idea was to early define
diastolic cardiomyopathy

3877
03:02:21,309 --> 03:02:25,046
before the problem
because there is no definition

3878
03:02:25,046 --> 03:02:28,650
because we have to stratify
the risk before the problems,

3879
03:02:28,650 --> 03:02:31,786
even in the pediatric
population, before PH onset,

3880
03:02:32,320 --> 03:02:35,456
that we can maybe reinforce
treatment or include them

3881
03:02:35,456 --> 03:02:37,358
in dedicated clinical trial.

3882
03:02:39,093 --> 03:02:41,429
So, I’m going to go quick
on this one.

3883
03:02:42,130 --> 03:02:47,368
We analyzed
this huge cohort of patients

3884
03:02:47,368 --> 03:02:50,138
and matched this cohort
to another one,

3885
03:02:50,805 --> 03:02:53,808
aged matched
with the most factors

3886
03:02:53,808 --> 03:02:55,944
in terms of confounding factors,
matched to a big,

3887
03:02:55,944 --> 03:02:59,714
big data sets of normal
echocardiography patients.

3888
03:02:59,714 --> 03:03:02,483
And what we can see is that --
you see in red here

3889
03:03:03,318 --> 03:03:07,355
that even young sickle
cell patients,

3890
03:03:07,355 --> 03:03:12,293
20 years old, they have lower
left ventricular compliance

3891
03:03:12,293 --> 03:03:15,396
than controls even young.

3892
03:03:16,064 --> 03:03:18,266
And this is actually
very parallel with --

3893
03:03:18,866 --> 03:03:24,806
it’s always lower through life,
like early aging, actually.

3894
03:03:25,306 --> 03:03:27,575
And when you check
the left atrial volume,

3895
03:03:27,575 --> 03:03:30,678
it’s quite flat
in the normal population.

3896
03:03:30,678 --> 03:03:32,513
But in the sickle
cell population,

3897
03:03:33,314 --> 03:03:36,951
you can see an exponential rise.
At 60 years old,

3898
03:03:36,951 --> 03:03:39,487
they usually have
a huge, huge, huge left atrial.

3899
03:03:40,121 --> 03:03:43,558
So, I have to go faster.

3900
03:03:43,558 --> 03:03:46,427
But I will be more than happy to
talk about all this data later.

3901
03:03:46,427 --> 03:03:48,329
Because I have some
very interesting messages

3902
03:03:48,329 --> 03:03:49,931
I want to share before the end.

3903
03:03:50,932 --> 03:03:53,334
When we do
the multivariate analysis

3904
03:03:53,334 --> 03:03:56,838
on a subgroup
of young SCA patients

3905
03:03:56,838 --> 03:04:00,274
where age does not impact
stiffness --

3906
03:04:00,274 --> 03:04:02,710
this is quite
a confounding factors --

3907
03:04:03,578 --> 03:04:07,615
only one criteria pops out,

3908
03:04:07,615 --> 03:04:12,887
and it’s good because
it’s left atrial compliance.

3909
03:04:13,588 --> 03:04:17,458
If a young patient --
the mean age was 26 years old.

3910
03:04:17,458 --> 03:04:21,362
If a young patient quite stable
without severe organ damage --

3911
03:04:21,362 --> 03:04:23,664
once again,
this is a selected population --

3912
03:04:23,664 --> 03:04:27,869
has a value of compliance
below 11, he reached 12 --

3913
03:04:28,669 --> 03:04:30,738
20 percent of mortality
in 10 years.

3914
03:04:31,472 --> 03:04:33,708
So, this is really interesting

3915
03:04:33,708 --> 03:04:36,077
because these are
very stable patients.

3916
03:04:36,778 --> 03:04:39,447
And if we go through time,
three years after,

3917
03:04:40,014 --> 03:04:42,850
the patient
with diastolic dysfunction,

3918
03:04:42,850 --> 03:04:44,852
with brackets once again,

3919
03:04:44,852 --> 03:04:50,258
they are starting to show
functional impairment

3920
03:04:50,258 --> 03:04:52,293
on six-minute walking tests.

3921
03:04:52,293 --> 03:04:55,396
They are going to show
a trend to a higher TRV

3922
03:04:55,396 --> 03:05:00,835
because disease is evolving.
Left atrial volume is pumping,

3923
03:05:00,835 --> 03:05:03,337
pressure rising --
there is a process --

3924
03:05:03,337 --> 03:05:07,442
and the glomerular
filtration rate is going down.

3925
03:05:07,975 --> 03:05:11,379
So, there is consistency
in all this data.

3926
03:05:12,613 --> 03:05:19,454
Though, this is just a summary
that we can, I think,

3927
03:05:19,454 --> 03:05:24,358
identify patients at risk
before pulmonary hypertension

3928
03:05:25,259 --> 03:05:30,298
by exploring left ventricular
compliance at younger age,

3929
03:05:30,298 --> 03:05:31,899
more frequently.

3930
03:05:32,967 --> 03:05:34,569
In five minutes --

3931
03:05:35,770 --> 03:05:39,107
when I arrived
in 2018 in Henri Mondor,

3932
03:05:40,308 --> 03:05:43,644
I started the DREPACOEUR
registry cohort of care.

3933
03:05:44,178 --> 03:05:47,215
It’s a multi-disciplinary
evaluation of all patients,

3934
03:05:47,215 --> 03:05:48,416
sickle cell patients,

3935
03:05:48,416 --> 03:05:51,586
that were suspected
of having heart problems.

3936
03:05:51,586 --> 03:05:54,021
It was very wide inclusion
criteria,

3937
03:05:54,021 --> 03:05:58,025
basically all that can make you
think that there’s a problem.

3938
03:05:58,659 --> 03:06:02,363
So, the idea was to --
for me to see a lot of patients

3939
03:06:02,363 --> 03:06:04,699
and to try to understand
the patterns.

3940
03:06:05,366 --> 03:06:08,569
And they all had,
in a day hospital,

3941
03:06:08,569 --> 03:06:12,673
a very systematic examination
with clinical exam,

3942
03:06:12,673 --> 03:06:14,876
EKG, biology sample,
echocardiography,

3943
03:06:14,876 --> 03:06:18,146
24-hour Holter EKG,
six-minute working test.

3944
03:06:18,713 --> 03:06:20,848
If we suspected
pulmonary hypertension,

3945
03:06:20,848 --> 03:06:26,220
they went to the referral center
for dedicated exploration,

3946
03:06:26,220 --> 03:06:29,924
and they all had cardiac MRI
and CT coronary angiogram

3947
03:06:31,192 --> 03:06:33,828
for the reason
I will explain after.

3948
03:06:35,663 --> 03:06:38,466
We now have more --
in the day hospital,

3949
03:06:38,466 --> 03:06:41,402
there are more than
350 SCD patients followed.

3950
03:06:41,402 --> 03:06:43,037
Most of them are SS.

3951
03:06:43,037 --> 03:06:45,873
And as we said before,
12 percent are SC.

3952
03:06:45,873 --> 03:06:51,712
And this is something that we
are -- the work is ongoing.

3953
03:06:51,712 --> 03:06:54,615
After describing
all the SS complication,

3954
03:06:54,615 --> 03:06:56,584
the SC is actually ongoing

3955
03:06:56,584 --> 03:06:58,319
because, as you said,
this is a problem.

3956
03:06:58,319 --> 03:07:00,054
And the first problematic
for me,

3957
03:07:00,054 --> 03:07:02,823
at the forefront,
there is, in this population,

3958
03:07:02,823 --> 03:07:06,160
10 to 15 percent of sudden
deaths that we can’t explain.

3959
03:07:06,727 --> 03:07:09,997
And in cardiology
or in intensive care,

3960
03:07:09,997 --> 03:07:14,168
a sudden death is not very --
there’s not a thousand cause.

3961
03:07:14,168 --> 03:07:17,038
It’s a stroke
or ventricular arrhythmia

3962
03:07:17,038 --> 03:07:18,639
or pulmonary embolism.

3963
03:07:18,639 --> 03:07:22,210
Must be one of the three.
There is no other explanation.

3964
03:07:22,810 --> 03:07:25,346
We know that this patient
have a lot of stroke,

3965
03:07:25,980 --> 03:07:28,015
53 percent in silent,

3966
03:07:28,015 --> 03:07:32,220
infarct below 30, 4 percent
severe stroke below 40.

3967
03:07:32,987 --> 03:07:34,922
But regarding
ventricular arrhythmia

3968
03:07:34,922 --> 03:07:36,891
or other arrhythmia,
we don’t know.

3969
03:07:38,025 --> 03:07:41,128
But yet, pathophysiology
tells us

3970
03:07:41,128 --> 03:07:44,298
that they are the best candidate
to develop

3971
03:07:44,298 --> 03:07:48,069
both ventricular arrhythmia
and atrial arrhythmia.

3972
03:07:48,069 --> 03:07:50,304
Ventricular arrhythmia
can lead to sudden death.

3973
03:07:50,304 --> 03:07:53,674
Atrial arrhythmia can lead
to atrial fibrillation,

3974
03:07:54,342 --> 03:07:58,246
one of the first risk
of stroke in the population,

3975
03:07:59,614 --> 03:08:01,749
just after
systemic hypertension.

3976
03:08:02,283 --> 03:08:04,418
They have all that is needed.

3977
03:08:04,418 --> 03:08:08,089
It’s basically an experimental
model for rhythmic disorder.

3978
03:08:08,089 --> 03:08:11,225
They have the fibrosis,
the atrial enlargement,

3979
03:08:11,959 --> 03:08:14,228
the -- some triggers,
such as inflammation,

3980
03:08:14,228 --> 03:08:15,997
hypoxemia,
electrolytic disorder.

3981
03:08:15,997 --> 03:08:18,599
They have everything needed
to develop

3982
03:08:18,599 --> 03:08:20,268
ventricular
or atrial fibrillation.

3983
03:08:20,268 --> 03:08:24,038
So, first, the first description
we did

3984
03:08:24,038 --> 03:08:27,108
on the first 100 patients,
it was more like an alert.

3985
03:08:28,376 --> 03:08:31,946
Last year, was --
you see 100 SCA patient,

3986
03:08:31,946 --> 03:08:35,316
40 years old,
so it’s not a very young cohort.

3987
03:08:36,717 --> 03:08:39,787
We did all the exams
I talked to you about before,

3988
03:08:39,787 --> 03:08:43,524
and we diagnosed 22 percent
of ventricular arrhythmia,

3989
03:08:43,524 --> 03:08:45,726
non-symptomatic
ventricular arrhythmia,

3990
03:08:45,726 --> 03:08:47,928
on 24-hour EKG monitoring.

3991
03:08:49,230 --> 03:08:53,367
In every disease, it has been
linked to sudden deaths,

3992
03:08:53,934 --> 03:08:55,469
not in sickle cell disease.

3993
03:08:55,469 --> 03:08:59,273
So, we can just assume,
but it’s not proven.

3994
03:09:00,341 --> 03:09:03,744
And interestingly, it concerns
more male gender

3995
03:09:03,744 --> 03:09:05,813
with an odds ratio of 4.3,

3996
03:09:05,813 --> 03:09:08,716
which is actually exactly the
same in other cardiomyopathy.

3997
03:09:08,716 --> 03:09:11,519
Men are more exposed
to ventricular arrhythmia.

3998
03:09:12,553 --> 03:09:16,190
And a feature
on echocardiography is strain,

3999
03:09:16,190 --> 03:09:18,459
which I have no time
to explain,

4000
03:09:18,459 --> 03:09:20,127
but I will be happy
to explain later.

4001
03:09:20,127 --> 03:09:25,966
But there is a measurement that,
if it is bad, can --

4002
03:09:27,501 --> 03:09:34,475
yeah, you can do an Holter EKG
based on this measurement

4003
03:09:34,475 --> 03:09:36,077
with a good predictive value.

4004
03:09:37,311 --> 03:09:42,116
And just to finish fast,
atrial arrhythmia,

4005
03:09:42,116 --> 03:09:45,786
same story, different --
very different kind of patients,

4006
03:09:45,786 --> 03:09:48,789
but 26 percent
of atrial arrhythmia

4007
03:09:48,789 --> 03:09:51,192
in this patient,
26 percent.

4008
03:09:51,192 --> 03:09:54,729
This is a huge,
huge, huge amount.

4009
03:09:55,629 --> 03:09:57,898
And like in
the general population,

4010
03:09:58,532 --> 03:10:00,634
age was independently
associated

4011
03:10:00,634 --> 03:10:04,805
with a number of premature
atrial complex per day.

4012
03:10:04,805 --> 03:10:06,107
This is a logarith [sic] scale.

4013
03:10:06,107 --> 03:10:08,376
So, from here,
this is more than

4014
03:10:08,376 --> 03:10:10,644
10,000 premature atrial
complex per day.

4015
03:10:10,644 --> 03:10:13,280
So, it’s basically
atrial fibrillation,

4016
03:10:14,248 --> 03:10:15,816
left atrial volume.

4017
03:10:16,450 --> 03:10:19,086
And very interestingly,
we have the luck

4018
03:10:19,086 --> 03:10:23,958
to have very good expertise
in the stroke field.

4019
03:10:24,959 --> 03:10:28,329
When looking at the patient
with a history of stroke

4020
03:10:28,329 --> 03:10:33,334
without underlying vasculopathy,
there was an odds ratio of 6.6.

4021
03:10:34,335 --> 03:10:40,441
So, this changes the way we took
care of patients in our center,

4022
03:10:40,441 --> 03:10:46,847
the local guidelines.
And now, we do actually this,

4023
03:10:47,848 --> 03:10:49,984
which was just published in
Blood Advances.

4024
03:10:51,185 --> 03:10:57,358
It’s when to do arrhythmic
evaluation in adult patients

4025
03:10:57,858 --> 03:11:01,195
and in what presentation.

4026
03:11:01,195 --> 03:11:05,900
If it’s just without any
symptoms, you do systematic EKG.

4027
03:11:05,900 --> 03:11:10,738
And if you get older or with
a very large left atrial volume,

4028
03:11:10,738 --> 03:11:13,941
then you are going
for 24-hour monitoring.

4029
03:11:14,708 --> 03:11:16,544
And then I have to stop.
Okay.

4030
03:11:16,544 --> 03:11:19,713
And same thing with
if you had a stroke

4031
03:11:19,713 --> 03:11:21,315
without underlying vasculopathy,

4032
03:11:21,315 --> 03:11:25,486
we go very easily to prolonged
more than 24 hours,

4033
03:11:25,486 --> 03:11:29,557
but 14 days monitoring
with excellent results.

4034
03:11:30,624 --> 03:11:34,662
I can tell you about this later.
They don’t have coronaropathy.

4035
03:11:34,662 --> 03:11:36,363
And I will end with this.

4036
03:11:36,363 --> 03:11:42,002
Even this new population
aging with kidney failure,

4037
03:11:42,002 --> 03:11:45,873
with systemic hypertension,
they don’t have --

4038
03:11:45,873 --> 03:11:49,009
still don’t have any
coronary artery disease.

4039
03:11:49,009 --> 03:11:52,546
So, I won’t talk
a lot about this,

4040
03:11:52,546 --> 03:11:55,649
but I just raise one question
that, for me, is very important.

4041
03:11:57,518 --> 03:12:00,354
What happens when we cure them,

4042
03:12:01,121 --> 03:12:04,225
when we restore normal
or high lipidemia,

4043
03:12:04,225 --> 03:12:06,861
and we level down
the bilirubin protective role

4044
03:12:07,628 --> 03:12:10,564
on the systemic vasculopathy
that is already existing?

4045
03:12:11,499 --> 03:12:12,833
This just --
this is just a question.

4046
03:12:12,833 --> 03:12:14,068
I don’t have the answer.

4047
03:12:14,068 --> 03:12:19,573
But they might be prone to
a very high cardiovascular risk

4048
03:12:19,573 --> 03:12:23,844
knowing that we are restoring
all that is needed to develop

4049
03:12:23,844 --> 03:12:26,247
some atherosclerosis
they didn’t have.

4050
03:12:26,881 --> 03:12:29,917
So, this is, for me, a point
of attention, very important.

4051
03:12:30,684 --> 03:12:32,286
And I’m out.

4052
03:12:33,187 --> 03:12:36,690
So, this is, yeah,
a very difficult,

4053
03:12:37,291 --> 03:12:39,093
unique presentation
of heart failure.

4054
03:12:39,093 --> 03:12:42,496
You have to have
dedicated cardiologists

4055
03:12:42,496 --> 03:12:44,698
or people interested
in this disease

4056
03:12:44,698 --> 03:12:46,300
to have a good evaluation.

4057
03:12:46,867 --> 03:12:48,903
There is a high prevalence,
very high,

4058
03:12:48,903 --> 03:12:51,071
of ventricular
and atrial arrhythmia

4059
03:12:51,071 --> 03:12:54,341
with a potential implication in
both sudden deaths and stroke.

4060
03:12:54,341 --> 03:12:58,946
So, we have to be careful.
Thank you.

4061
03:12:59,513 --> 03:13:04,184
[applause]

4062
03:13:04,184 --> 03:13:05,486
Angela Rankine-Mullings: Thank
you.

4063
03:13:05,486 --> 03:13:06,687
Thomas d’Humières:
[unintelligible]

4064
03:13:06,687 --> 03:13:07,955
Angela Rankine-Mullings:
Thank you so much.

4065
03:13:07,955 --> 03:13:09,223
Thank you, Dr. d’Humières.

4066
03:13:09,223 --> 03:13:10,424
[applause]

4067
03:13:10,424 --> 03:13:14,562
We can only have one question
because we are behind.

4068
03:13:15,429 --> 03:13:17,898
You’ll take one question,
the first hand.

4069
03:13:17,898 --> 03:13:20,734
I’ll encourage everyone
to catch him at lunch.

4070
03:13:22,102 --> 03:13:24,471
John Tisdale: Thank you for
a really great presentation.

4071
03:13:24,471 --> 03:13:25,906
I’m always wondering,

4072
03:13:25,906 --> 03:13:30,110
when we think about heart
disease and sickle cell disease,

4073
03:13:30,644 --> 03:13:33,080
we’re often attributing it
to hemolysis

4074
03:13:33,080 --> 03:13:35,215
and NO scavenging
and the like.

4075
03:13:35,215 --> 03:13:39,954
And hemolysis
also reduces hemoglobin.

4076
03:13:39,954 --> 03:13:43,724
So, I’m just wondering to what
extent the hemoglobin itself,

4077
03:13:43,724 --> 03:13:45,793
the anemia itself,
is contributing.

4078
03:13:45,793 --> 03:13:49,363
Because in thalassemia
intermedia back in the day,

4079
03:13:49,363 --> 03:13:51,165
you know, there was
this finding that

4080
03:13:51,165 --> 03:13:53,567
when you leave hemoglobins
chronically low,

4081
03:13:54,101 --> 03:13:58,005
patients develop heart failure
and pulmonary hypertension.

4082
03:13:58,005 --> 03:14:00,507
And in this disease, we leave
hemoglobins chronically low.

4083
03:14:00,507 --> 03:14:01,742
Thomas d’Humières: Yeah.

4084
03:14:01,742 --> 03:14:02,977
John Tisdale: So,
I’m just wondering

4085
03:14:02,977 --> 03:14:04,578
to what extent the anemia --

4086
03:14:05,179 --> 03:14:07,881
not the hemolysis and all
the downstream complications

4087
03:14:07,881 --> 03:14:09,149
that we know about,

4088
03:14:09,149 --> 03:14:11,318
but just the anemia itself --
has contributed?

4089
03:14:11,318 --> 03:14:13,253
Thomas d’Humières: Yeah.
This is a good question.

4090
03:14:13,253 --> 03:14:16,190
Anemia itself will contribute
to the remodeling, of course,

4091
03:14:16,190 --> 03:14:18,492
because there is a need
to increase the cardiac output

4092
03:14:18,492 --> 03:14:24,398
and to increase the left
ventricular volume each bit.

4093
03:14:24,398 --> 03:14:28,068
But there is a very good,
interesting paper

4094
03:14:28,068 --> 03:14:30,270
with preclinical model of mice.

4095
03:14:30,270 --> 03:14:36,410
One is a Townes model,
if I’m remembering good.

4096
03:14:36,410 --> 03:14:39,713
One is just
an anemic model of mice

4097
03:14:39,713 --> 03:14:43,484
with the same level
from the -- from birth,

4098
03:14:43,484 --> 03:14:45,519
and they are
following heart remodeling.

4099
03:14:46,053 --> 03:14:49,423
And the association
of hemolysis behind

4100
03:14:49,423 --> 03:14:53,961
is crucial in the development
of remodeling like fibrosis

4101
03:14:53,961 --> 03:14:55,996
and microvascular impairment.

4102
03:14:55,996 --> 03:15:00,467
That is not observed
in the chronic anemia situation.

4103
03:15:00,467 --> 03:15:03,370
Actually, we have a lot of
patients with chronic anemia.

4104
03:15:03,370 --> 03:15:05,072
We never see
that kind of pattern

4105
03:15:05,072 --> 03:15:07,007
we have with
sickle cell disease.

4106
03:15:07,007 --> 03:15:10,978
For me -- yeah, it’s just by
doing the echocardiography,

4107
03:15:10,978 --> 03:15:13,347
I can tell you that,
no, he’s not --

4108
03:15:13,347 --> 03:15:15,115
he doesn’t have sickle cell.

4109
03:15:15,115 --> 03:15:17,618
It was a mistake.
He’s just ASR. It was a mistake.

4110
03:15:18,419 --> 03:15:20,287
So, there is
a really good pattern,

4111
03:15:20,287 --> 03:15:23,590
and I think this is
the conjunction of both.

4112
03:15:25,626 --> 03:15:26,860
Angela Rankine-Mullings: Thank
you.

4113
03:15:26,860 --> 03:15:28,562
Thank you so much.

4114
03:15:28,562 --> 03:15:31,999
[applause]

4115
03:15:31,999 --> 03:15:36,704
Our third speaker is
Dr. Lori-Ann Fisher.

4116
03:15:37,504 --> 03:15:39,273
And we invite her
to come to the podium.

4117
03:15:39,273 --> 03:15:41,075
Dr. Fisher is a nephrologist

4118
03:15:41,075 --> 03:15:42,810
at the University
of the West Indies

4119
03:15:42,810 --> 03:15:45,245
and will share with us
on renal disease.

4120
03:15:45,245 --> 03:15:46,914
Let us welcome her.

4121
03:15:46,914 --> 03:15:49,717
[applause]

4122
03:15:49,717 --> 03:15:51,985
Lori-Ann Fisher: Okay.
So -- okay.

4123
03:15:51,985 --> 03:15:56,090
So, I’ve been warned that
I must be within the time.

4124
03:15:56,090 --> 03:16:02,096
So, I’m going to check this out.
Hold on. Okay. Cool.

4125
03:16:02,096 --> 03:16:04,498
So, thank you very much
to the organizers

4126
03:16:04,498 --> 03:16:08,736
for allowing me to present
or inviting me to present.

4127
03:16:08,736 --> 03:16:11,038
And I just wanted to start off
with a clinical case

4128
03:16:11,038 --> 03:16:14,074
because, you know,
first, I’m a clinician.

4129
03:16:14,074 --> 03:16:16,076
So --
and a lot of my questions

4130
03:16:16,076 --> 03:16:18,679
often come
from being on service.

4131
03:16:18,679 --> 03:16:20,914
So just last month,
I was on service.

4132
03:16:20,914 --> 03:16:22,516
I’m on service again.

4133
03:16:23,217 --> 03:16:26,320
A 60-year-old gentleman
came into the hospital.

4134
03:16:26,320 --> 03:16:29,123
He’s referred from
the renal clinic

4135
03:16:29,123 --> 03:16:30,691
with hyperkalemia and anemia.

4136
03:16:30,691 --> 03:16:32,359
It was noticed
on his blood test.

4137
03:16:32,993 --> 03:16:34,762
We don’t do chronic
transfusions here,

4138
03:16:34,762 --> 03:16:36,597
so people will have
a baseline hemoglobin,

4139
03:16:36,597 --> 03:16:38,899
and his baseline hemoglobin
is about 6.7.

4140
03:16:39,466 --> 03:16:40,968
He has sickle cardiomyopathy.

4141
03:16:40,968 --> 03:16:42,703
I’m so sorry
to the last presenter.

4142
03:16:43,737 --> 03:16:47,207
Yeah, that’s
a bad term [laughs].

4143
03:16:47,207 --> 03:16:50,210
But he has CKD, and he’s
followed by the renal clinic.

4144
03:16:50,210 --> 03:16:54,648
He has a baseline creatinine
of about 130 to 147.

4145
03:16:54,648 --> 03:16:58,819
That’s about 1.4 to 1.6, 1.7
for the Americans amongst us.

4146
03:17:00,354 --> 03:17:04,658
And he has kidney disease
stage 3a A3.

4147
03:17:04,658 --> 03:17:06,426
Nephrologists like
to make things complicated.

4148
03:17:06,426 --> 03:17:08,862
So, essentially,
his GFR is about 45,

4149
03:17:08,862 --> 03:17:11,799
and he has more than 300
milligrams of alminuria [sic].

4150
03:17:11,799 --> 03:17:13,534
So, he has severe alminuria,
right?

4151
03:17:14,735 --> 03:17:16,770
He’s on medication.
So, he’s on Resonium.

4152
03:17:17,638 --> 03:17:20,307
And now -- and he’s on Resonium
because he gave a history of,

4153
03:17:20,307 --> 03:17:24,711
you know, prior hyperkalemia,
which -- and enalapril.

4154
03:17:24,711 --> 03:17:26,180
He’s on allopurinol.

4155
03:17:26,180 --> 03:17:29,983
MIRCERA, which is
long-acting erythropoietin,

4156
03:17:30,617 --> 03:17:34,788
200 micrograms once monthly,
folic acid, and Lasix.

4157
03:17:35,689 --> 03:17:37,891
So, he had been having shortness
of breath

4158
03:17:37,891 --> 03:17:40,994
when riding his bicycle.
He was having some light --

4159
03:17:40,994 --> 03:17:42,529
he didn’t have
any lightheadedness

4160
03:17:42,529 --> 03:17:43,997
or symptoms of heart failure,

4161
03:17:43,997 --> 03:17:46,066
but he had some fatigue
and weakness.

4162
03:17:46,967 --> 03:17:50,671
No cough, fever, chills, or any
respiratory symptoms, no pain.

4163
03:17:50,671 --> 03:17:52,806
His exam was just noted
for like pallor.

4164
03:17:52,806 --> 03:17:54,708
His abdomen was non --
was benign.

4165
03:17:54,708 --> 03:17:57,311
He didn’t have hepatomegaly.
He had some edema.

4166
03:17:57,311 --> 03:17:59,413
His chest was equal.
There’s no crackles.

4167
03:18:00,147 --> 03:18:02,950
But his hemoglobin was 4.1,
[laughs]

4168
03:18:02,950 --> 03:18:06,753
and his platelets, 152.
He was hyperkalemia, 6.3.

4169
03:18:06,753 --> 03:18:08,288
And his creatinine
was above his baseline.

4170
03:18:08,288 --> 03:18:13,827
It was 171, so he had AKI,
and his bilis were elevated.

4171
03:18:13,827 --> 03:18:17,297
I’m sorry, the --
I didn’t put the units there.

4172
03:18:17,297 --> 03:18:19,333
But these are within
his baseline, though,

4173
03:18:19,333 --> 03:18:22,502
when I looked through
all his prior labs.

4174
03:18:23,136 --> 03:18:26,039
And this is what happened.
So, he came into the hospital.

4175
03:18:26,540 --> 03:18:28,242
Oh, boy,
this thing is really confusing.

4176
03:18:28,242 --> 03:18:30,811
So, he got two units
of red cells,

4177
03:18:31,478 --> 03:18:33,213
because that’s
what we could get,

4178
03:18:33,213 --> 03:18:35,582
and he came back up
to hemoglobin of 6.

4179
03:18:35,582 --> 03:18:38,051
His potassium dropped.
His bicarb came up,

4180
03:18:38,051 --> 03:18:40,554
and his creatinine came back
down to his baseline.

4181
03:18:41,788 --> 03:18:44,324
So, that kind of highlights
a lot of kind of issues

4182
03:18:44,324 --> 03:18:47,261
that we face
in a lot of patients

4183
03:18:47,261 --> 03:18:50,063
that we tend to see in Jamaica,

4184
03:18:50,831 --> 03:18:54,868
which is like the super
small dot right here.

4185
03:18:54,868 --> 03:18:57,671
I don’t know. All right. I think
that’s in the region there.

4186
03:18:57,671 --> 03:18:58,939
Yeah.

4187
03:18:58,939 --> 03:19:00,207
And we have -- as we know,

4188
03:19:00,207 --> 03:19:03,877
we have one of the highest rates
of sickle cell disease

4189
03:19:04,912 --> 03:19:07,547
in the world,
second to West Africa.

4190
03:19:07,547 --> 03:19:10,017
And this is data
from Professor Knight-Madden.

4191
03:19:10,017 --> 03:19:12,219
I actually just used her data.

4192
03:19:13,020 --> 03:19:16,790
You know, 0.7 percent
have sickle cell disease,

4193
03:19:16,790 --> 03:19:19,927
about 15 percent
have either S or C trait.

4194
03:19:21,161 --> 03:19:25,032
And, you know, we’ve gotten
much better in the region,

4195
03:19:25,032 --> 03:19:26,867
as we know,
and many people have said this,

4196
03:19:26,867 --> 03:19:29,336
at, you know,
the infectious complications

4197
03:19:29,336 --> 03:19:31,538
that people are living
much longer in this data

4198
03:19:31,538 --> 03:19:33,707
from the sickle cell cohort.

4199
03:19:34,675 --> 03:19:38,211
And what we found is that,
you know, chronic kidney disease

4200
03:19:38,211 --> 03:19:40,480
is one of the leading
causes of death --

4201
03:19:41,615 --> 03:19:44,318
oh, boy, yeah, chronic kidney
disease is right there --

4202
03:19:44,318 --> 03:19:48,155
as patients with sickle cell
disease get older, right?

4203
03:19:49,556 --> 03:19:51,591
So, everybody knows
the pathophy.

4204
03:19:51,591 --> 03:19:53,160
I feel like this is high school

4205
03:19:53,160 --> 03:19:54,928
compared to people
in the audience.

4206
03:19:54,928 --> 03:19:57,497
But we know that in areas

4207
03:19:57,497 --> 03:19:59,299
[laughs], you know,
in sickle cell disease,

4208
03:19:59,299 --> 03:20:01,768
you know, there’s low --
when there’s low oxygen tension,

4209
03:20:01,768 --> 03:20:04,338
you get polymerization
of the sickle cell moiety

4210
03:20:04,338 --> 03:20:06,173
that leads to
macrovascular damage,

4211
03:20:06,173 --> 03:20:09,076
cell adhesion,
vasoconstriction,

4212
03:20:09,076 --> 03:20:11,445
and the resultant
vascular injury.

4213
03:20:12,279 --> 03:20:14,414
When you think of sickle
cell nephropathy, again,

4214
03:20:14,414 --> 03:20:17,284
it’s a very basic
kind of pathogenesis.

4215
03:20:17,284 --> 03:20:20,721
You think of like viscosity
or vaso-occlusive phenomena.

4216
03:20:20,721 --> 03:20:24,057
And if you remember
from your physiology,

4217
03:20:24,057 --> 03:20:25,325
your renal physiology,

4218
03:20:25,325 --> 03:20:29,529
the medulla is a very low
oxygen tension space, right?

4219
03:20:29,529 --> 03:20:33,066
And so, recurrent sickling
can cause interstitial damage.

4220
03:20:35,068 --> 03:20:38,105
As a nephrologist,
the favorite part of the tubule

4221
03:20:38,105 --> 03:20:40,440
is the distal
convoluted tubule.

4222
03:20:40,440 --> 03:20:42,943
Yes, nephrologists have
a favorite part of their tubule.

4223
03:20:42,943 --> 03:20:44,144
[laughter]

4224
03:20:44,144 --> 03:20:48,281
So, the distal nephron
gets affected in kidney --

4225
03:20:48,982 --> 03:20:50,417
in sickle nephropathy.

4226
03:20:50,417 --> 03:20:54,254
So, you actually have a problem
with ammonium secretion,

4227
03:20:54,254 --> 03:20:57,991
and essentially that leads
to a failure of acidification.

4228
03:20:57,991 --> 03:21:01,028
So, they’re prone to metabolic
acidosis and hyperkalemia

4229
03:21:01,028 --> 03:21:03,063
because if you can’t
excrete potassium,

4230
03:21:03,063 --> 03:21:06,166
you can’t get rid of --
can’t excrete hydrogen ions,

4231
03:21:06,166 --> 03:21:08,101
you can’t get rid
of potassium as well.

4232
03:21:08,101 --> 03:21:10,303
They also -- my second favorite
part of the tubule

4233
03:21:10,303 --> 03:21:13,340
is also affected, which is
the proximal convoluted tubule.

4234
03:21:13,340 --> 03:21:17,411
So, you get an increase
in phosphorus reabsorption

4235
03:21:17,411 --> 03:21:24,651
and uric acid reabsorption
and also increased or impairment

4236
03:21:24,651 --> 03:21:27,988
in clearance of creatinine.
And so, you get this --

4237
03:21:28,522 --> 03:21:32,125
also this artifactual increase
in creatinine clearance.

4238
03:21:32,125 --> 03:21:35,162
The other kind of mediated
effect is hemolysis

4239
03:21:35,162 --> 03:21:38,398
and hyperperfusion,

4240
03:21:38,398 --> 03:21:41,635
reperfusion injury that leads
to endothelial dysfunction

4241
03:21:41,635 --> 03:21:43,537
and hyperfiltration,
which is such well --

4242
03:21:43,537 --> 03:21:46,373
which is well described
in our patients,

4243
03:21:46,373 --> 03:21:50,510
which ultimately leads
to sclerosis, alminuria,

4244
03:21:50,510 --> 03:21:53,213
and, you know, changes --

4245
03:21:53,213 --> 03:21:55,982
and, you know, over time,
progressive nephron loss.

4246
03:21:57,384 --> 03:21:58,718
It’s actually
quite interesting,

4247
03:21:58,718 --> 03:22:02,722
because the hyperfiltration
mechanism is actually similar

4248
03:22:02,722 --> 03:22:06,193
to diabetic kidney disease.
And that’s really important

4249
03:22:06,193 --> 03:22:08,728
when I talk about
kind of therapeutics later.

4250
03:22:10,931 --> 03:22:14,334
Ultimately, you know,
they’re at risk for two things,

4251
03:22:14,334 --> 03:22:16,403
acute kidney injury
and chronic kidney disease.

4252
03:22:16,403 --> 03:22:19,940
So, in kids, one of the early
signs of kidney disease

4253
03:22:19,940 --> 03:22:22,042
or at least one
early effects of kidney

4254
03:22:22,042 --> 03:22:23,643
is actually
nocturnal enuresis,

4255
03:22:23,643 --> 03:22:27,714
and that’s because of a failure
of concentration of urine,

4256
03:22:27,714 --> 03:22:30,417
and that’s because of
interstitial and tubular damage.

4257
03:22:31,318 --> 03:22:33,186
Also, you know,
infection risk,

4258
03:22:33,186 --> 03:22:35,388
all these other things
lead to AKI.

4259
03:22:35,388 --> 03:22:37,991
In people who do not
have sickle cell disease,

4260
03:22:37,991 --> 03:22:40,827
AKI increases risk
of chronic kidney disease.

4261
03:22:41,328 --> 03:22:44,531
So, there’s kind of this
bidirectional relationship

4262
03:22:44,531 --> 03:22:47,667
between acute kidney injury
and chronic kidney disease.

4263
03:22:48,869 --> 03:22:50,137
This is really great work
from --

4264
03:22:50,137 --> 03:22:55,041
I think this is
Jeff Lebensburger in UAB.

4265
03:22:55,041 --> 03:22:56,643
And essentially, we --
what we know is

4266
03:22:56,643 --> 03:23:00,714
that hyperfiltration
predates alminuria, right?

4267
03:23:00,714 --> 03:23:03,884
So, people who hyperfilter
are more likely

4268
03:23:03,884 --> 03:23:06,153
to develop
alminuria over time.

4269
03:23:07,254 --> 03:23:09,189
And this is some work
with people in the audience,

4270
03:23:09,189 --> 03:23:12,259
including Monika,
that showed that, you know,

4271
03:23:12,259 --> 03:23:14,327
a lot of patient
people have alminuria.

4272
03:23:14,327 --> 03:23:17,364
You can have
transient alminuria

4273
03:23:17,364 --> 03:23:20,167
without it being
a true kidney disease.

4274
03:23:20,167 --> 03:23:22,269
That’s why the definition
of kidney disease

4275
03:23:22,269 --> 03:23:25,238
is persistent alminuria
for more than three months.

4276
03:23:25,238 --> 03:23:27,807
What we found is, like,
you know,

4277
03:23:27,807 --> 03:23:31,211
a cutoff point
of 100 initial baseline

4278
03:23:31,211 --> 03:23:35,582
alminuria predicts
persistent albuminuria, right?

4279
03:23:35,582 --> 03:23:38,151
And those people with persistent
alminuria meaning --

4280
03:23:38,151 --> 03:23:42,522
and this study
was two values of greater

4281
03:23:42,522 --> 03:23:46,126
than 30 milligrams per gram
in a two-year period.

4282
03:23:46,126 --> 03:23:50,130
Those people were more likely
to have GFR decline, right?

4283
03:23:50,130 --> 03:23:54,034
So persistent alminuria bad.

4284
03:23:55,135 --> 03:23:57,204
We know that sickle
cell nephropathy

4285
03:23:57,737 --> 03:24:00,707
leading to kidney disease
is really, really common.

4286
03:24:00,707 --> 03:24:02,842
Gets more common
as we get older.

4287
03:24:03,810 --> 03:24:06,846
And it’s higher
in sickle SS disease

4288
03:24:06,846 --> 03:24:09,583
and sickle ß0 thalassemia,

4289
03:24:09,583 --> 03:24:14,721
less common in HBS --
sickle SC but does occur.

4290
03:24:16,489 --> 03:24:18,525
We used to think sickle cell
trait was benign,

4291
03:24:18,525 --> 03:24:19,859
but it doesn’t.
It’s not very benign.

4292
03:24:19,859 --> 03:24:22,996
There’s U.S. data that shows
that sickle cell trait

4293
03:24:22,996 --> 03:24:25,065
is associated with
chronic kidney disease.

4294
03:24:25,865 --> 03:24:31,438
And in our data from the Jamaica
Health and Lifestyle Survey,

4295
03:24:31,938 --> 03:24:33,740
sickle cell trait was associated
with a twofold

4296
03:24:33,740 --> 03:24:35,775
increased risk of reduced GFR.

4297
03:24:36,376 --> 03:24:38,144
This is an old slide because
we don’t really

4298
03:24:38,144 --> 03:24:40,247
call it microalbuminuria
and macroalbuminuria.

4299
03:24:40,247 --> 03:24:42,616
It’s called moderate
and severe alminuria.

4300
03:24:42,616 --> 03:24:45,485
So, macro --
microalminuria is 30 to 300,

4301
03:24:46,086 --> 03:24:47,954
macro is above 300.

4302
03:24:48,555 --> 03:24:56,429
Gray is SC and HbSS, so much --
you know, high rates of --

4303
03:24:57,264 --> 03:25:03,103
in this cohort, high rates
of alminuria in people,

4304
03:25:03,103 --> 03:25:09,342
which SC -- SS up to 40 percent,
and it increases with age.

4305
03:25:09,342 --> 03:25:12,379
So, presenting some data
from our population,

4306
03:25:13,246 --> 03:25:15,515
you know, this is from --
data from Monika Asnani

4307
03:25:15,515 --> 03:25:20,920
and Prof. Reid, CKD and above,
in 99 people with --

4308
03:25:21,554 --> 03:25:23,523
Jamaicans with
sickle cell disease,

4309
03:25:23,523 --> 03:25:25,125
and this is HSS,

4310
03:25:26,326 --> 03:25:29,529
about two-thirds of them
had alminuria.

4311
03:25:30,230 --> 03:25:31,464
Commonly, you know,

4312
03:25:31,464 --> 03:25:34,267
hyperfiltration occurred
in about a quarter of patients.

4313
03:25:35,135 --> 03:25:38,271
And what was really important is
that they actually measured GFR

4314
03:25:38,271 --> 03:25:41,174
and did creatinine
and cystatin testing.

4315
03:25:41,174 --> 03:25:44,311
And, you know,
compared to measured GFR,

4316
03:25:45,211 --> 03:25:47,347
you know,
creatinine-based estimates

4317
03:25:47,347 --> 03:25:50,650
of GFR
performed very poorly, right?

4318
03:25:50,650 --> 03:25:53,019
So, you actually --
we actually know that creatinine

4319
03:25:53,019 --> 03:25:54,921
was a very
insensitive marker of,

4320
03:25:54,921 --> 03:25:58,124
which the audience tends
to know, renal dysfunction,

4321
03:25:58,124 --> 03:26:01,795
and actually only started rising
after measured GFR fell.

4322
03:26:03,530 --> 03:26:05,265
So, as I said before,

4323
03:26:05,265 --> 03:26:07,667
sickle cell trait
really is not benign,

4324
03:26:07,667 --> 03:26:11,805
and it’s associated with kidney
disease amongst Black people.

4325
03:26:12,639 --> 03:26:15,175
And, you know, whether or not
it interacts with equal one

4326
03:26:15,175 --> 03:26:16,876
[phonetic sp]
is unclear. Some data says no.

4327
03:26:16,876 --> 03:26:18,278
Some data says yes.

4328
03:26:18,278 --> 03:26:20,447
But certainly,
sickle cell disease --

4329
03:26:20,447 --> 03:26:23,116
sickle cell trait is associated
with chronic kidney disease

4330
03:26:23,116 --> 03:26:25,785
and alminuria
in African Americans

4331
03:26:25,785 --> 03:26:27,387
and also in Brazil.

4332
03:26:29,522 --> 03:26:31,558
Kidney function declines
faster in people

4333
03:26:31,558 --> 03:26:32,992
with sickle cell disease.

4334
03:26:32,992 --> 03:26:37,697
So, this is some data which was
published about three years ago.

4335
03:26:37,697 --> 03:26:39,833
Comparing the second line
here is --

4336
03:26:39,833 --> 03:26:42,402
oops, this thing confuses me.
I’m sorry.

4337
03:26:43,503 --> 03:26:46,673
You know, GFR slopes or GFR

4338
03:26:46,673 --> 03:26:51,444
decline compared to age
AA controls.

4339
03:26:51,444 --> 03:26:55,048
And really and truly compared
to the reference group,

4340
03:26:56,015 --> 03:26:59,586
you know, GFR declined
in the order of kind of 2

4341
03:26:59,586 --> 03:27:02,255
to 3 mils per minute per year

4342
03:27:02,889 --> 03:27:06,526
in sickle cell
SS versus AS disease.

4343
03:27:08,361 --> 03:27:09,796
Going back to our patient,
remember,

4344
03:27:09,796 --> 03:27:14,000
he’s very hyperkalemic
and had metabolic acidosis.

4345
03:27:14,000 --> 03:27:15,535
And this is some really
great work from --

4346
03:27:15,535 --> 03:27:18,705
I think this is Santosh Saraf
that looked at,

4347
03:27:18,705 --> 03:27:21,007
you know, multicenter
cross-sectional study.

4348
03:27:21,007 --> 03:27:24,277
And what they found was
that compared to controls,

4349
03:27:24,277 --> 03:27:28,748
you had higher rates of
hyperkalemia metabolic acidosis

4350
03:27:28,748 --> 03:27:32,085
in what they termed
the severe SCDs genotype.

4351
03:27:32,085 --> 03:27:34,954
So that’s SS
and S beta-thal-not .

4352
03:27:36,156 --> 03:27:37,857
And it occurred
at higher GFR.

4353
03:27:37,857 --> 03:27:40,193
So, you’re looking
at a GFR here of --

4354
03:27:41,027 --> 03:27:46,466
oopsie -- like 85 to 91,
about 21 percent of them

4355
03:27:46,466 --> 03:27:50,970
had acidosis
and 13 percent had hyperkalemia.

4356
03:27:54,007 --> 03:27:56,643
And, you know, a pretty sore
point for me

4357
03:27:56,643 --> 03:27:57,977
as a nephrologist is,

4358
03:27:57,977 --> 03:28:00,713
how do I manage these people
or patients

4359
03:28:00,713 --> 03:28:02,549
with sickle cell disease
who have CKD.

4360
03:28:02,549 --> 03:28:04,150
How do I manage their anemia?

4361
03:28:04,918 --> 03:28:07,487
And, you know, there’s not
a lot of data on this.

4362
03:28:08,054 --> 03:28:10,323
The -- we know that
they have really high and --

4363
03:28:10,323 --> 03:28:12,192
especially in our country,

4364
03:28:12,192 --> 03:28:13,860
where we don’t have
a lot of blood,

4365
03:28:13,860 --> 03:28:15,328
and we don’t have a lot of --

4366
03:28:15,328 --> 03:28:17,530
we don’t have
transfusion programs.

4367
03:28:17,530 --> 03:28:20,967
What dose of ESA agents
should we be using?

4368
03:28:20,967 --> 03:28:22,836
And, you know,
this is some preliminary --

4369
03:28:22,836 --> 03:28:24,771
this is actually a letter
to the editor

4370
03:28:25,705 --> 03:28:30,076
looking at kind of EPO responses
or hemoglobin responses

4371
03:28:30,076 --> 03:28:33,947
to hemoglobin agents
with or without hydroxyurea.

4372
03:28:33,947 --> 03:28:38,284
And they actually found
increasing hemoglobin responses.

4373
03:28:39,118 --> 03:28:43,056
The concern that had been raised
in prior papers from the ‘90s

4374
03:28:43,056 --> 03:28:46,626
was that, you know, ESA agents
may increase risk of veno

4375
03:28:46,626 --> 03:28:51,297
[sic]-occlusive crisis may
increase risk of DVTs and PEs.

4376
03:28:51,297 --> 03:28:54,133
Didn’t really quite find
that in their data.

4377
03:28:54,133 --> 03:28:57,303
Didn’t have increased
risk of veno-occlusive crisis.

4378
03:28:58,104 --> 03:29:01,574
They had one or two VTE events,

4379
03:29:02,108 --> 03:29:04,644
but not statistically
kind of significant.

4380
03:29:04,644 --> 03:29:07,013
Please know that this is
a really small sample size,

4381
03:29:07,013 --> 03:29:10,884
so kind of hard
to kind of do associations,

4382
03:29:10,884 --> 03:29:13,586
but certainly an area
that needs further study

4383
03:29:14,587 --> 03:29:17,457
and especially because we have
such high rates

4384
03:29:17,457 --> 03:29:20,960
of kind of EPO resistance
in our patients,

4385
03:29:20,960 --> 03:29:24,097
especially with advanced
kidney disease with sickle cell.

4386
03:29:26,332 --> 03:29:29,168
We know that creatinine is not
a very good biomarker

4387
03:29:29,168 --> 03:29:30,970
for kidney disease in general.

4388
03:29:30,970 --> 03:29:33,439
It’s even worse
in sickle cell disease.

4389
03:29:33,439 --> 03:29:37,577
So, this is a paper from,
again, Monika and Marvin.

4390
03:29:38,478 --> 03:29:41,247
And they kind of looked at,
you know, crack --

4391
03:29:41,247 --> 03:29:45,318
the different -- the performance
of different equations,

4392
03:29:45,952 --> 03:29:52,792
including the CKD-EPI equation
compared to measured GFR.

4393
03:29:52,792 --> 03:29:55,028
And, you know, the bias of --

4394
03:29:55,028 --> 03:29:58,097
especially even the new
CKD-EPI equation

4395
03:29:58,097 --> 03:30:01,034
was pretty, pretty bad,
about 45 mils per minute.

4396
03:30:03,269 --> 03:30:05,505
So, recently,
in the nephrology world,

4397
03:30:05,505 --> 03:30:11,244
there is a move towards
removing race from equations.

4398
03:30:11,244 --> 03:30:13,746
So, you’ll notice
that most times in --

4399
03:30:13,746 --> 03:30:17,216
or in the newer reporting
of GFR,

4400
03:30:17,216 --> 03:30:18,985
there’s race out
of the equation.

4401
03:30:18,985 --> 03:30:22,388
And this is some newer
CKD cystatin-based

4402
03:30:22,388 --> 03:30:23,590
race-free equation.

4403
03:30:23,590 --> 03:30:27,493
This is the European version,
which is the EKC version.

4404
03:30:27,493 --> 03:30:29,295
And, you know,
I always use MDCalc.

4405
03:30:29,295 --> 03:30:31,931
So, you’ll see like
four different equations.

4406
03:30:31,931 --> 03:30:35,001
But newer recommendation
is to exclude race.

4407
03:30:35,702 --> 03:30:39,339
And we actually, myself
and Monika,

4408
03:30:39,339 --> 03:30:43,009
are actually looking at
how these newer equations

4409
03:30:43,009 --> 03:30:45,178
compare to measured GFR.

4410
03:30:45,178 --> 03:30:49,616
And, you know, what we found
was that the European equation,

4411
03:30:50,283 --> 03:30:53,686
EKFC equation, the P30,
which just means, you know,

4412
03:30:54,721 --> 03:31:02,996
how the percentage of persons
with the GFR,

4413
03:31:03,896 --> 03:31:07,133
how close they are to 30 percent
of the measured GFR

4414
03:31:08,001 --> 03:31:10,903
or within 30 percent
of the measured GFR.

4415
03:31:10,903 --> 03:31:14,607
I mean, all these equations
perform pretty poorly

4416
03:31:14,607 --> 03:31:16,576
when you compare them to,

4417
03:31:17,243 --> 03:31:19,212
you know, people
without sickle cell disease.

4418
03:31:19,212 --> 03:31:21,214
So, you know,
the best correlation

4419
03:31:21,214 --> 03:31:24,017
was actually the EKFC equation,

4420
03:31:24,017 --> 03:31:25,485
which is a European-based
equation

4421
03:31:25,485 --> 03:31:29,522
that was about 67 percent
versus the new equation,

4422
03:31:29,522 --> 03:31:31,791
which is CKD-EPI, 57 percent.

4423
03:31:32,425 --> 03:31:34,460
The cystatin C-based equation

4424
03:31:34,460 --> 03:31:36,763
actually did not perform
as well,

4425
03:31:36,763 --> 03:31:40,066
50 percent compared
to the old equation,

4426
03:31:40,066 --> 03:31:43,302
which was like 27 percent
when you included Black race.

4427
03:31:44,504 --> 03:31:47,640
So well, we’re working
on that publication.

4428
03:31:47,640 --> 03:31:49,108
I promise, Monika [laughs].

4429
03:31:49,108 --> 03:31:50,476
All right.

4430
03:31:50,476 --> 03:31:55,314
So, you know, hydroxyurea has
been talked about many times.

4431
03:31:55,314 --> 03:31:57,950
And I’m running out of time,
so I’m so sorry.

4432
03:31:57,950 --> 03:31:59,352
You know, it does reduce --

4433
03:31:59,352 --> 03:32:01,487
may reduce hyperfiltration
in children.

4434
03:32:02,088 --> 03:32:05,625
This is information from the --
data from the BABY HUG trial.

4435
03:32:06,526 --> 03:32:08,394
And so, you know,
they found indirect markers

4436
03:32:08,394 --> 03:32:10,096
of improved kidney function.

4437
03:32:10,096 --> 03:32:13,066
And certainly,
this is a study from out --

4438
03:32:13,633 --> 03:32:16,135
from a UC [sic]
Chapel Hill, I think.

4439
03:32:16,936 --> 03:32:21,107
You know, they looked at
patients with hydroxyurea use.

4440
03:32:21,107 --> 03:32:22,308
And they found that, you know,

4441
03:32:22,308 --> 03:32:24,911
alminuria certainly,
in their cohort,

4442
03:32:25,545 --> 03:32:29,849
was less in patients with --
treated with hydroxyurea.

4443
03:32:29,849 --> 03:32:35,655
And they were less likely
to develop alminuria.

4444
03:32:35,655 --> 03:32:37,490
And I think,
based on the conversation I had

4445
03:32:37,490 --> 03:32:40,560
with someone in Brazil,
yeah, it seems kind of [laughs]

4446
03:32:40,560 --> 03:32:43,796
-- yeah, it seems to be kind of
congruent with their findings.

4447
03:32:43,796 --> 03:32:45,031
The case for the ACE.

4448
03:32:45,031 --> 03:32:47,166
As a nephrologist,
I love ACE inhibitors.

4449
03:32:47,166 --> 03:32:51,070
They’re the best. And certainly,
captopril, enalapril,

4450
03:32:51,838 --> 03:32:53,406
and other agents
have been used

4451
03:32:53,406 --> 03:32:56,776
with variable kind of,
you know, benefits in alminuria.

4452
03:32:56,776 --> 03:32:59,846
There’s not a lot of data on,
like, long-term effects, like,

4453
03:32:59,846 --> 03:33:01,247
long-term use
over time

4454
03:33:01,247 --> 03:33:04,717
and reduction of progression
of kidney disease,

4455
03:33:05,485 --> 03:33:08,287
but certainly a very good data
in sickle --

4456
03:33:08,287 --> 03:33:10,757
in non-sickle
cell disease, right?

4457
03:33:11,557 --> 03:33:12,825
Other agents to consider

4458
03:33:12,825 --> 03:33:17,663
because nephrology in the last
five years has kind of advanced.

4459
03:33:17,663 --> 03:33:22,668
It’s really exciting to be
a nephrologist now and [laughs]

4460
03:33:22,668 --> 03:33:25,037
-- because we have no --
other than ACE inhibitors,

4461
03:33:25,037 --> 03:33:27,406
we have really new agents,
right?

4462
03:33:27,406 --> 03:33:30,743
So, this is a trial looking
at -- again,

4463
03:33:30,743 --> 03:33:33,813
from Santosh Saraf
and his colleagues --

4464
03:33:33,813 --> 03:33:37,383
looking at preliminary data
for voxelotor,

4465
03:33:37,383 --> 03:33:39,852
and they actually found
a reduction in alminuria.

4466
03:33:39,852 --> 03:33:42,688
But more recently,
we have newer agents,

4467
03:33:42,688 --> 03:33:45,658
so GLP-1 agonists.
For the non-informed,

4468
03:33:45,658 --> 03:33:49,462
these are agents that we use
in diabetic kidney disease

4469
03:33:49,462 --> 03:33:55,101
and now actually expanded across
many different applications.

4470
03:33:55,101 --> 03:33:59,105
SGLT-2 inhibitors, which is
sodium glucose transport

4471
03:33:59,739 --> 03:34:01,340
receptor inhibitors.

4472
03:34:03,009 --> 03:34:05,578
They have been used in
proteinuric kidney disease.

4473
03:34:06,479 --> 03:34:10,049
And, you know, the -- this
essentially reduce alminuria

4474
03:34:10,049 --> 03:34:12,518
by essentially
reducing hyperfiltration.

4475
03:34:12,518 --> 03:34:13,853
It makes me wonder that,
you know,

4476
03:34:13,853 --> 03:34:16,222
this may potentially
be beneficial,

4477
03:34:16,222 --> 03:34:19,325
given the pathogenesis
of sickle cell disease.

4478
03:34:19,325 --> 03:34:20,693
The only problem
with these agents,

4479
03:34:20,693 --> 03:34:22,028
potentially, is because --

4480
03:34:22,028 --> 03:34:25,731
especially with the GLP-1
agonist and SGLT-2 inhibitors,

4481
03:34:25,731 --> 03:34:29,101
is that they tend
to cause dehydration.

4482
03:34:29,101 --> 03:34:31,838
SGLT-2 is because
they cause osmotic diuresis,

4483
03:34:32,572 --> 03:34:34,507
and GLP-1 agonist as well.

4484
03:34:34,507 --> 03:34:37,009
So, you know, the concern
clearly would be kind of

4485
03:34:37,009 --> 03:34:38,244
increased

4486
03:34:38,244 --> 03:34:41,681
sickling and vaso-occlusive
crises in these patients.

4487
03:34:41,681 --> 03:34:46,252
So, this was an experience by,
again, Santosh Saraf

4488
03:34:46,252 --> 03:34:47,987
and his group in Illinois.

4489
03:34:47,987 --> 03:34:50,389
And they actually
looked at off-label

4490
03:34:50,389 --> 03:34:52,325
use of SGLT-2 inhibitors.

4491
03:34:52,859 --> 03:34:54,727
They actually had a reduction
in proteinuria,

4492
03:34:54,727 --> 03:34:57,763
but they had more, you know,

4493
03:34:57,763 --> 03:35:01,234
slightly increased rates
of vaso-occlusive events.

4494
03:35:01,234 --> 03:35:04,804
So, I mean, I think
that would be a caveat to that,

4495
03:35:04,804 --> 03:35:06,539
but certainly,
you know, promising.

4496
03:35:08,374 --> 03:35:11,377
End-stage renal disease,
in terms of data on that,

4497
03:35:12,078 --> 03:35:13,446
so, certainly most of the data

4498
03:35:13,446 --> 03:35:16,048
is from the U.S.
with their NHANES

4499
03:35:16,048 --> 03:35:17,583
[phonetic sp].
About 0.1 of --

4500
03:35:17,583 --> 03:35:22,855
percent of the U.S.-based
end-stage renal population

4501
03:35:23,556 --> 03:35:26,025
has sickle-related
kidney disease.

4502
03:35:27,193 --> 03:35:29,462
Mean age was younger,
so 40 years.

4503
03:35:30,396 --> 03:35:32,999
SCN patients were less
likely to relive --

4504
03:35:32,999 --> 03:35:35,001
receive renal transplantation.

4505
03:35:35,001 --> 03:35:37,737
And, you know, renal transplants
have occurred in patients

4506
03:35:37,737 --> 03:35:39,005
with sickle cell disease.

4507
03:35:39,005 --> 03:35:42,408
There’s a nice series
of about 105 patients

4508
03:35:43,009 --> 03:35:45,177
who underwent renal
transplantation.

4509
03:35:45,177 --> 03:35:46,479
They’re less likely --

4510
03:35:46,479 --> 03:35:49,115
their graph of survival
was less, though,

4511
03:35:49,115 --> 03:35:51,884
compared to
the general population.

4512
03:35:52,785 --> 03:35:56,455
And, you know,
as we kind of understand

4513
03:35:57,023 --> 03:36:00,026
sickle cell nephropathy,
patients were independently

4514
03:36:00,026 --> 03:36:02,061
associated with
risk of mortality.

4515
03:36:03,396 --> 03:36:06,299
I kind of shared this slide
because I think it was pretty.

4516
03:36:06,299 --> 03:36:08,334
But I thought it was
really cool [laughs].

4517
03:36:08,334 --> 03:36:10,970
So, essentially, you know,
we should be screening patients.

4518
03:36:10,970 --> 03:36:13,105
And I think the sickle cell unit
has been really good

4519
03:36:13,105 --> 03:36:16,575
at sending the patients
to us in nephrology with --

4520
03:36:16,575 --> 03:36:19,679
especially if it was patients
with persistent alminuria.

4521
03:36:19,679 --> 03:36:22,949
And we should be
screening pretty early.

4522
03:36:22,949 --> 03:36:24,317
We should be
repeating the test.

4523
03:36:24,317 --> 03:36:27,053
Because, you know, the problem
with alminuria

4524
03:36:27,053 --> 03:36:28,587
is that many other things
can cause it.

4525
03:36:28,587 --> 03:36:30,489
If you have a urinary
tract infection,

4526
03:36:31,090 --> 03:36:34,093
if you just finished exercising,
if you’re acutely ill,

4527
03:36:34,927 --> 03:36:36,162
these things can increase.

4528
03:36:36,162 --> 03:36:38,264
So, what’s really important is,
you know,

4529
03:36:38,264 --> 03:36:39,966
serial checking
and persistence.

4530
03:36:40,800 --> 03:36:43,869
And certainly, please send
the patients to us.

4531
03:36:43,869 --> 03:36:47,340
I’m really happy managing
our sickle cell patients.

4532
03:36:47,340 --> 03:36:49,875
And I think, you know,
we continue to do --

4533
03:36:49,875 --> 03:36:51,177
continue checking.

4534
03:36:51,177 --> 03:36:54,847
I mean, early ACE initiation,
hydroxyurea,

4535
03:36:54,847 --> 03:36:56,349
and a better argument,
I should say,

4536
03:36:56,349 --> 03:36:58,818
for hydroxyurea
initiation as well.

4537
03:36:59,719 --> 03:37:02,621
I’m going to shift gears,
and I’m going to talk about AKI.

4538
03:37:03,456 --> 03:37:07,026
You know, so as an --
as another hat,

4539
03:37:07,026 --> 03:37:08,627
which is my intensivist hat,

4540
03:37:08,627 --> 03:37:10,563
you know, we see AKI
a lot in ICU,

4541
03:37:10,563 --> 03:37:12,365
and it’s associated
with mortality.

4542
03:37:13,032 --> 03:37:15,134
This is a series from Batte --

4543
03:37:16,035 --> 03:37:19,305
Anthony Batte
and his colleagues in Uganda.

4544
03:37:19,972 --> 03:37:23,175
They found a high rate of AKI,
70 percent of children

4545
03:37:23,175 --> 03:37:24,777
and 10 percent of adults.

4546
03:37:25,611 --> 03:37:32,284
You know, of 185 people admitted
with acute SCD pain, 60 --

4547
03:37:32,284 --> 03:37:34,053
33 percent of them,
a third of them,

4548
03:37:34,053 --> 03:37:35,988
actually had AKI on admission,

4549
03:37:35,988 --> 03:37:39,492
and another quarter
developed AKI.

4550
03:37:40,126 --> 03:37:43,195
And we kind of did
a preliminary analysis.

4551
03:37:43,763 --> 03:37:46,766
Myself and Monika presented
this last year at ASN.

4552
03:37:46,766 --> 03:37:49,101
And what we found was,
amongst our patients,

4553
03:37:49,101 --> 03:37:52,738
144 persons admitted to --
with sickle cell disease.

4554
03:37:53,539 --> 03:37:56,509
Mean age was 27, 62 female.

4555
03:37:56,509 --> 03:38:00,279
Most of them were HbSS disease.
A third were children.

4556
03:38:00,279 --> 03:38:04,050
We had a rate of about
20 percent had AKI.

4557
03:38:04,050 --> 03:38:05,918
Most of them were KDIGO stage 1.

4558
03:38:07,153 --> 03:38:09,789
And what we found is,
like, you know,

4559
03:38:10,589 --> 03:38:14,326
blood transfusion
was associated with AKI

4560
03:38:14,326 --> 03:38:16,095
in adults,
but not children.

4561
03:38:16,095 --> 03:38:17,763
Bearing in mind,
our numbers are small.

4562
03:38:17,763 --> 03:38:19,799
We actually are going to present
the --

4563
03:38:19,799 --> 03:38:23,369
we’re actually in the process
of analyzing the larger sample,

4564
03:38:23,369 --> 03:38:25,471
which is about 500 participants.

4565
03:38:25,471 --> 03:38:29,175
And those results
are not quite ready yet.

4566
03:38:29,175 --> 03:38:33,045
Yes, I’m sorry,
Monika [laughs].

4567
03:38:33,045 --> 03:38:35,581
So, again, this is something

4568
03:38:35,581 --> 03:38:37,783
we kind of looked at
in our patients.

4569
03:38:37,783 --> 03:38:40,119
We looked at a case series
of our people

4570
03:38:40,119 --> 03:38:44,056
that were admitted with AKI
in COVID in sickle cell disease.

4571
03:38:44,890 --> 03:38:46,859
Especially with COVID
admissions,

4572
03:38:46,859 --> 03:38:48,994
over half of them had AKI,

4573
03:38:48,994 --> 03:38:50,696
was associated with
a long length of stay.

4574
03:38:50,696 --> 03:38:53,566
It was important as most of our
patients were not vaccinated,

4575
03:38:53,566 --> 03:38:56,135
and most of them
were not in hydroxyurea.

4576
03:38:56,135 --> 03:38:59,338
But certainly, blood transfusion
again was associated,

4577
03:38:59,338 --> 03:39:01,073
as well as a decrease
from baseline

4578
03:39:01,073 --> 03:39:06,112
was associated with AKI.
And I think that’s it.

4579
03:39:06,112 --> 03:39:10,282
And CKD frequently complicates
or occurs

4580
03:39:10,282 --> 03:39:11,984
in sickle cell disease,
as we all knew.

4581
03:39:11,984 --> 03:39:13,285
Creatinine measures are really,

4582
03:39:13,285 --> 03:39:16,188
really poor biomarkers
for kidney function.

4583
03:39:16,856 --> 03:39:19,125
Consider using
the EKFC equation,

4584
03:39:20,192 --> 03:39:21,894
which is available online,
and, you know,

4585
03:39:21,894 --> 03:39:24,263
it highlights the need for
biomarkers in kidney disease,

4586
03:39:24,263 --> 03:39:25,965
especially in
sickle cell disease.

4587
03:39:27,032 --> 03:39:30,669
We know that hydroxyurea
and ACEs may reduce progression,

4588
03:39:30,669 --> 03:39:32,004
but we may need to look at,

4589
03:39:32,004 --> 03:39:34,974
like,
newer disease-modifying drugs,

4590
03:39:34,974 --> 03:39:37,676
so SGLT-2 inhibitors,
MRAs. More recently,

4591
03:39:37,676 --> 03:39:40,880
endothelial-1
receptor antagonists.

4592
03:39:41,614 --> 03:39:43,716
So that would be kind of
something that’s interesting.

4593
03:39:43,716 --> 03:39:46,218
It’s kind of changed the shape
of IgA nephropathy.

4594
03:39:47,186 --> 03:39:49,855
But I think we need
new therapeutic targets, right?

4595
03:39:49,855 --> 03:39:54,126
So, kind of understanding
the targets and pathways

4596
03:39:54,126 --> 03:39:55,728
in sickle nephropathy.

4597
03:39:56,595 --> 03:39:58,097
AKI occurs frequently,

4598
03:39:58,097 --> 03:40:00,399
and we need to kind of have
longitudinal studies

4599
03:40:00,399 --> 03:40:03,369
looking at the impact of AKI
events in sickle cell disease.

4600
03:40:03,369 --> 03:40:04,603
I’m really proud of myself

4601
03:40:04,603 --> 03:40:05,938
because I finished
in one minute.

4602
03:40:05,938 --> 03:40:07,139
[laughter]

4603
03:40:07,139 --> 03:40:09,074
[applause]
So, thank you, guys.

4604
03:40:15,781 --> 03:40:19,285
Angela Rankine-Mullings:
Thank you so much, Dr. Fisher.

4605
03:40:19,285 --> 03:40:26,125
And we’ll just take
two questions [laughs].

4606
03:40:26,125 --> 03:40:28,661
Male Speaker: Yeah. Great talk.

4607
03:40:28,661 --> 03:40:31,330
I -- I’m just curious
if you thought about the risk

4608
03:40:31,330 --> 03:40:34,099
of renal medulla carcinoma
in sickle cell patients

4609
03:40:34,099 --> 03:40:35,868
and how we might be able
to modulate that,

4610
03:40:35,868 --> 03:40:37,102
maybe with some
of these therapies

4611
03:40:37,102 --> 03:40:38,804
or even with
fetal hemoglobin induction.

4612
03:40:38,804 --> 03:40:40,005
Lori-Ann Fisher: Right.

4613
03:40:40,005 --> 03:40:41,941
So, yeah,
that’s a really good question.

4614
03:40:41,941 --> 03:40:44,843
And I haven’t found --
I actually looked around,

4615
03:40:45,411 --> 03:40:47,146
looking at data on this,

4616
03:40:48,147 --> 03:40:49,448
and I haven’t really
found anything.

4617
03:40:49,448 --> 03:40:51,317
And I suspect,
because, you know,

4618
03:40:51,850 --> 03:40:55,120
the renal medulla carcinoma is,

4619
03:40:55,120 --> 03:40:56,388
you know, repeated
ischemic events.

4620
03:40:56,388 --> 03:40:58,691
If we can reduce
that progression,

4621
03:40:59,225 --> 03:41:03,229
even understanding more of
the endothelial dysfunction

4622
03:41:03,229 --> 03:41:05,631
that happens
in sickle nephropathy,

4623
03:41:05,631 --> 03:41:07,099
we could actually
modulate that.

4624
03:41:07,099 --> 03:41:10,903
And I think that’s a course
for future studies on this.

4625
03:41:12,204 --> 03:41:13,906
Manu Platt: Yes.
Hi. This was really cool.

4626
03:41:13,906 --> 03:41:16,208
Lot to learn, and I’m interested
in the sickle nephropathy

4627
03:41:16,208 --> 03:41:17,509
and endothelial part.

4628
03:41:17,509 --> 03:41:19,078
But one thing we’ve been
talking about here

4629
03:41:19,078 --> 03:41:21,747
are large arteries and
their issues with sickle cell.

4630
03:41:21,747 --> 03:41:24,250
What do you all see
with the renal artery

4631
03:41:24,250 --> 03:41:25,684
with sickle cell complications?

4632
03:41:25,684 --> 03:41:27,186
Lori-Ann Fisher: Oh,
that’s a really good question.

4633
03:41:27,186 --> 03:41:28,988
And I think the problem
with that

4634
03:41:28,988 --> 03:41:31,023
is that we don’t
look at it, right?

4635
03:41:31,023 --> 03:41:35,561
So, I mean -- and if -- we talk
about non-sickle cell disease,

4636
03:41:35,561 --> 03:41:37,930
even diabetic kidney disease,
it’s microvascular,

4637
03:41:37,930 --> 03:41:40,266
but also macrovascular
changes which happen.

4638
03:41:40,866 --> 03:41:44,670
And that’s pretty well
documented.

4639
03:41:44,670 --> 03:41:46,839
But we don’t really look
very well at,

4640
03:41:46,839 --> 03:41:48,574
like, what happens
with large blood vessels.

4641
03:41:48,574 --> 03:41:51,443
We know that, you know,
there may not be much --

4642
03:41:51,443 --> 03:41:53,545
there’s not much
accelerated atherosclerosis

4643
03:41:53,545 --> 03:41:56,949
or any changes in blood flow,
but certainly, you know, again,

4644
03:41:56,949 --> 03:41:59,084
another area
to kind of look into

4645
03:41:59,084 --> 03:42:00,552
because we haven’t really --

4646
03:42:00,552 --> 03:42:02,821
we seem to -- as nephrologists,
we’re alminuric.

4647
03:42:02,821 --> 03:42:04,556
We like alminuria,
but we don’t look

4648
03:42:04,556 --> 03:42:06,659
at other big kinds of things

4649
03:42:06,659 --> 03:42:09,828
that may influence
renal function.

4650
03:42:11,230 --> 03:42:12,998
Angela Rankine-Mullings: Thank
you so much.

4651
03:42:12,998 --> 03:42:18,570
[applause]

4652
03:42:18,570 --> 03:42:23,976
Our fourth and final speaker
is Dr. France Pirenne,

4653
03:42:24,510 --> 03:42:29,181
and she is from the University
Paris Est Créteil, France.

4654
03:42:30,049 --> 03:42:33,285
She will speak to us
on alloimmunization

4655
03:42:33,285 --> 03:42:34,987
and hyperhemolysis.
Help me welcome her.

4656
03:42:34,987 --> 03:42:36,188
[applause]

4657
03:42:36,188 --> 03:42:39,992
France Pirenne: It’s this one?
This? Okay.

4658
03:42:39,992 --> 03:42:41,493
Angela Rankine-Mullings: Yeah.

4659
03:42:41,493 --> 03:42:42,728
France Pirenne: Okay.

4660
03:42:42,728 --> 03:42:44,496
Thank you very much
for the invitation

4661
03:42:44,496 --> 03:42:46,899
to speak about this topic.

4662
03:42:46,899 --> 03:42:51,136
And back to the French accent
also [laughs]. Does it work?

4663
03:42:51,136 --> 03:42:52,338
Angela Rankine-Mullings:
[affirmative]

4664
03:42:52,338 --> 03:42:55,007
The other one. The other.

4665
03:42:55,007 --> 03:42:59,078
France Pirenne: Oh. Okay.

4666
03:43:00,212 --> 03:43:05,117
So, the first description of all
the characteristic features

4667
03:43:05,117 --> 03:43:07,920
of the sickle hemolytic
transfusion reaction syndrome

4668
03:43:08,587 --> 03:43:13,258
dates from 1997.
And after the transfusion,

4669
03:43:13,258 --> 03:43:16,462
the patient developed
vaso-occlusive symptoms

4670
03:43:16,462 --> 03:43:17,930
with reticulopenia

4671
03:43:17,930 --> 03:43:20,299
generally that worsen
the hemolytic anemia.

4672
03:43:21,166 --> 03:43:24,203
And generally,
in the very severe case,

4673
03:43:24,970 --> 03:43:28,307
there is the post-transfusion
hemoglobin is below.

4674
03:43:28,307 --> 03:43:29,908
It doesn’t work.

4675
03:43:30,709 --> 03:43:32,678
Angela Rankine-Mullings: No.
It needs to be --

4676
03:43:32,678 --> 03:43:36,348
France Pirenne: Oh, okay.
I have to [laughs]

4677
03:43:36,348 --> 03:43:39,118
-- so the post-transfusion
hemoglobin

4678
03:43:39,118 --> 03:43:42,354
is below the pre-transfusion
hemoglobin level.

4679
03:43:42,354 --> 03:43:45,657
It is the reason why we speak
about hyperhemolysis.

4680
03:43:45,657 --> 03:43:48,227
So, the hyperhemolysis,
the most severe form,

4681
03:43:48,761 --> 03:43:51,630
is because of the hemolysis
of donor red blood cell,

4682
03:43:51,630 --> 03:43:55,768
but also with the destruction of
the own patient red blood cell.

4683
03:43:55,768 --> 03:43:58,036
It’s called
bystander hemolysis,

4684
03:43:58,036 --> 03:44:01,373
and it is increased
the low level of hemoglobin

4685
03:44:01,373 --> 03:44:03,409
by this reticulopenia.

4686
03:44:04,076 --> 03:44:08,213
So, a new transfusion
can exacerbate the hemolysis.

4687
03:44:08,213 --> 03:44:11,717
And when we performed
the immunohematological workup,

4688
03:44:11,717 --> 03:44:15,254
we found alloantibodies
but also autoantibodies.

4689
03:44:15,254 --> 03:44:18,424
And in many case,
which is quite enigmatic,

4690
03:44:18,424 --> 03:44:20,559
no detectable antibodies.

4691
03:44:20,559 --> 03:44:23,762
There could be a gradual
improvement in some cases,

4692
03:44:23,762 --> 03:44:27,433
and it’s frequently
recurrent situation.

4693
03:44:27,433 --> 03:44:30,636
So, the known risk factor
for this syndrome

4694
03:44:30,636 --> 03:44:33,772
has been described
in many theory,

4695
03:44:33,772 --> 03:44:36,442
case theory of this accident.

4696
03:44:36,442 --> 03:44:40,245
Generally, the main risk
is when patients are transfused

4697
03:44:40,245 --> 03:44:41,513
for an acute or punctual

4698
03:44:41,513 --> 03:44:42,714
[phonetic sp]

4699
03:44:42,714 --> 03:44:47,653
indication and when they have
history of alloimmunization,

4700
03:44:47,653 --> 03:44:50,489
history of this
transfusion reaction,

4701
03:44:50,489 --> 03:44:52,825
and generally,
they have been transfused

4702
03:44:52,825 --> 03:44:56,762
with a few units in the past.
And infection has been shown

4703
03:44:56,762 --> 03:44:59,598
also as an independent
risk factor

4704
03:44:59,598 --> 03:45:02,568
for the post-transfusion
hemolysis.

4705
03:45:03,936 --> 03:45:07,206
So, classically, the trigger
of a post-transfusion hemolysis

4706
03:45:07,206 --> 03:45:08,574
is the --

4707
03:45:08,574 --> 03:45:12,711
is because of the reappearance
of evanescent antibodies

4708
03:45:12,711 --> 03:45:15,948
through reexposition of --
to the antigen.

4709
03:45:15,948 --> 03:45:20,986
So, alloimmunization has really
a pivotal role in this reaction.

4710
03:45:20,986 --> 03:45:23,155
And the rate of alloimmunization

4711
03:45:23,155 --> 03:45:26,959
is particularly high
in sickle cell transfusion,

4712
03:45:26,959 --> 03:45:30,429
first, because
of difference between --

4713
03:45:30,429 --> 03:45:33,332
in blood group between
donors and the patient.

4714
03:45:33,332 --> 03:45:37,035
And when donor --
when patient --

4715
03:45:37,936 --> 03:45:41,640
Black patients are in
a European environment,

4716
03:45:41,640 --> 03:45:44,142
there is different
for the common antigen.

4717
03:45:44,142 --> 03:45:47,145
Frequently, the patient
is negative for big C,

4718
03:45:47,145 --> 03:45:48,780
big E, JKB, Duffy,

4719
03:45:48,780 --> 03:45:51,717
which we spoke
about Duffy before, big S.

4720
03:45:52,584 --> 03:45:54,686
There is also a characteristic.

4721
03:45:54,686 --> 03:45:57,623
There is many what
we call partial antigen

4722
03:45:57,623 --> 03:46:02,060
in the RH blood groups.
So, a partial antigen is defined

4723
03:46:02,060 --> 03:46:05,631
by the absence
of some immunogenic epitopes.

4724
03:46:05,631 --> 03:46:10,202
Then when the patient is exposed
to the complete molecule,

4725
03:46:10,202 --> 03:46:15,073
it can produce antibody against
the missing immunogenic epitope,

4726
03:46:15,073 --> 03:46:19,378
which explains the production
of anti-RH in patient

4727
03:46:19,378 --> 03:46:23,181
despite RH matching
and in various high incidence,

4728
03:46:23,181 --> 03:46:27,185
for example, of partial D
in Black individuals.

4729
03:46:27,986 --> 03:46:30,222
There are also specific rare
blood types.

4730
03:46:30,222 --> 03:46:32,157
A rare blood type is defined

4731
03:46:32,157 --> 03:46:36,295
by the absence of expression
of high-frequency antigen.

4732
03:46:36,295 --> 03:46:39,398
When a patient carry
a rare blood type,

4733
03:46:39,398 --> 03:46:41,833
when you receive common blood,

4734
03:46:41,833 --> 03:46:44,436
it’s always exposed
to the high-frequency antigen

4735
03:46:44,436 --> 03:46:47,873
and can produce the antibodies
leading, generally,

4736
03:46:47,873 --> 03:46:49,474
to transfusion deadlock.

4737
03:46:50,208 --> 03:46:51,710
And the last characteristic

4738
03:46:51,710 --> 03:46:54,446
is what we call
low-frequency antigen.

4739
03:46:54,446 --> 03:46:57,416
So, individuals
of African ancestry

4740
03:46:57,416 --> 03:47:00,886
have many low-frequency
antigen expressed.

4741
03:47:00,886 --> 03:47:02,921
We say low frequency
in reference

4742
03:47:02,921 --> 03:47:05,524
to the European population.

4743
03:47:05,524 --> 03:47:08,460
But it’s not low
in this population.

4744
03:47:08,460 --> 03:47:11,229
Then the mismatch
can be very frequent

4745
03:47:11,229 --> 03:47:15,334
if a patient receives blood
from a Black donor.

4746
03:47:15,334 --> 03:47:18,937
But the problem is that the
antibodies are not detectable

4747
03:47:18,937 --> 03:47:22,808
at the common screening test
because the red blood cells

4748
03:47:22,808 --> 03:47:26,979
that are used do
not express this antigen.

4749
03:47:28,113 --> 03:47:32,284
So, the second very important
factor for a higher rate

4750
03:47:32,284 --> 03:47:34,453
of alloimmunization
is inflammation.

4751
03:47:34,453 --> 03:47:38,023
It has been known for a long
time in the murine model.

4752
03:47:38,023 --> 03:47:42,361
But inflammation is a risk
factor for alloimmunization.

4753
03:47:42,361 --> 03:47:44,796
And it has been shown
also in patients

4754
03:47:44,796 --> 03:47:48,100
that red blood cell
alloimmunization is influenced

4755
03:47:48,100 --> 03:47:50,869
by the recipient
inflammatory status

4756
03:47:50,869 --> 03:47:54,806
at time of transfusion
in sickle cell disease patient.

4757
03:47:56,074 --> 03:47:59,778
So, the rate of alloimmunization
is quite -- it’s very high.

4758
03:47:59,778 --> 03:48:01,980
It’s between --
depending on the study,

4759
03:48:01,980 --> 03:48:04,249
depending on
the transfusion protocol

4760
03:48:04,249 --> 03:48:05,651
in the different country,

4761
03:48:05,651 --> 03:48:08,954
it’s between 30
and 50 percent of patients

4762
03:48:08,954 --> 03:48:10,255
who are allo immunized.

4763
03:48:10,255 --> 03:48:15,694
In our last survey in France,
among 6,000 patients,

4764
03:48:15,694 --> 03:48:18,764
27 percent of the patient
were immunized.

4765
03:48:20,465 --> 03:48:22,234
So, not all patients
get immunized

4766
03:48:22,234 --> 03:48:24,770
when exposed to foreign antigen.

4767
03:48:24,770 --> 03:48:27,906
We can say that there is
a high and low responder,

4768
03:48:27,906 --> 03:48:31,777
and the characterization
of these two population

4769
03:48:31,777 --> 03:48:35,847
would allow to apply preventive
measure to high responder only,

4770
03:48:35,847 --> 03:48:39,317
such as to extend
the matching to RH/K.

4771
03:48:39,317 --> 03:48:42,154
It’s done for everybody,
but also to Duffy,

4772
03:48:42,154 --> 03:48:45,490
JK, and MNS,
the other immunogenic group,

4773
03:48:45,490 --> 03:48:50,629
or also to provide additional
immunosuppressive therapy.

4774
03:48:50,629 --> 03:48:52,597
However, on a routine basis,

4775
03:48:52,597 --> 03:48:55,033
there is no easy way
to differentiate,

4776
03:48:55,033 --> 03:48:59,604
at first opportunity, a patient
who is high or low responder.

4777
03:48:59,604 --> 03:49:02,074
There have been many study

4778
03:49:02,074 --> 03:49:04,743
on the immunogenetic background
of the patient,

4779
03:49:05,510 --> 03:49:07,879
but these results
are very interesting.

4780
03:49:07,879 --> 03:49:09,948
It cannot be used
on a routine basis.

4781
03:49:10,515 --> 03:49:13,118
So, the only way we have
is we know

4782
03:49:13,118 --> 03:49:15,721
that an immunized patient
is a high responder.

4783
03:49:15,721 --> 03:49:18,623
It will produce, again,
more antibodies.

4784
03:49:18,623 --> 03:49:22,327
And also, we could show
on a very big cohort

4785
03:49:22,327 --> 03:49:28,033
that after transfusion of 20
RH/K match red blood cell,

4786
03:49:28,033 --> 03:49:30,235
when the patient
did not get immunized

4787
03:49:30,936 --> 03:49:33,905
against the other
blood group system,

4788
03:49:33,905 --> 03:49:37,242
it could be considered
as low responders

4789
03:49:37,242 --> 03:49:39,911
and not be longer
get immunized.

4790
03:49:40,612 --> 03:49:43,448
So, alloimmunization is
very important

4791
03:49:43,448 --> 03:49:45,317
in the trigger
of this reaction.

4792
03:49:45,317 --> 03:49:48,587
However, antibodies
are not always detectable.

4793
03:49:49,187 --> 03:49:51,556
In many cases,
there is no antibodies.

4794
03:49:52,457 --> 03:49:55,894
And also, very important,
the severity of the reaction

4795
03:49:55,894 --> 03:49:58,764
is not correlated
to the type of antibody

4796
03:49:58,764 --> 03:50:01,066
and also to the presence
of antibodies.

4797
03:50:01,933 --> 03:50:05,437
And it was shown recently
that extended phenotyping

4798
03:50:05,437 --> 03:50:08,106
prevention to Duffy,
JK, and MNS

4799
03:50:08,106 --> 03:50:12,444
does not always prevent
against this reaction.

4800
03:50:13,712 --> 03:50:17,315
So, there is [laughs]
an enigmatic mechanism.

4801
03:50:17,315 --> 03:50:19,217
We try to speak
about it a little.

4802
03:50:20,018 --> 03:50:23,755
So, no antibody --
or no detectable antibody

4803
03:50:23,755 --> 03:50:25,590
or no antibody at all.

4804
03:50:26,124 --> 03:50:29,027
It’s likely also that sometimes
it’s not the good time

4805
03:50:29,027 --> 03:50:31,863
for the screening test,
so it has to be repeated.

4806
03:50:32,564 --> 03:50:35,467
Also, it could be that
there is a low concentration

4807
03:50:35,467 --> 03:50:38,904
under the screening test
sensitivity.

4808
03:50:38,904 --> 03:50:41,239
Flow cytometry could be used,
for example,

4809
03:50:41,239 --> 03:50:42,674
to find the antibodies.

4810
03:50:42,674 --> 03:50:45,177
And also, these --
there are these antibodies

4811
03:50:45,177 --> 03:50:48,713
I spoke about against
low-frequency antigen.

4812
03:50:49,514 --> 03:50:52,417
And for that, we have to use
a specific red blood cell

4813
03:50:52,417 --> 03:50:56,388
carrying the antigen
to find these antibodies.

4814
03:50:56,388 --> 03:50:58,890
However, many cases
are described

4815
03:50:58,890 --> 03:51:02,761
with no antibody at all,
despite repeated workup.

4816
03:51:03,361 --> 03:51:08,967
And we know that rituximab
do not prevent always DHTR

4817
03:51:08,967 --> 03:51:10,202
in sickle cell disease.

4818
03:51:10,202 --> 03:51:13,972
So, it can be also
an indirect proof of situation

4819
03:51:13,972 --> 03:51:16,208
with no antibodies.
Then what is it?

4820
03:51:17,475 --> 03:51:21,513
So, it is likely that DHTR
with hyperhemolysis,

4821
03:51:21,513 --> 03:51:30,222
the most severe case of DHTR,
is like a two-hit phenomenon.

4822
03:51:30,222 --> 03:51:32,791
You know, the first hit
is the background of the patient

4823
03:51:32,791 --> 03:51:38,296
that we are going to
speak about with heme,

4824
03:51:38,296 --> 03:51:41,266
activation of complement,
and many other things.

4825
03:51:41,266 --> 03:51:43,535
And the second hit
is the antibodies,

4826
03:51:43,535 --> 03:51:45,437
even at very low concentration,

4827
03:51:45,971 --> 03:51:50,041
or maybe something else
that we do not know about yet.

4828
03:51:51,076 --> 03:51:54,346
So, it’s important to understand
the destruction pathway

4829
03:51:54,346 --> 03:51:58,316
and the potentiating mechanism
to get preventive measure.

4830
03:51:58,316 --> 03:52:00,085
But we don’t know --

4831
03:52:00,085 --> 03:52:03,455
there is many gap in our
understanding, unfortunately.

4832
03:52:03,989 --> 03:52:07,859
So, the more --
the most important way

4833
03:52:07,859 --> 03:52:12,264
to destroy the red blood cell
is the complement.

4834
03:52:12,264 --> 03:52:15,166
The complement has
a very important role.

4835
03:52:15,166 --> 03:52:19,070
And when red blood cells are
sensitized with the antibody,

4836
03:52:19,070 --> 03:52:23,408
it is the classical pathway,
classically, that is activated.

4837
03:52:23,408 --> 03:52:26,878
And there is two ways for the
destruction of red blood cells.

4838
03:52:27,545 --> 03:52:32,083
First, if complement
is activated to C3b,

4839
03:52:33,118 --> 03:52:37,088
the complex are destroyed
by the macrophages

4840
03:52:37,088 --> 03:52:38,857
in the spleen or in the liver.

4841
03:52:39,524 --> 03:52:42,727
And the second way is
when complement is activated

4842
03:52:42,727 --> 03:52:44,396
to the membrane
attack complex,

4843
03:52:44,396 --> 03:52:46,698
it is an intravascular
hemolysis,

4844
03:52:46,698 --> 03:52:50,101
and it’s generally
the more severe situation.

4845
03:52:50,769 --> 03:52:56,074
So, in sickle cell disease,
there is a heme circulating.

4846
03:52:56,074 --> 03:53:00,879
And it has been shown that heme
can recruit the macrophage

4847
03:53:00,879 --> 03:53:03,782
in the spleen,
hence seeing the possibility

4848
03:53:03,782 --> 03:53:07,118
for the sensitized red
blood cell to be destroyed.

4849
03:53:07,118 --> 03:53:09,821
So, it is one
potentiating mechanism.

4850
03:53:09,821 --> 03:53:11,957
And it has been shown
also that

4851
03:53:12,557 --> 03:53:15,327
the circulating
free hemoglobin S

4852
03:53:15,327 --> 03:53:17,896
can induce monocyte activation.

4853
03:53:19,197 --> 03:53:20,865
So, in everybody,

4854
03:53:20,865 --> 03:53:23,735
and also in patients
with sickle cell disease,

4855
03:53:23,735 --> 03:53:26,705
there is a permanent
low-level activation

4856
03:53:26,705 --> 03:53:32,377
of the alternate pathway.
And it has been shown that heme,

4857
03:53:32,377 --> 03:53:36,014
which is produced by
the intravascular hemolysis,

4858
03:53:36,014 --> 03:53:39,617
can interfere with complement
factor, the protector --

4859
03:53:39,617 --> 03:53:43,121
the protecting factor
against the activation,

4860
03:53:43,121 --> 03:53:46,224
and also intravascular
hemolysis

4861
03:53:46,224 --> 03:53:49,527
activates complement
via cell-free heme

4862
03:53:49,527 --> 03:53:52,897
and also heme-loaded
microvesicles.

4863
03:53:52,897 --> 03:53:57,802
So, there is an increase
in potentiating of this pathway.

4864
03:53:57,802 --> 03:54:01,773
It’s likely that there
is increased potentiation

4865
03:54:01,773 --> 03:54:03,508
of this pathway in --

4866
03:54:03,508 --> 03:54:06,778
of intravascular hemolysis
in sickle cell patients.

4867
03:54:06,778 --> 03:54:12,317
So, this is just a case,
a reporting case,

4868
03:54:12,317 --> 03:54:15,987
showing that an increase
of a BB molecule with sign --

4869
03:54:15,987 --> 03:54:20,358
which signed the activation
of the alternate pathway

4870
03:54:20,358 --> 03:54:23,128
in a case of hyperhemolysis.

4871
03:54:24,396 --> 03:54:28,366
So, it could be that there are
other potentiating mechanisms.

4872
03:54:28,900 --> 03:54:31,269
For example, it’s like --

4873
03:54:32,337 --> 03:54:35,507
we studied the effect
of the sickle cell plasma

4874
03:54:35,507 --> 03:54:37,876
on stored red blood cells,
those red blood cells

4875
03:54:37,876 --> 03:54:40,178
that we are going to
transfuse to the patient.

4876
03:54:41,346 --> 03:54:45,950
And we show that when we take
plasma from acute patient,

4877
03:54:45,950 --> 03:54:47,952
this plasma induce increased

4878
03:54:47,952 --> 03:54:50,655
senescence of stored
red blood cell

4879
03:54:50,655 --> 03:54:55,126
as compared to steady-state
patient and also to control.

4880
03:54:55,126 --> 03:54:58,430
So, we want to know
which component

4881
03:54:58,430 --> 03:55:02,634
of the plasma of acute patient
is responsible for this effect.

4882
03:55:02,634 --> 03:55:04,369
In fact, we do not know yet
[laughs].

4883
03:55:04,369 --> 03:55:06,137
Maybe heme.
What else?

4884
03:55:06,137 --> 03:55:09,140
So, we wanted to know
if there was something related

4885
03:55:09,140 --> 03:55:12,277
to the proinflammatory disease.

4886
03:55:12,277 --> 03:55:14,913
So, we do --
we did the same experiment

4887
03:55:14,913 --> 03:55:17,215
with the plasma
of sepsis patient

4888
03:55:17,215 --> 03:55:19,384
with non-sickle cell disease.

4889
03:55:19,384 --> 03:55:22,821
And in that experiment,
we did not see the same thing.

4890
03:55:22,821 --> 03:55:26,157
Only the age of red blood cells
was correlated

4891
03:55:26,157 --> 03:55:28,760
with an accelerated
senescence in vitro.

4892
03:55:29,694 --> 03:55:34,833
So, it’s not something linked
to the inflammatory situation.

4893
03:55:36,201 --> 03:55:38,002
Another potentiating mechanism.

4894
03:55:38,002 --> 03:55:39,838
So, you can see,
it’s a lot of question.

4895
03:55:39,838 --> 03:55:41,773
I don’t --
I do not have a response.

4896
03:55:41,773 --> 03:55:47,345
It’s maybe G6PD deficiency.
So, it is very frequent

4897
03:55:47,345 --> 03:55:51,082
to have G6PD deficiency
in Black individual.

4898
03:55:51,082 --> 03:55:55,487
And it was shown that G6PD
deficiency red blood cell unit

4899
03:55:55,487 --> 03:55:59,891
were associated with decreased
post-transfusion red blood cell

4900
03:55:59,891 --> 03:56:02,527
survival in children
with sickle cell disease.

4901
03:56:02,527 --> 03:56:06,064
That was a study in patient
transfused chronically.

4902
03:56:06,064 --> 03:56:10,001
We do not have data correlated
to hyperhemolysis

4903
03:56:10,001 --> 03:56:12,504
or increase of hyperhemolysis

4904
03:56:12,504 --> 03:56:16,841
when we transfuse patient
with G6PD deficiency unit.

4905
03:56:16,841 --> 03:56:21,513
But it is -- we know that the
blood quality for transfusion

4906
03:56:21,513 --> 03:56:24,215
is not as good
when the red blood cell --

4907
03:56:24,215 --> 03:56:27,018
when the donor
has G6PD deficiency.

4908
03:56:27,018 --> 03:56:29,821
And in a cohort of Black donors,

4909
03:56:30,355 --> 03:56:35,927
we could see that 14 percent
of the unit had G6PD deficiency,

4910
03:56:35,927 --> 03:56:39,764
which is -- which was expected
looking to the --

4911
03:56:40,498 --> 03:56:43,067
over a study
in over the country.

4912
03:56:44,469 --> 03:56:46,037
So now, there is --

4913
03:56:46,037 --> 03:56:48,072
I said that there was also
the destruction

4914
03:56:48,072 --> 03:56:49,741
of the own patient
red blood cell.

4915
03:56:49,741 --> 03:56:52,443
We call that
bystander hemolysis.

4916
03:56:52,443 --> 03:56:55,380
So, it is the destruction
of a patient --

4917
03:56:55,380 --> 03:56:58,850
the destruction of patient
red blood cells represents,

4918
03:56:58,850 --> 03:57:01,819
finally, collateral damage
in a conflict

4919
03:57:01,819 --> 03:57:04,522
that does not concern them
in the first place.

4920
03:57:05,089 --> 03:57:08,826
So, when the donor red
blood cells are destroyed,

4921
03:57:09,627 --> 03:57:12,230
there is a big release
of free hemoglobin

4922
03:57:12,230 --> 03:57:16,834
A and free heme, and the natural
defense against free hemoglobin

4923
03:57:16,834 --> 03:57:19,037
A and free heme
are overwhelmed.

4924
03:57:20,104 --> 03:57:27,579
And there are also a huge amount
of circulating C5b-9,

4925
03:57:27,579 --> 03:57:32,116
the end-activated factors
of the complement.

4926
03:57:32,650 --> 03:57:36,321
Then the autologous red
blood cells are also destroyed.

4927
03:57:36,321 --> 03:57:37,589
We know that.

4928
03:57:37,589 --> 03:57:43,027
And they release free
hemoglobin S and free heme also.

4929
03:57:43,027 --> 03:57:46,397
But -- so what is the mechanism
for this destruction?

4930
03:57:46,397 --> 03:57:50,168
So, it is known that there is
a sensitivity of sickle cell,

4931
03:57:50,168 --> 03:57:51,402
red blood cell,

4932
03:57:51,402 --> 03:57:54,138
to complement attack
with the potentialization

4933
03:57:54,138 --> 03:57:58,409
of the alternate pathway
by circulating free heme.

4934
03:57:58,409 --> 03:58:01,879
And also, it has been shown
that some molecules

4935
03:58:01,879 --> 03:58:04,015
at the surface
of the red blood cell --

4936
03:58:04,015 --> 03:58:06,918
of dense red blood cell
of sickle patient,

4937
03:58:06,918 --> 03:58:10,255
they have a decreased
expression of protection,

4938
03:58:10,255 --> 03:58:14,759
such as CD35, CD55, and CD59.

4939
03:58:15,260 --> 03:58:18,529
So, it could be also the
production of autoantibodies

4940
03:58:18,529 --> 03:58:20,565
when there is alloimmunization,

4941
03:58:20,565 --> 03:58:23,468
frequently, patients
that produce autoantibodies,

4942
03:58:23,468 --> 03:58:26,337
that can participate
through this destruction

4943
03:58:26,337 --> 03:58:29,040
of the autologous
red blood cell.

4944
03:58:29,040 --> 03:58:31,709
And it’s not destruction,

4945
03:58:31,709 --> 03:58:34,779
but no more production,
the reticulopenia.

4946
03:58:34,779 --> 03:58:40,051
And it could be the effect.
It could be a necrosis --

4947
03:58:40,051 --> 03:58:45,290
a bone marrow necrosis through
circulating activation --

4948
03:58:45,290 --> 03:58:48,793
circulating activated
complement molecule,

4949
03:58:48,793 --> 03:58:51,963
as shown in this --
in a case report.

4950
03:58:53,731 --> 03:58:57,068
So -- and there is also
a vicious circle of inflammation

4951
03:58:57,068 --> 03:59:00,271
with the release
of this hemoglobin S

4952
03:59:00,271 --> 03:59:04,442
and this heme from
the autologous red blood cells.

4953
03:59:05,176 --> 03:59:09,714
So, patients can die
from this syndrome.

4954
03:59:11,049 --> 03:59:14,519
And the reason of death
is generally organ damage.

4955
03:59:14,519 --> 03:59:18,323
So, with the release
of hemoglobin

4956
03:59:18,323 --> 03:59:21,392
A, hemoglobin S,
and heme in the blood flow,

4957
03:59:22,093 --> 03:59:25,830
it has an impact on the
NO biodisponibility.

4958
03:59:25,830 --> 03:59:27,565
It has an impact on the --

4959
03:59:27,565 --> 03:59:31,169
it increase the damage
of the endothelial cells

4960
03:59:31,169 --> 03:59:33,604
and the little pieces
of membrane

4961
03:59:33,604 --> 03:59:37,542
that are released from
the hemolytic red blood cell.

4962
03:59:38,176 --> 03:59:41,145
They induce,
which [unintelligible] --

4963
03:59:41,145 --> 03:59:44,215
they induce
a proinflammatory phenotype

4964
03:59:44,215 --> 03:59:45,783
of the endothelial cell.

4965
03:59:45,783 --> 03:59:49,687
Altogether, it is the reason why
we have vaso-occlusive crisis,

4966
03:59:49,687 --> 03:59:53,458
but also kidney failure
to the first organ,

4967
03:59:53,458 --> 03:59:57,128
which is fail --
which failed,

4968
03:59:57,128 --> 04:00:00,231
and also multiorgan damage
at the end.

4969
04:00:02,266 --> 04:00:05,002
So now, how to prevent
alloimmunization and

4970
04:00:05,002 --> 04:00:07,071
hyperhemolysis
with what we know?

4971
04:00:07,071 --> 04:00:09,640
So, we have to know
the history of the patient

4972
04:00:09,640 --> 04:00:14,479
to do recommendations.
So, when a patient is transfused

4973
04:00:14,479 --> 04:00:16,981
for an acute
or punctual situation,

4974
04:00:16,981 --> 04:00:18,950
if he has history
of immunization

4975
04:00:18,950 --> 04:00:21,986
and history of DHTR,

4976
04:00:22,520 --> 04:00:26,424
we provide RH-matched
red blood cell,

4977
04:00:26,424 --> 04:00:27,959
like for all sickle patient,

4978
04:00:27,959 --> 04:00:32,163
but we extend the matching
to Duffy, JK, and MNS.

4979
04:00:32,163 --> 04:00:36,401
And when they have this
very severe history

4980
04:00:36,401 --> 04:00:37,802
of hyperhemolysis,

4981
04:00:37,802 --> 04:00:40,505
generally, we discuss
the additional --

4982
04:00:40,505 --> 04:00:44,742
an additional immunosuppressive
therapy such as rituximab.

4983
04:00:46,077 --> 04:00:48,179
However, to apply
this recommendation,

4984
04:00:48,179 --> 04:00:50,248
we have to know
the history of the patient,

4985
04:00:50,248 --> 04:00:53,251
and we have also to prevent
the risk of reexposure

4986
04:00:53,251 --> 04:00:59,056
of an evanescent alloantibodies
with a very good file

4987
04:00:59,056 --> 04:01:00,658
with the history
of the patient.

4988
04:01:02,360 --> 04:01:05,463
So now, how to treat
this hyperhemolysis

4989
04:01:05,463 --> 04:01:08,166
when it’s very severe
with what we know?

4990
04:01:08,166 --> 04:01:12,370
So, the goal of the treatment
is to provide supportive care,

4991
04:01:12,937 --> 04:01:15,440
also stimulation
of erythropoiesis,

4992
04:01:15,440 --> 04:01:18,209
especially for those
with reticulopenia,

4993
04:01:18,209 --> 04:01:20,445
with erythropoietin mainly.

4994
04:01:20,445 --> 04:01:23,614
And we have to act
on the destruction mechanism.

4995
04:01:23,614 --> 04:01:28,085
So, it can be
the macrophage activity.

4996
04:01:28,085 --> 04:01:31,155
So, some -- generally,
on the first basis,

4997
04:01:31,155 --> 04:01:34,158
we can give to the patient
intravascular immunoglobulin.

4998
04:01:34,158 --> 04:01:36,694
But it doesn’t work
very quickly,

4999
04:01:36,694 --> 04:01:39,297
and it can be
also contraindicated

5000
04:01:39,297 --> 04:01:42,667
in sickle patient.
And also, we can act.

5001
04:01:42,667 --> 04:01:44,669
It’s what we do
in the very severe case

5002
04:01:44,669 --> 04:01:48,539
now on the complement cascade
activation with eculizumab.

5003
04:01:50,107 --> 04:01:51,943
It’s important if we can do it

5004
04:01:51,943 --> 04:01:54,745
to eliminate
the toxic component.

5005
04:01:54,745 --> 04:01:58,349
So, some case reports show you
the use of plasmapheresis.

5006
04:01:58,349 --> 04:02:01,919
But to use plasmapheresis,
we need to have a high --

5007
04:02:03,087 --> 04:02:04,689
good hematocrit.

5008
04:02:05,189 --> 04:02:08,960
Some study with hemopexin,
haptoglobin are ongoing.

5009
04:02:09,494 --> 04:02:11,929
So, the anti-inflammatory
action is very good.

5010
04:02:11,929 --> 04:02:14,532
So, you know,
I know clinicians

5011
04:02:14,532 --> 04:02:16,467
do not want to use
corticosteroids,

5012
04:02:16,467 --> 04:02:20,538
so there is a risk balance.
But in many -- in few,

5013
04:02:20,538 --> 04:02:23,808
so not many,
but some case report,

5014
04:02:23,808 --> 04:02:28,813
they use the tocilizumab
as an anti-inflammatory action.

5015
04:02:29,647 --> 04:02:32,049
And after that, if we have
to retranfuse the patient

5016
04:02:32,049 --> 04:02:34,218
because he has a very low
hemoglobin level,

5017
04:02:35,686 --> 04:02:37,755
in all the case reports.

5018
04:02:38,523 --> 04:02:41,259
Each has its own,
I would say, recipe.

5019
04:02:41,893 --> 04:02:45,062
Some use transfusion
plus rituximab plus eculizumab

5020
04:02:45,062 --> 04:02:46,330
plus corticosteroids.

5021
04:02:46,330 --> 04:02:51,769
So, there is no consensus
on all these treatments.

5022
04:02:51,769 --> 04:02:58,175
And it’s not real-based science
with many.

5023
04:02:58,175 --> 04:03:02,380
It’s -- there is no really
good studies that say,

5024
04:03:02,380 --> 04:03:04,916
"Oh, you have to do this"
or "you have to do that."

5025
04:03:05,917 --> 04:03:09,153
So, for two years now,
in our country

5026
04:03:09,153 --> 04:03:10,488
and at the national level,

5027
04:03:10,488 --> 04:03:15,226
we have organized
multi-consulting meetings.

5028
04:03:15,226 --> 04:03:19,196
And so, we have clinician.
We have transfusion specialist.

5029
04:03:19,997 --> 04:03:24,001
And so, we discuss
the transfusion protocol

5030
04:03:24,001 --> 04:03:26,537
for patients
who are at high risk

5031
04:03:27,071 --> 04:03:31,008
to develop
post-transfusion hemolysis.

5032
04:03:31,709 --> 04:03:34,211
So, we always --
we discuss the indication,

5033
04:03:34,211 --> 04:03:35,746
but generally it’s maintained.

5034
04:03:35,746 --> 04:03:37,748
So, we discuss the transfusion
protocol.

5035
04:03:37,748 --> 04:03:41,319
We discuss additional therapy
and also we give advice

5036
04:03:41,319 --> 04:03:44,288
for very close
post-transfusion monitoring

5037
04:03:44,288 --> 04:03:46,390
to be able to detect
very quickly

5038
04:03:46,390 --> 04:03:49,026
if a patient developed a new --

5039
04:03:49,026 --> 04:03:51,862
despite all this record,
all the prevention,

5040
04:03:52,830 --> 04:03:56,734
if the patient developed again
a post-transfusion hemolysis

5041
04:03:56,734 --> 04:04:01,172
to be able to act very quickly
and to treat if it’s severe.

5042
04:04:03,040 --> 04:04:07,445
So, to finish my last slide,
how to advance our understanding

5043
04:04:07,445 --> 04:04:11,248
of the pathophysiology
of DHTR and hyperhemolysis

5044
04:04:11,248 --> 04:04:12,683
in sickle cell disease.

5045
04:04:12,683 --> 04:04:16,153
So now, we are on the process
to organize a registry

5046
04:04:16,787 --> 04:04:19,757
for all the case
in France of DHTR.

5047
04:04:20,358 --> 04:04:22,760
Just to give you a number,

5048
04:04:22,760 --> 04:04:26,030
we have about
30,000 patients in France.

5049
04:04:26,030 --> 04:04:30,468
Every year, we have about 5,000
patients who are transfused.

5050
04:04:30,468 --> 04:04:34,438
And last year,
we had 45 declared cases --

5051
04:04:35,006 --> 04:04:36,907
and I think
it’s underappreciated --

5052
04:04:36,907 --> 04:04:39,543
45 declared cases of DHTR.

5053
04:04:39,543 --> 04:04:41,312
So, it’s going to be
very interesting

5054
04:04:41,312 --> 04:04:43,381
to collect all the data of --

5055
04:04:44,148 --> 04:04:46,517
clinical and biological data
of the patient

5056
04:04:47,518 --> 04:04:53,691
and also to collect a sample
to be able to do more studies,

5057
04:04:53,691 --> 04:04:57,028
such as, for example,
the exploration of a complement

5058
04:04:57,028 --> 04:05:00,197
and to revert all the subunit,

5059
04:05:00,197 --> 04:05:03,067
submolecule of the activation,
some --

5060
04:05:03,067 --> 04:05:05,970
to do some complement
genetic in patient also,

5061
04:05:06,671 --> 04:05:09,607
to explore what we have
in the plasma of patient,

5062
04:05:10,908 --> 04:05:14,245
an example, to do proteomic
in this plasma of patient

5063
04:05:14,245 --> 04:05:20,751
who developed this
very severe hyperhemolysis,

5064
04:05:20,751 --> 04:05:24,388
and also to do
very sensitive technique

5065
04:05:24,388 --> 04:05:27,558
to really know if really
we do not have antibodies

5066
04:05:28,426 --> 04:05:29,927
using flow cytometry

5067
04:05:29,927 --> 04:05:33,197
and also looking to
the different

5068
04:05:33,197 --> 04:05:35,132
characteristic of
the antibodies.

5069
04:05:35,132 --> 04:05:38,069
And what about IgA?
Nobody look at IgA.

5070
04:05:38,069 --> 04:05:39,737
In fact,
we should look at IgA

5071
04:05:39,737 --> 04:05:42,206
because they are in
autoimmune hemolytic anemia,

5072
04:05:42,206 --> 04:05:46,977
very severe case with IgA
that give Coombs negative.

5073
04:05:46,977 --> 04:05:51,115
So, it’s -- why not [laughs]?
And also, murine studies,

5074
04:05:51,115 --> 04:05:52,917
because there is
no murine studies

5075
04:05:52,917 --> 04:05:55,619
on this specific problem.

5076
04:05:56,454 --> 04:06:00,391
So, I’m done.
And I would like to thank my --

5077
04:06:00,391 --> 04:06:03,661
all the people
of my research teams

5078
04:06:03,661 --> 04:06:07,665
in Henri Mondor Hospital
and all the clinicians.

5079
04:06:07,665 --> 04:06:11,368
And we all work together
to try to improve

5080
04:06:12,203 --> 04:06:14,505
transfusion for this patient --

5081
04:06:14,505 --> 04:06:16,841
safety of transfusion
for this patient.

5082
04:06:16,841 --> 04:06:18,709
Thank you for your attention.

5083
04:06:18,709 --> 04:06:23,881
[applause]

5084
04:06:23,881 --> 04:06:25,116
Angela Rankine-Mullings: Thank
you.

5085
04:06:25,116 --> 04:06:28,519
Thank you so much, Dr. Pirenne.
We will take a few questions.

5086
04:06:28,519 --> 04:06:32,323
We are very late into lunch.
So --

5087
04:06:32,890 --> 04:06:38,896
[laughter]

5088
04:06:38,896 --> 04:06:43,834
Marilyn Telen: I don’t know.

5089
04:06:44,902 --> 04:06:50,241
So, well -- so I think so much
valuable data on the syndrome

5090
04:06:50,241 --> 04:06:54,345
has come out of France.
And this was a wonderful talk.

5091
04:06:55,079 --> 04:07:00,551
I wonder what you think, though,
about the frequency

5092
04:07:01,852 --> 04:07:06,757
with which we can’t
find antibodies to explain

5093
04:07:06,757 --> 04:07:08,826
what appears
to be hyperhemolysis

5094
04:07:08,826 --> 04:07:11,729
in which I think, I agree,
I think is hyperhemolysis.

5095
04:07:12,797 --> 04:07:14,865
I’ve spent a lot of time
telling people

5096
04:07:15,599 --> 04:07:17,468
that not finding an antibody

5097
04:07:17,468 --> 04:07:19,370
doesn’t mean it isn’t
hyperhemolysis.

5098
04:07:20,371 --> 04:07:22,606
I mean, I guess by biases,

5099
04:07:22,606 --> 04:07:26,210
IgA is still a pretty rare
reason why we can’t find it.

5100
04:07:26,844 --> 04:07:30,915
And I wonder whether you think
that there’s just,

5101
04:07:30,915 --> 04:07:34,018
you know, phenomenal clearance
of the antibody

5102
04:07:34,018 --> 04:07:36,487
because it’s very avid
and it clears the red cells.

5103
04:07:36,487 --> 04:07:38,556
There’s no antibody excess.

5104
04:07:38,556 --> 04:07:40,758
You know, are there
particular antigens

5105
04:07:40,758 --> 04:07:43,627
that are just sometimes
hard to detect?

5106
04:07:43,627 --> 04:07:45,830
The antibodies too,
Kidd is one which,

5107
04:07:45,830 --> 04:07:47,264
at least by the literature,

5108
04:07:47,264 --> 04:07:49,967
they say it is hard
to detect sometimes.

5109
04:07:49,967 --> 04:07:52,670
So, you know, is it titer?
Is it specificity?

5110
04:07:54,071 --> 04:07:55,306
Just wonder what you think.

5111
04:07:55,306 --> 04:07:56,507
France Pirenne: Yes.

5112
04:07:56,507 --> 04:07:58,843
We had a prospective study
a long time ago.

5113
04:07:58,843 --> 04:08:02,279
And we follow --
yes, because it can be --

5114
04:08:02,947 --> 04:08:05,683
it can disappear
with the red blood cell

5115
04:08:05,683 --> 04:08:08,118
if it’s bound to the red
blood cell, the antibody.

5116
04:08:08,118 --> 04:08:10,588
But we follow
with screening test

5117
04:08:10,588 --> 04:08:14,792
and sensitize screening test
after one month, two months,

5118
04:08:14,792 --> 04:08:17,394
because generally
it can reappear,

5119
04:08:17,394 --> 04:08:21,432
and we didn’t find anything.
And also, in many --

5120
04:08:22,166 --> 04:08:25,369
we are not the only ones to say
that we do not find antibodies.

5121
04:08:25,369 --> 04:08:29,707
So, there are also this antibody
against low-frequency antigen.

5122
04:08:29,707 --> 04:08:31,141
Now, we try to look for them.

5123
04:08:31,141 --> 04:08:32,343
Swee Lay Thein: Yeah.

5124
04:08:32,343 --> 04:08:34,845
France Pirenne: But sometimes
we have one like that,

5125
04:08:35,546 --> 04:08:39,316
but there is really a case
where there is nothing.

5126
04:08:39,316 --> 04:08:40,618
So --

5127
04:08:40,618 --> 04:08:41,852
Swee Lay Thein: Yeah.

5128
04:08:41,852 --> 04:08:43,187
Marilyn Telen: So,
the cooperative study

5129
04:08:43,187 --> 04:08:44,788
of sickle cell disease

5130
04:08:44,788 --> 04:08:48,225
showed very nicely
that antibodies disappear

5131
04:08:48,225 --> 04:08:49,493
quite frequently.

5132
04:08:49,493 --> 04:08:51,095
France Pirenne: Yes.

5133
04:08:51,962 --> 04:08:53,631
Swee Lay Thein: France,
that was really very good.

5134
04:08:53,631 --> 04:08:56,967
Thank you so much.
I have two comments to make.

5135
04:08:57,534 --> 04:09:01,138
One is that I think we --

5136
04:09:01,138 --> 04:09:04,708
it’s very important to
clinicians who are not aware --

5137
04:09:04,708 --> 04:09:05,976
France Pirenne: Yeah.

5138
04:09:05,976 --> 04:09:09,146
Swee Lay Thein: -- of DHTR that
when a patient comes in --

5139
04:09:09,146 --> 04:09:10,414
France Pirenne: Yeah.

5140
04:09:10,414 --> 04:09:12,283
Swee Lay Thein: --
with acute pain or crisis,

5141
04:09:12,283 --> 04:09:17,154
first question is, when did you
receive a transfusion?

5142
04:09:17,154 --> 04:09:18,555
Did you receive one recently?

5143
04:09:18,555 --> 04:09:19,757
France Pirenne: Yes.

5144
04:09:19,757 --> 04:09:22,660
Swee Lay Thein: Because that
could trigger

5145
04:09:23,294 --> 04:09:24,828
a mimic SL acute pain.

5146
04:09:24,828 --> 04:09:26,230
France Pirenne: Yeah.

5147
04:09:26,230 --> 04:09:27,464
Swee Lay Thein: That’s one
thing.

5148
04:09:27,464 --> 04:09:32,503
Secondly, during VOC,
they should always measure LDH.

5149
04:09:32,503 --> 04:09:33,837
France Pirenne: Yes.

5150
04:09:33,837 --> 04:09:36,106
Swee Lay Thein: And always do
a HPLC --

5151
04:09:36,106 --> 04:09:37,341
France Pirenne: Yes.

5152
04:09:37,341 --> 04:09:39,043
Swee Lay Thein: --
especially when they transfuse.

5153
04:09:39,043 --> 04:09:43,948
Because when you talk
about DHTR, we see hemolysis.

5154
04:09:43,948 --> 04:09:45,549
We should see
a parallel drop --

5155
04:09:45,549 --> 04:09:46,750
France Pirenne: Yes.

5156
04:09:46,750 --> 04:09:49,086
Swee Lay Thein: -- of
the transfused hemoglobin A

5157
04:09:49,086 --> 04:09:50,688
and the hemoglobin S.

5158
04:09:52,122 --> 04:09:54,925
That’s -- I think
this should be made clear

5159
04:09:54,925 --> 04:09:58,963
to the trainees and clinicians,
so they’re aware of this.

5160
04:09:58,963 --> 04:10:02,399
And I often get so upset that
they don’t do LDH, you know.

5161
04:10:02,399 --> 04:10:03,667
France Pirenne: Yes, yes.

5162
04:10:03,667 --> 04:10:05,002
And like that,
we have a basic --

5163
04:10:05,002 --> 04:10:06,337
Swee Lay Thein: Yeah.
France Pirenne: --

5164
04:10:06,337 --> 04:10:07,538
and if it’s increased,
something happened.

5165
04:10:07,538 --> 04:10:08,772
Swee Lay Thein: Yeah.

5166
04:10:08,772 --> 04:10:10,007
France Pirenne: But you’re
right.

5167
04:10:10,007 --> 04:10:11,241
I mean, I didn’t think
about all that.

5168
04:10:11,241 --> 04:10:13,110
Swee Lay Thein: And then when
the LDH is very high, 3-,

5169
04:10:13,110 --> 04:10:14,645
4,000 --
France Pirenne: Yeah, yeah.

5170
04:10:14,645 --> 04:10:16,146
Swee Lay Thein: -- then
it’s time to give eculizumab.

5171
04:10:16,146 --> 04:10:17,381
France Pirenne: Yes.

5172
04:10:17,381 --> 04:10:18,615
Swee Lay Thein: That has
a role in that.

5173
04:10:18,615 --> 04:10:19,850
France Pirenne: Yes.

5174
04:10:19,850 --> 04:10:21,085
Swee Lay Thein: The second thing
is, in fact,

5175
04:10:21,085 --> 04:10:24,321
you and John actually did
a very nice study

5176
04:10:24,321 --> 04:10:27,424
that was published
maybe a year or two years ago.

5177
04:10:27,424 --> 04:10:30,828
And we’re working with him.
And he actually found a locus.

5178
04:10:30,828 --> 04:10:33,797
I think it’s on --
I can’t remember the chromosome,

5179
04:10:33,797 --> 04:10:36,867
but it’s upstream
of an immune response gene.

5180
04:10:37,768 --> 04:10:41,638
And there are people with that
variant and then people without.

5181
04:10:41,638 --> 04:10:43,574
And these are kind
of more common

5182
04:10:43,574 --> 04:10:47,378
in people of African descent
or Black people.

5183
04:10:47,978 --> 04:10:50,981
And this may be account
partly for the reason

5184
04:10:50,981 --> 04:10:55,819
why alloantibodies occur
more commonly during,

5185
04:10:56,620 --> 04:10:58,822
you know, acute --
when they’re transfused,

5186
04:10:58,822 --> 04:11:01,191
during acute pain,
when inflammation is high --

5187
04:11:01,191 --> 04:11:02,593
France Pirenne: Yes.

5188
04:11:02,593 --> 04:11:06,630
Swee Lay Thein: -- and also,
why in Black people.

5189
04:11:08,632 --> 04:11:11,168
It’s worth looking
at your patient cohort.

5190
04:11:11,168 --> 04:11:12,469
France Pirenne: Yes, yes,
yes, yes.

5191
04:11:12,469 --> 04:11:16,173
You should give me
the paper in that.

5192
04:11:16,740 --> 04:11:19,009
[laughter]

5193
04:11:19,009 --> 04:11:20,644
Male Speaker: Should I go,
or should you go?

5194
04:11:20,644 --> 04:11:21,879
Female Speaker: No.

5195
04:11:21,879 --> 04:11:23,480
Male Speaker: All right.

5196
04:11:23,480 --> 04:11:25,983
Well, some of what I wanted
to say has been,

5197
04:11:26,683 --> 04:11:29,053
in a sense, addressed
slightly by Swee Lay.

5198
04:11:29,987 --> 04:11:32,523
I just wanted to ask you
a few things, though.

5199
04:11:32,523 --> 04:11:37,728
So, in your high responders
versus your non-high responders,

5200
04:11:37,728 --> 04:11:39,596
if your hypothesis is correct,

5201
04:11:39,596 --> 04:11:42,666
that is probably driven
to some extent by heme

5202
04:11:42,666 --> 04:11:47,638
or hemoglobin S perhaps,
then we would expect at baseline

5203
04:11:47,638 --> 04:11:49,940
that there should be
a difference in those levels

5204
04:11:49,940 --> 04:11:53,610
between both groups.
Go ahead.

5205
04:11:53,610 --> 04:11:54,878
France Pirenne: No.

5206
04:11:54,878 --> 04:11:58,615
What I say
about hemoglobin S and heme --

5207
04:11:59,149 --> 04:12:03,821
I said that it potentiate
the hemolysis,

5208
04:12:03,821 --> 04:12:05,022
in fact, when it occur.

5209
04:12:05,022 --> 04:12:06,256
Male Speaker: Right.

5210
04:12:06,256 --> 04:12:09,193
France Pirenne: But I didn’t
speak about alloimmunization

5211
04:12:09,193 --> 04:12:13,097
versus the circulation
of free heme

5212
04:12:13,097 --> 04:12:14,565
and free hemoglobin S.

5213
04:12:14,565 --> 04:12:17,734
There is only one study
from Karina Yazdanbakhsh,

5214
04:12:17,734 --> 04:12:21,238
from the New York Blood Center.
She showed that heme can --

5215
04:12:21,238 --> 04:12:25,342
it’s an immunomodulator
for alloimmunization,

5216
04:12:25,342 --> 04:12:27,845
but it’s quite complicated
the way it works.

5217
04:12:27,845 --> 04:12:31,181
In fact, we could think that
it’s increased, but not always.

5218
04:12:31,181 --> 04:12:32,749
So, it’s -- yeah. So --

5219
04:12:32,749 --> 04:12:34,017
Male Speaker: Right. All right.

5220
04:12:34,017 --> 04:12:37,688
So, my follow-up question
is that comes from your side,

5221
04:12:37,688 --> 04:12:42,126
in terms of your algorithm
for screening individuals

5222
04:12:42,126 --> 04:12:44,328
to prevent hyperhemolysis

5223
04:12:44,328 --> 04:12:48,332
as part of your treatment
options moving forward.

5224
04:12:49,066 --> 04:12:50,767
And so,
in the absence of the --

5225
04:12:51,602 --> 04:12:54,338
in the observations,
for example,

5226
04:12:54,338 --> 04:12:59,109
that the extended matching
for the extended heme --

5227
04:13:00,043 --> 04:13:04,381
RBCs antigen,
they don’t necessarily prevent

5228
04:13:05,449 --> 04:13:08,986
or identify individual
with any degree of specificity

5229
04:13:09,887 --> 04:13:12,389
who are likely to develop
the hyperhemolysis.

5230
04:13:12,389 --> 04:13:14,391
From a family medicine
perspective,

5231
04:13:14,391 --> 04:13:17,461
then we would say then perhaps
one needs to comment

5232
04:13:17,461 --> 04:13:21,064
on the cost effectiveness
of actually doing that,

5233
04:13:21,064 --> 04:13:23,066
vis-à-vis just depending
on the history.

5234
04:13:24,268 --> 04:13:25,536
France Pirenne: Yeah.

5235
04:13:25,536 --> 04:13:28,639
But we do not do that to many,
many patients, you know.

5236
04:13:28,639 --> 04:13:32,176
We do that -- we extend --
once a patient, for example,

5237
04:13:32,176 --> 04:13:34,278
I ask to produce anti-JKB,

5238
04:13:34,278 --> 04:13:36,280
which is quite frequent
in our cohort,

5239
04:13:36,914 --> 04:13:40,484
we know that is going to
continue to produce antibody.

5240
04:13:40,484 --> 04:13:44,221
For specific patients,
we try to extend the matching,

5241
04:13:44,221 --> 04:13:48,225
you know, especially
if they are patients who are --

5242
04:13:48,225 --> 04:13:49,826
not transfused a lot.

5243
04:13:49,826 --> 04:13:52,796
But for the chronic patient
who are transfused,

5244
04:13:52,796 --> 04:13:55,999
and everything is going okay,
if I want antibody,

5245
04:13:55,999 --> 04:13:58,602
we just take into account
the antibody,

5246
04:13:58,602 --> 04:13:59,836
and we do not extend.

5247
04:13:59,836 --> 04:14:03,740
It’s really when
they are transfused

5248
04:14:03,740 --> 04:14:06,310
for an acute indication
that we do that.

5249
04:14:06,310 --> 04:14:09,613
We try to do that.
But yes, in many case,

5250
04:14:09,613 --> 04:14:11,014
maybe in 50 percent
of the case --

5251
04:14:11,014 --> 04:14:12,616
because we did it quite often --

5252
04:14:13,483 --> 04:14:15,819
a score in 50 percent
of the case,

5253
04:14:15,819 --> 04:14:17,521
we will not develop --
we can --

5254
04:14:18,021 --> 04:14:22,059
they would have been okay
with regular protocol.

5255
04:14:22,059 --> 04:14:24,494
But we don’t know
who are these 50 percent

5256
04:14:24,494 --> 04:14:27,831
who would have
a good follow up.

5257
04:14:27,831 --> 04:14:29,433
Swee Lay Thein: You want to
go next?

5258
04:14:30,033 --> 04:14:32,903
I have another comment to make.
You want to go next?

5259
04:14:32,903 --> 04:14:35,072
[laughter]

5260
04:14:35,072 --> 04:14:36,340
Female Speaker: Okay.
Yes.

5261
04:14:36,340 --> 04:14:39,910
Well, I think this is
a general comment.

5262
04:14:39,910 --> 04:14:42,412
You’re obviously
interested in this topic.

5263
04:14:42,412 --> 04:14:48,518
And when Swee Lay talked about
the need to do LDH and HPLC,

5264
04:14:48,518 --> 04:14:50,420
I said that’s
where we practice.

5265
04:14:50,921 --> 04:14:53,023
The patient will not
be able to --

5266
04:14:53,023 --> 04:14:54,458
France Pirenne: No.
Female Speaker: -- do this test.

5267
04:14:54,458 --> 04:14:58,962
So, I’m just wondering if we
are missing an opportunity,

5268
04:14:58,962 --> 04:15:02,866
especially with the presence
of disease registries now

5269
04:15:03,400 --> 04:15:07,337
and trying to follow
our patients prospectively,

5270
04:15:07,337 --> 04:15:09,506
if this is not something
that we should look

5271
04:15:09,506 --> 04:15:13,443
at more closely in areas
of high burden,

5272
04:15:14,177 --> 04:15:16,380
you know,
by working together maybe.

5273
04:15:16,380 --> 04:15:17,581
France Pirenne: Yeah.

5274
04:15:17,581 --> 04:15:18,815
Female Speaker: Thank you.

5275
04:15:18,815 --> 04:15:21,285
France Pirenne: So, you know
the previous talk about

5276
04:15:22,019 --> 04:15:25,922
having kidney failure in patient
transfused, it’s been --

5277
04:15:25,922 --> 04:15:30,560
it’s likely
because they got a reaction

5278
04:15:30,560 --> 04:15:31,895
to transfusion, you know.
So, it’s --

5279
04:15:31,895 --> 04:15:33,096
Swee Lay Thein: Exactly.

5280
04:15:33,096 --> 04:15:34,464
France Pirenne: Yeah.

5281
04:15:34,464 --> 04:15:36,366
Swee Lay Thein: I can tell you,
it’s a very scary time

5282
04:15:36,366 --> 04:15:39,469
when you manage somebody
with DHTR,

5283
04:15:39,469 --> 04:15:41,071
and hemoglobin fall to 2 --
France Pirenne: Yeah, yeah.

5284
04:15:41,071 --> 04:15:42,272
Swee Lay Thein: --
and the clinicians

5285
04:15:42,272 --> 04:15:43,507
want to transfuse,

5286
04:15:43,507 --> 04:15:45,208
and I said,
"Only if dying, okay?"

5287
04:15:45,208 --> 04:15:46,443
France Pirenne: Yes.

5288
04:15:46,443 --> 04:15:48,378
Swee Lay Thein: "Because you’re
going to make problems worse."

5289
04:15:48,378 --> 04:15:49,646
France Pirenne: Yes.

5290
04:15:49,646 --> 04:15:51,748
Swee Lay Thein: One thing I like
to ask you, France.

5291
04:15:51,748 --> 04:15:57,854
I manage patients with DHTR too,
except the LDH is not so high.

5292
04:15:57,854 --> 04:16:00,824
It’s extravascular.
I can see the spleen.

5293
04:16:00,824 --> 04:16:02,025
France Pirenne: Yeah.

5294
04:16:02,025 --> 04:16:03,427
Swee Lay Thein: No, the liver,
not the spleen.

5295
04:16:03,427 --> 04:16:03,627
France Pirenne: Yeah.

5296
04:16:03,627 --> 04:16:05,629
Swee Lay Thein: The liver going
right down to the pelvic area.

5297
04:16:05,629 --> 04:16:06,830
France Pirenne: Yes.

5298
04:16:06,830 --> 04:16:09,333
Swee Lay Thein: So, it’s almost
all the blood that we transfuse

5299
04:16:09,333 --> 04:16:10,934
has gone to the liver.

5300
04:16:12,469 --> 04:16:16,606
Can you differentiate why some
patients have this extravascular

5301
04:16:16,606 --> 04:16:18,508
and some more intravascular?

5302
04:16:19,443 --> 04:16:20,977
France Pirenne: No.
I don’t have a response.

5303
04:16:20,977 --> 04:16:22,913
And really,
it’s something that --

5304
04:16:22,913 --> 04:16:25,415
I think that with this registry,
you know,

5305
04:16:25,415 --> 04:16:27,050
with every clinical staff
and everything --

5306
04:16:27,050 --> 04:16:28,318
Swee Lay Thein: Yeah.

5307
04:16:28,318 --> 04:16:29,720
France Pirenne: -- maybe
we will understand --

5308
04:16:29,720 --> 04:16:32,389
we can do cohorts.
We can divide patient with this

5309
04:16:32,389 --> 04:16:36,159
and that and maybe understand
better what happened.

5310
04:16:36,159 --> 04:16:37,627
Because right now,
it’s like [laughs] --

5311
04:16:37,627 --> 04:16:38,829
Swee Lay Thein: Yeah.

5312
04:16:38,829 --> 04:16:40,097
France Pirenne: Yes.

5313
04:16:40,097 --> 04:16:41,798
And it’s only case report
in literature and also --

5314
04:16:41,798 --> 04:16:42,999
Swee Lay Thein: Yeah, yeah.

5315
04:16:42,999 --> 04:16:44,368
France Pirenne: --
it’s difficult to --

5316
04:16:44,368 --> 04:16:45,702
there is no history
of the patient,

5317
04:16:45,702 --> 04:16:47,037
so we don’t know exactly what --
yeah.

5318
04:16:47,037 --> 04:16:48,338
Angela Rankine-Mullings: Thank
you.

5319
04:16:48,338 --> 04:16:48,805
France Pirenne: Sorry [laughs].

5320
04:16:48,805 --> 04:16:50,006
Angela Rankine-Mullings: Thank
you very much.

5321
04:16:50,006 --> 04:16:51,241
France Pirenne: Yes. Thank you.

5322
04:16:51,241 --> 04:16:54,911
Angela Rankine-Mullings: Okay.
Okay. Sorry.

5323
04:16:54,911 --> 04:16:56,213
John Tisdale: Go ahead.

5324
04:16:56,213 --> 04:16:57,581
Angela Rankine-Mullings: Well,
I was about to end this session

5325
04:16:57,581 --> 04:16:58,815
because it’s our lunch.
John Tisdale: Yes. Sure.

5326
04:16:58,815 --> 04:17:00,450
Angela Rankine-Mullings: Okay.
Thank you so much.

5327
04:17:00,450 --> 04:17:02,586
So, we thank
all our speakers.

5328
04:17:03,120 --> 04:17:05,288
[applause]

5329
04:17:05,288 --> 04:17:11,828
Thank you for participating.
Our return time is 1:40 p.m.

5330
04:17:13,143 --> 04:17:15,011
John Tisdale: So, while
you’re getting settled in

5331
04:17:15,011 --> 04:17:16,613
with finishing up your lunch,

5332
04:17:16,613 --> 04:17:18,214
we’re going to go ahead
and get started.

5333
04:17:18,214 --> 04:17:21,551
Our first speaker
is Wynona Coles.

5334
04:17:21,551 --> 04:17:25,889
Nona currently serves as
a senior technical lab manager

5335
04:17:25,889 --> 04:17:27,924
for both the cellular
molecular therapeutics

5336
04:17:27,924 --> 04:17:30,493
and the molecular
and clinical therapeutics

5337
04:17:30,493 --> 04:17:34,364
branches within the NHLBI.

5338
04:17:34,364 --> 04:17:38,668
And she has been actually
screening individuals

5339
04:17:38,668 --> 04:17:41,137
with sickle cell disease
for research protocols

5340
04:17:41,905 --> 04:17:46,142
for decades now.
And she’s going to talk to us

5341
04:17:46,142 --> 04:17:49,212
about the five most frequently
asked questions.

5342
04:17:49,212 --> 04:17:51,081
Nona?

5343
04:17:51,081 --> 04:18:00,557
[applause]

5344
04:18:01,124 --> 04:18:03,793
Wynona Coles: All right.
Good afternoon.

5345
04:18:03,793 --> 04:18:05,395
Female Speaker: Good afternoon.

5346
04:18:06,629 --> 04:18:07,931
Wynona Coles: Thank you
for that, John.

5347
04:18:07,931 --> 04:18:10,667
Thank you for the skiff
organizers for inviting me

5348
04:18:10,667 --> 04:18:12,268
to speak this afternoon.

5349
04:18:14,104 --> 04:18:16,806
So far, everything
has been very lovely.

5350
04:18:16,806 --> 04:18:19,676
I’ve enjoyed wonderful
conversations,

5351
04:18:19,676 --> 04:18:21,311
meeting new faces.

5352
04:18:21,311 --> 04:18:24,981
I’ve enjoyed carpool
karaoke with Manu.

5353
04:18:25,515 --> 04:18:27,417
[laughter]

5354
04:18:27,417 --> 04:18:30,420
I’ve seen some old faces.
Not that their faces are old,

5355
04:18:30,420 --> 04:18:31,955
but it’s been a while
since I’ve seen them.

5356
04:18:31,955 --> 04:18:33,123
[laughter]

5357
04:18:33,123 --> 04:18:35,358
So, it’s been lovely
to see all of you.

5358
04:18:35,358 --> 04:18:36,960
So, thank you again.

5359
04:18:38,828 --> 04:18:41,931
So, I’d like to share
my experiences and observations

5360
04:18:41,931 --> 04:18:46,703
as the transplant referral
coordinator for NHLBI cellular

5361
04:18:46,703 --> 04:18:50,473
molecular therapeutics branch
at the NIH on John Tisdale’s

5362
04:18:50,473 --> 04:18:52,709
and Courtney Fitzhugh’s
clinical teams

5363
04:18:52,709 --> 04:18:55,145
and present the common questions
we receive

5364
04:18:55,145 --> 04:18:57,147
from patients
who are considering transplant

5365
04:18:57,147 --> 04:18:59,682
as a curative option
for their sickle cell disease.

5366
04:19:02,018 --> 04:19:04,854
We receive referrals
from many sources,

5367
04:19:04,854 --> 04:19:06,156
some from other physicians

5368
04:19:06,156 --> 04:19:08,925
who are aware of the transplant
clinical trials at NIH.

5369
04:19:09,459 --> 04:19:12,262
Online searches have led
to the largest referral source,

5370
04:19:12,962 --> 04:19:14,964
with some patients
emailing directly to me

5371
04:19:14,964 --> 04:19:16,432
or to Dr. Tisdale

5372
04:19:16,432 --> 04:19:18,935
or contacting our office
of patient recruitment

5373
04:19:18,935 --> 04:19:21,404
using
the clinicaltrial.gov website.

5374
04:19:21,404 --> 04:19:24,007
We’ve also received referrals
from Swee Lay’s

5375
04:19:24,007 --> 04:19:27,076
natural history of sickle cell
disease screening study.

5376
04:19:27,076 --> 04:19:29,879
And now, after 20 years
and over 100 transplants,

5377
04:19:29,879 --> 04:19:33,149
a large number of referrals
are coming by word of mouth.

5378
04:19:33,149 --> 04:19:35,418
Patients and their family
members are openly sharing

5379
04:19:35,418 --> 04:19:37,787
their transplant experiences
in their communities,

5380
04:19:37,787 --> 04:19:39,589
support groups,
and on social media.

5381
04:19:40,523 --> 04:19:42,358
Roughly, a quarter
of our transplanted patients

5382
04:19:42,358 --> 04:19:44,194
are actually international
and, again,

5383
04:19:44,194 --> 04:19:46,896
have found out about
our clinical trials online

5384
04:19:46,896 --> 04:19:48,498
or through word of mouth.

5385
04:19:51,067 --> 04:19:53,937
So, the first question asked
is sometimes

5386
04:19:53,937 --> 04:19:56,406
actually from the transplant
team to the patient.

5387
04:19:57,574 --> 04:20:00,343
How much do you know
about what a transplant is?

5388
04:20:00,343 --> 04:20:03,413
Even with information widely
available on the internet,

5389
04:20:03,413 --> 04:20:06,115
I still think it’s hard
to visualize how this is done.

5390
04:20:06,649 --> 04:20:09,752
Is there surgery?
Will I be awake? Will it hurt?

5391
04:20:10,486 --> 04:20:12,922
What is the difference
between a bone marrow transplant

5392
04:20:12,922 --> 04:20:14,524
and a stem cell transplant?

5393
04:20:15,191 --> 04:20:17,927
Our team of staff clinicians,
advanced practitioners,

5394
04:20:17,927 --> 04:20:20,296
and research nurses
do a great job

5395
04:20:20,296 --> 04:20:22,532
of providing
this pivotal education

5396
04:20:22,532 --> 04:20:24,467
and making it easier
to understand.

5397
04:20:27,036 --> 04:20:29,839
So, bone marrow transplant,
stem cell transplant,

5398
04:20:29,839 --> 04:20:32,809
hematopoietic stem
cell transplant, the adage,

5399
04:20:32,809 --> 04:20:35,545
a rose by any other name
is still a rose, applies here,

5400
04:20:36,246 --> 04:20:38,014
and these are used
interchangeably.

5401
04:20:38,681 --> 04:20:40,617
Specific to sickle cell disease,

5402
04:20:40,617 --> 04:20:42,485
a bone marrow transplant
is a procedure

5403
04:20:42,485 --> 04:20:44,487
that can cure
sickle cell disease

5404
04:20:44,487 --> 04:20:47,090
by replacing abnormal bone
marrow stem cells

5405
04:20:47,090 --> 04:20:48,891
with healthy ones
from a donor.

5406
04:20:52,962 --> 04:20:56,799
We oftentimes meet a prospective
patient virtually first,

5407
04:20:56,799 --> 04:20:59,702
and using graphics are helpful
in educating patients

5408
04:20:59,702 --> 04:21:01,537
on how the bone marrow works
as a factory

5409
04:21:01,537 --> 04:21:03,940
producing all the blood
stem cells for the body.

5410
04:21:04,974 --> 04:21:07,176
I like how Dr. Mathew Hsieh

5411
04:21:07,176 --> 04:21:09,946
sometimes describes it
as cracking open a chicken bone

5412
04:21:09,946 --> 04:21:13,049
and what’s inside [laughs],
the part that we enjoy the most.

5413
04:21:15,084 --> 04:21:17,620
We also let the patient know
how in sickle cell disease,

5414
04:21:17,620 --> 04:21:18,855
the red cells are abnormal,

5415
04:21:18,855 --> 04:21:21,324
containing defective
sickle hemoglobin,

5416
04:21:21,324 --> 04:21:23,893
which distorts the shape
of the red cells,

5417
04:21:23,893 --> 04:21:25,194
blocking blood flow,

5418
04:21:25,194 --> 04:21:27,964
and causing many of the
complications of their disease.

5419
04:21:29,399 --> 04:21:31,501
Usually, our virtual
meeting will not have --

5420
04:21:31,501 --> 04:21:33,569
will not only have
just the patient present

5421
04:21:33,569 --> 04:21:36,105
but also their potential donor,
if applicable.

5422
04:21:36,105 --> 04:21:38,408
So, we explain
the process of collecting

5423
04:21:38,408 --> 04:21:41,344
the donor’s peripheral
stem cells via apheresis,

5424
04:21:41,344 --> 04:21:42,578
or in the case
of gene therapy,

5425
04:21:42,578 --> 04:21:46,449
the collection of their own stem
cells using the same procedure.

5426
04:21:47,183 --> 04:21:49,419
After all the basics
are discussed,

5427
04:21:49,419 --> 04:21:52,322
the next slide is what makes me
eligible for a transplant.

5428
04:21:53,423 --> 04:21:55,024
Next question, sorry.

5429
04:21:56,125 --> 04:21:58,695
I try to be sensitive to those
who seek participation

5430
04:21:58,695 --> 04:22:00,063
in a clinical trial,

5431
04:22:00,063 --> 04:22:02,465
not to dismiss how
they perceive their illness.

5432
04:22:02,999 --> 04:22:06,269
Patients may ask, "How sick do
I have to be for a transplant?

5433
04:22:06,269 --> 04:22:08,137
Why am I not eligible?

5434
04:22:08,137 --> 04:22:10,540
Why do I have to wait
until my disease worsens?"

5435
04:22:11,407 --> 04:22:12,909
I explained that
in clinical trials,

5436
04:22:12,909 --> 04:22:14,977
we evaluate whether
the potential benefits

5437
04:22:14,977 --> 04:22:17,847
outweigh the potential risks
in determining eligibility.

5438
04:22:18,481 --> 04:22:20,717
Transplant is not
without significant risk,

5439
04:22:20,717 --> 04:22:23,519
and therefore determining
disease severity is essential.

5440
04:22:25,755 --> 04:22:28,725
So, I start by reviewing
lots of medical records,

5441
04:22:28,725 --> 04:22:30,626
trying to determine
disease severity

5442
04:22:30,626 --> 04:22:32,562
and if standard treatments
were maximized

5443
04:22:32,562 --> 04:22:34,797
before advancing to
an experimental therapy.

5444
04:22:35,932 --> 04:22:38,534
Mild disease would indicate
that current medical treatment

5445
04:22:38,534 --> 04:22:41,637
is working by showing little
to no organ damage,

5446
04:22:41,637 --> 04:22:43,606
infrequent hospitalizations,

5447
04:22:43,606 --> 04:22:47,110
and that the patient is able
to achieve their life goals,

5448
04:22:47,110 --> 04:22:48,711
going to school,
working,

5449
04:22:48,711 --> 04:22:50,913
participating in activities
that they enjoy.

5450
04:22:51,981 --> 04:22:54,417
Skip over the moderate
and jump to the severe disease,

5451
04:22:54,417 --> 04:22:55,651
which on the other hand

5452
04:22:55,651 --> 04:22:58,621
is indicated by debilitating
complications such as stroke,

5453
04:22:59,188 --> 04:23:01,824
frequent hospitalizations,
or ER visits.

5454
04:23:01,824 --> 04:23:05,194
Records would show permanent
or progressive organ damage,

5455
04:23:05,194 --> 04:23:08,231
sometimes multiple organs
requiring intensive management.

5456
04:23:08,765 --> 04:23:11,834
Reviewing past medical history
and lab results can show us

5457
04:23:11,834 --> 04:23:13,669
if standard treatments
are effective.

5458
04:23:14,337 --> 04:23:16,773
And the moderate is kind
of somewhere in between.

5459
04:23:19,075 --> 04:23:22,645
This next slide shows
the hematologic changes

5460
04:23:22,645 --> 04:23:25,715
with consistent use of
available standard treatments.

5461
04:23:25,715 --> 04:23:28,618
The first block shows
common lab values if untreated.

5462
04:23:29,519 --> 04:23:31,220
The next block
shows the lab changes

5463
04:23:31,220 --> 04:23:33,156
by introducing hydroxyurea,

5464
04:23:33,156 --> 04:23:35,892
which was approved
in March of 1998

5465
04:23:35,892 --> 04:23:37,193
for treating sickle cell disease

5466
04:23:37,193 --> 04:23:38,895
and its corresponding
lab changes.

5467
04:23:40,196 --> 04:23:44,367
If there’s a good response,
there can be as much as a 20

5468
04:23:44,367 --> 04:23:46,769
to 30 percent displacement
of hemoglobin S,

5469
04:23:46,769 --> 04:23:49,038
leading to reduced pain
and hospitalizations.

5470
04:23:49,906 --> 04:23:53,509
L-glutamine was approved in 2017
to reduce acute pain,

5471
04:23:53,509 --> 04:23:55,111
including chronic pain,

5472
04:23:55,111 --> 04:23:58,114
potentially leading
to reduced hospitalizations

5473
04:23:58,114 --> 04:24:00,149
but no real hematologic
changes.

5474
04:24:01,050 --> 04:24:04,554
In 2019, both crizanlizumab
and later voxelotor

5475
04:24:05,087 --> 04:24:07,223
were approved similarly
to reduce pain.

5476
04:24:07,223 --> 04:24:09,192
However,
some five years later,

5477
04:24:09,192 --> 04:24:11,027
their efficacy is
still questionable.

5478
04:24:11,661 --> 04:24:14,397
You can see -- you can also see
that there is little

5479
04:24:14,397 --> 04:24:17,900
to no hematological
changes for crizanlizumab,

5480
04:24:17,900 --> 04:24:21,037
but voxelotor may provide
a bump to total hemoglobin.

5481
04:24:22,171 --> 04:24:25,274
So, nearly 30 years later,
even with newly approved drugs,

5482
04:24:25,274 --> 04:24:27,510
we still have not found
a pharmacological treatment

5483
04:24:27,510 --> 04:24:29,111
as effective as hydroxyurea.

5484
04:24:30,046 --> 04:24:32,915
So, when someone reaches out
asking about transplant,

5485
04:24:32,915 --> 04:24:36,486
is it -- it is recommended
that they have been treated

5486
04:24:36,486 --> 04:24:38,020
with some standard
sickle therapy,

5487
04:24:38,020 --> 04:24:41,324
preferably hydroxyurea,
for a minimum of six months

5488
04:24:41,324 --> 04:24:43,493
before thinking
about a clinical trial.

5489
04:24:46,362 --> 04:24:47,663
But what are the options

5490
04:24:47,663 --> 04:24:50,299
for those who are intolerant
to hydroxyurea

5491
04:24:50,299 --> 04:24:52,835
or do not have access to it
or other treatments,

5492
04:24:52,835 --> 04:24:54,937
or even for those
who did respond initially

5493
04:24:54,937 --> 04:24:56,572
but now have reached
a plateau

5494
04:24:56,572 --> 04:24:58,474
in response
to their disease symptoms?

5495
04:24:59,075 --> 04:25:01,210
Is the best next option
now transplant?

5496
04:25:02,445 --> 04:25:04,380
Understanding the patient’s
transplant goal

5497
04:25:04,380 --> 04:25:06,215
is also important
to make sure

5498
04:25:06,215 --> 04:25:08,251
that expectations
are reasonable.

5499
04:25:08,251 --> 04:25:10,820
Some organs may have
irreversible damage

5500
04:25:10,820 --> 04:25:12,522
from complications
such as stroke.

5501
04:25:13,556 --> 04:25:16,626
Transplant will not restore
joints destroyed by AVN.

5502
04:25:17,193 --> 04:25:20,696
Borderline kidney function may
actually worsen with transplant.

5503
04:25:20,696 --> 04:25:22,198
And so, transplant education

5504
04:25:22,198 --> 04:25:24,700
is essential to provide
realistic outcomes.

5505
04:25:25,668 --> 04:25:27,303
Right now, we don’t know
if transplant

5506
04:25:27,303 --> 04:25:29,405
is the best option
for everyone.

5507
04:25:29,405 --> 04:25:31,340
Long-term studies
are needed to decide

5508
04:25:31,340 --> 04:25:32,942
what provides
the best outcomes.

5509
04:25:33,943 --> 04:25:36,212
After disease severity
has been identified

5510
04:25:36,212 --> 04:25:38,915
and expectations discussed,
the next question is,

5511
04:25:38,915 --> 04:25:40,516
what type of transplant is best.

5512
04:25:43,052 --> 04:25:44,720
Curative options
for sickle cell disease

5513
04:25:44,720 --> 04:25:47,123
can be divided into donor
versus non-donor

5514
04:25:47,123 --> 04:25:49,392
or allogeneic versus
autologous transplant.

5515
04:25:50,126 --> 04:25:51,861
Decades after
the first allogeneic

5516
04:25:51,861 --> 04:25:53,963
bone marrow transplant
in sickle cell disease

5517
04:25:53,963 --> 04:25:56,265
confirmed that indeed
a cure is possible.

5518
04:25:56,265 --> 04:25:58,868
Researchers have been
refining the treatment

5519
04:25:58,868 --> 04:26:01,904
to make it safer and more
effective and more accessible.

5520
04:26:02,805 --> 04:26:04,073
Clinical trials have shown

5521
04:26:04,073 --> 04:26:05,975
that even patients
with severe diseases

5522
04:26:05,975 --> 04:26:08,044
can tolerate
milder conditioning regimens

5523
04:26:08,044 --> 04:26:09,946
with successful outcomes.

5524
04:26:09,946 --> 04:26:11,581
Therefore,
the challenge therein lies

5525
04:26:11,581 --> 04:26:13,182
in finding a suitable donor.

5526
04:26:13,883 --> 04:26:16,252
Data has shown
that successful outcomes

5527
04:26:16,252 --> 04:26:18,321
are critically tied
to donor selection.

5528
04:26:19,088 --> 04:26:22,091
Currently, having a full HLA
match related donor

5529
04:26:22,091 --> 04:26:23,426
is the best choice,

5530
04:26:23,426 --> 04:26:26,228
but there is only
a 25 percent chance

5531
04:26:26,228 --> 04:26:28,230
that a sibling who shares
the same parents

5532
04:26:28,230 --> 04:26:30,333
may be a full HLA
or tissue match.

5533
04:26:31,267 --> 04:26:33,402
That percentage increases
to 50 percent

5534
04:26:33,402 --> 04:26:35,004
for haplo-related donors,

5535
04:26:35,004 --> 04:26:37,573
where a healthy sibling
who was not a full match,

5536
04:26:37,573 --> 04:26:40,610
a parent, or a relative can help
to enlarge the donor pool.

5537
04:26:41,777 --> 04:26:43,079
Donors with sickle cell --

5538
04:26:43,079 --> 04:26:44,847
with sickle traits
are not excluded

5539
04:26:44,847 --> 04:26:48,217
and are equally successful
as donors without.

5540
04:26:49,885 --> 04:26:51,554
But donor selection
goes beyond

5541
04:26:51,554 --> 04:26:54,357
just donor-recipient
histocompatibility.

5542
04:26:54,357 --> 04:26:59,295
Other factors such as donor age,
size, gender, viral status,

5543
04:26:59,295 --> 04:27:03,332
and ABO compatibility also play
a role in transplant outcomes.

5544
04:27:04,900 --> 04:27:06,802
Gene therapy, on the other hand,

5545
04:27:06,802 --> 04:27:09,271
eliminates the need
for a donor altogether.

5546
04:27:09,271 --> 04:27:11,440
The patient’s stem cells
are genetically modified

5547
04:27:11,440 --> 04:27:12,942
in several different ways

5548
04:27:12,942 --> 04:27:14,744
to cure them
of sickle cell disease.

5549
04:27:15,711 --> 04:27:18,014
Gene addition involves
adding one or more copies

5550
04:27:18,014 --> 04:27:21,717
of the normal beta-globin gene
like bluebird bio’s LYFGENIA,

5551
04:27:21,717 --> 04:27:24,320
which was recently FDA approved
in the U.S.

5552
04:27:24,320 --> 04:27:25,921
for treatment
of severe disease.

5553
04:27:26,656 --> 04:27:29,525
Gene edit transplant
changes the existing gene

5554
04:27:29,525 --> 04:27:31,727
to alter production
of fetal hemoglobin.

5555
04:27:32,294 --> 04:27:35,331
CRISPR Therapeutics’ CASGEVY
reactivates gamma globin

5556
04:27:35,331 --> 04:27:38,034
to increase fetal hemoglobin,
which is not sickle,

5557
04:27:38,034 --> 04:27:41,570
thereby curing the patient
by reducing or eliminated --

5558
04:27:41,570 --> 04:27:43,305
eliminating
sickle complications.

5559
04:27:44,373 --> 04:27:47,143
Lastly, gene correction is the
next level in gene therapies,

5560
04:27:47,143 --> 04:27:49,912
which works by actually
correcting the sickle mutation.

5561
04:27:52,682 --> 04:27:55,117
Once we know what type
of transplant a patient

5562
04:27:55,117 --> 04:27:56,385
is eligible for,

5563
04:27:56,385 --> 04:27:59,455
we explain the procedures
involved to prepare

5564
04:27:59,455 --> 04:28:02,825
and go through transplant.
For the allotransplants,

5565
04:28:02,825 --> 04:28:05,261
the stem cells
are collected from the donor.

5566
04:28:05,261 --> 04:28:06,562
Meanwhile, for the patient,

5567
04:28:06,562 --> 04:28:09,331
they start to undergo
their pre-transplant testing,

5568
04:28:09,331 --> 04:28:12,101
like dental exams,
pulmonary function tests,

5569
04:28:12,101 --> 04:28:15,004
bone marrow biopsies,
social work, et cetera,

5570
04:28:15,004 --> 04:28:18,174
and possibly auto collection
for backup from the recipient

5571
04:28:18,174 --> 04:28:19,775
is occurring simultaneously.

5572
04:28:20,509 --> 04:28:23,512
This may take up to about two
to three months to accomplish.

5573
04:28:24,046 --> 04:28:25,614
After all the pre-transplant
tests

5574
04:28:25,614 --> 04:28:27,249
and procedures are done,

5575
04:28:27,249 --> 04:28:29,985
the patient is admitted
to the hospital for transplant.

5576
04:28:30,619 --> 04:28:33,355
Month-long admission includes
preparing the recipient

5577
04:28:33,355 --> 04:28:35,324
in the first half
of the hospitalization,

5578
04:28:35,324 --> 04:28:36,525
excuse me,

5579
04:28:36,525 --> 04:28:38,994
by using non-myeloablative
conditioning regimens

5580
04:28:39,595 --> 04:28:41,230
that make space
in the marrow --

5581
04:28:41,230 --> 04:28:44,200
that’s so by using chemo
or radiation --

5582
04:28:44,200 --> 04:28:47,837
reduces or lowers
the immune system,

5583
04:28:47,837 --> 04:28:51,307
either, again, by chemoradiation
and antibody therapies,

5584
04:28:51,307 --> 04:28:54,310
and then medications to prevent
graft versus host disease,

5585
04:28:55,211 --> 04:28:58,347
such as sirolimus or post-
transplant cyclophosphamide.

5586
04:28:59,915 --> 04:29:03,419
Infusion of donor stem cells
is typically one day,

5587
04:29:03,419 --> 04:29:04,754
and the remainder
of the hospital stay

5588
04:29:04,754 --> 04:29:06,856
is waiting for
the donor cells to engraft.

5589
04:29:07,690 --> 04:29:10,292
After discharge, patients return
to clinic for frequent

5590
04:29:10,292 --> 04:29:13,162
monitoring for
transplant-related complications

5591
04:29:13,162 --> 04:29:16,766
such as viral reactivation,
GVHD monitoring,

5592
04:29:16,766 --> 04:29:18,667
and to adjust
transplant medications.

5593
04:29:19,268 --> 04:29:21,771
After about 100 days
after transplant,

5594
04:29:21,771 --> 04:29:24,473
the patient is typically
stable enough to return home.

5595
04:29:25,608 --> 04:29:29,378
For the auto transplant,
the hydroxyurea is actually

5596
04:29:29,378 --> 04:29:33,249
stopped in advance
of their auto collection,

5597
04:29:33,249 --> 04:29:34,950
and the patient is started
on red cell

5598
04:29:34,950 --> 04:29:36,552
exchanges for about two months.

5599
04:29:38,120 --> 04:29:43,092
Same thing, part of the
preparation is going under --

5600
04:29:43,092 --> 04:29:45,394
undergoing all
the pre-transplant testings

5601
04:29:45,394 --> 04:29:46,996
that I mentioned before.

5602
04:29:48,063 --> 04:29:49,965
After the auto collection,
this time,

5603
04:29:49,965 --> 04:29:52,935
instead of banking it,
the cells are actually sent off

5604
04:29:52,935 --> 04:29:55,171
to manufacturing
for genetic modification.

5605
04:29:55,704 --> 04:29:57,706
And this may require
more than one collection

5606
04:29:57,706 --> 04:29:59,608
to reach adequate cell dosing.

5607
04:30:02,912 --> 04:30:05,147
When the patient is admitted
for their month-long stay,

5608
04:30:05,147 --> 04:30:08,384
the recipient now receives
full myeloablative conditioning

5609
04:30:08,384 --> 04:30:09,852
to make enough space
in the marrow

5610
04:30:09,852 --> 04:30:13,088
to give the genetically modified
stem cells advantage to engraft.

5611
04:30:13,756 --> 04:30:15,825
In the future, the hope is
that this can be achieved

5612
04:30:15,825 --> 04:30:17,226
using antibody therapy

5613
04:30:17,226 --> 04:30:19,562
and avoid the toxicity
associated with chemo.

5614
04:30:22,164 --> 04:30:24,233
These modified
stem cells make --

5615
04:30:24,233 --> 04:30:25,568
then make healthy red cells.

5616
04:30:25,568 --> 04:30:28,370
And similar to allotransplant,
once discharged,

5617
04:30:28,370 --> 04:30:31,173
the patients are followed
closely as an outpatient

5618
04:30:31,173 --> 04:30:34,543
for any post-transplant
complications for up to 90 days.

5619
04:30:37,713 --> 04:30:39,748
Next question we may receive

5620
04:30:39,748 --> 04:30:42,117
is what are the possible
side effects?

5621
04:30:44,553 --> 04:30:46,822
This slide can be divided
into those side effects

5622
04:30:46,822 --> 04:30:49,191
that may be considered par
for the course,

5623
04:30:49,191 --> 04:30:51,660
such as nausea,
vomiting, hair loss.

5624
04:30:52,828 --> 04:30:56,765
So, those are the ones that we
anticipate will likely happen.

5625
04:30:56,765 --> 04:30:59,134
Then on the other side are
the ones that we try to avoid,

5626
04:30:59,134 --> 04:31:00,870
such as graft
versus host disease,

5627
04:31:01,503 --> 04:31:04,840
the secondary malignancies,
transplant failure,

5628
04:31:05,441 --> 04:31:08,510
infertility, seizures,
and tragically, even death.

5629
04:31:11,180 --> 04:31:13,482
These risks are higher or lower

5630
04:31:13,482 --> 04:31:16,585
depending on the degree
of conditioning.

5631
04:31:16,585 --> 04:31:18,254
So, this slide shows
the difference

5632
04:31:18,254 --> 04:31:21,390
between our partial or mini
bone marrow transplant

5633
04:31:21,390 --> 04:31:22,892
versus the full transplant.

5634
04:31:22,892 --> 04:31:26,095
You can see the differences
in the success rates,

5635
04:31:26,095 --> 04:31:29,031
what can happen
with blood counts, infections,

5636
04:31:29,031 --> 04:31:32,134
the incidences of graft
versus host disease,

5637
04:31:32,968 --> 04:31:35,604
and other complications
such as thyroid disease.

5638
04:31:38,741 --> 04:31:41,076
Fourth common question is
how successful

5639
04:31:41,076 --> 04:31:42,811
is the transplant?

5640
04:31:42,811 --> 04:31:47,182
The answer may be influenced by
factors such as age of recipient

5641
04:31:47,182 --> 04:31:50,386
at the time of transplant,
organ injury, donor selection,

5642
04:31:50,386 --> 04:31:51,987
and conditioning regimen used.

5643
04:31:53,756 --> 04:31:56,892
But overall,
the success rate is favorable.

5644
04:31:56,892 --> 04:32:00,296
90 to 95 percent for a matched
sibling donor transplant,

5645
04:32:00,796 --> 04:32:03,499
80 to 90 percent
success rate for haplo,

5646
04:32:04,033 --> 04:32:06,468
and then 80 to 90 percent
of gene therapy

5647
04:32:06,468 --> 04:32:07,803
will have some
kind of effect,

5648
04:32:07,803 --> 04:32:12,341
like increased in the modified
gene hemoglobin T87Q

5649
04:32:12,341 --> 04:32:13,943
or increased fetal hemoglobin,

5650
04:32:13,943 --> 04:32:15,644
depending on type
of gene therapy.

5651
04:32:16,979 --> 04:32:19,615
Overall, the younger the patient
and the younger the donor

5652
04:32:19,615 --> 04:32:23,252
seem to be the best combination
for good transplant outcomes.

5653
04:32:26,455 --> 04:32:30,726
Last question is what is
the recovery process like?

5654
04:32:34,096 --> 04:32:36,298
I touched on this
a little bit before,

5655
04:32:36,298 --> 04:32:37,766
but the post-transplant
clinical trial

5656
04:32:37,766 --> 04:32:40,269
is essentially the same
for most transplant

5657
04:32:40,269 --> 04:32:42,104
with frequent monitoring

5658
04:32:42,104 --> 04:32:45,307
for both allo
and auto transplants,

5659
04:32:46,108 --> 04:32:48,877
and that goes up to about
100 days after transplant.

5660
04:32:49,511 --> 04:32:52,314
They may have a repeat
of pre-transplant testing,

5661
04:32:52,314 --> 04:32:54,350
like we might repeat
the echo [laughs],

5662
04:32:54,350 --> 04:32:56,385
the pulmonary
function testing.

5663
04:32:57,419 --> 04:33:00,556
I’m telling you; it comes back
to bite you right at the time.

5664
04:33:00,556 --> 04:33:02,124
[laughter]

5665
04:33:02,124 --> 04:33:04,927
Bone marrow transplants,
bone marrow biopsies,

5666
04:33:05,627 --> 04:33:07,963
not only for clinical
purposes now, but for --

5667
04:33:08,497 --> 04:33:11,166
especially as it relates
to clinical research trial

5668
04:33:11,166 --> 04:33:13,769
where we’re evaluating
the long-term effects

5669
04:33:13,769 --> 04:33:15,871
of transplant
on organ function over time.

5670
04:33:17,172 --> 04:33:20,342
There is more intensive follow
up required for gene therapies,

5671
04:33:20,342 --> 04:33:23,612
with the U.S. FDA mandating
at least 15 years

5672
04:33:23,612 --> 04:33:25,247
of follow up post-transplant.

5673
04:33:27,316 --> 04:33:29,752
Medications, for some patients,

5674
04:33:29,752 --> 04:33:31,253
they may leave
on more medications

5675
04:33:31,253 --> 04:33:32,621
than they actually
came in with,

5676
04:33:32,621 --> 04:33:35,357
so that requires [laughs]
a lot of education,

5677
04:33:35,357 --> 04:33:37,993
especially about their
immunosuppressive medications,

5678
04:33:37,993 --> 04:33:39,728
antivirals,
antibacterials.

5679
04:33:40,796 --> 04:33:42,865
So, some of these meds
may stay on

5680
04:33:42,865 --> 04:33:44,700
until the patient
is revaccinated,

5681
04:33:44,700 --> 04:33:46,769
if needed for
the allotransplant.

5682
04:33:47,503 --> 04:33:49,338
One of the previous
slides again

5683
04:33:49,338 --> 04:33:51,073
talked about
thyroid dysfunction.

5684
04:33:51,874 --> 04:33:54,309
So, there may be some meds
that have to be added

5685
04:33:54,309 --> 04:33:58,147
that they hadn’t expected,
like high blood pressure

5686
04:33:58,147 --> 04:33:59,882
or a number of other
health issues

5687
04:34:00,549 --> 04:34:02,217
as it relates
to the transplant.

5688
04:34:03,152 --> 04:34:06,155
Education on the importance
of post-transplant medication

5689
04:34:06,155 --> 04:34:08,223
adherence is necessary.

5690
04:34:08,223 --> 04:34:11,293
I always emphasize how failing
to take post-transplant meds

5691
04:34:11,293 --> 04:34:14,496
as prescribed can attribute
to even losing the graft.

5692
04:34:15,330 --> 04:34:18,367
To go through this whole thing
only to lose it for a reason,

5693
04:34:18,367 --> 04:34:22,237
such as not taking your
medications, would be tragic.

5694
04:34:22,237 --> 04:34:25,607
And in fact,
Dr. Courtney Fitzhugh

5695
04:34:25,607 --> 04:34:29,478
considers a patient’s medication
compliance before transplants.

5696
04:34:29,478 --> 04:34:31,513
It’s a good gauge to see

5697
04:34:31,513 --> 04:34:33,816
if they will be compliant
post transplants.

5698
04:34:33,816 --> 04:34:35,417
She doesn’t play that.

5699
04:34:37,719 --> 04:34:41,223
So similar to my slide
that showed key hematologic

5700
04:34:41,223 --> 04:34:44,193
changes to the standard
sickle cell treatment --

5701
04:34:44,193 --> 04:34:45,694
treatments,
this slide now shows

5702
04:34:45,694 --> 04:34:49,298
the hematological
changes on hydroxyurea,

5703
04:34:49,865 --> 04:34:52,468
red cell exchange,
and then the final three boxes

5704
04:34:52,468 --> 04:34:54,970
based on auto
versus allotransplant.

5705
04:35:00,542 --> 04:35:03,745
So, my final thoughts as a
transplant referral coordinator.

5706
04:35:03,745 --> 04:35:07,749
My goal is to provide
as much education as I can

5707
04:35:07,749 --> 04:35:10,319
so that patients can make
an informed decision

5708
04:35:10,319 --> 04:35:12,287
to review not only
the potential benefits

5709
04:35:12,287 --> 04:35:14,756
but also to discuss
the potential risks.

5710
04:35:15,357 --> 04:35:18,193
Dr. Mathew Hsieh often says
that during these talks

5711
04:35:18,193 --> 04:35:20,262
that it’s our job
to kind of be the bad guy,

5712
04:35:20,262 --> 04:35:22,931
the Debbie Downer,
and emphasize the risks,

5713
04:35:23,465 --> 04:35:26,101
because this is not a decision
to be made lightly.

5714
04:35:27,803 --> 04:35:31,039
Matt provides other options
for consideration,

5715
04:35:31,039 --> 04:35:34,343
even when the option
may not even be transplant.

5716
04:35:35,110 --> 04:35:37,279
Lastly, making sure
that patients have realistic

5717
04:35:37,279 --> 04:35:38,981
expectations and understand

5718
04:35:38,981 --> 04:35:41,817
that the goal is to move
the needle towards overall

5719
04:35:41,817 --> 04:35:43,418
improved quality of life.

5720
04:35:45,654 --> 04:35:47,356
So, I’d like to thank you all
for your time

5721
04:35:47,356 --> 04:35:48,590
and to the clinical teams

5722
04:35:48,590 --> 04:35:50,959
that I’m blessed to work
with every day.

5723
04:35:50,959 --> 04:35:54,296
A special thank you to John
for inviting me and for --

5724
04:35:54,296 --> 04:35:56,732
to Swee Lay
for inviting me as well.

5725
04:35:56,732 --> 04:36:00,769
I’d like to thank Matt Hsieh
for helping with the slides.

5726
04:36:00,769 --> 04:36:04,540
I’ve actually called him
my Matt GPT instead of ChatGPT.

5727
04:36:04,540 --> 04:36:06,175
[laughter]

5728
04:36:06,175 --> 04:36:08,944
And again, just to John
for supporting me

5729
04:36:08,944 --> 04:36:11,513
over these many,
many, many years. Thank you.

5730
04:36:12,047 --> 04:36:17,186
[applause]

5731
04:36:17,186 --> 04:36:18,787
John Tisdale: I see the hands
up already.

5732
04:36:19,488 --> 04:36:22,024
So, let’s start with Haydar
and then Jane.

5733
04:36:33,001 --> 04:36:34,336
Haydar Frangoul: That’s great.

5734
04:36:34,336 --> 04:36:36,505
You did a great job
explaining the story here.

5735
04:36:36,505 --> 04:36:39,708
So, what is your experience
when you talk to patients

5736
04:36:39,708 --> 04:36:43,579
about a gene therapy
versus an allo?

5737
04:36:43,579 --> 04:36:46,615
In allo, you can go to
within a month.

5738
04:36:47,349 --> 04:36:50,052
While gene therapy, it’s a year
before they get the product.

5739
04:36:50,052 --> 04:36:52,287
Although, on the back end,
they recover much faster

5740
04:36:52,287 --> 04:36:54,289
because it’s
an autologous graft.

5741
04:36:54,289 --> 04:36:55,791
What’s your experience in that

5742
04:36:55,791 --> 04:36:58,126
when patients
are making decisions

5743
04:36:58,126 --> 04:37:00,462
between gene therapy
and allogeneic transplant?

5744
04:37:01,330 --> 04:37:04,666
Wynona Coles: I mean, I think
the way we’re headed

5745
04:37:04,666 --> 04:37:09,771
is going to be gene therapy.
And I think for patients,

5746
04:37:09,771 --> 04:37:11,940
they also are kind of leaning
in that direction

5747
04:37:11,940 --> 04:37:14,243
because they don’t want to have
to involve a family member.

5748
04:37:14,243 --> 04:37:15,444
A lot of them are concerned,

5749
04:37:15,444 --> 04:37:17,546
"What’s going to happen
to my donor

5750
04:37:17,546 --> 04:37:20,616
who’s decided
to do this procedure?"

5751
04:37:20,616 --> 04:37:24,319
So, I think gene therapy
is definitely the future.

5752
04:37:24,319 --> 04:37:25,587
And even the time thing

5753
04:37:25,587 --> 04:37:28,023
isn’t really that big of
a difference for them.

5754
04:37:28,023 --> 04:37:30,425
I think they’re just
more concerned about,

5755
04:37:30,425 --> 04:37:32,995
"Well, if I don’t have to
involve my brother, sister,

5756
04:37:32,995 --> 04:37:35,797
my mom, dad, or whatever,
I prefer that route, actually.

5757
04:37:35,797 --> 04:37:38,934
If this can just all be on me
is what I would prefer."

5758
04:37:40,302 --> 04:37:42,004
Jane Little: Nona,
that was really great.

5759
04:37:42,004 --> 04:37:43,872
Thank you so much.

5760
04:37:43,872 --> 04:37:46,775
You know,
I looked at your slides,

5761
04:37:46,775 --> 04:37:48,343
which are --
some of which are great.

5762
04:37:48,343 --> 04:37:50,279
There was you or Matt.
Doesn’t matter [laughs].

5763
04:37:50,279 --> 04:37:52,948
But, you know, not every center
is going to have a Nona.

5764
04:37:52,948 --> 04:37:54,816
Like you’re not that common,
I hate to tell you.

5765
04:37:54,816 --> 04:37:56,018
[laughter]

5766
04:37:56,018 --> 04:38:00,656
And I do believe that the
personal discussions

5767
04:38:00,656 --> 04:38:01,990
are the most,

5768
04:38:01,990 --> 04:38:04,526
you know, important part
of that counseling.

5769
04:38:04,526 --> 04:38:08,930
Are there any web-based tools
that you found help you for --

5770
04:38:08,930 --> 04:38:10,999
you know, to help get
people up to speed?

5771
04:38:12,701 --> 04:38:14,936
Wynona Coles: I wish there were
[laughs],

5772
04:38:14,936 --> 04:38:18,607
because I do have
a large backlog of patients

5773
04:38:18,607 --> 04:38:20,575
just to get back
in touch with them.

5774
04:38:20,575 --> 04:38:22,844
No, I don’t.
I don’t know of any.

5775
04:38:22,844 --> 04:38:27,582
I’m not aware of any. I’m happy
to consider that for sure.

5776
04:38:28,317 --> 04:38:31,787
I think people now --
their phone is their friend,

5777
04:38:31,787 --> 04:38:33,755
is their tool.
That’s what they use.

5778
04:38:34,756 --> 04:38:37,793
We’re starting to --
support groups are also great,

5779
04:38:37,793 --> 04:38:40,062
just talking to other people
in the community.

5780
04:38:40,929 --> 04:38:43,465
YouTube podcast is just where
a lot of people

5781
04:38:43,465 --> 04:38:45,300
are finding their information.

5782
04:38:45,300 --> 04:38:47,869
Now, as far as getting plugged
into being able to enroll

5783
04:38:47,869 --> 04:38:49,104
into a clinical trial,

5784
04:38:49,104 --> 04:38:52,040
I think that part
is still backed up.

5785
04:38:52,040 --> 04:38:54,976
So, you know, if we get
referrals from mouth to mouth

5786
04:38:54,976 --> 04:38:58,680
or from a physician through our
office of patient recruitment,

5787
04:38:58,680 --> 04:39:01,083
but it’s still a backlog,
so we’re just waiting.

5788
04:39:02,217 --> 04:39:04,653
I guess I’ve heard other people
say, you know, thankfully,

5789
04:39:04,653 --> 04:39:07,255
this isn’t a decision
that has to be made overnight,

5790
04:39:07,789 --> 04:39:10,225
so that we do have
a little bit of luxury of time

5791
04:39:10,225 --> 04:39:14,396
for you to consider the benefits
and the risks of moving forward.

5792
04:39:14,396 --> 04:39:16,798
But yeah, Jane, I wish there
was something like that,

5793
04:39:16,798 --> 04:39:18,367
but not that I’m aware of.

5794
04:39:18,367 --> 04:39:19,968
Jane Little: Thank you.

5795
04:39:21,203 --> 04:39:22,804
John Tisdale: Other questions?

5796
04:39:27,509 --> 04:39:29,444
Manu Platt: Thank you.
It’s an easy one, Nona.

5797
04:39:30,178 --> 04:39:32,414
How does this conversation
change if it’s a child,

5798
04:39:32,414 --> 04:39:35,884
and at what age do you
explain it to the child

5799
04:39:35,884 --> 04:39:37,853
or just the parents?
How do you make those decisions?

5800
04:39:37,853 --> 04:39:39,054
Wynona Coles: Yeah.

5801
04:39:39,054 --> 04:39:41,289
So, you know, obviously,
it’s going to be the parents

5802
04:39:41,289 --> 04:39:43,225
that are reaching out
to us for this.

5803
04:39:43,225 --> 04:39:48,497
But working with Matt
and Emily Limerick,

5804
04:39:48,497 --> 04:39:52,467
Mary Lacy Grecco, Liz Harvey,
they’re just phenomenal

5805
04:39:53,201 --> 04:39:57,773
with speaking even to
the younger patient population.

5806
04:39:57,773 --> 04:40:00,342
They really do explain it
in terms that they understand.

5807
04:40:00,342 --> 04:40:02,778
Obviously, when it comes
to clinical trials,

5808
04:40:02,778 --> 04:40:07,048
we have assent from
these pediatric patients.

5809
04:40:07,048 --> 04:40:10,018
We have assent
informed documents

5810
04:40:10,018 --> 04:40:13,989
that it’s broken down into
that level of understanding.

5811
04:40:15,257 --> 04:40:19,628
Also, at the NIH,
we have a psychosocial --

5812
04:40:19,628 --> 04:40:22,063
social work department
that’s responsible

5813
04:40:22,063 --> 04:40:24,499
for the pediatric donors,

5814
04:40:25,066 --> 04:40:27,903
making sure that they clearly
understand their role too.

5815
04:40:27,903 --> 04:40:30,071
I think it’s pivotal for them
to understand

5816
04:40:30,071 --> 04:40:34,075
that if they do this
for their sibling

5817
04:40:34,075 --> 04:40:36,978
who has sickle cell disease
and the transplant doesn’t work,

5818
04:40:36,978 --> 04:40:40,315
what kind of guilt does that
put on that young child.

5819
04:40:40,315 --> 04:40:42,951
So, they have
several counseling sessions

5820
04:40:42,951 --> 04:40:46,121
with the psychosocial group
at NIH, which is key.

5821
04:40:47,088 --> 04:40:48,723
And then it’s just
constant education

5822
04:40:48,723 --> 04:40:51,326
as much as we can
for the younger kids.

5823
04:40:51,326 --> 04:40:53,929
We have a great
pediatric department as well.

5824
04:40:53,929 --> 04:40:55,931
So, I think it’s just
constantly having

5825
04:40:55,931 --> 04:40:57,532
that conversation with them.

5826
04:40:59,100 --> 04:41:00,402
Male Speaker: Yeah.

5827
04:41:00,402 --> 04:41:01,703
I was wondering
if you could speak more

5828
04:41:01,703 --> 04:41:04,473
to the international referrals
you get

5829
04:41:04,473 --> 04:41:07,442
and how you deal with that kind
of more complicated issue

5830
04:41:07,442 --> 04:41:09,377
of an international patient.

5831
04:41:10,846 --> 04:41:12,047
Wynona Coles: Yeah.

5832
04:41:12,047 --> 04:41:15,917
International patients
are great [laughs].

5833
04:41:15,917 --> 04:41:17,886
It’s always fun
trying to figure out visas

5834
04:41:17,886 --> 04:41:19,354
and how they’re
going to get here.

5835
04:41:19,354 --> 04:41:24,092
So, while we do it,
and in fact,

5836
04:41:24,092 --> 04:41:27,462
we’re doing more
and more international patients,

5837
04:41:27,462 --> 04:41:30,632
it just takes more
in terms of logistics.

5838
04:41:32,467 --> 04:41:35,837
It’s really difficult to receive
medical records that --

5839
04:41:36,972 --> 04:41:40,909
to help establish
the eligibility criteria.

5840
04:41:40,909 --> 04:41:43,278
So, you know, we’re constantly
being monitored to make sure

5841
04:41:43,278 --> 04:41:46,081
that all the patients
are appropriately enrolled.

5842
04:41:46,081 --> 04:41:48,316
And when we have to review,
you know,

5843
04:41:48,316 --> 04:41:52,721
handwritten paper documents
that may have been, you know --

5844
04:41:52,721 --> 04:41:54,289
it’s just
really difficult [laughs]

5845
04:41:54,289 --> 04:41:56,324
to get some of
these documents.

5846
04:41:56,324 --> 04:41:59,661
And a lot of centers don’t even
keep long-term documentation,

5847
04:41:59,661 --> 04:42:01,263
so that also
makes it difficult.

5848
04:42:02,931 --> 04:42:06,368
I think what’s great
from our point of view

5849
04:42:06,368 --> 04:42:09,437
is that we kind of just give
international patients

5850
04:42:09,437 --> 04:42:10,805
benefit the doubt, right?

5851
04:42:10,805 --> 04:42:13,575
Because we understand that
there’s all these challenges

5852
04:42:13,575 --> 04:42:14,943
to keeping medical records

5853
04:42:14,943 --> 04:42:16,978
and getting those
medical records to you.

5854
04:42:16,978 --> 04:42:22,851
And so, we offer,
at the NIH, for --

5855
04:42:22,851 --> 04:42:25,921
if you can get to NIH,
then I think after that,

5856
04:42:25,921 --> 04:42:27,522
everything is
pretty much covered.

5857
04:42:27,522 --> 04:42:29,791
So, of course,
the transplant is covered.

5858
04:42:29,791 --> 04:42:31,026
Medications are covered.

5859
04:42:31,026 --> 04:42:32,861
In some instances,
housing is covered.

5860
04:42:32,861 --> 04:42:34,129
Everything is covered.

5861
04:42:34,129 --> 04:42:35,764
So, it’s nice.
So, it’s just getting here.

5862
04:42:35,764 --> 04:42:40,168
And then also the commitment of
making sure that you can return,

5863
04:42:40,168 --> 04:42:42,804
because again, we’re collecting
information and data.

5864
04:42:44,773 --> 04:42:48,843
And then we again assist
with visa extensions

5865
04:42:48,843 --> 04:42:50,745
as long as you’re needed
to be there.

5866
04:42:50,745 --> 04:42:54,382
So, we do have a good system
all kind of set up

5867
04:42:55,116 --> 04:42:56,718
for the international referrals.

5868
04:42:56,718 --> 04:42:58,620
It’s just that we still need
a lot of --

5869
04:42:58,620 --> 04:43:01,856
we still have a lot
of physical barriers,

5870
04:43:01,856 --> 04:43:05,794
such as getting them here
and then when they return.

5871
04:43:05,794 --> 04:43:07,262
So sometimes
we have to make sure

5872
04:43:07,262 --> 04:43:10,765
that you can receive
post-transplant medications.

5873
04:43:10,765 --> 04:43:12,901
Does your country
take FedEx packages?

5874
04:43:13,835 --> 04:43:16,972
Do you have a physician
that’s going to be able to feel

5875
04:43:16,972 --> 04:43:18,740
comfortable taking care
of you, post-transplant,

5876
04:43:18,740 --> 04:43:20,775
in case there’s any
long-term complications?

5877
04:43:20,775 --> 04:43:22,944
So those are things
we have to check for.

5878
04:43:22,944 --> 04:43:24,446
John Tisdale: All right.

5879
04:43:24,446 --> 04:43:25,647
Wynona Coles: All right.

5880
04:43:25,647 --> 04:43:27,248
John Tisdale: Thank you
so much, Nona.

5881
04:43:28,917 --> 04:43:30,251
Wynona Coles: Thank you.

5882
04:43:30,251 --> 04:43:31,453
[applause]

5883
04:43:31,453 --> 04:43:32,754
John Tisdale: Okay.
So, our next talk

5884
04:43:32,754 --> 04:43:35,824
is a recorded talk
from Julie Kanter,

5885
04:43:35,824 --> 04:43:38,727
who’s the director
of the adult sickle cell clinic

5886
04:43:38,727 --> 04:43:41,162
at the University of Alabama
at Birmingham.

5887
04:43:42,063 --> 04:43:44,966
So, if you can
queue that up.

5888
04:43:44,966 --> 04:43:47,002
Julie Kanter: Hi.
Thank you so much to the --

5889
04:43:48,269 --> 04:43:49,871
these are my --

5890
04:44:04,386 --> 04:44:05,720
hi.

5891
04:44:05,720 --> 04:44:07,455
Thank you so much
to the organizers

5892
04:44:07,455 --> 04:44:09,290
for having me here
with you today.

5893
04:44:09,290 --> 04:44:12,227
And I’m so sorry that I cannot
be there in Jamaica

5894
04:44:12,227 --> 04:44:15,830
with you in person.
My name is Dr. Julie Kanter.

5895
04:44:16,531 --> 04:44:19,601
I’m the director of our adult
sickle cell disease program

5896
04:44:19,601 --> 04:44:21,903
at the University of Alabama
in Birmingham.

5897
04:44:22,804 --> 04:44:24,272
And today,
I’m going to talk about

5898
04:44:24,272 --> 04:44:27,942
how we measure the therapeutic
efficacy of genetic therapies

5899
04:44:27,942 --> 04:44:29,544
for sickle cell disease.

5900
04:44:32,781 --> 04:44:37,585
Hi. Thank you so much to the --
these are my disclosures.

5901
04:44:39,888 --> 04:44:42,057
So, I hope at the end
of this time,

5902
04:44:42,057 --> 04:44:43,358
you will have
a better understanding

5903
04:44:43,358 --> 04:44:46,728
of why we’re using gene therapy
for sickle cell disease,

5904
04:44:46,728 --> 04:44:49,964
though I think you’ve heard
quite a lot about that already.

5905
04:44:49,964 --> 04:44:52,701
You will also have an increased
understanding of the current

5906
04:44:52,701 --> 04:44:54,736
two genetic therapies
that have been approved

5907
04:44:54,736 --> 04:44:57,005
by the FDA
for sickle cell disease

5908
04:44:57,005 --> 04:44:59,674
and a few that are on their way
down the pipeline.

5909
04:44:59,674 --> 04:45:01,710
And then, of course, our focus.
We’re going to talk about

5910
04:45:01,710 --> 04:45:04,312
how we’re going to measure
the therapeutic efficacy.

5911
04:45:06,281 --> 04:45:08,583
So, why sickle cell disease?

5912
04:45:09,284 --> 04:45:11,519
Well, I know
this audience understands

5913
04:45:11,519 --> 04:45:14,989
the devastating complications
of sickle cell disease.

5914
04:45:14,989 --> 04:45:17,926
It affects every organ,
as every organ relies on blood.

5915
04:45:18,993 --> 04:45:20,795
And the downstream
effect of pain

5916
04:45:20,795 --> 04:45:25,667
due to vaso-occlusion and tissue
damage is truly horrendous,

5917
04:45:25,667 --> 04:45:28,703
and we need a better therapy
to improve our outcomes.

5918
04:45:29,671 --> 04:45:31,940
But specifically,
why sickle cell disease?

5919
04:45:31,940 --> 04:45:35,343
Because it is actually a genetic
abnormality, as you all know.

5920
04:45:36,044 --> 04:45:38,980
And the idea is, if we could fix
that genetic abnormality,

5921
04:45:38,980 --> 04:45:43,051
perhaps we can change or alter
all of these downstream effects.

5922
04:45:45,153 --> 04:45:46,988
So, when we think about
the current treatment

5923
04:45:46,988 --> 04:45:48,757
strategies
for sickle cell disease --

5924
04:45:48,757 --> 04:45:50,291
and I do want to credit
my colleague,

5925
04:45:50,291 --> 04:45:52,360
John Tisdale,
for this fantastic slide --

5926
04:45:53,128 --> 04:45:55,697
we think about the many
different drug treatments

5927
04:45:55,697 --> 04:45:58,800
that are both available now
and coming down the pipeline.

5928
04:45:59,667 --> 04:46:02,070
We think about the importance
of transplant

5929
04:46:02,070 --> 04:46:04,072
and allogeneic transplant
specifically,

5930
04:46:04,072 --> 04:46:06,674
which is curative
for sickle cell disease.

5931
04:46:07,609 --> 04:46:09,744
We also think about
blood transfusions

5932
04:46:09,744 --> 04:46:12,447
and how impactful they have been
on the care of individuals

5933
04:46:12,447 --> 04:46:14,048
with sickle cell disease.

5934
04:46:14,048 --> 04:46:17,218
And then the future
of in-vivo gene therapies,

5935
04:46:17,218 --> 04:46:20,221
as well as our current gene
therapies, gene addition,

5936
04:46:20,221 --> 04:46:22,257
and gene editing
that have been approved.

5937
04:46:24,125 --> 04:46:25,627
So, what are the goals
of treatment

5938
04:46:25,627 --> 04:46:28,329
with these genetic therapies
in sickle cell disease?

5939
04:46:29,230 --> 04:46:31,199
Well, as everyone knows,
and as I mentioned,

5940
04:46:31,199 --> 04:46:32,967
this is a genetic disorder.

5941
04:46:32,967 --> 04:46:36,504
So, in theory, our goal
is to actually fix the gene

5942
04:46:36,504 --> 04:46:38,106
that causes sickle cell disease.

5943
04:46:38,773 --> 04:46:41,342
At current, none of our
therapies actually do that.

5944
04:46:41,910 --> 04:46:45,013
However, they use different
methods to reduce red blood cell

5945
04:46:45,013 --> 04:46:47,615
sickling by adding
healthy hemoglobin.

5946
04:46:48,449 --> 04:46:50,518
This will help improve
oxygen delivery,

5947
04:46:50,518 --> 04:46:52,320
eliminate or reduce hemolysis,

5948
04:46:53,221 --> 04:46:55,456
decrease adhesion
within the blood vessels,

5949
04:46:56,324 --> 04:46:58,726
which, of course,
should prevent vaso-occlusion,

5950
04:46:59,294 --> 04:47:01,763
reduce or eliminate
blood vessel injury,

5951
04:47:01,763 --> 04:47:03,198
and prevent the organ damage

5952
04:47:03,198 --> 04:47:05,767
that is caused by all of
those mechanisms I listed.

5953
04:47:07,535 --> 04:47:09,704
There are other goals
of treatment as well.

5954
04:47:10,238 --> 04:47:12,073
These are related to function.

5955
04:47:12,073 --> 04:47:14,642
We want to improve
or resolve fatigue,

5956
04:47:15,543 --> 04:47:20,014
pain, decrease the organ damage
that causes so much discomfort,

5957
04:47:20,014 --> 04:47:22,350
improve energy
due to improving anemia,

5958
04:47:23,284 --> 04:47:25,854
and then, of course, be able
to improve attention, memory,

5959
04:47:25,854 --> 04:47:28,489
and focus
and so many other functions

5960
04:47:28,489 --> 04:47:31,259
that are lost during
complications of sickle cell.

5961
04:47:33,394 --> 04:47:35,663
So, let’s spend just a minute
reviewing the current gene

5962
04:47:35,663 --> 04:47:38,533
therapies with a focus
on the recent FDA approvals.

5963
04:47:39,934 --> 04:47:44,305
Lovo-cel is a one-time ex-vivo
lentiviral vector gene therapy.

5964
04:47:45,039 --> 04:47:46,941
It introduces
a beta-globin gene

5965
04:47:47,642 --> 04:47:49,577
modified for the production
of hemoglobin

5966
04:47:49,577 --> 04:47:52,513
A, specifically with
the mutation T87Q

5967
04:47:53,081 --> 04:47:55,516
that makes it
a little more anti-sickling.

5968
04:47:55,516 --> 04:47:58,553
But very important,
this does not remove or change

5969
04:47:58,553 --> 04:48:00,521
any of the existing genes.

5970
04:48:00,521 --> 04:48:03,124
So, we’d love
the ideal goal of gene therapy

5971
04:48:03,124 --> 04:48:05,793
to actually fix
the hemoglobin S mutation.

5972
04:48:06,461 --> 04:48:07,695
But this doesn’t do that,

5973
04:48:07,695 --> 04:48:10,632
and instead it adds a new gene
that makes hemoglobin A.

5974
04:48:13,134 --> 04:48:16,371
These are the articles
discussing lovo-cel gene therapy

5975
04:48:16,371 --> 04:48:18,072
that I’m going
to reference today.

5976
04:48:20,141 --> 04:48:23,244
Then we have exa-cel,
an ex-vivo gene editing therapy

5977
04:48:23,912 --> 04:48:27,582
that first uses electroporation
and then uses CRISPR-Cas9,

5978
04:48:27,582 --> 04:48:30,485
which targets the BCL11A
erythroid-specific enhancer.

5979
04:48:31,586 --> 04:48:33,321
By doing this,
it can reactivate

5980
04:48:33,321 --> 04:48:34,956
hemoglobin F
or fetal hemoglobin.

5981
04:48:35,790 --> 04:48:37,492
But again,
we are not actually

5982
04:48:37,492 --> 04:48:39,294
targeting the hemoglobin
S mutation,

5983
04:48:39,827 --> 04:48:42,563
instead reactivating
fetal hemoglobin,

5984
04:48:42,563 --> 04:48:45,233
hopefully to overcome
the amount of sickle hemoglobin

5985
04:48:46,100 --> 04:48:47,702
and lead to better outcomes.

5986
04:48:49,170 --> 04:48:50,405
How does it work?

5987
04:48:50,405 --> 04:48:52,674
Well, specifically,
the role of BCL11A

5988
04:48:53,408 --> 04:48:55,643
is in globin gene expression,

5989
04:48:55,643 --> 04:48:58,313
and you can see here
that it’s on chromosome 2.

5990
04:48:59,314 --> 04:49:03,151
And so, what happens normally
is this complex results

5991
04:49:03,151 --> 04:49:06,654
in turning off fetal hemoglobin
so that as adults grow older,

5992
04:49:06,654 --> 04:49:08,723
of course, we don’t have
fetal hemoglobin.

5993
04:49:09,924 --> 04:49:12,327
Instead, by editing a BCL11A,

5994
04:49:12,994 --> 04:49:16,197
we’re able to actually turn
that fetal hemoglobin back on

5995
04:49:16,197 --> 04:49:17,999
to produce healthy
fetal hemoglobin.

5996
04:49:20,034 --> 04:49:22,303
I’m going to be
referencing this paper today.

5997
04:49:24,105 --> 04:49:25,707
So, let’s compare these trials,

5998
04:49:25,707 --> 04:49:28,142
specifically looking
at their points of efficacy.

5999
04:49:30,111 --> 04:49:33,314
I know you’ve already heard
quite a lot about these studies,

6000
04:49:33,314 --> 04:49:35,783
so I’m not going to
review this in detail.

6001
04:49:35,783 --> 04:49:37,485
Just here,
showing the demographics

6002
04:49:37,485 --> 04:49:39,554
and patient characteristics
side by side,

6003
04:49:40,221 --> 04:49:42,623
pointing out the genotype
of individuals

6004
04:49:43,424 --> 04:49:47,261
who are included in the trial,
the genotype for alpha-globin,

6005
04:49:47,261 --> 04:49:50,031
which we now know may change
the outcomes of the trial,

6006
04:49:50,665 --> 04:49:52,633
as well as the number
of individuals

6007
04:49:52,633 --> 04:49:55,737
who had adjudicated
vaso-occlusive events,

6008
04:49:55,737 --> 04:49:57,839
serious vaso-occlusive events,

6009
04:49:57,839 --> 04:50:00,575
their total hemoglobin,
and their stroke history.

6010
04:50:01,376 --> 04:50:04,379
Importantly, only individuals
in the lovo-cel trial

6011
04:50:04,379 --> 04:50:06,581
had a history
of prior overt stroke.

6012
04:50:08,783 --> 04:50:11,452
Now, I want to talk more
about the results.

6013
04:50:11,452 --> 04:50:13,054
First, focusing on biology.

6014
04:50:14,856 --> 04:50:17,291
Here, we’re seeing the results
from the lovo-cel studies

6015
04:50:17,291 --> 04:50:20,495
that I showed earlier.
And I want to specifically note

6016
04:50:20,495 --> 04:50:22,897
that we’re looking
at biologic outcomes

6017
04:50:22,897 --> 04:50:25,967
with the goal being that blue
bar or blue hemoglobin.

6018
04:50:26,601 --> 04:50:28,936
Again, total hemoglobin
is the y axis,

6019
04:50:28,936 --> 04:50:31,305
and the x axis
is months from therapy.

6020
04:50:32,006 --> 04:50:33,241
And blue is our goal,

6021
04:50:33,241 --> 04:50:35,676
our therapeutic hemoglobin
A T87Q.

6022
04:50:36,511 --> 04:50:39,180
And what you can see here
is a substantial

6023
04:50:39,180 --> 04:50:43,151
almost 50 percent
of the total hemoglobin

6024
04:50:43,151 --> 04:50:46,521
is now hemoglobin T87Q.

6025
04:50:50,291 --> 04:50:52,894
We also know, when we think
about biology,

6026
04:50:52,894 --> 04:50:55,163
the importance of hemolysis
or cell breakdown.

6027
04:50:55,730 --> 04:50:56,998
And here we show

6028
04:50:56,998 --> 04:50:59,100
that the reticulocytes
in the upper graph,

6029
04:50:59,734 --> 04:51:01,969
indirect bilirubin
in your lower left corner,

6030
04:51:03,004 --> 04:51:05,273
and lactate dehydrogenase
in the right corner

6031
04:51:06,007 --> 04:51:07,642
are all remarkably improved

6032
04:51:07,642 --> 04:51:11,512
and, in many cases, normalized
after this treatment.

6033
04:51:13,581 --> 04:51:15,650
Now, let’s change over
to exa-cel.

6034
04:51:16,584 --> 04:51:19,520
Again, we’re talking about
total hemoglobin here,

6035
04:51:19,520 --> 04:51:22,123
as well as the percentage
of fetal hemoglobin,

6036
04:51:22,123 --> 04:51:25,626
which is the therapeutic goal,
and F cells.

6037
04:51:25,626 --> 04:51:28,429
F cells means the number
of red cells or percentage,

6038
04:51:28,429 --> 04:51:31,666
in this case, of red cells that
contain that fetal hemoglobin.

6039
04:51:33,434 --> 04:51:35,903
The top graph is looking
at total hemoglobin

6040
04:51:35,903 --> 04:51:37,939
and how much
of that hemoglobin

6041
04:51:37,939 --> 04:51:40,374
is actually due
to the fetal hemoglobin,

6042
04:51:41,709 --> 04:51:45,313
average around 40 percent.
And you can also see

6043
04:51:45,313 --> 04:51:48,182
an almost pancellular
expression of F cells.

6044
04:51:50,384 --> 04:51:53,354
Similarly, we saw significant
improvement in hemolysis.

6045
04:51:54,355 --> 04:51:57,792
Here, you can see reduction
in LDH in that top graph.

6046
04:51:58,426 --> 04:52:02,196
And on the bottom, you can see
other improvements in hemolysis.

6047
04:52:04,799 --> 04:52:06,968
Now, as I talked about earlier,

6048
04:52:06,968 --> 04:52:09,403
we don’t want to just see
biologic improvements

6049
04:52:09,403 --> 04:52:10,805
with these therapies.

6050
04:52:10,805 --> 04:52:15,009
We really want to understand how
they affect individual function.

6051
04:52:17,745 --> 04:52:20,615
Now, I know I’m not
showing anything new here,

6052
04:52:20,615 --> 04:52:24,385
as just about everyone has seen
this slide in some way.

6053
04:52:25,119 --> 04:52:27,889
On the y axis
is each individual person,

6054
04:52:27,889 --> 04:52:31,559
on the x axis,
represented by little dots,

6055
04:52:31,559 --> 04:52:35,730
are the numbers of VOEs
or vaso-occlusive events.

6056
04:52:35,730 --> 04:52:37,298
And as you can see
on the left,

6057
04:52:37,298 --> 04:52:40,635
prior to receiving lovo-cel,
there were quite a lot of dots.

6058
04:52:40,635 --> 04:52:42,970
And on the right,
there are very few.

6059
04:52:42,970 --> 04:52:45,573
And in fact,
88.2 percent of people

6060
04:52:46,073 --> 04:52:48,242
had complete resolution
of all VOEs.

6061
04:52:49,644 --> 04:52:51,812
Here, though, we’re really
talking about pain

6062
04:52:51,812 --> 04:52:53,080
and pain events,

6063
04:52:53,080 --> 04:52:55,483
and we all know that these can
be incredibly difficult

6064
04:52:55,483 --> 04:52:57,518
to measure
and remain very subjective.

6065
04:52:58,319 --> 04:52:59,720
For this trial,
like other trials,

6066
04:52:59,720 --> 04:53:03,724
vaso-occlusive events required
a visit to a healthcare facility

6067
04:53:04,292 --> 04:53:07,895
and were basically
defined by pain

6068
04:53:07,895 --> 04:53:09,597
that did not have
any other cause.

6069
04:53:10,231 --> 04:53:12,600
And so, we certainly recognize

6070
04:53:12,600 --> 04:53:15,436
that there could be better
biologic endpoints.

6071
04:53:15,436 --> 04:53:18,039
But this is truly a functional
endpoint in terms

6072
04:53:18,039 --> 04:53:20,675
of how much pain did people
have after the therapy

6073
04:53:20,675 --> 04:53:23,044
that required a visit
to a healthcare facility.

6074
04:53:23,644 --> 04:53:25,813
Whether it was truly
a vaso-occlusive event

6075
04:53:25,813 --> 04:53:29,283
versus other pain can be
very difficult to differentiate.

6076
04:53:30,051 --> 04:53:33,187
We do know that adolescent
patients enrolled in this study

6077
04:53:33,187 --> 04:53:34,555
had even better improvement

6078
04:53:34,555 --> 04:53:37,959
with 100 percent
of resolution of VOEs.

6079
04:53:40,494 --> 04:53:43,898
Similar here, we’re looking
at the severe VOEs.

6080
04:53:43,898 --> 04:53:45,933
Again, lots of red dots
on the left,

6081
04:53:45,933 --> 04:53:47,602
very few red dots
on the right.

6082
04:53:48,269 --> 04:53:51,672
And we see that lovo-cel
again reduced

6083
04:53:51,672 --> 04:53:53,608
significantly
the number of severe

6084
04:53:53,608 --> 04:53:56,444
VOEs, so that 94 percent
of individuals

6085
04:53:56,444 --> 04:53:59,213
had a complete resolution
of all severe VOEs.

6086
04:54:00,081 --> 04:54:01,983
There were very few people
that did have

6087
04:54:01,983 --> 04:54:04,585
some hospitalizations
after treatment.

6088
04:54:05,519 --> 04:54:08,256
And as you can see on the bottom
right above my head,

6089
04:54:08,256 --> 04:54:10,424
that even in these
very few people,

6090
04:54:10,424 --> 04:54:11,826
they had a significant reduction

6091
04:54:11,826 --> 04:54:13,527
in the number
of hospitalizations,

6092
04:54:13,527 --> 04:54:17,198
as well as a huge
reduction in hospital days,

6093
04:54:17,198 --> 04:54:21,102
from a median of 15.75
to a median of 2.

6094
04:54:24,705 --> 04:54:27,375
Now, we’re talking about
patient-reported outcomes.

6095
04:54:27,375 --> 04:54:29,877
And we really chose
the three here,

6096
04:54:29,877 --> 04:54:33,080
pain interference, fatigue,
and pain intensity,

6097
04:54:33,614 --> 04:54:35,583
as some that we felt
were the most important

6098
04:54:35,583 --> 04:54:37,585
to individuals
with sickle cell disease.

6099
04:54:38,219 --> 04:54:40,521
And we’re showing here
the significant improvements

6100
04:54:40,521 --> 04:54:43,024
that you can see displayed
on this graph,

6101
04:54:43,658 --> 04:54:46,027
noted as early as six months
after therapy

6102
04:54:46,027 --> 04:54:49,463
and sustained for up to 36
and really 48 months.

6103
04:54:50,197 --> 04:54:52,700
We saw clinically meaningful
improvements in pain intensity,

6104
04:54:52,700 --> 04:54:55,936
pain interference,
and fatigue in 57, 64,

6105
04:54:55,936 --> 04:54:57,972
and 64 percent of patients,
respectively.

6106
04:54:58,906 --> 04:55:00,675
Now, some individuals
didn’t have a lot

6107
04:55:00,675 --> 04:55:04,412
of pain interference or fatigue,
and so we couldn’t reduce beyond

6108
04:55:05,246 --> 04:55:07,248
where they were already
at a good level.

6109
04:55:07,948 --> 04:55:09,684
Again, these are
functional outcomes

6110
04:55:09,684 --> 04:55:11,752
that are also really
important to assess.

6111
04:55:13,688 --> 04:55:15,856
Now we’re switching over
to exa-cel,

6112
04:55:15,856 --> 04:55:17,892
where we see
a very similar

6113
04:55:17,892 --> 04:55:19,827
and very clinically
meaningful benefit.

6114
04:55:20,895 --> 04:55:22,830
In the same type
of swimmers’ plot,

6115
04:55:22,830 --> 04:55:24,932
the y axis is
the number of individuals,

6116
04:55:25,599 --> 04:55:28,069
the x axis is
the number of dots

6117
04:55:28,069 --> 04:55:30,137
representing vaso-occlusive
crises.

6118
04:55:30,738 --> 04:55:32,940
Again, you can see
a lot of dots on the left

6119
04:55:32,940 --> 04:55:35,009
and a lot less on the right.

6120
04:55:35,009 --> 04:55:37,578
And we can see that,
as we saw previously,

6121
04:55:37,578 --> 04:55:40,214
some individuals do continue
to have pain events

6122
04:55:40,748 --> 04:55:42,350
after this therapy.

6123
04:55:42,350 --> 04:55:45,986
Again, these events are defined
by having pain

6124
04:55:45,986 --> 04:55:49,023
with no other discernible cause
and, of course,

6125
04:55:49,023 --> 04:55:52,993
recognizing that pain can be
very difficult to differentiate.

6126
04:55:54,695 --> 04:55:56,597
They saw consistent efficacy

6127
04:55:56,597 --> 04:55:58,065
and clinically
meaningful benefit

6128
04:55:58,065 --> 04:56:01,502
from exa-cel in
both adolescents and adults.

6129
04:56:01,502 --> 04:56:03,704
Adults on the top portion
of the graph here,

6130
04:56:03,704 --> 04:56:06,374
and the bottom portion,
adolescents.

6131
04:56:06,374 --> 04:56:09,110
Again, very few blue dots
post-treatment

6132
04:56:09,110 --> 04:56:11,445
really showing us
significant improvement.

6133
04:56:13,814 --> 04:56:16,350
We also saw significant
improvement in patient

6134
04:56:16,350 --> 04:56:17,818
reported outcomes.

6135
04:56:17,818 --> 04:56:19,487
Now, these outcomes
are displayed differently

6136
04:56:19,487 --> 04:56:21,155
than the ones
I showed you before,

6137
04:56:21,155 --> 04:56:23,424
and there are slightly
different outcomes.

6138
04:56:23,424 --> 04:56:24,859
And this is from the recent
New England

6139
04:56:24,859 --> 04:56:27,795
Journal paper
that showed improvements,

6140
04:56:29,263 --> 04:56:33,367
specifically looking at
the EuroQol visual analog scale

6141
04:56:34,068 --> 04:56:36,437
as well as
the numeric rating system

6142
04:56:36,437 --> 04:56:38,005
and showing the minimally

6143
04:56:38,005 --> 04:56:39,740
clinically important
differences.

6144
04:56:42,276 --> 04:56:44,945
Let’s briefly compare
the results of these two trials.

6145
04:56:44,945 --> 04:56:48,249
Again, we’re really talking here
about the therapeutic efficacy,

6146
04:56:48,249 --> 04:56:50,551
and that’s what I want
to compare here.

6147
04:56:50,551 --> 04:56:51,952
So, you can see, similarly,

6148
04:56:51,952 --> 04:56:54,088
the primary outcome
for both trials

6149
04:56:54,088 --> 04:56:56,157
was the number
of vaso-occlusive events.

6150
04:56:56,157 --> 04:56:58,125
For lovo-cel,
we saw an 88 percent

6151
04:56:58,125 --> 04:57:01,762
complete resolution of VOEs
from month 6 to 18.

6152
04:57:02,730 --> 04:57:04,465
And if we measure
it the same way

6153
04:57:04,465 --> 04:57:07,368
that exa-cel measured it,
that would have been 93 percent.

6154
04:57:07,935 --> 04:57:10,037
That’s because the measurement
was a fixed point

6155
04:57:10,037 --> 04:57:11,839
starting at 6 months
to 18 months.

6156
04:57:12,706 --> 04:57:15,176
Exa-cel saw 96 percent achieve

6157
04:57:15,176 --> 04:57:18,145
at least 12 months
without a VOC.

6158
04:57:18,145 --> 04:57:21,582
And in this case,
that was a sliding 12 months

6159
04:57:21,582 --> 04:57:24,952
of any 12 months in which it was
consecutively without a VOC.

6160
04:57:25,953 --> 04:57:28,989
Secondary outcomes, we see
that 94 percent of patients

6161
04:57:28,989 --> 04:57:31,192
had a complete resolution
of severe VOEs.

6162
04:57:32,460 --> 04:57:34,762
In the lower cell group
and in the exa-cel group,

6163
04:57:34,762 --> 04:57:36,964
that number was 100 percent
for at least

6164
04:57:36,964 --> 04:57:38,666
those consecutive
12 months again.

6165
04:57:39,967 --> 04:57:42,002
Now, one thing that we’re going
to call attention to

6166
04:57:42,002 --> 04:57:43,304
is the difference in duration.

6167
04:57:43,304 --> 04:57:46,006
At the time these were approved,
we saw a median duration

6168
04:57:46,006 --> 04:57:51,078
for lovo-cel of 35.5 months
and exa-cel of 22.4 months.

6169
04:57:51,946 --> 04:57:54,482
Globin response,
or the biologic response, again,

6170
04:57:55,049 --> 04:57:57,251
is really thinking about
the total hemoglobin

6171
04:57:57,251 --> 04:57:58,953
and the percent
of that hemoglobin

6172
04:57:58,953 --> 04:58:02,189
that comes from the therapeutic
gene therapy that’s intended.

6173
04:58:02,990 --> 04:58:05,092
And the way this was defined
for lovo-cel,

6174
04:58:05,626 --> 04:58:08,762
86.8 percent of individuals
achieved globin response

6175
04:58:09,363 --> 04:58:11,232
with that very
complicated definition.

6176
04:58:12,233 --> 04:58:14,935
And on the right, you can
see that 100 percent

6177
04:58:14,935 --> 04:58:16,203
achieved a globin response

6178
04:58:16,203 --> 04:58:19,340
with a less complicated
definition of fetal hemoglobin

6179
04:58:19,340 --> 04:58:22,676
greater than 20 percent
with a pancellular distribution.

6180
04:58:24,178 --> 04:58:25,880
Again, just calling
attention here,

6181
04:58:25,880 --> 04:58:29,183
the definitions for resolution
of vaso-occlusive crises

6182
04:58:29,183 --> 04:58:30,551
were slightly different.

6183
04:58:30,551 --> 04:58:32,319
So, when you’re
comparing efficacy,

6184
04:58:32,319 --> 04:58:33,988
recognize that they are
slightly different

6185
04:58:33,988 --> 04:58:35,189
in the way they’re defined

6186
04:58:35,189 --> 04:58:37,625
and the time
that they’re defined over.

6187
04:58:37,625 --> 04:58:40,327
And similarly, there’s a pretty
significant difference

6188
04:58:40,327 --> 04:58:43,364
in median duration at the time
these therapies were improved.

6189
04:58:45,199 --> 04:58:46,567
So, what about other gene
therapies

6190
04:58:46,567 --> 04:58:48,969
that are ongoing
in sickle cell disease?

6191
04:58:48,969 --> 04:58:52,373
Well, the three most
far along therapies

6192
04:58:52,373 --> 04:58:55,643
are the RUBY or Editas therapy.
It’s a gene editing therapy.

6193
04:58:56,176 --> 04:58:59,780
Instead of aiming at BCL11A,
it’s a CRISPR-Cas12 deletion

6194
04:58:59,780 --> 04:59:02,283
at the gamma globin
hemoglobin F promotor.

6195
04:59:03,117 --> 04:59:07,521
We know the GRASP therapy
is the BMT-CTN 2001.

6196
04:59:07,521 --> 04:59:10,758
It uses a viral vector,
that same lentiviral vector,

6197
04:59:10,758 --> 04:59:14,995
to insert a small hairpin RNA
that does affect BCL11A

6198
04:59:14,995 --> 04:59:16,597
to reactivate fetal hemoglobin.

6199
04:59:17,398 --> 04:59:19,934
And the BEAM-101 or BEACON
therapy

6200
04:59:19,934 --> 04:59:21,635
is a base editing therapy,

6201
04:59:21,635 --> 04:59:23,771
so no longer
a double-stranded DNA break.

6202
04:59:24,405 --> 04:59:26,574
This is actually
targeting a promoter

6203
04:59:27,107 --> 04:59:30,711
that is also preventing
an edit of a repressor,

6204
04:59:31,245 --> 04:59:34,114
so it prevents the turning off
of the gamma globin gene.

6205
04:59:35,115 --> 04:59:36,550
In all three of these,

6206
04:59:36,550 --> 04:59:38,786
we’re looking to assess
the total hemoglobin

6207
04:59:39,286 --> 04:59:41,055
and the percent fetal
hemoglobin,

6208
04:59:41,055 --> 04:59:44,191
as well as vaso-occlusive crises
and patient-reported outcomes.

6209
04:59:46,327 --> 04:59:49,296
So, the real question, though,
is how are we going to define

6210
04:59:49,296 --> 04:59:51,699
the therapeutic efficacy
of these treatments?

6211
04:59:54,134 --> 04:59:56,203
So, when we think
about efficacy,

6212
04:59:56,203 --> 04:59:57,805
we need to start with biology.

6213
04:59:58,305 --> 05:00:00,808
As we discussed earlier,
none of the current gene

6214
05:00:00,808 --> 05:00:03,310
therapies are actually targeting
the mutation

6215
05:00:03,310 --> 05:00:04,912
that leads to hemoglobin S.

6216
05:00:06,013 --> 05:00:09,249
Instead, they are producing
different hemoglobin

6217
05:00:10,017 --> 05:00:12,953
that will help prevent
the hemoglobin polymerization

6218
05:00:13,487 --> 05:00:15,889
that leads to all
the downstream complications.

6219
05:00:16,423 --> 05:00:18,826
So, all individuals receiving
these therapies

6220
05:00:18,826 --> 05:00:20,828
will continue
to make sickle hemoglobin,

6221
05:00:21,629 --> 05:00:24,665
but they need to make enough
hemoglobin ATV-7Q

6222
05:00:24,665 --> 05:00:26,300
or fetal hemoglobin,

6223
05:00:26,300 --> 05:00:28,502
to prevent that
polymerization and sickling.

6224
05:00:29,303 --> 05:00:32,706
Not only should they prevent
the polymerization,

6225
05:00:32,706 --> 05:00:35,175
they also really have
to prevent hemolysis.

6226
05:00:35,743 --> 05:00:37,978
They have to completely
resolve hemolysis

6227
05:00:37,978 --> 05:00:40,014
to prevent ongoing
endothelial damage

6228
05:00:40,581 --> 05:00:42,683
and to prevent the loss
of nitric oxide

6229
05:00:42,683 --> 05:00:44,284
in the vessel system.

6230
05:00:44,852 --> 05:00:47,988
And they have to act
like a red cell that is healthy.

6231
05:00:48,922 --> 05:00:52,292
And one measure of that is
somebody with sickle cell trait.

6232
05:00:52,292 --> 05:00:53,894
That’s because
in sickle cell trait,

6233
05:00:53,894 --> 05:00:56,363
all of the red cells
actually contain hemoglobin

6234
05:00:56,363 --> 05:00:58,732
A and S,
and we know that

6235
05:00:58,732 --> 05:01:01,802
unless they’re under
significant hypoxic conditions,

6236
05:01:01,802 --> 05:01:03,337
those cells act normally.

6237
05:01:03,337 --> 05:01:05,472
They don’t hemolyze
or break down,

6238
05:01:05,472 --> 05:01:07,741
and they’re squishy
and not adhesive.

6239
05:01:07,741 --> 05:01:09,743
So, we want to make sure
that the individuals

6240
05:01:09,743 --> 05:01:12,680
who get these therapies
end up with rheology

6241
05:01:13,414 --> 05:01:15,616
at least equivalent
to sickle cell trait.

6242
05:01:16,350 --> 05:01:19,453
And those would be biologic
definitions of efficacy.

6243
05:01:19,453 --> 05:01:20,754
And if you could meet
all of them,

6244
05:01:20,754 --> 05:01:22,723
you could even suggest
a biologic cure.

6245
05:01:24,992 --> 05:01:26,427
But again, we also need
to think about

6246
05:01:26,427 --> 05:01:28,462
those functional definitions
of efficacy.

6247
05:01:29,263 --> 05:01:31,398
So, one difficulty,
as I mentioned,

6248
05:01:31,398 --> 05:01:34,201
is that we are measuring pain.
And as everyone knows,

6249
05:01:34,201 --> 05:01:37,004
not all pain is caused
by vaso-occlusive crises,

6250
05:01:37,604 --> 05:01:39,707
even individuals
with sickle cell disease.

6251
05:01:40,274 --> 05:01:43,310
And as individuals age
and have different types

6252
05:01:43,310 --> 05:01:45,679
of bone and muscular damage
or just get older,

6253
05:01:46,547 --> 05:01:48,148
they will experience more pain.

6254
05:01:48,682 --> 05:01:50,184
And we need to understand
what pain

6255
05:01:50,184 --> 05:01:52,286
is actually caused
by sickle cell disease

6256
05:01:52,286 --> 05:01:54,221
versus other pain
that we can all have.

6257
05:01:55,022 --> 05:01:56,957
I was speaking recently
to a patient of mine

6258
05:01:56,957 --> 05:02:01,295
who’s 23, beautiful,
wonderful young man, and I --

6259
05:02:01,295 --> 05:02:02,796
he was explaining
his pain to me.

6260
05:02:02,796 --> 05:02:04,932
And what he was explaining
is that his pain

6261
05:02:04,932 --> 05:02:06,867
is really severe
after he’s been working.

6262
05:02:06,867 --> 05:02:08,969
And he works in a factory,
lifting things.

6263
05:02:09,570 --> 05:02:11,572
And at the end of the day,
he notes that he’s got

6264
05:02:11,572 --> 05:02:14,108
a lot of pain in his shoulders,
and he’s very sore.

6265
05:02:14,842 --> 05:02:17,277
And he said, "Well, of course,
it’s my sickle cell pain.

6266
05:02:17,277 --> 05:02:18,879
What other pain could there be?"

6267
05:02:19,413 --> 05:02:22,516
And I tried to explain that
there are lots of other pains.

6268
05:02:22,516 --> 05:02:25,586
Because he clearly
had musculoskeletal pain

6269
05:02:25,586 --> 05:02:28,155
because he was lifting
very heavy packages.

6270
05:02:28,155 --> 05:02:30,758
So, we really need to start
to figure out a better way

6271
05:02:30,758 --> 05:02:34,394
to differentiate pain versus
vaso-occlusive-induced pain.

6272
05:02:35,129 --> 05:02:37,865
We need to make sure
we keep acute pain crisis

6273
05:02:37,865 --> 05:02:39,600
in our title
of these episodes.

6274
05:02:39,600 --> 05:02:42,603
Because it is a crisis.
Because pain is a crisis.

6275
05:02:43,203 --> 05:02:45,706
But perhaps we need to look
at other definitions

6276
05:02:45,706 --> 05:02:48,842
to differentiate acute
pain crisis

6277
05:02:48,842 --> 05:02:52,746
and acute vaso-occlusive crises
to really help us understand

6278
05:02:52,746 --> 05:02:54,715
this functional definition
of efficacy.

6279
05:02:55,415 --> 05:02:57,184
What about fatigue?

6280
05:02:57,184 --> 05:03:00,087
So, I’m sure none of you in the
audience are ever fatigued,

6281
05:03:00,087 --> 05:03:02,589
even if you don’t have
sickle cell disease, right?

6282
05:03:03,090 --> 05:03:05,125
Well, of course,
this can be really challenging.

6283
05:03:05,125 --> 05:03:08,028
How do we know what fatigue
is from life and adulting,

6284
05:03:08,629 --> 05:03:11,231
and what fatigue is
from sickle cell disease?

6285
05:03:11,231 --> 05:03:12,733
And how are we going
to differentiate

6286
05:03:12,733 --> 05:03:14,334
that as time goes on?

6287
05:03:15,035 --> 05:03:17,070
I have another individual
that I take care of,

6288
05:03:17,070 --> 05:03:20,674
who’s a wonderful woman who,
after receiving gene therapy,

6289
05:03:20,674 --> 05:03:22,776
actually began working
as a teacher’s aide

6290
05:03:22,776 --> 05:03:24,511
for a first-grade class.

6291
05:03:25,612 --> 05:03:27,915
She’d never had
a full-time job before.

6292
05:03:27,915 --> 05:03:30,984
And she was exhausted
at the end of the day

6293
05:03:30,984 --> 05:03:33,554
and wanted to know why
her fatigue hadn’t improved.

6294
05:03:34,154 --> 05:03:36,123
Remember, before she got
this gene therapy,

6295
05:03:36,123 --> 05:03:37,357
she hadn’t worked at all.

6296
05:03:37,357 --> 05:03:39,426
So, this was all
very new to her.

6297
05:03:39,426 --> 05:03:41,228
So, again, how do
we measure fatigue

6298
05:03:41,929 --> 05:03:44,198
when it may not be
the same before and after?

6299
05:03:45,065 --> 05:03:47,067
And what about quality of life?

6300
05:03:47,067 --> 05:03:48,936
How do we make sure
that what we’re measuring

6301
05:03:48,936 --> 05:03:52,306
are changes in quality of life
due to sickle cell disease

6302
05:03:52,806 --> 05:03:55,909
and not due to other things
that can happen in our lives?

6303
05:03:55,909 --> 05:03:58,846
We know that fixing sickle
cell disease is just one part,

6304
05:03:59,479 --> 05:04:01,215
and it may not fix everything.

6305
05:04:01,215 --> 05:04:02,749
So, how can we measure
these differences

6306
05:04:02,749 --> 05:04:03,984
when we’re really thinking

6307
05:04:03,984 --> 05:04:06,320
about functional definitions
of efficacy?

6308
05:05:17,925 --> 05:05:20,160
John Tisdale: Are you working
on the volume back there?

6309
05:05:20,160 --> 05:05:21,762
Because we’re not hearing.

6310
05:05:43,583 --> 05:05:46,486
Yes.
We can’t hear anything still.

6311
05:05:49,122 --> 05:05:52,926
Okay. It’s over.
Well, sorry about that.

6312
05:05:52,926 --> 05:05:55,495
I think we’re going to have to
move on in the interest of time,

6313
05:05:55,495 --> 05:05:58,799
but we lost
the last bit of audio.

6314
05:05:58,799 --> 05:06:01,201
Hopefully, you were able
to read the slides

6315
05:06:02,135 --> 05:06:05,305
for Julie’s final comments.

6316
05:06:05,305 --> 05:06:08,875
So, we’re going to move now back
to a live presentation.

6317
05:06:08,875 --> 05:06:11,745
Dr. Melanie Fields
is going to talk to us

6318
05:06:11,745 --> 05:06:15,115
about cerebral hemodynamics.
She’s a pediatric hematologist

6319
05:06:15,115 --> 05:06:17,951
and tenured professor
at the Department of Pediatrics

6320
05:06:17,951 --> 05:06:21,088
and Neurology at Washington
University in St Louis.

6321
05:06:21,088 --> 05:06:23,123
So, I hand it over to you,
Melanie.

6322
05:06:32,265 --> 05:06:34,601
Melanie Fields: So, Julie is
a tough act to follow,

6323
05:06:34,601 --> 05:06:36,536
even when she freezes.

6324
05:06:36,536 --> 05:06:39,172
[laughter]

6325
05:06:39,172 --> 05:06:41,074
There we go. Okay.

6326
05:06:41,074 --> 05:06:45,145
So, thank you to everyone.
Thanks for the invitation.

6327
05:06:45,145 --> 05:06:47,981
This is an incredible crowd
to get to meet with

6328
05:06:47,981 --> 05:06:51,918
and speak in front of.
I’m going to focus on the brain,

6329
05:06:51,918 --> 05:06:54,721
which I can talk about
for a very long time.

6330
05:06:54,721 --> 05:06:56,690
So, we’ll try to do this
in 20 minutes.

6331
05:06:57,791 --> 05:07:01,762
Except that’s not it.
Yeah. These are my disclosures.

6332
05:07:03,563 --> 05:07:05,399
I’m going to give an overview
first

6333
05:07:05,399 --> 05:07:08,635
of what I mean when I say
cerebral hemodynamics

6334
05:07:08,635 --> 05:07:11,471
and start with where
the field started.

6335
05:07:12,072 --> 05:07:13,874
And then as this field
has grown,

6336
05:07:14,408 --> 05:07:16,243
some controversies
have developed,

6337
05:07:17,077 --> 05:07:21,181
conflicting data, and I want
to dive briefly into that.

6338
05:07:21,181 --> 05:07:24,885
And then lastly, end with how
we can potentially use

6339
05:07:24,885 --> 05:07:28,321
these biomarkers
to assess treatment efficacy.

6340
05:07:30,023 --> 05:07:32,426
So, as everyone in this room
knows,

6341
05:07:32,426 --> 05:07:36,096
the neurologic comorbidities
of sickle cell disease

6342
05:07:36,096 --> 05:07:37,697
are significant,

6343
05:07:38,331 --> 05:07:42,035
from overt stroke,
vasculopathy, silent stroke,

6344
05:07:43,036 --> 05:07:45,038
and cognitive complications.

6345
05:07:45,038 --> 05:07:47,607
The pathophysiology
of these comorbidities

6346
05:07:47,607 --> 05:07:49,209
really remains unclear.

6347
05:07:50,377 --> 05:07:52,379
Current guidelines,
at least in the U.S.,

6348
05:07:52,379 --> 05:07:54,314
recommend that
a screening MRI

6349
05:07:54,314 --> 05:07:57,584
be obtained when a child
becomes school age,

6350
05:07:57,584 --> 05:08:00,053
so between the ages
of five to seven.

6351
05:08:00,053 --> 05:08:03,023
And that MRI is really
for structural purposes.

6352
05:08:03,023 --> 05:08:04,991
Because if there
are silent infarcts,

6353
05:08:04,991 --> 05:08:06,393
if there’s vasculopathy,

6354
05:08:06,393 --> 05:08:09,096
that might mean that we would
recommend a change

6355
05:08:09,096 --> 05:08:10,831
in primary disease
modification.

6356
05:08:11,398 --> 05:08:13,633
However, once that infarction
has occurred,

6357
05:08:13,633 --> 05:08:15,836
once that vasculopathy
has occurred,

6358
05:08:16,937 --> 05:08:19,039
I would say we really
missed the boat.

6359
05:08:19,739 --> 05:08:26,913
And can we use technology
to define and prevent this,

6360
05:08:26,913 --> 05:08:28,982
instead of just then
having to move forward

6361
05:08:28,982 --> 05:08:30,584
with secondary prevention?

6362
05:08:31,118 --> 05:08:33,453
So, we’re going
to talk about that.

6363
05:08:34,855 --> 05:08:37,224
So, when you put someone
in an MRI scanner,

6364
05:08:37,224 --> 05:08:38,658
you can take
a lot of pictures.

6365
05:08:38,658 --> 05:08:42,729
Clinically, we really take
almost all structural pictures.

6366
05:08:42,729 --> 05:08:43,964
However, with the scanner,

6367
05:08:43,964 --> 05:08:46,333
you can look at the function
of the brain.

6368
05:08:46,333 --> 05:08:49,069
You can look
at white matter microstructure.

6369
05:08:49,069 --> 05:08:50,604
And then you can look
at these measures

6370
05:08:50,604 --> 05:08:53,440
of cerebral blood flow
and oxygen extraction,

6371
05:08:53,440 --> 05:08:55,642
which is what we’re going
to focus on today.

6372
05:08:56,843 --> 05:09:00,013
And then really exciting data
is coming out of Emory,

6373
05:09:00,013 --> 05:09:01,314
Dr. Erin Buckley’s lab,

6374
05:09:01,314 --> 05:09:05,986
where she’s using
this bedside tool, DCNIRS

6375
05:09:06,520 --> 05:09:10,123
[sic], to obtain similar
measurements regionally.

6376
05:09:10,123 --> 05:09:12,159
So, we’ll also
briefly touch on her data.

6377
05:09:12,993 --> 05:09:15,595
So, when I say
cerebral hemodynamics,

6378
05:09:15,595 --> 05:09:17,164
I think we have to start
with the fact

6379
05:09:17,164 --> 05:09:21,735
the brain consumes 20 percent
of the oxygen supply

6380
05:09:21,735 --> 05:09:23,970
to meet what we call
the cerebral rate

6381
05:09:23,970 --> 05:09:26,873
of oxygen utilization,
or CMRO2.

6382
05:09:27,674 --> 05:09:29,442
We calculate CMRO2

6383
05:09:29,442 --> 05:09:33,480
by multiplying arterial oxygen
content by cerebral blood flow,

6384
05:09:34,047 --> 05:09:37,617
or the rate of arterial
blood delivery to the brain,

6385
05:09:37,617 --> 05:09:40,020
and then by oxygen
extraction fraction,

6386
05:09:40,020 --> 05:09:41,655
which is the percentage
of oxygen

6387
05:09:41,655 --> 05:09:43,957
removed from the blood
into the brain tissue.

6388
05:09:45,759 --> 05:09:49,863
So, old -- very old,
PET imaging data in the setting

6389
05:09:49,863 --> 05:09:53,233
of carotid stenosis
arterial ischemic stroke,

6390
05:09:53,233 --> 05:09:55,235
has shown us that there
are multiple phases

6391
05:09:55,235 --> 05:09:58,338
of hemodynamic compromise in
the setting of arterial stroke.

6392
05:09:58,939 --> 05:10:01,174
So, when you have stenosis
or an occlusion,

6393
05:10:01,174 --> 05:10:03,610
you have decreased
focal perfusion pressure.

6394
05:10:04,811 --> 05:10:08,715
However, even with that stenosis
or that occlusion,

6395
05:10:08,715 --> 05:10:11,484
cerebral blood flow
regionally remains stable

6396
05:10:11,484 --> 05:10:14,387
due to autoregulation
or dilatation

6397
05:10:14,387 --> 05:10:16,456
of the surrounding
blood vessels.

6398
05:10:16,456 --> 05:10:18,525
So, cerebral blood
flow stays steady.

6399
05:10:18,525 --> 05:10:20,594
CMRO2 stays normal.

6400
05:10:20,594 --> 05:10:23,863
However, if that stenosis
or occlusion persists,

6401
05:10:23,863 --> 05:10:26,166
all the surrounding blood
vessels are vasodilated.

6402
05:10:26,166 --> 05:10:29,369
You will eventually see a
decline in cerebral blood flow.

6403
05:10:30,136 --> 05:10:32,105
The brain can then
further compensate

6404
05:10:32,105 --> 05:10:34,374
from pulling
more blood from --

6405
05:10:34,374 --> 05:10:36,343
more oxygen
from the limited blood

6406
05:10:36,343 --> 05:10:38,144
that is available
to the tissue,

6407
05:10:38,745 --> 05:10:42,148
and that’s this increase
in oxygen extraction fraction,

6408
05:10:42,148 --> 05:10:45,252
again, to maintain the CMRO2.

6409
05:10:45,752 --> 05:10:48,822
When both of these
compensatory mechanisms max out,

6410
05:10:48,822 --> 05:10:51,558
though, that’s when you see
a drop in CMRO2,

6411
05:10:51,558 --> 05:10:53,360
and that’s when
brain tissue dies.

6412
05:10:54,594 --> 05:10:56,363
So, what does this mean
for sickle cell disease?

6413
05:10:56,363 --> 05:11:00,233
So, a minority of patients
do have stenosis;

6414
05:11:00,233 --> 05:11:01,801
the majority don’t, though.

6415
05:11:01,801 --> 05:11:04,471
However, they are all
severely anemic

6416
05:11:04,471 --> 05:11:06,940
with a decrease
in arterial oxygen content.

6417
05:11:06,940 --> 05:11:09,542
So, this field really took off

6418
05:11:09,542 --> 05:11:11,544
when there’s a desire
to understand,

6419
05:11:11,544 --> 05:11:14,214
how does the brain compensate
for that decrease

6420
05:11:14,214 --> 05:11:16,283
in arterial oxygen content,

6421
05:11:16,283 --> 05:11:19,085
when it comes to these metrics
of blood flow

6422
05:11:19,085 --> 05:11:20,720
and oxygen extraction?

6423
05:11:21,321 --> 05:11:24,591
So -- and there was
a delay in this

6424
05:11:24,591 --> 05:11:29,396
because we don’t like to use
PET imaging and research

6425
05:11:29,396 --> 05:11:30,697
because it involves radiation.

6426
05:11:30,697 --> 05:11:33,833
So, MRI had to come far enough
along that

6427
05:11:33,833 --> 05:11:36,803
we could look at these measures
without radiation exposure.

6428
05:11:37,604 --> 05:11:39,572
And so, to start off,

6429
05:11:40,607 --> 05:11:43,710
a study out of my lab looked
at these metrics in children

6430
05:11:43,710 --> 05:11:45,045
and then a study
out of Vanderbilt

6431
05:11:45,045 --> 05:11:46,680
looked at these metrics
in adults.

6432
05:11:46,680 --> 05:11:49,582
And we found that there was
a significant increase,

6433
05:11:49,582 --> 05:11:51,217
about a 1.5-fold increase,

6434
05:11:51,217 --> 05:11:55,455
in cerebral blood flow
in both children and adults.

6435
05:11:55,455 --> 05:11:58,158
And then when we looked
at oxygen extraction fraction --

6436
05:11:58,158 --> 05:11:59,993
and this this data
is globally --

6437
05:11:59,993 --> 05:12:03,596
we found a similar increase
in oxygen extraction fraction.

6438
05:12:04,297 --> 05:12:05,965
And in these two figures,

6439
05:12:05,965 --> 05:12:09,803
you’re looking at arterial
oxygen content on your x axis

6440
05:12:09,803 --> 05:12:12,205
and then these metrics of blood
flow on your left,

6441
05:12:12,205 --> 05:12:16,476
oxygen extraction on your right.
The more anemic the participant,

6442
05:12:16,476 --> 05:12:18,011
the higher
the cerebral blood flow,

6443
05:12:18,011 --> 05:12:19,879
the higher
the oxygen extraction.

6444
05:12:20,680 --> 05:12:23,283
But how do these metrics
relate to the clinical outcomes

6445
05:12:23,283 --> 05:12:24,517
that we care about?

6446
05:12:24,517 --> 05:12:28,355
So, this is some data published
by Dr. Andrea Ford at WashU,

6447
05:12:28,355 --> 05:12:31,458
and she had
a great opportunity.

6448
05:12:31,458 --> 05:12:34,060
She took all the brain MRIs
from the SIT trial.

6449
05:12:34,060 --> 05:12:36,963
She outlined where those
silent infarcts occurred

6450
05:12:36,963 --> 05:12:38,565
and created a heat map.

6451
05:12:39,833 --> 05:12:44,671
And so, she also had
imaging data to help us define

6452
05:12:44,671 --> 05:12:48,708
the regions of the brain with
the lowest cerebral blood flow.

6453
05:12:48,708 --> 05:12:52,011
And so, in the bottom figure,
what you see in red

6454
05:12:52,011 --> 05:12:55,448
is the region of the brain
where blood flow is the lowest.

6455
05:12:56,149 --> 05:12:58,485
Overlaid on top of that,
in blue,

6456
05:12:58,485 --> 05:13:00,587
is that heat map
where silent strokes

6457
05:13:00,587 --> 05:13:03,089
are most likely to occur.
And then in purple,

6458
05:13:03,089 --> 05:13:06,459
you see that these regions
really nicely overlap.

6459
05:13:06,960 --> 05:13:11,131
The figure up top is looking
at infarct density in the brain

6460
05:13:11,131 --> 05:13:13,266
and what you see --
on the x axis.

6461
05:13:13,266 --> 05:13:16,169
And then on the y axis,
you see cerebral blood flow.

6462
05:13:16,169 --> 05:13:18,004
And you see that
the region of the brain

6463
05:13:18,004 --> 05:13:20,106
with the greatest
infarct density

6464
05:13:20,106 --> 05:13:22,175
has a significantly lower
cerebral blood

6465
05:13:22,175 --> 05:13:26,012
flow compared to the region
with the lowest infarct density.

6466
05:13:26,012 --> 05:13:28,715
So, what this would suggest is,
is that where this --

6467
05:13:28,715 --> 05:13:30,016
where blood flow drops,

6468
05:13:30,016 --> 05:13:32,118
that’s where
these infarcts occur.

6469
05:13:32,118 --> 05:13:34,387
And then when we think
about oxygen extraction --

6470
05:13:34,387 --> 05:13:37,657
so, this was a different study
out of my lab,

6471
05:13:37,657 --> 05:13:40,026
but we took a cohort of kids
with sickle cell disease

6472
05:13:40,026 --> 05:13:41,895
and a cohort of kids unaffected.

6473
05:13:42,429 --> 05:13:45,165
And we averaged
their oxygen extraction mass.

6474
05:13:45,165 --> 05:13:47,100
We then made a ratio metric map,

6475
05:13:47,100 --> 05:13:49,169
meaning that in each voxel
of the brain,

6476
05:13:49,169 --> 05:13:53,139
you had a ratio of that average
sickle cell oxygen extraction

6477
05:13:53,139 --> 05:13:55,742
to the average control
oxygen extraction,

6478
05:13:55,742 --> 05:13:57,243
and then thresholded it.

6479
05:13:57,243 --> 05:14:00,079
And then the top figure
you see in blue,

6480
05:14:00,079 --> 05:14:03,183
the three regions of the brain,
where there’s a 60, 70,

6481
05:14:03,183 --> 05:14:06,386
and 80 percent increase
in oxygen extraction

6482
05:14:06,386 --> 05:14:08,388
in that average sickle cell map

6483
05:14:08,388 --> 05:14:10,156
compared to
that average control map.

6484
05:14:10,156 --> 05:14:11,591
So, this is the region

6485
05:14:11,591 --> 05:14:13,726
where oxygen extraction
is the highest.

6486
05:14:14,227 --> 05:14:16,429
In an independent
cohort of kids,

6487
05:14:16,429 --> 05:14:19,199
we again created
an embark density heat map,

6488
05:14:19,199 --> 05:14:21,801
just shown in red.
And when you overlay the region

6489
05:14:21,801 --> 05:14:24,103
with increased
oxygen extraction,

6490
05:14:24,103 --> 05:14:26,406
you see that they again,
nicely colocalized.

6491
05:14:26,406 --> 05:14:29,676
So, together,
these two studies suggest

6492
05:14:29,676 --> 05:14:32,278
that where blood flow nadirs

6493
05:14:32,278 --> 05:14:34,514
and oxygen extraction
is the highest,

6494
05:14:35,048 --> 05:14:37,984
that’s the region of the brain
that’s vulnerable to infarction.

6495
05:14:37,984 --> 05:14:39,886
And this potentially
gives us insight

6496
05:14:40,553 --> 05:14:43,690
into why these infarctions
may be occurring.

6497
05:14:46,125 --> 05:14:49,095
And then this is data
out of Vanderbilt,

6498
05:14:49,095 --> 05:14:52,499
and this is a really nice study,
where they were correlating

6499
05:14:52,499 --> 05:14:55,568
these imaging biomarkers
with cognitive measures.

6500
05:14:55,568 --> 05:14:58,238
And so, through
multivariate modeling,

6501
05:14:58,238 --> 05:14:59,973
where they’re controlling
for age,

6502
05:14:59,973 --> 05:15:02,208
household income,
presence of stroke,

6503
05:15:02,208 --> 05:15:05,545
they found that blood flow
was inversely related

6504
05:15:05,545 --> 05:15:08,047
to working memory
and inhibitory control.

6505
05:15:08,548 --> 05:15:10,617
And oxygen extraction
was inversely

6506
05:15:10,617 --> 05:15:12,785
related to processing speed.

6507
05:15:12,785 --> 05:15:15,889
And then when they removed
the patients that had infarcts,

6508
05:15:15,889 --> 05:15:19,692
they found similar relationships
between blood flow and memory

6509
05:15:19,692 --> 05:15:22,128
and oxygen extraction
and processing speed.

6510
05:15:23,663 --> 05:15:27,867
So, again, this was the start
of the field to understand

6511
05:15:27,867 --> 05:15:29,636
how the brain is compensating

6512
05:15:29,636 --> 05:15:31,538
in the setting
of sickle cell disease.

6513
05:15:32,205 --> 05:15:35,909
As the field has grown,
there’s been conflicting data.

6514
05:15:35,909 --> 05:15:39,546
There’s been a lot of progress,
and there’s some controversies.

6515
05:15:39,546 --> 05:15:44,784
And so, I want to talk about
why those controversies exist

6516
05:15:44,784 --> 05:15:49,155
and how we’re kind of learning
to better understand that data.

6517
05:15:49,155 --> 05:15:50,623
So, the first thing
to understand is that

6518
05:15:50,623 --> 05:15:53,493
when we talk about
oxygen extraction fraction,

6519
05:15:53,493 --> 05:15:55,528
there are two different
MR sequences

6520
05:15:55,528 --> 05:15:59,499
that can be used
to obtain this measure.

6521
05:15:59,499 --> 05:16:02,035
So, there’s what we call
a TRUST sequence.

6522
05:16:02,602 --> 05:16:06,205
And with the TRUST sequence,
there’s a single measurement

6523
05:16:06,205 --> 05:16:10,276
of the MR T2 signal
in the superior sagittal sinus.

6524
05:16:10,276 --> 05:16:12,845
That measurement
of the T2 signal,

6525
05:16:12,845 --> 05:16:17,183
they then use calibration models
to convert that measurement

6526
05:16:17,183 --> 05:16:20,086
to a measurement
of venous oxygen saturation,

6527
05:16:20,086 --> 05:16:21,287
from which you can

6528
05:16:21,287 --> 05:16:23,923
then calculate
the oxygen extraction fraction.

6529
05:16:23,923 --> 05:16:27,160
However, that’s a single
measurement that’s applied

6530
05:16:27,160 --> 05:16:29,128
globally to the entire brain.

6531
05:16:30,296 --> 05:16:33,499
In contrast, you can also use
an arterial spin --

6532
05:16:33,499 --> 05:16:37,537
sorry, an ASE or asymmetric
spin echo sequence,

6533
05:16:38,404 --> 05:16:40,073
which is a tissue-based
measurement

6534
05:16:40,073 --> 05:16:42,041
of oxygen extraction fraction.

6535
05:16:42,041 --> 05:16:47,013
And so, this measurement
is based on the variation

6536
05:16:47,013 --> 05:16:48,448
in the MR field

6537
05:16:48,448 --> 05:16:52,352
caused by deoxyhemoglobin
in the microvasculature.

6538
05:16:52,885 --> 05:16:54,821
So, then you’re getting
a measurement

6539
05:16:54,821 --> 05:16:58,958
of oxygen extraction fraction
within each voxel of the brain.

6540
05:16:58,958 --> 05:17:01,127
So, if you’re seeing
regional data,

6541
05:17:01,127 --> 05:17:03,196
not just global measurements,

6542
05:17:03,196 --> 05:17:06,065
that data was obtained
with an ASE sequence.

6543
05:17:08,067 --> 05:17:11,237
So, the reason why
I’m explaining this to you

6544
05:17:11,237 --> 05:17:14,574
is because a couple of years
later, this was published.

6545
05:17:14,574 --> 05:17:19,412
So, on your left, you see
some great data out of LA

6546
05:17:20,346 --> 05:17:21,948
using a TRUST sequence,

6547
05:17:22,915 --> 05:17:25,218
showing that in kids
with sickle cell disease,

6548
05:17:25,218 --> 05:17:27,286
that whole brain
oxygen extraction

6549
05:17:27,286 --> 05:17:29,956
is significantly lower
than healthy controls.

6550
05:17:30,590 --> 05:17:33,192
And then a year later,
our lab published data

6551
05:17:33,192 --> 05:17:35,995
showing that in kids
with sickle cell disease,

6552
05:17:36,829 --> 05:17:40,299
whole brain oxygen extraction
was significantly elevated

6553
05:17:40,299 --> 05:17:42,835
compared
to healthy controls and children

6554
05:17:42,835 --> 05:17:45,004
who are anemic
for other reasons.

6555
05:17:45,004 --> 05:17:47,273
So, you don’t have to be
a scientist

6556
05:17:47,273 --> 05:17:50,176
to see the data is moving
in the exact opposite direction.

6557
05:17:51,411 --> 05:17:53,880
So, why is this?

6558
05:17:54,447 --> 05:17:57,750
So, like I said,
with the TRUST sequence,

6559
05:17:58,251 --> 05:18:00,353
you get an MR measurement
that then --

6560
05:18:00,353 --> 05:18:02,522
is then calibrated or converted

6561
05:18:02,522 --> 05:18:04,524
to this venous
oxygen saturation.

6562
05:18:05,391 --> 05:18:08,828
And that --
those calibration models --

6563
05:18:08,828 --> 05:18:10,263
there are different ones.

6564
05:18:10,263 --> 05:18:12,498
The original one
was this bovine model.

6565
05:18:12,498 --> 05:18:15,868
It was based off of experiments
done with bovine blood.

6566
05:18:15,868 --> 05:18:20,973
And the criticism of that model
is that they --

6567
05:18:20,973 --> 05:18:23,042
the samples
weren’t anemic, right?

6568
05:18:23,042 --> 05:18:24,410
And so, then
you’re applying that

6569
05:18:24,410 --> 05:18:26,446
to a very anemic population.

6570
05:18:27,046 --> 05:18:28,915
And then the Bush model came,

6571
05:18:28,915 --> 05:18:33,786
and that used hemoglobin S
containing blood and was anemic.

6572
05:18:33,786 --> 05:18:36,189
But they didn’t control
for the hemoglobin level,

6573
05:18:36,189 --> 05:18:38,958
and then the Li model
and then the Li-Bush model.

6574
05:18:38,958 --> 05:18:40,626
And this data that
I’m showing here

6575
05:18:40,626 --> 05:18:43,296
comes from a paper
from Dr. Eboni Lance

6576
05:18:43,996 --> 05:18:48,568
at Kennedy Krieger,
looking at TRUST data obtained

6577
05:18:49,702 --> 05:18:51,204
and the relationship
with hemoglobin

6578
05:18:51,204 --> 05:18:53,239
using these four
different models.

6579
05:18:53,239 --> 05:18:55,475
And you can see that
with the original bovine model,

6580
05:18:55,475 --> 05:18:58,144
the more anemic the patient,
the higher the OEF,

6581
05:18:58,144 --> 05:19:00,079
and then
an opposite relationship

6582
05:19:00,613 --> 05:19:02,415
with the other three models.

6583
05:19:02,415 --> 05:19:06,185
And then this, very recently,
was published out of WashU.

6584
05:19:06,185 --> 05:19:08,888
And so, this was a cohort
of adults who were scanned

6585
05:19:08,888 --> 05:19:12,191
and had OEF measurements
using both sequences.

6586
05:19:12,191 --> 05:19:16,095
And on the x axis,
you see the ASE OEF measurement.

6587
05:19:16,095 --> 05:19:19,232
On the y axis,
you see the TRUST measurement

6588
05:19:19,232 --> 05:19:20,600
with three different models,

6589
05:19:20,600 --> 05:19:22,702
and you see
a significant correlation

6590
05:19:22,702 --> 05:19:27,540
between ASE OEF and TRUST OEF
when the Li model is used,

6591
05:19:27,540 --> 05:19:30,476
but we lose significance with
the Bush and Li-Bush model.

6592
05:19:31,244 --> 05:19:33,246
So, why?

6593
05:19:35,148 --> 05:19:37,617
So, I’m [laughs]
-- this is really complicated,

6594
05:19:37,617 --> 05:19:39,152
and I have 20 minutes.

6595
05:19:39,152 --> 05:19:42,155
So, I’m not going
to go through the data

6596
05:19:42,155 --> 05:19:44,157
from these three papers
cited here,

6597
05:19:44,157 --> 05:19:48,027
but these three papers
are trying to get at why.

6598
05:19:48,027 --> 05:19:51,531
So, maybe not one is right,
and one is wrong.

6599
05:19:51,531 --> 05:19:54,066
Maybe these are two
different sequences,

6600
05:19:54,066 --> 05:19:55,802
two very different techniques,

6601
05:19:55,802 --> 05:19:58,104
and they’re each
telling us something.

6602
05:19:58,104 --> 05:20:02,175
And the TRUST sequence, again,
it’s that single measurement

6603
05:20:02,175 --> 05:20:03,776
in the superior sagittal sinus.

6604
05:20:03,776 --> 05:20:07,146
It’s a flow-weighted measurement
from -- you know,

6605
05:20:07,146 --> 05:20:10,616
it’s a vein that’s draining
huge territories of the brain.

6606
05:20:11,184 --> 05:20:14,754
And so, there’s something called
cerebral capillary shunting.

6607
05:20:14,754 --> 05:20:17,456
And these three papers
are using MRI

6608
05:20:17,456 --> 05:20:22,094
to try to quantify
cerebral capillary shunting

6609
05:20:22,094 --> 05:20:25,264
and explain how,
with a TRUST sequence,

6610
05:20:25,264 --> 05:20:29,735
you could see, actually a lower
oxygen extraction fraction.

6611
05:20:29,735 --> 05:20:31,571
So, just conceptually,

6612
05:20:31,571 --> 05:20:34,740
the concept
is that in a healthy brain,

6613
05:20:34,740 --> 05:20:37,476
you have an artery
supplying blood to the brain.

6614
05:20:37,476 --> 05:20:40,379
The blood then travels
through the capillary.

6615
05:20:40,379 --> 05:20:44,283
The capillaries, at a normal
transit time, offloads oxygen

6616
05:20:44,283 --> 05:20:45,985
and then goes
to the draining vein.

6617
05:20:45,985 --> 05:20:47,954
In compensated anemia,

6618
05:20:47,954 --> 05:20:50,890
that supplying
artery vasodilates.

6619
05:20:50,890 --> 05:20:52,925
The blood travels
a little faster,

6620
05:20:53,526 --> 05:20:56,362
offloads its oxygen,
though normally,

6621
05:20:56,362 --> 05:20:59,765
and you again have a normal
oxygen extraction fraction.

6622
05:20:59,765 --> 05:21:03,369
However, when anemia
is uncompensated,

6623
05:21:03,369 --> 05:21:07,073
you have further vasodilation.
The blood is moving even faster,

6624
05:21:07,073 --> 05:21:10,343
and it’s unable to properly
offload the oxygen.

6625
05:21:10,843 --> 05:21:12,245
And then in that vein,

6626
05:21:12,245 --> 05:21:15,047
you have a lower
oxygen extraction fraction.

6627
05:21:15,047 --> 05:21:18,818
And some really fancy
imaging in humans is being used

6628
05:21:18,818 --> 05:21:21,387
to try to demonstrate this
in sickle cell disease.

6629
05:21:22,154 --> 05:21:27,760
So, I think -- and I’ve read
all of this literature.

6630
05:21:27,760 --> 05:21:31,898
So, my takeaway is --
my job depends on it.

6631
05:21:31,898 --> 05:21:33,232
[laughter]

6632
05:21:33,232 --> 05:21:38,271
My takeaway is that regardless
of the sequence

6633
05:21:38,271 --> 05:21:40,439
that’s used
or the calibration model,

6634
05:21:40,439 --> 05:21:44,777
the majority of literature would
suggest that sickle cell disease

6635
05:21:44,777 --> 05:21:47,513
impacts the cerebral
hemodynamics of the brain.

6636
05:21:48,114 --> 05:21:52,618
And that possibly, we don’t have
to say one’s right or wrong,

6637
05:21:52,618 --> 05:21:55,254
but we could learn different
things about the physiology

6638
05:21:55,254 --> 05:21:57,023
from the different sequences.

6639
05:21:58,090 --> 05:22:01,994
And I just hope
that we don’t throw this away

6640
05:22:01,994 --> 05:22:05,865
because it’s a little confusing
and then not potentially use it

6641
05:22:05,865 --> 05:22:08,401
as a biomarker
to help our patients.

6642
05:22:08,401 --> 05:22:10,670
Okay. That’s my hill.

6643
05:22:10,670 --> 05:22:16,609
Now, I’m going to tell you about
how these biomarkers thus far

6644
05:22:16,609 --> 05:22:18,311
have been used to understand

6645
05:22:18,311 --> 05:22:20,579
the different treatments
that are available.

6646
05:22:20,579 --> 05:22:23,382
And this has been hit upon
at least 10 times

6647
05:22:23,382 --> 05:22:25,985
in the past two days,
so I’m not going to review it.

6648
05:22:26,585 --> 05:22:29,522
But just the timeline,
at least in the United States,

6649
05:22:29,522 --> 05:22:32,858
of different treatments that
are available to our patients.

6650
05:22:33,492 --> 05:22:35,528
And I’m going to start
with chronic transfusion therapy

6651
05:22:35,528 --> 05:22:39,365
because it’s really
the cornerstone for our patients

6652
05:22:39,365 --> 05:22:42,535
with sickle cell disease,
and that’s what we fall back on.

6653
05:22:42,535 --> 05:22:49,075
So, on the left is a figure from
a paper out of WashU using MRI,

6654
05:22:49,075 --> 05:22:51,944
the ASE sequence,
to look at cerebral blood flow

6655
05:22:51,944 --> 05:22:53,279
and oxygen extraction

6656
05:22:53,279 --> 05:22:55,147
immediately before
and immediately

6657
05:22:55,147 --> 05:22:58,284
after a transfusion
in a cohort of 21 children.

6658
05:22:58,985 --> 05:23:02,388
And what we found is that there
was a significant reduction

6659
05:23:02,388 --> 05:23:05,424
in cerebral blood flow
and oxygen extraction

6660
05:23:05,424 --> 05:23:09,895
after a single transfusion,
while CMRO2 remains stable.

6661
05:23:09,895 --> 05:23:12,932
Then more recently,
the group out of Emory,

6662
05:23:12,932 --> 05:23:15,334
using a diffuse optical
spectroscopy --

6663
05:23:15,334 --> 05:23:18,204
which is that bedside tool
with the sensor

6664
05:23:18,204 --> 05:23:22,108
on the brain measuring light
frequencies and wavelengths.

6665
05:23:22,108 --> 05:23:23,342
And I’m not a physicist,

6666
05:23:23,342 --> 05:23:25,911
but it’s a really
cool technology.

6667
05:23:25,911 --> 05:23:28,647
And they get regional
measurements of cerebral blood

6668
05:23:28,647 --> 05:23:31,217
flow and oxygen extraction
with this tool.

6669
05:23:31,217 --> 05:23:32,752
And they found similar results,

6670
05:23:32,752 --> 05:23:35,221
showing that cerebral blood
flow decreased,

6671
05:23:35,221 --> 05:23:36,822
oxygen extraction decreased,

6672
05:23:36,822 --> 05:23:40,726
and CMRO2 didn’t change
with a single transfusion.

6673
05:23:40,726 --> 05:23:42,895
And so,
this gives you some insight

6674
05:23:42,895 --> 05:23:46,465
into why chronic transfusion
therapy

6675
05:23:46,465 --> 05:23:48,667
is neuroprotective
in this population.

6676
05:23:49,468 --> 05:23:52,805
So, contrasting that figure
on the left out of WashU

6677
05:23:52,805 --> 05:23:55,174
with the figure on the right
out of Vanderbilt,

6678
05:23:55,174 --> 05:23:57,009
and they did
a very similar study,

6679
05:23:57,009 --> 05:23:59,412
but they included adults
in their study.

6680
05:23:59,412 --> 05:24:01,914
And so, on the bottom
is their pediatric data

6681
05:24:01,914 --> 05:24:03,249
showing similar results,

6682
05:24:03,249 --> 05:24:05,785
that cerebral blood flow
and oxygen extraction,

6683
05:24:06,519 --> 05:24:09,755
each significantly went down
with transfusion.

6684
05:24:09,755 --> 05:24:12,124
But in adults, they found
that cerebral blood flow

6685
05:24:12,124 --> 05:24:13,392
didn’t change,

6686
05:24:13,392 --> 05:24:15,928
and oxygen extraction
was significantly reduced.

6687
05:24:15,928 --> 05:24:19,432
And so, I think here
is our first hint, right,

6688
05:24:19,432 --> 05:24:23,269
that our therapies don’t impact
adults necessarily,

6689
05:24:23,269 --> 05:24:26,238
in the same way that they impact
children with sickle cell.

6690
05:24:27,840 --> 05:24:29,775
So, this is a paper
out of my lab.

6691
05:24:29,775 --> 05:24:32,945
This was a cross-sectional study
in children and adults

6692
05:24:32,945 --> 05:24:37,349
with sickle cell disease,
87 patients, a cohort of 23

6693
05:24:37,349 --> 05:24:39,718
not on primary disease
modification --

6694
05:24:39,718 --> 05:24:44,190
not because we didn’t try --
a cohort on hydroxyurea only,

6695
05:24:44,190 --> 05:24:46,392
and a cohort that’s
chronically transfused.

6696
05:24:46,892 --> 05:24:49,128
And on the left side
of each brain,

6697
05:24:49,128 --> 05:24:51,530
you see the average
oxygen extraction map.

6698
05:24:51,530 --> 05:24:55,468
On the right side of each brain,
you see a generic brain atlas,

6699
05:24:55,468 --> 05:24:57,803
but overlaid on that
in teal and white,

6700
05:24:58,337 --> 05:24:59,638
are the regions of the brain

6701
05:24:59,638 --> 05:25:02,108
with increased
oxygen extraction.

6702
05:25:02,108 --> 05:25:06,145
And there was a significant
reduction in the volume of brain

6703
05:25:06,145 --> 05:25:08,414
with elevated oxygen extraction

6704
05:25:08,414 --> 05:25:11,283
as a potential biomarker
of metabolic stress

6705
05:25:11,283 --> 05:25:14,253
in the people
with sickle cell disease

6706
05:25:14,253 --> 05:25:16,122
receiving treatment
with hydroxyurea

6707
05:25:16,122 --> 05:25:18,891
compared to those not
receiving disease modification.

6708
05:25:19,425 --> 05:25:22,161
However, the volume
of threshold of brain

6709
05:25:22,161 --> 05:25:24,964
was significantly higher
than the cohort

6710
05:25:24,964 --> 05:25:27,500
that was receiving
chronic transfusion therapy.

6711
05:25:28,467 --> 05:25:33,506
So, you know, it’s probably not
equivalent, but it definitely --

6712
05:25:33,506 --> 05:25:36,375
I think this suggests
and further supports

6713
05:25:36,375 --> 05:25:37,877
the mounting evidence

6714
05:25:37,877 --> 05:25:40,579
that hydroxyurea
is neuroprotective

6715
05:25:40,579 --> 05:25:42,481
in the setting
of sickle cell disease.

6716
05:25:43,816 --> 05:25:47,419
And then voxelotor,
which we’ve talked about --

6717
05:25:47,419 --> 05:25:51,123
and I think there have been
discussions in the past day

6718
05:25:51,123 --> 05:25:54,960
or so about concerns
about the drug’s ability

6719
05:25:54,960 --> 05:25:59,431
to appropriately offload oxygen
in vulnerable organs.

6720
05:26:00,166 --> 05:26:03,469
So, this paper was published
this year, again, out of Emory,

6721
05:26:03,469 --> 05:26:05,905
in a very small cohort,
eight children,

6722
05:26:06,839 --> 05:26:10,476
using that bed site tool to look
at regional cerebral blood

6723
05:26:10,476 --> 05:26:12,444
flow and oxygen extraction.

6724
05:26:12,444 --> 05:26:16,448
And they found reduction
in both of these biomarkers,

6725
05:26:16,448 --> 05:26:19,285
four weeks after
initiating voxelotor,

6726
05:26:19,285 --> 05:26:21,954
and that persisted out
to 12 weeks.

6727
05:26:22,688 --> 05:26:27,159
So, I think we’re starting
to see physiologic data

6728
05:26:27,159 --> 05:26:28,861
about the impact
of this drug.

6729
05:26:30,229 --> 05:26:33,365
Okay. Last but not least,
transplant.

6730
05:26:34,033 --> 05:26:37,703
We have no gene therapy data.
So, transplant.

6731
05:26:37,703 --> 05:26:40,406
So, this is a paper published
by Dr. Monica Hulbert,

6732
05:26:40,406 --> 05:26:44,243
who’s now in Boston,
and she looked at this data --

6733
05:26:45,077 --> 05:26:49,582
these measurements, before and
after transplant in children.

6734
05:26:49,582 --> 05:26:51,917
So, these were 10 children
that underwent curative

6735
05:26:51,917 --> 05:26:53,352
stem cell transplant.

6736
05:26:53,352 --> 05:26:57,056
So, in orange, you see pre-,
post-transfusion data.

6737
05:26:57,723 --> 05:27:01,360
In like teal green, you see
pre-, post-transplant data.

6738
05:27:01,894 --> 05:27:04,630
And then you see data
from healthy controls in blue.

6739
05:27:04,630 --> 05:27:08,534
And what you see is that
after curative transplant --

6740
05:27:08,534 --> 05:27:10,636
this is about a year
out from transplant --

6741
05:27:10,636 --> 05:27:13,572
you see a significant reduction
in cerebral blood

6742
05:27:13,572 --> 05:27:16,442
flow on the left and
oxygen extraction on the right.

6743
05:27:17,076 --> 05:27:21,213
You also see that it’s reduced,
not only from pre-transplant,

6744
05:27:21,213 --> 05:27:24,717
but reduced from pre-
and post-transfusion.

6745
05:27:25,417 --> 05:27:27,519
And you see that
there’s no longer a difference

6746
05:27:27,519 --> 05:27:29,488
when compared
to the healthy controls.

6747
05:27:30,256 --> 05:27:32,291
And this was
a pediatric-only study.

6748
05:27:33,892 --> 05:27:37,263
In adults, this is data
out of Vanderbilt

6749
05:27:38,397 --> 05:27:40,399
using a TRUST sequence.

6750
05:27:40,933 --> 05:27:42,701
And their calibration curve,
I believe,

6751
05:27:42,701 --> 05:27:44,703
was hemoglobin F-based.

6752
05:27:44,703 --> 05:27:46,405
So, you have
healthy controls in red.

6753
05:27:46,405 --> 05:27:47,606
And then you have pre-,

6754
05:27:47,606 --> 05:27:49,575
post-transplant
in blue and green.

6755
05:27:49,575 --> 05:27:52,411
And you see a reduction
in cerebral blood flow,

6756
05:27:52,411 --> 05:27:56,615
post-transplant, without
a change in oxygen extraction.

6757
05:27:56,615 --> 05:27:59,351
When they further dove
into their OEF data,

6758
05:27:59,985 --> 05:28:01,854
they found that in the adults

6759
05:28:01,854 --> 05:28:04,623
who started with a higher
oxygen extraction,

6760
05:28:04,623 --> 05:28:07,259
they had a greater decrease,
post-transplant.

6761
05:28:07,259 --> 05:28:08,627
However, if they were starting

6762
05:28:08,627 --> 05:28:11,463
with a more normal
oxygen extraction,

6763
05:28:11,463 --> 05:28:13,165
they didn’t see that decrease.

6764
05:28:13,899 --> 05:28:16,302
And then this is data
very recently published

6765
05:28:16,302 --> 05:28:18,604
out of the Netherlands
using a TRUST sequence,

6766
05:28:18,604 --> 05:28:20,439
a different calibration curve,

6767
05:28:20,439 --> 05:28:22,875
so the data is moving
in the opposite direction.

6768
05:28:23,375 --> 05:28:26,345
However, again, you have your
healthy controls for comparison.

6769
05:28:26,345 --> 05:28:28,881
And what they see is a --

6770
05:28:30,549 --> 05:28:33,786
they see a decrease
in cerebral blood flow

6771
05:28:33,786 --> 05:28:37,089
and an increase in oxygen
extraction towards,

6772
05:28:37,089 --> 05:28:38,691
again,
the healthy controls,

6773
05:28:38,691 --> 05:28:42,361
so a normalization
of hemodynamic metrics,

6774
05:28:42,361 --> 05:28:43,562
post-transplant.

6775
05:28:43,562 --> 05:28:45,964
Again, this is about a year
out from transplant.

6776
05:28:47,533 --> 05:28:53,939
So, hopefully, I can convince
you guys

6777
05:28:53,939 --> 05:28:55,808
that advanced neuroimaging

6778
05:28:55,808 --> 05:28:58,377
has provided us
with some insight

6779
05:28:58,911 --> 05:29:01,980
into the impact of sickle
cell disease on the brain.

6780
05:29:02,648 --> 05:29:04,116
And today, I really focused

6781
05:29:04,116 --> 05:29:05,984
on these cerebral
hemodynamic measures.

6782
05:29:05,984 --> 05:29:08,887
There’s much more that MRI
can offer us, again,

6783
05:29:08,887 --> 05:29:10,823
looking at function
and structure.

6784
05:29:13,058 --> 05:29:16,095
But learning more can help us

6785
05:29:16,095 --> 05:29:19,498
then possibly link
these biomarkers

6786
05:29:19,498 --> 05:29:22,167
with the clinical outcomes
that we care about.

6787
05:29:22,835 --> 05:29:27,539
And then can we use these tools
to assess for risk prediction

6788
05:29:27,539 --> 05:29:29,775
but also to assess
treatment efficacy?

6789
05:29:29,775 --> 05:29:35,147
Because trials looking for
stroke take a very long time,

6790
05:29:35,147 --> 05:29:37,616
and that damage has then
already occurred.

6791
05:29:38,183 --> 05:29:40,686
And those brain cells
cannot be saved.

6792
05:29:40,686 --> 05:29:46,625
So, can we use technology
to look in a different way?

6793
05:29:46,625 --> 05:29:48,494
Okay. Thanks.

6794
05:29:49,061 --> 05:29:54,566
[applause]

6795
05:29:54,566 --> 05:29:56,935
John Tisdale: Trying to sneak
off before the questions.

6796
05:29:56,935 --> 05:29:58,170
[laughter]

6797
05:29:58,170 --> 05:29:59,772
Swee Lay is already up, so --

6798
05:30:01,106 --> 05:30:02,975
Swee Lay Thein: Melanie,
that was really wonderful.

6799
05:30:03,709 --> 05:30:07,112
I’ve got two questions.

6800
05:30:07,112 --> 05:30:11,683
One is, when you talk about
pre-, post-transplant, pre/post,

6801
05:30:12,785 --> 05:30:16,555
you have discussed a lot about,
in the patient, variability.

6802
05:30:17,089 --> 05:30:19,958
It is comparing it
with the patient.

6803
05:30:20,959 --> 05:30:22,961
And the change that you give,

6804
05:30:22,961 --> 05:30:25,798
is the change from baseline
and the average?

6805
05:30:26,365 --> 05:30:29,134
Or is it taken across --

6806
05:30:29,701 --> 05:30:31,537
you see what I’m saying --
the baseline?

6807
05:30:31,537 --> 05:30:36,875
Melanie Fields: It’s a pre/post.
It’s the same patient before --

6808
05:30:36,875 --> 05:30:38,143
Swee Lay Thein: Right.

6809
05:30:38,143 --> 05:30:41,046
So, there’s a huge
difference in that.

6810
05:30:41,046 --> 05:30:45,684
Because let’s say you,
pre and post,

6811
05:30:45,684 --> 05:30:48,454
then I should take your change
from your baseline.

6812
05:30:49,354 --> 05:30:53,125
Mine changed from baseline,
and we average the baseline.

6813
05:30:53,125 --> 05:30:55,861
We average the change.
Is that what you did?

6814
05:30:57,162 --> 05:30:58,564
Melanie Fields: Yeah.

6815
05:30:58,564 --> 05:31:01,733
And I think in some studies,
they show you --

6816
05:31:01,733 --> 05:31:04,636
well, I didn’t show it to you,
but in like -- for instance,

6817
05:31:04,636 --> 05:31:06,638
I just published a paper
using connectivity,

6818
05:31:06,638 --> 05:31:10,242
and I showed that measure
for each person, pre and post.

6819
05:31:10,242 --> 05:31:12,344
So, you can see the delta.

6820
05:31:13,378 --> 05:31:15,614
Swee Lay Thein: Yes.
This one took in the delta --

6821
05:31:15,614 --> 05:31:17,249
average of the delta,
the change.

6822
05:31:17,249 --> 05:31:18,450
Melanie Fields: [affirmative]

6823
05:31:18,450 --> 05:31:19,818
Swee Lay Thein: Sorry, John.
Can I ask another question?

6824
05:31:19,818 --> 05:31:21,053
John Tisdale: Yeah.

6825
05:31:21,053 --> 05:31:23,088
Swee Lay Thein: The second is,
when you talk about --

6826
05:31:23,088 --> 05:31:24,289
[laughter]
John Tisdale: [unintelligible]

6827
05:31:24,289 --> 05:31:25,791
Melanie Fields: I think you’re
the boss.

6828
05:31:25,791 --> 05:31:27,426
Swee Lay Thein: Okay.

6829
05:31:27,426 --> 05:31:30,095
You talk about
decompensated anemia.

6830
05:31:31,163 --> 05:31:35,767
So, this could -- maybe could
explain why, you know, like,

6831
05:31:35,767 --> 05:31:38,570
say, when someone
with sickle cell disease

6832
05:31:38,570 --> 05:31:42,841
who is a decent hemoglobin,
not like, say, 8,

6833
05:31:42,841 --> 05:31:47,913
and then maybe has a crisis
or a parvovirus

6834
05:31:47,913 --> 05:31:50,983
and they dropped.
Then it’s not compensated.

6835
05:31:51,884 --> 05:31:55,454
And is this associated
with a high incidence

6836
05:31:56,522 --> 05:31:57,756
of cerebral infarc?

6837
05:31:57,756 --> 05:31:59,024
Melanie Fields: Yeah.

6838
05:31:59,024 --> 05:32:01,093
So, there was a really
great paper published

6839
05:32:01,093 --> 05:32:05,998
about 2010-ish in Blood,
where they did acute

6840
05:32:05,998 --> 05:32:10,669
imaging in the setting of acute
anemia in sickle cell disease

6841
05:32:10,669 --> 05:32:13,572
and in patients who are
anemic for other reasons,

6842
05:32:13,572 --> 05:32:16,542
and showed these
acute ischemic events

6843
05:32:17,109 --> 05:32:23,916
using a different sequence.
So, I’m going to go with yes.

6844
05:32:24,716 --> 05:32:30,455
And with the recent
parvovirus surge, you know,

6845
05:32:30,455 --> 05:32:34,026
we have patients come in
with neurologic symptoms

6846
05:32:34,026 --> 05:32:36,628
as their hemoglobin drop,
so exactly.

6847
05:32:36,628 --> 05:32:40,265
I think -- but I do think
that there’s also chronic damage

6848
05:32:40,265 --> 05:32:44,803
from this persistent anemia.
So, I think it’s both.

6849
05:32:46,338 --> 05:32:47,539
John Tisdale: Okay.

6850
05:32:47,539 --> 05:32:48,974
I’m going to jump in
with a quick question.

6851
05:32:48,974 --> 05:32:50,208
And then we have
one more question.

6852
05:32:50,208 --> 05:32:52,311
And then we’ll move on.
And that is,

6853
05:32:52,311 --> 05:32:55,113
I’m always struck --
and I always ask this question

6854
05:32:55,647 --> 05:32:57,449
by about this time
in the meeting --

6855
05:32:58,283 --> 05:33:01,553
I’m always struck by
how much better the patients

6856
05:33:01,553 --> 05:33:04,489
that are getting transfusions
look by every measure,

6857
05:33:04,489 --> 05:33:08,460
by kidney function, by heart
function, by brain imaging.

6858
05:33:08,460 --> 05:33:12,764
I mean, the difference
was pretty stark, I thought.

6859
05:33:13,298 --> 05:33:16,635
So, are we not transfusing
enough in this disease?

6860
05:33:18,737 --> 05:33:20,672
Melanie Fields: I like how I got
the loaded question.

6861
05:33:20,672 --> 05:33:21,974
[laughter]

6862
05:33:21,974 --> 05:33:25,744
I think each hematologist
has their --

6863
05:33:25,744 --> 05:33:28,413
I mean, there’s the concrete,
like, you have to transfuse

6864
05:33:28,413 --> 05:33:29,881
for X, Y and Z, right?

6865
05:33:29,881 --> 05:33:32,284
TCD is, all of you
have to do it.

6866
05:33:32,284 --> 05:33:33,919
And then there’s
the middle ground.

6867
05:33:33,919 --> 05:33:36,822
And I think then the way
that I practice

6868
05:33:36,822 --> 05:33:41,393
is that that’s a very serious
conversation with each family

6869
05:33:41,393 --> 05:33:43,295
about the benefits
and the risks.

6870
05:33:43,996 --> 05:33:46,164
And then we go through
every risk,

6871
05:33:46,164 --> 05:33:50,002
and it’s probably 50/50,
where we land.

6872
05:33:51,637 --> 05:33:55,040
But I don’t think we have --
I don’t think we can --

6873
05:33:55,040 --> 05:33:57,175
it is an art, right?
I don’t think we can practice,

6874
05:33:57,175 --> 05:33:59,411
but we can only transfuse
for X, Y and Z.

6875
05:33:59,978 --> 05:34:02,514
So -- but I agree;
they feel better.

6876
05:34:03,515 --> 05:34:04,750
John Tisdale: Okay,
last question.

6877
05:34:04,750 --> 05:34:05,984
Male Speaker: I need to add
to this --

6878
05:34:05,984 --> 05:34:07,853
great presentation, by the way.
Love it.

6879
05:34:07,853 --> 05:34:10,489
So, how much do you think
this is all hemoglobin?

6880
05:34:10,489 --> 05:34:13,125
You mean -- I’m going to add to
what has been said thus far --

6881
05:34:13,125 --> 05:34:14,359
you mean,

6882
05:34:14,359 --> 05:34:17,062
because transplant corrects
the hemoglobin the most,

6883
05:34:17,062 --> 05:34:19,865
so they are having
a hemoglobin of 13 or 14?

6884
05:34:19,865 --> 05:34:22,501
While voxelotor might
increase you by one gram.

6885
05:34:22,501 --> 05:34:24,469
Correct?
And their baseline is lower.

6886
05:34:24,469 --> 05:34:28,073
So, how much of this is truly
all based on hemoglobin?

6887
05:34:28,073 --> 05:34:29,374
Melanie Fields: Yeah.

6888
05:34:29,374 --> 05:34:33,011
So, I don’t think
that it’s all hemoglobin.

6889
05:34:33,845 --> 05:34:38,083
I think that hemolysis --

6890
05:34:38,083 --> 05:34:40,719
the endothelial activation,
plays a role.

6891
05:34:40,719 --> 05:34:44,423
I think the metabolic
demands differ.

6892
05:34:45,223 --> 05:34:48,493
And there’s old data,
I think, from Jamaica --

6893
05:34:48,493 --> 05:34:51,496
don’t quote me on that --
looking in the periphery.

6894
05:34:51,496 --> 05:34:54,533
So, I don’t think
it’s just the anemia.

6895
05:34:54,533 --> 05:34:58,170
And every time I have presented
this over the many years,

6896
05:34:59,104 --> 05:35:00,338
a hematologist asks me

6897
05:35:00,338 --> 05:35:03,075
if it’s just a really
expensive test for CBC?

6898
05:35:03,075 --> 05:35:06,178
And I don’t think it’s --
and which is why

6899
05:35:06,178 --> 05:35:09,915
I did that study with kids
with sickle cell disease,

6900
05:35:09,915 --> 05:35:12,117
kids who are anemic,
for other reasons,

6901
05:35:12,117 --> 05:35:14,953
and healthy controls, because --
I didn’t show it.

6902
05:35:14,953 --> 05:35:16,188
It’s only 20 minutes.

6903
05:35:16,188 --> 05:35:18,356
But in that data set,
the kids who are anemic

6904
05:35:18,356 --> 05:35:21,159
for other reasons
were equally anemic.

6905
05:35:21,159 --> 05:35:24,863
So, their hemoglobin
were sitting at 7 and 8,

6906
05:35:24,863 --> 05:35:29,601
and their oxygen extraction
was significantly lower

6907
05:35:29,601 --> 05:35:31,903
than the kids
with sickle cell disease.

6908
05:35:31,903 --> 05:35:34,606
So, I mean, that’s not --
it’s a cross-sectional study,

6909
05:35:34,606 --> 05:35:36,074
but I think it supports the idea

6910
05:35:36,074 --> 05:35:37,876
that it’s more than
just the anemia.

6911
05:35:39,611 --> 05:35:41,613
John Tisdale: All right. Great.
Thank you, Melanie.

6912
05:35:41,613 --> 05:35:42,848
[applause]

6913
05:35:42,848 --> 05:35:44,049
All right.

6914
05:35:44,049 --> 05:35:47,953
So, to finish up this session,
we have Dr. Parker Ruhl,

6915
05:35:47,953 --> 05:35:52,290
who’s my colleague
and my neighbor in D.C.,

6916
05:35:52,290 --> 05:35:54,426
who’s going to talk to us
about cardiopulmonary function

6917
05:35:54,426 --> 05:35:55,627
in sickle cell disease.

6918
05:35:55,627 --> 05:35:59,498
She’s an associate research
physician in the Physiology Unit

6919
05:35:59,498 --> 05:36:04,803
of the Laboratory of Malaria
and Vector Research at NIAID

6920
05:36:06,104 --> 05:36:09,007
and in the Pulmonary
Branch of NHLBI.

6921
05:36:09,007 --> 05:36:10,375
So, welcome Parker.

6922
05:36:10,375 --> 05:36:12,444
You can bring us
to the end of this session.

6923
05:36:12,444 --> 05:36:13,879
Parker Ruhl: Thank you.

6924
05:36:13,879 --> 05:36:16,014
[applause]

6925
05:36:16,014 --> 05:36:17,616
Thank you, everyone.

6926
05:36:18,283 --> 05:36:21,653
So, I wear many hats,
as many people in this room do,

6927
05:36:22,387 --> 05:36:25,757
but certainly pulmonary function
in sickle cell disease

6928
05:36:25,757 --> 05:36:28,160
is something
that’s close to my heart.

6929
05:36:29,528 --> 05:36:32,631
And today, I will talk about
lung function

6930
05:36:32,631 --> 05:36:35,333
after non-myeloablative
hematopoietic

6931
05:36:35,333 --> 05:36:38,603
cell transplant for patients
with sickle cell disease

6932
05:36:39,271 --> 05:36:42,174
from a cohort that’s all from
the NIH Clinical Center.

6933
05:36:46,077 --> 05:36:50,115
Getting used to the --
oh, the other.

6934
05:36:55,220 --> 05:37:00,992
Oh, the big green button.
All right.

6935
05:37:00,992 --> 05:37:02,894
So, I will start off
with a background

6936
05:37:02,894 --> 05:37:04,529
in sickle cell lung disease.

6937
05:37:04,529 --> 05:37:06,765
I will do a little primer
on pulmonary function

6938
05:37:06,765 --> 05:37:10,468
testing and spirometry for the
non-pulmonologists in the room.

6939
05:37:11,469 --> 05:37:14,239
And then I will go through
our own data on PFTs

6940
05:37:14,239 --> 05:37:18,109
after transplant and then talk
about some next plan steps.

6941
05:37:18,109 --> 05:37:20,812
And I’d love to hear everybody
else’s ideas and input.

6942
05:37:22,013 --> 05:37:24,749
So, starting off, we know
that sickle cell disease

6943
05:37:24,749 --> 05:37:26,351
is a multi-organ disease.

6944
05:37:27,219 --> 05:37:29,321
We’ve discussed
the contributions of that,

6945
05:37:29,321 --> 05:37:31,223
the polymerization
of hemoglobin S,

6946
05:37:31,223 --> 05:37:34,392
hemolytic anemia,
vaso-occlusive events.

6947
05:37:34,392 --> 05:37:37,295
I think if we think back
to medical school

6948
05:37:37,295 --> 05:37:39,564
and our training,
I remember the phrase,

6949
05:37:39,564 --> 05:37:41,733
if you know syphilis,
then you know medicine.

6950
05:37:41,733 --> 05:37:44,603
If you know HIV,
you know medicine.

6951
05:37:44,603 --> 05:37:46,071
And I think it’s really
important to consider

6952
05:37:46,071 --> 05:37:50,008
sickle cell disease
as truly a multi-organ disease.

6953
05:37:50,008 --> 05:37:52,644
So, in the pulmonary arena
alone,

6954
05:37:52,644 --> 05:37:58,717
we have acute chest syndrome.
We have pulmonary hypertension.

6955
05:37:58,717 --> 05:38:03,889
We have sleep-disordered
breathing, nocturnal hypoxemia.

6956
05:38:03,889 --> 05:38:06,324
And today, I’ll be focusing
on lower airways disease

6957
05:38:06,324 --> 05:38:07,926
and pulmonary function.

6958
05:38:08,827 --> 05:38:12,163
So, when we think about lung
health and sickle cell disease,

6959
05:38:12,163 --> 05:38:13,598
it’s really important
to consider

6960
05:38:13,598 --> 05:38:16,268
that it’s a cycle
that lung health

6961
05:38:16,268 --> 05:38:18,770
has a significant impact
on sickle cell disease

6962
05:38:18,770 --> 05:38:22,974
due to the oxygenation
of our bloodstream.

6963
05:38:22,974 --> 05:38:26,511
So, when we think about
otherwise more typical ailments

6964
05:38:26,511 --> 05:38:28,380
that impact the lung --

6965
05:38:28,380 --> 05:38:31,650
pneumonia, asthma,
lower airways disease,

6966
05:38:31,650 --> 05:38:33,285
sleep-disordered breathing,

6967
05:38:33,285 --> 05:38:37,522
recurrent hypoxemia, pulmonary
hypertension, pulmonary

6968
05:38:37,522 --> 05:38:39,124
and venous
thromboembolism --

6969
05:38:39,124 --> 05:38:40,892
that can cause damage
to the lung

6970
05:38:41,493 --> 05:38:44,195
but then also interact
with ongoing issues

6971
05:38:44,195 --> 05:38:45,797
that are more specific
to sickle cell disease,

6972
05:38:45,797 --> 05:38:49,434
so the V/Q mismatch that you
might get from atelectasis,

6973
05:38:49,434 --> 05:38:53,638
among the other issues that we
have, vaso-occlusive events,

6974
05:38:53,638 --> 05:38:58,043
fat emboli syndrome,
infarction pain, inflammation,

6975
05:38:58,043 --> 05:39:00,045
and how that cycle
then can continue.

6976
05:39:00,045 --> 05:39:01,446
It can damage
the lung further,

6977
05:39:01,446 --> 05:39:03,949
which can then impact
overall sickle cell disease.

6978
05:39:04,582 --> 05:39:05,917
And then, you know,
we’ve had a lot of,

6979
05:39:05,917 --> 05:39:07,619
I think, really
healthy discussions about

6980
05:39:07,619 --> 05:39:10,689
what is a high-income,
middle-, low-income country.

6981
05:39:10,689 --> 05:39:12,090
I actually majored in Economics,

6982
05:39:12,090 --> 05:39:14,492
so I really value
that Jennifer brought that in.

6983
05:39:15,193 --> 05:39:17,562
But we do know
that in otherwise

6984
05:39:18,163 --> 05:39:19,664
using the term
low-resource settings

6985
05:39:19,664 --> 05:39:23,001
or places where there’s other
particularly infectious insults,

6986
05:39:23,635 --> 05:39:26,771
that there are other things
that are impacting lung health.

6987
05:39:26,771 --> 05:39:28,473
And we know that the predictions

6988
05:39:28,473 --> 05:39:31,276
are the 85 percent
of expected births

6989
05:39:31,276 --> 05:39:33,345
will be occurring
in Sub-Saharan Africa,

6990
05:39:33,345 --> 05:39:37,515
where under-five child mortality
due to pneumonia

6991
05:39:37,515 --> 05:39:40,685
in all children
remains very high.

6992
05:39:40,685 --> 05:39:44,322
Tuberculosis, other infections
like malaria, tobacco,

6993
05:39:44,322 --> 05:39:47,125
vaping, air pollution
that’s indoor, outdoor,

6994
05:39:47,125 --> 05:39:50,462
all of those things can
additionally impact lung health.

6995
05:39:50,462 --> 05:39:52,230
I have a couple of pulmonary
colleagues in the U.S.

6996
05:39:52,230 --> 05:39:53,999
that are starting
to look at wildfires

6997
05:39:53,999 --> 05:39:55,667
and how that might impact
sickle cell disease.

6998
05:39:55,667 --> 05:39:58,503
So, there’s a lot
that can be factored in.

6999
05:39:59,938 --> 05:40:02,374
So, when we think about
pulmonary complications

7000
05:40:02,374 --> 05:40:04,743
of sickle cell
across the lifespan,

7001
05:40:04,743 --> 05:40:07,245
we know that they’re one
of the more common,

7002
05:40:07,245 --> 05:40:08,847
if not most common,

7003
05:40:08,847 --> 05:40:10,515
depending on different
study population studies

7004
05:40:10,515 --> 05:40:12,784
that you look at, morbidity
and mortality factors

7005
05:40:12,784 --> 05:40:15,420
in sickle cell.
And pulmonary complications

7006
05:40:15,420 --> 05:40:17,956
can affect nearly every
lung cell type and structure

7007
05:40:17,956 --> 05:40:19,324
as patients age.

7008
05:40:19,324 --> 05:40:21,493
And this is all happening
in racial and ethnic groups

7009
05:40:21,493 --> 05:40:22,694
that are profoundly impacted

7010
05:40:22,694 --> 05:40:25,096
by the social determinants
of health and commonly,

7011
05:40:25,096 --> 05:40:27,432
with poor access to health care.
Again, as we’ve discussed,

7012
05:40:27,432 --> 05:40:29,968
even in countries
with lots of resources,

7013
05:40:29,968 --> 05:40:31,703
those resources
are maldistributed.

7014
05:40:32,270 --> 05:40:34,773
So, these are all factors
leading to health disparities

7015
05:40:34,773 --> 05:40:37,776
and our limited understanding
of pulmonary complications.

7016
05:40:37,776 --> 05:40:40,979
You know, I think all of
the subspecialists in this room,

7017
05:40:40,979 --> 05:40:44,616
the nephrologist,
the cardiologist, neurologist,

7018
05:40:44,616 --> 05:40:46,184
everybody,
we’re all kind of talking about

7019
05:40:46,184 --> 05:40:48,086
how there’s a lot
of low-hanging fruit.

7020
05:40:48,086 --> 05:40:52,724
There’s so much that we can
research in sickle cell

7021
05:40:52,724 --> 05:40:54,692
because a lot has not been

7022
05:40:54,692 --> 05:40:56,494
appropriately funded
to investigate.

7023
05:40:57,028 --> 05:41:00,799
So, a group of pulmonologists
and myself,

7024
05:41:00,799 --> 05:41:03,968
both Liz Klings
and Robyn Cohen at BU

7025
05:41:03,968 --> 05:41:06,071
and Christy Sadreameli
at Hopkins,

7026
05:41:06,071 --> 05:41:09,841
got together a diverse
group of specialists

7027
05:41:09,841 --> 05:41:11,543
through the American
Thoracic Society.

7028
05:41:11,543 --> 05:41:14,279
And in 2019,
we published a workshop report.

7029
05:41:14,279 --> 05:41:15,513
And we were really proud

7030
05:41:15,513 --> 05:41:17,816
of how diverse
our interest holders were.

7031
05:41:17,816 --> 05:41:20,819
So, we had 18 pulmonologists,
12 hematologists,

7032
05:41:20,819 --> 05:41:23,521
two emergency medicine
physicians with pediatric

7033
05:41:23,521 --> 05:41:26,124
and adult represented
throughout that group.

7034
05:41:26,124 --> 05:41:27,725
We had hematologists
from Mali.

7035
05:41:27,725 --> 05:41:30,995
We had Dr. Jennifer
Knight-Madden from Jamaica,

7036
05:41:30,995 --> 05:41:34,933
from the U.K., Dr. Thein --
you know, Dr.

7037
05:41:34,933 --> 05:41:37,068
Thein gets to pick where
she says she’s from right now.

7038
05:41:37,068 --> 05:41:38,303
[laughter]

7039
05:41:38,303 --> 05:41:40,505
But we also had Ann Greenough,
Jo Howard.

7040
05:41:40,505 --> 05:41:43,174
So, it was a really
representative group.

7041
05:41:43,174 --> 05:41:46,377
And also, importantly, we had
a sickle cell disease advocate.

7042
05:41:46,377 --> 05:41:49,013
So, we had a patient
by the name of Debbie Bennett,

7043
05:41:49,013 --> 05:41:50,915
who has passed away,

7044
05:41:50,915 --> 05:41:54,385
but she was really proud
to be a part of this.

7045
05:41:54,385 --> 05:41:56,421
And I’ve had some discussions
with people here

7046
05:41:56,421 --> 05:41:58,890
in the room
that having a patient advocate,

7047
05:41:58,890 --> 05:42:00,492
literally,
the seat at the table,

7048
05:42:00,492 --> 05:42:02,093
it changes the tenor
of the discussions.

7049
05:42:02,093 --> 05:42:03,461
We all know that,

7050
05:42:03,461 --> 05:42:05,296
and I think it’s really
valuable and important.

7051
05:42:05,296 --> 05:42:06,898
We’ve had -- at the
American Thoracic Society,

7052
05:42:06,898 --> 05:42:09,167
we’ve had a number
of panel discussions,

7053
05:42:09,167 --> 05:42:11,970
where we would have invited
a sickle cell patient to come,

7054
05:42:11,970 --> 05:42:14,906
and we get standing room
only crowds.

7055
05:42:14,906 --> 05:42:17,442
So, it’s really important
to bring that patient in.

7056
05:42:18,610 --> 05:42:21,813
And so, through this process,
we did a systematic literature

7057
05:42:21,813 --> 05:42:23,515
review with our librarian
at the NIH,

7058
05:42:23,515 --> 05:42:25,683
and we had our expert
panel discussions.

7059
05:42:25,683 --> 05:42:27,752
Because there’s not
a high-enough level of evidence

7060
05:42:27,752 --> 05:42:30,255
to really write guidelines
for pulmonary care.

7061
05:42:30,255 --> 05:42:31,723
But we were able to document

7062
05:42:31,723 --> 05:42:33,892
that there’s inconsistent
or low quality of evidence

7063
05:42:33,892 --> 05:42:37,595
and study design,
nonuniform classifications

7064
05:42:37,595 --> 05:42:39,297
of things
like acute chest syndrome.

7065
05:42:39,297 --> 05:42:41,666
So, I don’t have enough time
to talk about that today,

7066
05:42:41,666 --> 05:42:43,134
but don’t get me started.
We all know that

7067
05:42:43,134 --> 05:42:46,237
it’s a very general definition
of acute chest.

7068
05:42:46,938 --> 05:42:50,074
And then inconsistent
strategies for interpreting

7069
05:42:50,074 --> 05:42:53,144
pulmonary function testing,
which I’ll go into more detail.

7070
05:42:53,144 --> 05:42:54,913
There’s a lack of
longitudinal data.

7071
05:42:54,913 --> 05:42:57,348
I know that Dr. Little
is very eager

7072
05:42:57,348 --> 05:42:59,684
to have consistent registries.

7073
05:42:59,684 --> 05:43:01,386
I’ll talk a little bit
about CF later,

7074
05:43:01,386 --> 05:43:04,255
but obviously, that’s one group
that’s benefited from having

7075
05:43:04,255 --> 05:43:06,558
a very consistent
registry program.

7076
05:43:07,625 --> 05:43:15,500
And then importantly, as
Dr. Fisher alluded to earlier,

7077
05:43:15,500 --> 05:43:18,369
there’s important factors
about race adjustment.

7078
05:43:18,369 --> 05:43:21,239
So, there’s a lack of ethnically
diverse normative data

7079
05:43:21,239 --> 05:43:23,241
for PFT reference
equations.

7080
05:43:23,241 --> 05:43:25,643
What that means is that the
global long initiative values

7081
05:43:25,643 --> 05:43:26,878
that we use,

7082
05:43:26,878 --> 05:43:30,381
there’s 59,000 healthy patients
that are included in those.

7083
05:43:30,381 --> 05:43:33,551
And there’s 4,500
that have African ancestry,

7084
05:43:33,551 --> 05:43:35,720
and they happen
to all be African American.

7085
05:43:36,487 --> 05:43:37,855
But then in addition to that,

7086
05:43:37,855 --> 05:43:40,725
there’s been an incorrect use
of race as an adjustment factor

7087
05:43:40,725 --> 05:43:43,194
when it does not have
a biological basis.

7088
05:43:43,194 --> 05:43:45,129
So, I think
the pulmonary community

7089
05:43:45,129 --> 05:43:48,299
is a bit behind the nephrologist
community, for example,

7090
05:43:48,299 --> 05:43:50,702
but that has come out
in the last year or two

7091
05:43:50,702 --> 05:43:53,204
to actually
reevaluate our guidelines.

7092
05:43:53,204 --> 05:43:55,406
And that’s moving through
pulmonary function testing labs.

7093
05:43:55,406 --> 05:43:57,208
We have made that change
at the NIH.

7094
05:43:57,709 --> 05:44:01,045
So, now, I’ll go through a
little primer on spirometry.

7095
05:44:02,080 --> 05:44:05,283
So, I know these curves
can be a little bit daunting,

7096
05:44:05,817 --> 05:44:08,753
but the important
thing to know is

7097
05:44:08,753 --> 05:44:10,955
that if you look at the y axis,

7098
05:44:10,955 --> 05:44:14,592
we have the volume exhaled,
and then we have the time.

7099
05:44:14,592 --> 05:44:17,695
So, the FEV1 is one of
the most important outcomes

7100
05:44:17,695 --> 05:44:18,963
in pulmonary medicine

7101
05:44:18,963 --> 05:44:22,000
because it’s been shown to be
associated with mortality

7102
05:44:22,000 --> 05:44:25,003
in a number of different
populations, COPD, CF,

7103
05:44:25,003 --> 05:44:27,105
also now,
in sickle cell disease.

7104
05:44:27,105 --> 05:44:29,140
So, the FEV1, you can see
at that one second,

7105
05:44:29,140 --> 05:44:30,908
that gray dotted line.

7106
05:44:30,908 --> 05:44:32,977
The FVC,
or forced vital capacity,

7107
05:44:33,645 --> 05:44:36,214
is a demonstration
of restrictive lung disease,

7108
05:44:36,214 --> 05:44:38,182
which we can see by scarring
to the lung tissue,

7109
05:44:38,182 --> 05:44:40,285
which can happen
in sickle cell disease.

7110
05:44:40,285 --> 05:44:42,920
And so, with that yellow line,
you can see that the plateau,

7111
05:44:42,920 --> 05:44:46,724
the FVC, is lower when
you have restrictive disease.

7112
05:44:46,724 --> 05:44:51,329
And then obstructive diseases,
so that’s more your asthma,

7113
05:44:51,329 --> 05:44:54,032
COPD,
when you have inflamed bronchi.

7114
05:44:54,032 --> 05:44:56,434
People with sickle cell disease
can also have obstruction,

7115
05:44:56,434 --> 05:44:57,969
and I’ll discuss that more.

7116
05:44:57,969 --> 05:45:00,471
In children,
it’s a more common finding.

7117
05:45:00,471 --> 05:45:01,806
But -- so, that’s when you have

7118
05:45:01,806 --> 05:45:04,275
the ratio
of FEV1 to FVC is lower.

7119
05:45:04,275 --> 05:45:06,611
And so, that you’ll see
in the blue line there.

7120
05:45:07,145 --> 05:45:10,214
And so, going back to this idea
of having race-neutral

7121
05:45:10,214 --> 05:45:11,516
equations,

7122
05:45:11,516 --> 05:45:18,623
so here, a group at UPenn looked
at about 2500 Black patients.

7123
05:45:19,257 --> 05:45:21,359
And what they did
was they reinterpreted --

7124
05:45:21,359 --> 05:45:24,529
so, using the pink
as the new interpretation,

7125
05:45:25,563 --> 05:45:29,701
they identified that
with race-neutral equations,

7126
05:45:29,701 --> 05:45:33,338
a significant chunk moved
from having a normal assessment

7127
05:45:33,905 --> 05:45:37,342
to having restrictive disease.
And so, those were people

7128
05:45:37,342 --> 05:45:39,377
that were being
underdiagnosed previously,

7129
05:45:39,377 --> 05:45:42,046
purely because someone had added
a variable to a formula

7130
05:45:42,046 --> 05:45:44,515
that didn’t necessarily
have a biological basis.

7131
05:45:45,550 --> 05:45:48,453
And so, here’s another example
in a multi-center cohort

7132
05:45:48,453 --> 05:45:50,621
that was analyzed through Mayo,

7133
05:45:51,155 --> 05:45:55,493
that found that both FEV1
and FVC --

7134
05:45:55,493 --> 05:45:58,062
so, those are both different
markers that look at --

7135
05:45:58,830 --> 05:46:01,265
you know, together,
they can look at obstruction;

7136
05:46:01,265 --> 05:46:03,768
apart, they can be
a sign of restriction.

7137
05:46:03,768 --> 05:46:07,538
So, looking at the old equations
on the left, the GLI-2012,

7138
05:46:07,538 --> 05:46:10,675
and then the GLI Global,
those values were lower,

7139
05:46:10,675 --> 05:46:12,643
meaning they were more likely
to be identified

7140
05:46:12,643 --> 05:46:16,114
as having restrictive disease.
They both do come down.

7141
05:46:16,114 --> 05:46:18,249
So, it looks like
that may not have impacted

7142
05:46:18,249 --> 05:46:20,651
the diagnosis of obstructive
disease as much.

7143
05:46:21,853 --> 05:46:26,023
So, looking at current
guidelines, NHLBI and ASH

7144
05:46:26,023 --> 05:46:29,394
have both actually come out
against screening

7145
05:46:29,394 --> 05:46:31,662
PFTs in asymptomatic patients.

7146
05:46:31,662 --> 05:46:34,065
And I think that’s the most
important thing to remember,

7147
05:46:34,065 --> 05:46:36,801
is that dyspnea,
as classified by the New York

7148
05:46:36,801 --> 05:46:40,004
Heart Association,
Class 2, is very common.

7149
05:46:40,004 --> 05:46:42,039
And we all know that it’s
a little bit of a gestalt

7150
05:46:42,039 --> 05:46:43,741
for your patient.
Is this a new dyspnea?

7151
05:46:43,741 --> 05:46:45,543
Is this something
that they’ve always had?

7152
05:46:45,543 --> 05:46:48,980
But no matter what, I feel,
and many folks agree,

7153
05:46:48,980 --> 05:46:51,516
that PFTs are essential
in evaluating patients

7154
05:46:51,516 --> 05:46:52,817
that that have dyspnea,

7155
05:46:52,817 --> 05:46:56,387
so that we don’t assume
it’s only the anemia.

7156
05:46:56,387 --> 05:46:58,356
I know it’s fun to hear
the hematologist argue

7157
05:46:58,356 --> 05:47:00,324
about how much
the anemia matters,

7158
05:47:00,324 --> 05:47:03,494
but you know, it’s there.
But let’s think about, you know,

7159
05:47:03,494 --> 05:47:05,563
do they have
something else going on?

7160
05:47:06,831 --> 05:47:09,634
And then in terms
of the reduced FEV1,

7161
05:47:09,634 --> 05:47:11,269
so I mentioned
that this has been associated

7162
05:47:11,269 --> 05:47:14,605
with mortality in sickle cell.
So, Kassim and colleagues

7163
05:47:14,605 --> 05:47:17,008
had looked at the cooperative
study of sickle cell.

7164
05:47:17,008 --> 05:47:18,276
So, you know,
this is a little bit

7165
05:47:18,276 --> 05:47:21,979
before the widely
accepted hydroxyurea era.

7166
05:47:21,979 --> 05:47:24,749
But they found
that 6 percent decrease

7167
05:47:24,749 --> 05:47:26,584
in FEV1-percent predicted
was associated

7168
05:47:26,584 --> 05:47:28,820
with an 11 percent
higher hazard of death,

7169
05:47:29,720 --> 05:47:32,924
and that the mean
decline of FEV1

7170
05:47:32,924 --> 05:47:34,425
was 47 milliliters per year,

7171
05:47:34,425 --> 05:47:36,327
which is twice
the general population.

7172
05:47:37,662 --> 05:47:39,363
One small study that I saw

7173
05:47:39,363 --> 05:47:41,632
that came out a bit
during the pandemic,

7174
05:47:41,632 --> 05:47:46,437
was a group looked at getting
lung CTs before transplant

7175
05:47:46,437 --> 05:47:47,638
and comparing that

7176
05:47:47,638 --> 05:47:50,241
to whether or not patients
had PFT abnormalities.

7177
05:47:50,241 --> 05:47:51,642
They were trying
to pick through,

7178
05:47:51,642 --> 05:47:54,178
do we really need to get PFTs
before a transplant?

7179
05:47:54,178 --> 05:47:55,613
And I thought
it was really interesting.

7180
05:47:55,613 --> 05:47:57,048
Now, this is in pediatrics.

7181
05:47:57,048 --> 05:48:02,186
But they found that CTs were
normal in 58 percent of children

7182
05:48:02,186 --> 05:48:03,955
that did have PFT abnormalities.

7183
05:48:03,955 --> 05:48:05,289
So, that, again,
just to underscore,

7184
05:48:05,289 --> 05:48:07,592
I think it’s really important
to get this baseline testing,

7185
05:48:07,592 --> 05:48:08,793
particularly before transplant.

7186
05:48:08,793 --> 05:48:10,895
But you know,
I think it’s important

7187
05:48:10,895 --> 05:48:12,864
if there’s any
possible lung issues.

7188
05:48:12,864 --> 05:48:15,700
So, this is one study looking at
PFT abnormalities

7189
05:48:15,700 --> 05:48:17,034
in sickle cell,

7190
05:48:17,034 --> 05:48:19,670
I think, is representative
of the of the population.

7191
05:48:19,670 --> 05:48:24,475
So, this is a study looking at
146 patients with SS or S-ßthal.

7192
05:48:25,243 --> 05:48:28,012
And this had 39 percent
of pediatric patients

7193
05:48:28,012 --> 05:48:29,247
had abnormalities,

7194
05:48:29,247 --> 05:48:32,216
with the most common being
obstructive, at 18 percent,

7195
05:48:32,950 --> 05:48:35,219
with the risk factors
being increasing age,

7196
05:48:35,219 --> 05:48:37,588
family/patient history
of asthma or wheezing,

7197
05:48:38,656 --> 05:48:40,258
and increased hemolysis.

7198
05:48:40,925 --> 05:48:45,263
And then looking at adults,
in adults,

7199
05:48:45,263 --> 05:48:49,100
there is a higher amount
of abnormal pulmonary function

7200
05:48:49,100 --> 05:48:50,334
testing that’s identified.

7201
05:48:50,334 --> 05:48:51,969
This is, again,
from the cooperative study,

7202
05:48:51,969 --> 05:48:53,704
so a bit older.

7203
05:48:53,704 --> 05:48:55,306
There’s a couple
of different studies

7204
05:48:55,306 --> 05:49:00,945
that show relatively similar
percentages of these.

7205
05:49:00,945 --> 05:49:02,213
But it is important to notice

7206
05:49:02,213 --> 05:49:03,481
that the restrictive
at the bottom

7207
05:49:03,481 --> 05:49:05,816
is the most common
finding in adults.

7208
05:49:06,817 --> 05:49:09,954
So, here’s a representative
normal chest CT.

7209
05:49:09,954 --> 05:49:14,725
You see a lot of black air,
those well-aerated alveoli.

7210
05:49:14,725 --> 05:49:15,993
And then so, on the right,

7211
05:49:15,993 --> 05:49:18,362
we have chest CT
from a sickle cell patient

7212
05:49:18,362 --> 05:49:22,099
that had a number of
severe acute chest episodes.

7213
05:49:22,099 --> 05:49:24,368
And so, they have what we call
honeycombing fibrosis.

7214
05:49:24,368 --> 05:49:28,072
So, not all sickle cell patients
have this dramatic of a CT scan,

7215
05:49:28,072 --> 05:49:30,408
but it’s representative to show
the degree of fibrosis

7216
05:49:30,408 --> 05:49:33,144
that you can get.
You can also have restrictions.

7217
05:49:33,144 --> 05:49:35,846
So, when you do blow and do
your pulmonary function testing,

7218
05:49:35,846 --> 05:49:39,116
you can have restriction
from impaired muscle function,

7219
05:49:39,116 --> 05:49:41,919
muscle ischemia,
from rib pain,

7220
05:49:41,919 --> 05:49:44,021
bone infarcts
that can all play out

7221
05:49:44,021 --> 05:49:45,690
into what is lung restriction.

7222
05:49:47,158 --> 05:49:49,026
In terms of looking
at diffusion capacity,

7223
05:49:49,026 --> 05:49:50,761
there are several
potential mechanisms,

7224
05:49:50,761 --> 05:49:53,698
so anemia, reduced lung volumes,

7225
05:49:53,698 --> 05:49:55,933
or reduced alveolar gas
exchange area,

7226
05:49:56,601 --> 05:49:58,135
the fibrosis that
we’ve talked about,

7227
05:49:58,135 --> 05:49:59,437
and then you could also
have reduced

7228
05:49:59,437 --> 05:50:03,407
pulmonary vascular perfusion.
I’m looking for a cardiologist.

7229
05:50:03,407 --> 05:50:05,109
Maybe he’s behind the pole,
but --

7230
05:50:06,277 --> 05:50:08,946
so, all of these factors
play a role together.

7231
05:50:08,946 --> 05:50:11,882
Oh, and then just otherwise,
real quickly.

7232
05:50:11,882 --> 05:50:15,086
So, this is a
cross-sectional graph

7233
05:50:15,086 --> 05:50:18,356
showing DLCO levels across age.

7234
05:50:18,356 --> 05:50:22,693
So, just showing that with age,
the DLCO can decrease

7235
05:50:23,427 --> 05:50:26,364
and why it’s important
to do studies in both pediatrics

7236
05:50:26,364 --> 05:50:28,199
and adults for everything
that we’re looking at.

7237
05:50:28,199 --> 05:50:29,934
Because there
will be differences.

7238
05:50:29,934 --> 05:50:34,338
So, this study was a neat
little snapshot

7239
05:50:34,338 --> 05:50:38,643
done at Vanderbilt University
by Hodges and Shaina Willen,

7240
05:50:39,543 --> 05:50:43,414
that looked at the rate of
annual decline in FEV1 in adults

7241
05:50:43,414 --> 05:50:44,982
that were living
with sickle cell disease

7242
05:50:44,982 --> 05:50:46,584
and cystic fibrosis.

7243
05:50:46,584 --> 05:50:50,087
And so, if you look at this,
the blue line is sickle cell.

7244
05:50:50,087 --> 05:50:55,693
And so, the median FEV1
at the starting age of 20

7245
05:50:55,693 --> 05:50:57,495
from this cohort, was 3.

7246
05:50:58,095 --> 05:51:00,831
And for cystic fibrosis, it was
a little bit lower at 2.7.

7247
05:51:00,831 --> 05:51:02,199
But already you’re seeing, okay,

7248
05:51:02,199 --> 05:51:04,235
wow, sickle cell disease
and cystic fibrosis,

7249
05:51:04,235 --> 05:51:07,104
they’re already somewhat
similarly impaired.

7250
05:51:07,104 --> 05:51:09,040
But then in their
statistical analysis,

7251
05:51:09,040 --> 05:51:10,708
there’s not
a statistical difference

7252
05:51:10,708 --> 05:51:11,976
in the rate of decline.

7253
05:51:11,976 --> 05:51:15,413
And this is, you know,
a pretty clear graph to see.

7254
05:51:15,413 --> 05:51:16,714
So, this is striking.

7255
05:51:16,714 --> 05:51:21,085
This is now before
the era of the cocktail

7256
05:51:21,085 --> 05:51:23,854
ivacaftor therapies
in cystic fibrosis.

7257
05:51:23,854 --> 05:51:25,156
But I think, if anything,

7258
05:51:25,156 --> 05:51:26,524
it’s really
important to have these data

7259
05:51:26,524 --> 05:51:30,327
to see what was happening
without some of these therapies,

7260
05:51:30,327 --> 05:51:32,063
to compare these two illnesses.

7261
05:51:33,164 --> 05:51:35,599
So, when we consider
other existing evidence

7262
05:51:35,599 --> 05:51:37,234
for post-transplant
pulmonary function

7263
05:51:37,234 --> 05:51:39,437
testing outcomes,
it’s difficult to extrapolate

7264
05:51:39,437 --> 05:51:42,440
from the effects of transplant
in malignant disease

7265
05:51:42,440 --> 05:51:45,810
to a chronic, lifelong disease
with vascular impairments.

7266
05:51:46,544 --> 05:51:48,012
The prior reports in sickle cell

7267
05:51:48,012 --> 05:51:51,148
for post-transplant
pulmonary function

7268
05:51:51,148 --> 05:51:53,417
are often of myeloablative
transplant protocols

7269
05:51:53,417 --> 05:51:55,453
or retrospective analyses.

7270
05:51:55,453 --> 05:51:58,923
There -- a couple of larger
studies with pediatrics

7271
05:51:58,923 --> 05:52:02,960
showed in one with 174,
a stable FEV1.

7272
05:52:02,960 --> 05:52:04,895
In 350, there were
about 2 percent

7273
05:52:04,895 --> 05:52:07,064
that had abnormal FEV1
at four years.

7274
05:52:07,064 --> 05:52:08,599
But these studies
were not prospective,

7275
05:52:08,599 --> 05:52:11,102
and they did not have
baseline data to compare to.

7276
05:52:11,836 --> 05:52:14,138
And so, the important gap
in literature

7277
05:52:14,138 --> 05:52:16,874
is prospectively collected,
multi-center data,

7278
05:52:16,874 --> 05:52:18,075
particularly in adults.

7279
05:52:18,075 --> 05:52:20,377
So, I’ll now walk you through --
or in a moment.

7280
05:52:20,377 --> 05:52:22,012
First, we’ve got to talk
about the heart.

7281
05:52:22,012 --> 05:52:24,248
I’ll walk you through
our single center data.

7282
05:52:24,248 --> 05:52:27,418
So, looking at cardiac outcomes
after a transplant,

7283
05:52:28,252 --> 05:52:31,322
as our colleague
earlier alluded to,

7284
05:52:32,523 --> 05:52:33,858
volume overload
in sickle cell

7285
05:52:33,858 --> 05:52:36,427
anemia can contribute
to cardiac remodeling.

7286
05:52:36,427 --> 05:52:38,329
I don’t think I used
the word cardiomyopathy.

7287
05:52:38,329 --> 05:52:39,930
So, I think I’m safe.

7288
05:52:40,898 --> 05:52:44,201
But Vandana Sachdev
and others at the NIH

7289
05:52:44,201 --> 05:52:46,604
looked at the NIH cohort
and then also in conjunction

7290
05:52:46,604 --> 05:52:48,506
with similar transplant
protocols

7291
05:52:48,506 --> 05:52:50,941
that were done at the University
of Illinois, Chicago,

7292
05:52:50,941 --> 05:52:52,176
and Riyadh, Saudi Arabia.

7293
05:52:52,176 --> 05:52:55,246
And what they found was that
two years after transplant,

7294
05:52:55,246 --> 05:52:57,815
there was an improvement
in the cardiac size,

7295
05:52:57,815 --> 05:53:00,251
mass, an improvement
in diastolic function,

7296
05:53:00,251 --> 05:53:06,290
an improvement in the tricuspid
regurgitant velocity.

7297
05:53:07,324 --> 05:53:10,394
And so, that’s
a really important finding,

7298
05:53:10,394 --> 05:53:13,330
that in the -- once the high
blood flow state was resolved,

7299
05:53:13,330 --> 05:53:16,400
that many cardiac changes
reversed.

7300
05:53:17,835 --> 05:53:20,504
So, now, looking at our data
in pulmonary function

7301
05:53:20,504 --> 05:53:21,906
after transplant.

7302
05:53:21,906 --> 05:53:26,076
So, our hypothesis was that
patients after transplant

7303
05:53:26,076 --> 05:53:27,845
would have stable
lung function.

7304
05:53:28,412 --> 05:53:30,848
And so, that we really
hypothesized

7305
05:53:30,848 --> 05:53:32,349
and kind of were eager
to demonstrate

7306
05:53:32,349 --> 05:53:34,885
that the transplant
wouldn’t hurt their lungs.

7307
05:53:34,885 --> 05:53:37,121
This has been shown in
non-myeloablative transplants

7308
05:53:37,121 --> 05:53:39,557
outside of sickle cell.
But we really need to not assume

7309
05:53:39,557 --> 05:53:41,158
that that’s going to be
the case in sickle cell

7310
05:53:41,158 --> 05:53:42,426
and look at it clearly.

7311
05:53:42,426 --> 05:53:45,396
So, our primary outcome measure
was FEV1-percent predicted.

7312
05:53:46,130 --> 05:53:49,600
And so, we looked at spirometry.
We also did lung volumes.

7313
05:53:50,167 --> 05:53:52,303
So, we have body
plethysmography or nitrogen

7314
05:53:52,303 --> 05:53:54,505
washout in all of our patients.
This is one of the luxuries

7315
05:53:54,505 --> 05:53:56,740
of being
at America’s research hospitals

7316
05:53:56,740 --> 05:53:59,076
is that we can get a number
of these different studies.

7317
05:53:59,076 --> 05:54:00,678
We looked at DLCO.

7318
05:54:00,678 --> 05:54:02,913
And then we also performed
six-minute walk distance

7319
05:54:02,913 --> 05:54:05,616
testing,
a sub-maximal exercise test.

7320
05:54:06,717 --> 05:54:09,420
And so, looking
at our flow diagram,

7321
05:54:09,420 --> 05:54:11,755
we had 112 people
living with sickle cell

7322
05:54:11,755 --> 05:54:15,693
that underwent transplant.
97 had baseline PFT data.

7323
05:54:15,693 --> 05:54:18,095
So, that’s the analysis
that we did.

7324
05:54:18,095 --> 05:54:24,401
Year 1, we had 91 patients,
72 at Year 2, and 55 at Year 3.

7325
05:54:24,401 --> 05:54:25,970
So, you see there is
some drop off.

7326
05:54:25,970 --> 05:54:29,807
By that Year 3, half of those
patients had lost a follow up.

7327
05:54:29,807 --> 05:54:31,542
And this is --
we had to exclude them

7328
05:54:31,542 --> 05:54:33,510
if they didn’t have
the pulmonary function data

7329
05:54:33,510 --> 05:54:34,945
at the beginning.

7330
05:54:34,945 --> 05:54:37,781
But either lost a follow up,
or some of them were censored.

7331
05:54:37,781 --> 05:54:39,216
Because when we pulled the data,

7332
05:54:39,216 --> 05:54:41,085
they hadn’t yet had
that observation.

7333
05:54:41,685 --> 05:54:44,021
In terms of preconditioning,
they all uniformly had

7334
05:54:44,021 --> 05:54:47,458
Alemtuzumab, total body
irradiation, and sirolimus.

7335
05:54:47,458 --> 05:54:50,261
And half received pentostatin
and cyclophosphamide.

7336
05:54:51,061 --> 05:54:54,398
So, looking at
our cohort characteristics --

7337
05:54:54,398 --> 05:54:56,967
so, these are just
descriptive statistics --

7338
05:54:57,601 --> 05:55:01,605
we can see that the median age
in the group is 31.8.

7339
05:55:01,605 --> 05:55:04,842
41 percent are female.
In terms of the hemoglobin --

7340
05:55:04,842 --> 05:55:06,310
I know you guys
would ask about that.

7341
05:55:06,310 --> 05:55:07,544
[laughter]

7342
05:55:07,544 --> 05:55:12,783
So, the baseline was 8.8.
And then the median in Year 3 --

7343
05:55:12,783 --> 05:55:15,119
and it’s pretty stable
throughout -- was 12.5.

7344
05:55:15,119 --> 05:55:18,155
So, we know that the transplant
in this population

7345
05:55:19,056 --> 05:55:20,457
increased the hemoglobin.

7346
05:55:20,457 --> 05:55:23,227
I will discuss;
this includes 22 patients

7347
05:55:23,227 --> 05:55:25,162
who experienced graft failure.

7348
05:55:25,162 --> 05:55:27,531
We made the decision to include
those within these data

7349
05:55:27,531 --> 05:55:30,234
because patients who might
experience graft failure

7350
05:55:30,234 --> 05:55:33,003
will also want to know what
their pulmonary outcomes are.

7351
05:55:33,003 --> 05:55:35,339
We didn’t want to just
exclude them from the beginning.

7352
05:55:35,339 --> 05:55:37,875
So, 87 percent
are hemoglobin SS.

7353
05:55:39,376 --> 05:55:41,178
And as we had hypothesized --

7354
05:55:41,178 --> 05:55:42,546
now, these are just
the descriptive data.

7355
05:55:42,546 --> 05:55:45,516
I’ll go through
some more detailed analyses.

7356
05:55:46,283 --> 05:55:48,619
The FEV1 and FEV1-percent
predicted

7357
05:55:48,619 --> 05:55:49,887
were really rock solid,

7358
05:55:49,887 --> 05:55:53,057
almost surprisingly solid.
So, that is encouraging.

7359
05:55:53,991 --> 05:55:57,127
And as Nona went through,
this is really something --

7360
05:55:57,127 --> 05:55:58,729
and I’ve been able to do this.

7361
05:55:58,729 --> 05:56:00,297
You can talk to
your patients about it.

7362
05:56:00,297 --> 05:56:03,534
You can say, you know,
that we have data that show

7363
05:56:03,534 --> 05:56:06,270
that your lungs
won’t be hurt by this."

7364
05:56:06,270 --> 05:56:07,838
And I think just
saying that alone --

7365
05:56:07,838 --> 05:56:09,373
whether or not we can say
there’s improvement

7366
05:56:09,373 --> 05:56:11,208
in some patients
is a different story,

7367
05:56:11,208 --> 05:56:13,577
but I think that’s an important
thing that you can communicate.

7368
05:56:13,577 --> 05:56:15,646
How we breathe is important.

7369
05:56:15,646 --> 05:56:18,615
That’s what we think
in the pulmonary world.

7370
05:56:19,350 --> 05:56:20,851
And then there’s also
not a change

7371
05:56:20,851 --> 05:56:23,854
in the sign of obstruction
with FEV1 over FVC.

7372
05:56:24,922 --> 05:56:27,257
So, looking at DLCO --

7373
05:56:27,257 --> 05:56:29,360
so, this is unadjusted
for hemoglobin.

7374
05:56:30,227 --> 05:56:32,863
As we might expect,
because the hemoglobin went up,

7375
05:56:32,863 --> 05:56:37,801
the DLCO does improve and stays
consistent out to Year 3.

7376
05:56:37,801 --> 05:56:40,304
We did also look at it,
adjusted for hemoglobin.

7377
05:56:41,005 --> 05:56:43,507
And I would say
this took me down

7378
05:56:43,507 --> 05:56:45,476
a whole rabbit hole
of understanding --

7379
05:56:46,443 --> 05:56:48,812
oftentimes,
when we all rely on something,

7380
05:56:48,812 --> 05:56:51,882
and you go back,
and there’s one paper from 1983

7381
05:56:51,882 --> 05:56:54,451
that enrolled 20 patients
that had anemia.

7382
05:56:54,451 --> 05:56:56,153
And that’s how they devised

7383
05:56:56,153 --> 05:56:58,589
the DLCO hemoglobin
adjustment formulas.

7384
05:56:58,589 --> 05:57:01,525
And I spoke with a number
of world-level experts

7385
05:57:01,525 --> 05:57:04,828
in physiology
to really be certain about that.

7386
05:57:04,828 --> 05:57:09,767
So, I felt really unconfident
about adjusting for hemoglobin,

7387
05:57:09,767 --> 05:57:12,603
particularly once people
come out of that anemic range.

7388
05:57:13,137 --> 05:57:16,240
But you can see, we looked at it
a couple different ways.

7389
05:57:16,240 --> 05:57:18,675
And overall,
it remains stable.

7390
05:57:19,343 --> 05:57:22,012
In terms of looking at
the six-minute walk distance,

7391
05:57:22,579 --> 05:57:26,450
there is an improvement
from 490 at baseline median

7392
05:57:26,450 --> 05:57:29,486
to 520 at Year 3.

7393
05:57:30,487 --> 05:57:33,057
And I’ll go through the minimal
clinically important difference

7394
05:57:33,057 --> 05:57:34,725
for that
in a little bit.

7395
05:57:34,725 --> 05:57:37,628
So, this is just a snapshot
of baseline comorbidities.

7396
05:57:37,628 --> 05:57:40,397
This is a national and
international referral network.

7397
05:57:41,598 --> 05:57:44,868
And so, there’s a relatively
high severity of illness

7398
05:57:44,868 --> 05:57:46,203
coming in.

7399
05:57:46,203 --> 05:57:48,105
So, one-third of patients
have had ACS

7400
05:57:48,105 --> 05:57:50,274
requiring ICU
or ventilator support.

7401
05:57:50,274 --> 05:57:52,976
10 percent had right heart
cath-defined PH.

7402
05:57:53,744 --> 05:57:56,346
About one in five
had thrombosis.

7403
05:57:57,848 --> 05:57:59,817
So, these are our models,
really looking

7404
05:57:59,817 --> 05:58:03,687
at the longitudinal
PFT change from baseline by year

7405
05:58:03,687 --> 05:58:05,856
using generalized
estimating equations.

7406
05:58:05,856 --> 05:58:07,157
And this gets to one
of the questions

7407
05:58:07,157 --> 05:58:08,392
that Dr. Thein had earlier

7408
05:58:08,392 --> 05:58:10,694
about looking
at inter-individual variation

7409
05:58:10,694 --> 05:58:12,963
versus the variation
of the cohort.

7410
05:58:12,963 --> 05:58:17,634
So, here, we do have
inter-individual evaluations.

7411
05:58:17,634 --> 05:58:21,171
And so, what we can see, if you
jump to the Year 3 column,

7412
05:58:21,171 --> 05:58:23,707
so for FEV1-percent
predicted and FVC,

7413
05:58:24,341 --> 05:58:25,809
there’s very little change.

7414
05:58:25,809 --> 05:58:29,546
So, that’s as compared back
to the baseline measurement.

7415
05:58:30,547 --> 05:58:32,783
So, if anything, it, you know --

7416
05:58:32,783 --> 05:58:35,586
our hypothesis bore out
that there was not change.

7417
05:58:35,586 --> 05:58:37,254
And then looking at the DLCO,

7418
05:58:37,988 --> 05:58:40,791
there was a statistically
significant increase.

7419
05:58:40,791 --> 05:58:42,893
But it’s not quite
what we would consider

7420
05:58:43,560 --> 05:58:46,263
a clinically important
difference.

7421
05:58:46,263 --> 05:58:48,398
So, we would want to see
a 10 percent change.

7422
05:58:48,398 --> 05:58:51,101
When people are reading
your pulmonary function testing,

7423
05:58:51,101 --> 05:58:53,237
and they say there’s
a significant change.

7424
05:58:53,237 --> 05:58:55,873
It’s really that they’ve seen
a 10 percent change.

7425
05:58:56,473 --> 05:59:02,346
That, again, is not based
on very robust data,

7426
05:59:02,346 --> 05:59:04,715
but that’s kind of where the
pulmonary community has settled.

7427
05:59:04,715 --> 05:59:08,452
So, overall, there was
a slight improvement in DLCO

7428
05:59:08,452 --> 05:59:10,754
but not a clinically
significant change.

7429
05:59:10,754 --> 05:59:13,290
But then looking at
the six-minute walk test,

7430
05:59:13,290 --> 05:59:15,559
there was a --
both clinically significant

7431
05:59:15,559 --> 05:59:17,661
and statistically
significant improvement,

7432
05:59:18,362 --> 05:59:22,065
with an increase in 26 meters
from the baseline.

7433
05:59:22,566 --> 05:59:26,336
There was additional lost data

7434
05:59:26,870 --> 05:59:28,605
in the six-minute walk
testing patients

7435
05:59:28,605 --> 05:59:30,974
compared to the pulmonary
function testing data.

7436
05:59:30,974 --> 05:59:32,543
So, it’s possible
that we missed some people

7437
05:59:32,543 --> 05:59:35,445
who weren’t able to do
the six-minute walk.

7438
05:59:35,445 --> 05:59:37,915
But I think this is
still very represented --

7439
05:59:37,915 --> 05:59:40,984
representational of the people
who were able to do that.

7440
05:59:41,785 --> 05:59:45,355
So, this colorful plot,
it’s called an alluvial plot,

7441
05:59:45,355 --> 05:59:50,294
and it was very fun to make.
But this is able to show us

7442
05:59:50,294 --> 05:59:53,764
the ventilatory patterns
in each year

7443
05:59:53,764 --> 05:59:56,633
and who moves in and out
of the different groups.

7444
05:59:56,633 --> 05:59:59,603
So, if we start at the top,
in the pink, that’s normal.

7445
05:59:59,603 --> 06:00:02,573
The yellow is obstructive.
Green is restrictive.

7446
06:00:03,407 --> 06:00:05,609
The blue is mixed.

7447
06:00:05,609 --> 06:00:07,945
Purple is nonspecific,
which is a little bit --

7448
06:00:08,645 --> 06:00:11,582
people you can’t quite
put in any bucket.

7449
06:00:11,582 --> 06:00:14,017
And then the pink
are the missing data.

7450
06:00:14,017 --> 06:00:16,486
And so, I think what’s most
important to see about this

7451
06:00:16,486 --> 06:00:20,624
is that the relative proportions
stay similar.

7452
06:00:20,624 --> 06:00:24,895
So, once you get rid of that
missing data over time, overall,

7453
06:00:24,895 --> 06:00:26,697
the proportions
of the categories

7454
06:00:26,697 --> 06:00:29,266
that people are in remain
stable over follow up.

7455
06:00:29,266 --> 06:00:31,702
And most importantly,
no patients were diagnosed

7456
06:00:31,702 --> 06:00:36,573
with pulmonary GVHD or other
moderate GVHD in this study.

7457
06:00:36,573 --> 06:00:38,709
And only two patients
went from having

7458
06:00:38,709 --> 06:00:40,043
had normal pulmonary function

7459
06:00:40,043 --> 06:00:43,513
testing at the beginning to
ending with obstructive disease.

7460
06:00:43,513 --> 06:00:46,483
So, that’s very reassuring
in a transplant setting.

7461
06:00:46,483 --> 06:00:49,653
So, in summary, FEV1
and pulmonary function

7462
06:00:49,653 --> 06:00:52,256
remained stable three years
after transplant.

7463
06:00:52,256 --> 06:00:53,690
There’s a clinically
meaningful improvement

7464
06:00:53,690 --> 06:00:55,792
in six-minute walk distance
from baseline,

7465
06:00:56,426 --> 06:00:57,894
a slight improvement in DLCO

7466
06:00:57,894 --> 06:00:59,830
in the setting
of increased hemoglobin,

7467
06:01:00,364 --> 06:01:02,366
ventilatory patterns
remained stable,

7468
06:01:02,966 --> 06:01:05,802
and no patients developed
pulmonary GVHD.

7469
06:01:05,802 --> 06:01:08,805
So, is it possible
that we can break the cycle

7470
06:01:08,805 --> 06:01:11,041
of sickle cell disease

7471
06:01:11,041 --> 06:01:13,977
by using curative
and transformative therapy?

7472
06:01:13,977 --> 06:01:16,313
So, I’m coming along
with the language.

7473
06:01:17,614 --> 06:01:19,750
I think there’s a lot that that
we can do

7474
06:01:19,750 --> 06:01:21,151
and communicate to our patients

7475
06:01:21,151 --> 06:01:25,589
by describing some of these
patient-relevant outcomes.

7476
06:01:25,589 --> 06:01:28,325
It means a lot to tell people
about the six-minute

7477
06:01:28,325 --> 06:01:30,427
walk distance.
I know that might not --

7478
06:01:30,427 --> 06:01:31,995
that might seem very wonky
for some of you,

7479
06:01:31,995 --> 06:01:33,530
but if you kind of explain it

7480
06:01:33,530 --> 06:01:35,265
and the fact that
they can walk further,

7481
06:01:35,265 --> 06:01:37,534
that’s something
that really can -- that sink in.

7482
06:01:37,534 --> 06:01:39,403
So, our paper was just
recently published

7483
06:01:39,403 --> 06:01:41,104
in the Annals
of American Thoracic Society.

7484
06:01:41,104 --> 06:01:42,773
So, one thing
I was excited about

7485
06:01:42,773 --> 06:01:45,842
is that now more pulmonologists
are seeing this as well.

7486
06:01:46,710 --> 06:01:52,582
And we’re looking ahead
to evaluate the association

7487
06:01:52,582 --> 06:01:54,184
of the six-minute walk distance

7488
06:01:54,184 --> 06:01:56,820
testing with health-related
quality of life.

7489
06:01:57,754 --> 06:01:58,955
One thing I kind of mentioned

7490
06:01:58,955 --> 06:02:01,024
was the minimal clinically
important difference.

7491
06:02:01,024 --> 06:02:02,859
So, that really
should be studied

7492
06:02:02,859 --> 06:02:04,194
in different populations.

7493
06:02:04,194 --> 06:02:09,800
So, within the cardiopulmonary
community,

7494
06:02:09,800 --> 06:02:13,804
ranges about 20 to 30.
And so, I think that’s one thing

7495
06:02:13,804 --> 06:02:16,073
that has not yet been done
in sickle cell.

7496
06:02:16,073 --> 06:02:18,275
So, I think, really setting
a minimal clinically important

7497
06:02:18,275 --> 06:02:20,377
difference would be important.
And then, of course,

7498
06:02:20,377 --> 06:02:22,979
looking at multicenter
and longitudinal studies.

7499
06:02:22,979 --> 06:02:27,617
So, COALESCE is an effort
to continue this study

7500
06:02:27,617 --> 06:02:29,720
across multiple sites.

7501
06:02:31,154 --> 06:02:33,890
And we will, at the NIH,
also be able to dive

7502
06:02:33,890 --> 06:02:36,660
in a little bit more
closely into deep phenotyping,

7503
06:02:36,660 --> 06:02:38,261
looking at long MRI

7504
06:02:39,896 --> 06:02:44,267
and working closely with
colleagues in Africa as well.

7505
06:02:45,769 --> 06:02:48,004
And I know I’m out of time,
so just going to jump ahead.

7506
06:02:48,004 --> 06:02:49,406
I just want to show
these shining faces

7507
06:02:49,406 --> 06:02:51,007
at the
American Thoracic Society,

7508
06:02:51,007 --> 06:02:53,543
who are all excited to learn
about sickle cell disease

7509
06:02:53,543 --> 06:02:55,846
and want to bring
in best practices

7510
06:02:55,846 --> 06:02:57,614
into their own clinics.

7511
06:02:58,348 --> 06:03:00,951
So, with that, I’ll thank you
for your attention,

7512
06:03:00,951 --> 06:03:03,053
and happy to take questions
if there’s time.

7513
06:03:03,053 --> 06:03:07,891
[applause]

7514
06:03:07,891 --> 06:03:11,128
John Tisdale: Any questions?
I can start with one.

7515
06:03:11,128 --> 06:03:12,729
Parker Ruhl: Okay. Yes.

7516
06:03:12,729 --> 06:03:14,364
John Tisdale: Another question
that I always ask.

7517
06:03:14,364 --> 06:03:16,800
So, when looking
at the effects

7518
06:03:16,800 --> 06:03:21,471
of an intervention
on some physiology,

7519
06:03:22,305 --> 06:03:25,342
it always seems to me
that one step further to go

7520
06:03:25,342 --> 06:03:29,379
is not just look at the whole
population that some failed,

7521
06:03:30,247 --> 06:03:32,315
but to only look at
the population where it worked,

7522
06:03:32,315 --> 06:03:33,583
so that --
Parker Ruhl: Yes.

7523
06:03:33,583 --> 06:03:34,851
John Tisdale: --
so that you’re understanding

7524
06:03:34,851 --> 06:03:37,187
whether making the hemoglobin
go to normal

7525
06:03:37,187 --> 06:03:41,425
and have all that hemoglobin
be hemoglobin A or AS

7526
06:03:44,461 --> 06:03:46,663
is valuable
for this measure of that.

7527
06:03:46,663 --> 06:03:49,633
So, it makes me wonder,
did you look at DLCO

7528
06:03:50,500 --> 06:03:53,870
in only those where it was
successful and compare?

7529
06:03:54,438 --> 06:03:55,639
Parker Ruhl: Yes.

7530
06:03:55,639 --> 06:03:58,041
So, we did also look
at the group

7531
06:03:59,543 --> 06:04:02,279
excluding patients
that had graft failure.

7532
06:04:03,113 --> 06:04:06,650
And for both the DLCO and
the six-minute walk distance,

7533
06:04:06,650 --> 06:04:08,251
the measures were
a bit higher.

7534
06:04:08,952 --> 06:04:10,787
So, they weren’t
dramatically different

7535
06:04:10,787 --> 06:04:12,756
but several points higher
for the DLCO.

7536
06:04:13,790 --> 06:04:18,094
So, for example, that went
from 3 to, I think it was 5.9.

7537
06:04:18,094 --> 06:04:19,596
And then for the six-minute
walk distance,

7538
06:04:19,596 --> 06:04:22,332
it went from 25.9 to 33.

7539
06:04:22,332 --> 06:04:27,137
So, I think higher but also
not dramatically higher

7540
06:04:27,137 --> 06:04:29,673
to really change your
interpretation of those data.

7541
06:04:30,240 --> 06:04:31,441
Yes.

7542
06:04:31,441 --> 06:04:33,043
Swee Lay Thein: Parker,
that was great.

7543
06:04:34,845 --> 06:04:36,313
You’ve seen this
in some of your patients,

7544
06:04:36,313 --> 06:04:38,582
and you also see,
you know,

7545
06:04:38,582 --> 06:04:42,085
other patients with pulmonary
function abnormalities.

7546
06:04:42,085 --> 06:04:45,355
Supposing I give you the PFTs,

7547
06:04:46,256 --> 06:04:50,327
and you can ask for blood,
everything.

7548
06:04:50,327 --> 06:04:51,795
Look at the pattern.

7549
06:04:51,795 --> 06:04:53,964
I won’t tell you what disease
the patient has.

7550
06:04:53,964 --> 06:04:55,198
Parker Ruhl: Yeah.

7551
06:04:55,198 --> 06:04:58,201
Swee Lay Thein: Is there a
typical signature from the PFT,

7552
06:04:58,201 --> 06:04:59,703
so you can say,

7553
06:04:59,703 --> 06:05:02,272
"I think this patient
has sickle cell disease"?

7554
06:05:02,272 --> 06:05:03,874
Parker Ruhl: So, I don’t think
I could do that,

7555
06:05:03,874 --> 06:05:06,877
like our cardiologist suggested
that he could earlier.

7556
06:05:06,877 --> 06:05:10,680
Now, there would be a pattern
that would be consistent,

7557
06:05:10,680 --> 06:05:12,382
but I think there’d be enough
other patients

7558
06:05:12,382 --> 06:05:13,717
that might also
have that pattern

7559
06:05:13,717 --> 06:05:15,418
that I wouldn’t be able
to see that,

7560
06:05:15,418 --> 06:05:17,587
you know, there’s a signature
for sickle cell.

7561
06:05:17,587 --> 06:05:19,823
But for example, you know,

7562
06:05:19,823 --> 06:05:21,625
if I’m allowed to look
at the hemoglobin,

7563
06:05:21,625 --> 06:05:23,193
there would be a --

7564
06:05:23,193 --> 06:05:25,762
you know, if you’re ordering
DLCO, please make sure

7565
06:05:25,762 --> 06:05:28,231
that there’s been CBC done
within the last week.

7566
06:05:29,900 --> 06:05:34,004
But then also, I think one thing
that I could say

7567
06:05:34,004 --> 06:05:38,174
that you would see,
is you might see a DLCO --

7568
06:05:38,174 --> 06:05:45,148
an isolated low DLCO or a DLCO
that’s lower out of proportion

7569
06:05:45,148 --> 06:05:46,750
to a mild restriction.

7570
06:05:47,484 --> 06:05:50,487
So, I think that’s something
that can be fairly common

7571
06:05:50,487 --> 06:05:53,256
in what we see
with that sort of gestalt

7572
06:05:53,256 --> 06:05:54,858
that you’re asking about.

7573
06:05:56,359 --> 06:05:58,595
Female Speaker
Parker, great -- sorry.

7574
06:05:58,595 --> 06:05:59,796
Go ahead.

7575
06:05:59,796 --> 06:06:00,997
Male Speaker: Please.

7576
06:06:00,997 --> 06:06:02,232
Female Speaker: Great
presentation.

7577
06:06:02,232 --> 06:06:03,500
I want to say --

7578
06:06:03,500 --> 06:06:05,635
you said that pulmonary
function didn’t improve,

7579
06:06:05,635 --> 06:06:07,337
but I want to say,
natural history

7580
06:06:07,337 --> 06:06:08,772
of pulmonary function
has declined.

7581
06:06:08,772 --> 06:06:09,973
Parker Ruhl: Yes.

7582
06:06:09,973 --> 06:06:11,575
Female Speaker: So,
if you can prevent decline,

7583
06:06:11,575 --> 06:06:13,176
that is improvement.
So, just --

7584
06:06:13,176 --> 06:06:14,978
Parker Ruhl: Yes.
No, that’s completely fair.

7585
06:06:14,978 --> 06:06:16,580
And so, I think part of this
is once

7586
06:06:16,580 --> 06:06:18,949
we’re looking
at a three-year window,

7587
06:06:18,949 --> 06:06:23,053
anytime we’re looking
at a long-term data point,

7588
06:06:23,053 --> 06:06:24,921
and we’re trying
to annualize it,

7589
06:06:24,921 --> 06:06:26,356
it can be a little tricky
to say,

7590
06:06:26,356 --> 06:06:28,491
"How meaningful is it to look
at these three years

7591
06:06:28,491 --> 06:06:30,393
versus the next three years?"

7592
06:06:30,393 --> 06:06:32,963
So, you know, you can look
at these annualized declines

7593
06:06:32,963 --> 06:06:38,635
of 49 ccs per year versus 25
in the general population.

7594
06:06:38,635 --> 06:06:40,270
That was also another deep dive
I did,

7595
06:06:40,270 --> 06:06:44,374
is looking at the general data
on what is an annual decline.

7596
06:06:44,374 --> 06:06:48,211
There’s not data
on the expected annual decline

7597
06:06:48,211 --> 06:06:50,347
in Black patients
or in Black --

7598
06:06:50,347 --> 06:06:52,849
or in patients with
African ancestry, for example.

7599
06:06:52,849 --> 06:06:56,219
But in the general population,
it’s also just a very --

7600
06:06:56,219 --> 06:06:57,520
it’s kind of so general

7601
06:06:57,520 --> 06:07:01,124
as to not be terribly meaningful
about year to year.

7602
06:07:01,124 --> 06:07:03,293
So, I think this is where
looking at coalesce,

7603
06:07:03,293 --> 06:07:05,862
multicenter,
and truly longitudinal data --

7604
06:07:05,862 --> 06:07:08,131
I think, as everyone here
has talked about,

7605
06:07:08,131 --> 06:07:10,133
really looking at five-
and 10-year data

7606
06:07:11,334 --> 06:07:14,938
is going to say
a lot more about that point.

7607
06:07:14,938 --> 06:07:16,706
But I agree with you,
with age --

7608
06:07:16,706 --> 06:07:19,042
and I have some other slides
I didn’t include for time

7609
06:07:19,042 --> 06:07:20,377
about just aging in the lung

7610
06:07:20,377 --> 06:07:22,412
as kind of one of
the additional insults.

7611
06:07:23,346 --> 06:07:25,749
But basically, you’re going
to see expected ages.

7612
06:07:25,749 --> 06:07:28,018
The beauty of these percent
predicted equations

7613
06:07:28,018 --> 06:07:30,553
is that it does account
for a lot of that.

7614
06:07:31,354 --> 06:07:32,722
But I agree.
I think this is --

7615
06:07:32,722 --> 06:07:35,659
should be seen as a success,
that it remains stable.

7616
06:07:36,259 --> 06:07:37,661
John Tisdale: Mark.

7617
06:07:37,661 --> 06:07:39,396
Mark Walters: That was that was
really wonderful.

7618
06:07:39,996 --> 06:07:41,965
I got nervous
because I was afraid

7619
06:07:41,965 --> 06:07:43,600
that you were going to show
a different conclusion

7620
06:07:43,600 --> 06:07:44,834
about lung function
[unintelligible].

7621
06:07:44,834 --> 06:07:46,069
Parker Ruhl: [laughs] I didn’t.

7622
06:07:46,069 --> 06:07:48,405
Mark Walters: Because children
we observed

7623
06:07:48,405 --> 06:07:51,841
in a much smaller cohort
in a real-world setting,

7624
06:07:51,841 --> 06:07:54,611
that we came
to the same conclusion.

7625
06:07:54,611 --> 06:07:57,313
But we don’t have
the luxury of John

7626
06:07:57,313 --> 06:07:59,349
and Matt Hsieh’s incredible work

7627
06:07:59,349 --> 06:08:01,217
where they don’t get graft
versus host disease

7628
06:08:01,217 --> 06:08:03,620
after allogeneic
transplantation.

7629
06:08:03,620 --> 06:08:05,722
And myeloablation with busulfan

7630
06:08:05,722 --> 06:08:08,391
is generally a part
of our world in children

7631
06:08:08,391 --> 06:08:09,626
and in gene therapy too.

7632
06:08:09,626 --> 06:08:10,960
So, what would be
your predictions

7633
06:08:10,960 --> 06:08:14,664
about the overlay of those
two potential toxicities?

7634
06:08:14,664 --> 06:08:16,032
Parker Ruhl: Yeah.
That’s a great question.

7635
06:08:16,032 --> 06:08:20,770
We should look at those data.
I think it’s going to be --

7636
06:08:21,438 --> 06:08:23,440
yeah, it’ll be
a different situation

7637
06:08:23,440 --> 06:08:25,041
than the non-myeloablative.

7638
06:08:25,642 --> 06:08:28,878
And I think that’s, again,
where looking at those --

7639
06:08:28,878 --> 06:08:31,715
you know, everyone’s talked
about how doing it younger --

7640
06:08:31,715 --> 06:08:33,650
with a younger donor
is a benefit.

7641
06:08:33,650 --> 06:08:36,419
So, I think as we consider
the aging lung,

7642
06:08:36,419 --> 06:08:38,755
I think you’re going to see
even more differences

7643
06:08:38,755 --> 06:08:43,927
in the impact in, for example,
20 decile, 30 decile.

7644
06:08:43,927 --> 06:08:46,096
If you were doing gene therapy
in a 40 decile,

7645
06:08:46,096 --> 06:08:47,764
I think that’s where
you’re going to see

7646
06:08:47,764 --> 06:08:49,365
increasing risk, for sure.

7647
06:08:49,999 --> 06:08:52,569
So, be very interesting
to look at that. Please. Yes.

7648
06:08:52,569 --> 06:08:53,837
Male Speaker: What’s the
difference

7649
06:08:53,837 --> 06:08:57,006
with the graft failure cohort?
Is there a difference?

7650
06:08:57,006 --> 06:08:58,308
Parker Ruhl: So, the graft
failure cohort,

7651
06:08:58,308 --> 06:09:00,210
they did not worsen.

7652
06:09:00,210 --> 06:09:02,746
So, their pulmonary function
test did not worsen.

7653
06:09:02,746 --> 06:09:06,316
But, for example, the DLCO
did not increase as much.

7654
06:09:08,585 --> 06:09:10,854
So, I think -- the reassuring
thing to me there is

7655
06:09:10,854 --> 06:09:12,489
that if you go
through the transplant

7656
06:09:12,489 --> 06:09:14,290
and you do experience
graft failure,

7657
06:09:14,858 --> 06:09:18,094
that, you know, there might be
other things that happen.

7658
06:09:18,094 --> 06:09:20,897
For example, there were
three deaths in this cohort,

7659
06:09:20,897 --> 06:09:23,800
but they weren’t related
directly to the transplant.

7660
06:09:24,567 --> 06:09:28,838
But just that, overall,
you don’t come out of having

7661
06:09:28,838 --> 06:09:31,775
had the transplant with --
even if you have graft failure

7662
06:09:31,775 --> 06:09:35,712
with additional injury
to the lung. Thank you.

7663
06:09:35,712 --> 06:09:37,080
[applause]

7664
06:09:37,080 --> 06:09:38,348
John Tisdale: Okay.
Thank you.

7665
06:09:38,348 --> 06:09:39,883
We’re going to take
a 10-minute break

7666
06:09:39,883 --> 06:09:41,651
and be back at 3:40 p.m.,

7667
06:09:42,185 --> 06:09:44,187
which is actually
an eight-minute break.

7668
06:09:44,360 --> 06:09:45,962
Swee Lay Thein: Okay.

7669
06:09:49,532 --> 06:09:50,902
It’s my pleasure
to continue the session

7670
06:09:50,902 --> 06:09:52,737
from the early session about
outcome of curative therapies

7671
06:09:52,737 --> 06:09:54,339
in sickle cell disease,

7672
06:09:54,339 --> 06:10:00,111
but this time it is to talk
about mutation of variant

7673
06:10:00,111 --> 06:10:03,806
screening to try to improve
the outcome.

7674
06:10:03,806 --> 06:10:08,210
And the first speaker that
we have is Dr. Lachelle Weeks.

7675
06:10:08,210 --> 06:10:11,313
And it’s really a pleasure
that she could come in person.

7676
06:10:11,947 --> 06:10:13,649
Because we’ve been
working with her,

7677
06:10:13,649 --> 06:10:17,286
and she’s been looking
at CH markers

7678
06:10:17,286 --> 06:10:19,588
in patients
with sickle cell disease.

7679
06:10:19,588 --> 06:10:22,791
So, Lachelle is
a physician scientist

7680
06:10:22,791 --> 06:10:25,661
at the Dana-Farber
Cancer Institute

7681
06:10:25,661 --> 06:10:27,796
in the Department
of Medical Oncology

7682
06:10:28,364 --> 06:10:30,833
and an assistant
professor of medicine

7683
06:10:30,833 --> 06:10:32,434
at Harvard Medical School.

7684
06:10:32,968 --> 06:10:35,638
And she’s probably
the only person in the world

7685
06:10:35,638 --> 06:10:39,441
who does the clinic
on clonal hematopoiesis.

7686
06:10:40,009 --> 06:10:42,144
[laughter]

7687
06:10:42,144 --> 06:10:44,847
[applause]

7688
06:10:44,847 --> 06:10:48,717
Lachelle Weeks: I thank you
so much

7689
06:10:48,717 --> 06:10:50,886
for that introduction, Swee Lay.

7690
06:10:50,886 --> 06:10:54,123
And I -- there are a couple
of different CHIP clinics

7691
06:10:54,123 --> 06:10:55,991
around the world.

7692
06:10:55,991 --> 06:10:59,061
And so, today, I’m hoping
to tell this audience,

7693
06:10:59,061 --> 06:11:02,197
which doesn’t think about
clonal hematopoiesis

7694
06:11:02,197 --> 06:11:04,433
and blood cancers
as often as I do,

7695
06:11:05,167 --> 06:11:08,270
to sort of orient you to the
idea of clonal hematopoiesis

7696
06:11:08,270 --> 06:11:12,408
as a complication, potentially,
of cellular therapies,

7697
06:11:12,408 --> 06:11:14,610
and then talk about
what we do in our CHIP clinic

7698
06:11:14,610 --> 06:11:16,812
for patients
who are diagnosed with CH.

7699
06:11:18,614 --> 06:11:22,318
So, these are my disclosures.
None, relevant to this talk.

7700
06:11:23,852 --> 06:11:26,288
So, cancer early detection
and interception

7701
06:11:26,288 --> 06:11:29,258
is something that we think
about a lot in our CHIP clinic.

7702
06:11:29,258 --> 06:11:31,427
And really, the framework
that we use for this

7703
06:11:31,427 --> 06:11:33,429
is that there are
three requirements

7704
06:11:33,429 --> 06:11:35,965
for any sort of effective
screening program.

7705
06:11:35,965 --> 06:11:37,333
You have to have the ability

7706
06:11:37,333 --> 06:11:40,402
to detect an asymptomatic
pre-cancer state,

7707
06:11:40,402 --> 06:11:42,538
like a colon polyp
or a skin mole.

7708
06:11:42,538 --> 06:11:45,140
You have to be able
to identify a population

7709
06:11:45,140 --> 06:11:47,710
that’s at risk
for developing malignancy.

7710
06:11:47,710 --> 06:11:49,712
And one must have
an intervention that

7711
06:11:49,712 --> 06:11:52,181
is likely to block progression
and improve survival.

7712
06:11:52,181 --> 06:11:54,283
Because that’s what
the sort of public health

7713
06:11:54,283 --> 06:11:55,918
relevance all boils down to.

7714
06:11:56,518 --> 06:11:58,320
And so, we think about this

7715
06:11:58,320 --> 06:12:00,189
in the context
of clonal hematopoiesis,

7716
06:12:00,189 --> 06:12:03,058
which is a precursor
of blood cancers

7717
06:12:03,058 --> 06:12:05,060
such as myelodysplastic syndrome

7718
06:12:05,060 --> 06:12:08,831
or MDS and acute
myeloid leukemia or AML.

7719
06:12:10,799 --> 06:12:13,068
There are currently no approved
screening programs

7720
06:12:13,068 --> 06:12:14,303
for blood cancers,

7721
06:12:14,303 --> 06:12:18,674
and our CHIP clinic is a hybrid
clinical and research endeavor.

7722
06:12:19,241 --> 06:12:21,977
And we’re working to develop
a screening program for MDS

7723
06:12:21,977 --> 06:12:23,212
and AML,

7724
06:12:23,212 --> 06:12:25,881
which are deadly cancers
that affect older adults.

7725
06:12:27,616 --> 06:12:29,251
So, what is
clonal hematopoiesis?

7726
06:12:29,952 --> 06:12:32,755
We know that as we age
and as our cells divide,

7727
06:12:32,755 --> 06:12:37,626
we acquire or accumulate genetic
alterations or mutations.

7728
06:12:37,626 --> 06:12:40,029
These are inevitable.
They’re ubiquitous.

7729
06:12:40,029 --> 06:12:42,264
And clonal hematopoiesis
really is describing

7730
06:12:42,264 --> 06:12:45,134
those somatic events
in our blood cells.

7731
06:12:45,134 --> 06:12:47,236
So, it’s the age-related
expansion

7732
06:12:47,236 --> 06:12:50,372
of a genetically related
population of blood

7733
06:12:50,372 --> 06:12:52,441
stem cells
or progenitor cells,

7734
06:12:52,441 --> 06:12:56,345
that’s detected as contributing
more than its fair share

7735
06:12:56,345 --> 06:12:57,846
to the development
of blood cells,

7736
06:12:57,846 --> 06:13:00,616
so red blood cells, white
blood cells, and platelets.

7737
06:13:00,616 --> 06:13:04,286
In 2014, Ben Ebert
and Sid Jaiswal

7738
06:13:04,286 --> 06:13:08,424
described clonal hematopoiesis
as a precursor of blood cancers,

7739
06:13:08,424 --> 06:13:10,559
showing that,
one, clonal hematopoiesis

7740
06:13:10,559 --> 06:13:12,861
was increasing with age,

7741
06:13:12,861 --> 06:13:15,531
and that, two,
clonal hematopoiesis

7742
06:13:15,531 --> 06:13:17,399
existed in this continuum

7743
06:13:17,399 --> 06:13:20,736
from the movement
from normal hematopoiesis

7744
06:13:20,736 --> 06:13:23,338
to the development
of clonal hematopoietic

7745
06:13:23,338 --> 06:13:25,407
blood cancer like MDS or AML.

7746
06:13:26,709 --> 06:13:28,277
There are many
different subtypes,

7747
06:13:28,277 --> 06:13:31,113
and I’ll bore you with
all of these data sort of later.

7748
06:13:31,847 --> 06:13:33,916
But subtypes
of clonal hematopoiesis

7749
06:13:33,916 --> 06:13:37,219
that we talk about the most have
to do with somatic mutations,

7750
06:13:37,219 --> 06:13:40,255
so mutations
in leukemia driver genes.

7751
06:13:40,255 --> 06:13:43,258
So, these are genes that
we think of as causing leukemia,

7752
06:13:43,258 --> 06:13:45,894
found in individuals who
don’t actually have leukemia,

7753
06:13:45,894 --> 06:13:47,496
who are otherwise healthy.

7754
06:13:48,030 --> 06:13:51,900
And we have now in the World
Health Organization,

7755
06:13:51,900 --> 06:13:53,135
CHIP and CCUSS,

7756
06:13:53,135 --> 06:13:55,204
which are two subtypes
of clonal hematopoiesis

7757
06:13:55,204 --> 06:13:58,273
that have been
formally defined in the WHO

7758
06:13:58,273 --> 06:14:01,710
fifth edition of blood
cancer classification.

7759
06:14:01,710 --> 06:14:03,746
There are other subtypes
of clonal hematopoiesis,

7760
06:14:03,746 --> 06:14:06,749
so basically, other ways
that you could have a single,

7761
06:14:06,749 --> 06:14:09,318
genetically related population
of cells, but we --

7762
06:14:09,318 --> 06:14:11,620
predominantly, when
we’re talking about CH,

7763
06:14:11,620 --> 06:14:15,491
we’re talking about the acquired
mutations in leukemia genes.

7764
06:14:17,092 --> 06:14:19,862
So, like I said, these are
precursors of blood cancers,

7765
06:14:19,862 --> 06:14:21,330
and they’re also quite common,

7766
06:14:21,330 --> 06:14:25,234
so about 15 percent of the
population over the age of 65

7767
06:14:25,234 --> 06:14:28,270
and about 30 percent
of the population by age 80.

7768
06:14:28,270 --> 06:14:31,406
With our standard clinical
next-generation sequencing,

7769
06:14:31,406 --> 06:14:33,942
we can detect these precursors
of blood cancers,

7770
06:14:33,942 --> 06:14:35,310
of MDS and AML.

7771
06:14:35,310 --> 06:14:38,213
So, again, these are people who
don’t actually have leukemia.

7772
06:14:38,213 --> 06:14:40,349
They don’t actually
have blood cancer.

7773
06:14:40,349 --> 06:14:41,583
They have the precursor.

7774
06:14:41,583 --> 06:14:44,319
So, they have the presence
of the mutation in the absence

7775
06:14:44,319 --> 06:14:47,389
of any of the clinical
characteristics of a cancer.

7776
06:14:47,389 --> 06:14:50,092
The rate of transformation
from the precursor state

7777
06:14:50,092 --> 06:14:53,595
to the blood cancer is about 0.5
to 1 percent per year.

7778
06:14:54,163 --> 06:14:57,032
And one of the questions
that we asked very early on was,

7779
06:14:57,866 --> 06:15:01,336
how do we actually determine
which people in the U.S.

7780
06:15:01,336 --> 06:15:02,638
or around the world,

7781
06:15:02,638 --> 06:15:04,907
who we identify
this precursor in,

7782
06:15:04,907 --> 06:15:08,243
should we actually be following
in our CHIP clinics,

7783
06:15:08,744 --> 06:15:11,480
should we actually be worried
about as being high risk

7784
06:15:11,480 --> 06:15:13,515
for developing a blood cancer?

7785
06:15:13,515 --> 06:15:17,252
In 2023, we developed a risk
stratification system with that

7786
06:15:17,252 --> 06:15:20,889
in mind to estimate the risk
of transformation from CH

7787
06:15:20,889 --> 06:15:22,491
to blood cancer.

7788
06:15:23,125 --> 06:15:26,528
And this risk stratification
system is available online.

7789
06:15:27,229 --> 06:15:29,631
It’s called the clonal
hematopoiesis risk score.

7790
06:15:29,631 --> 06:15:32,334
And it basically incorporates
eight different factors

7791
06:15:32,868 --> 06:15:34,469
that you would have
with a person

7792
06:15:34,469 --> 06:15:36,371
who you knew had
clonal hematopoiesis;

7793
06:15:37,072 --> 06:15:38,574
whether or not
they have low blood counts,

7794
06:15:38,574 --> 06:15:41,276
whether or not they have
more than one mutation,

7795
06:15:41,844 --> 06:15:45,280
what those mutations are,
how big the clone size is,

7796
06:15:45,280 --> 06:15:47,649
so what the allelic fraction is,

7797
06:15:47,649 --> 06:15:50,385
and then whether or not
their red blood cell indices,

7798
06:15:50,385 --> 06:15:53,822
the MCV and RDW,
are perturbed, and their age.

7799
06:15:54,556 --> 06:15:57,292
And using this risk
stratification score,

7800
06:15:57,292 --> 06:16:01,296
we can tell if a person
is high risk, intermediate risk,

7801
06:16:01,296 --> 06:16:04,466
or low risk for progression
to a blood cancer.

7802
06:16:04,466 --> 06:16:07,569
So, in a general population
setting,

7803
06:16:07,569 --> 06:16:09,504
individuals who are high risk
for progression

7804
06:16:09,504 --> 06:16:13,208
have over 50 percent risk of
transformation to blood cancer,

7805
06:16:13,208 --> 06:16:16,211
and individuals who are low risk
have a less than 1% chance.

7806
06:16:17,546 --> 06:16:19,681
This is just showing
what I just said.

7807
06:16:19,681 --> 06:16:23,118
So, individuals who are low risk
have a threefold increase

7808
06:16:23,118 --> 06:16:25,087
in their risk
of developing blood cancer

7809
06:16:25,087 --> 06:16:27,656
compared to individuals
without any mutations.

7810
06:16:27,656 --> 06:16:28,957
Individuals who are high risk

7811
06:16:28,957 --> 06:16:31,894
have a 348-fold
increase in their risk.

7812
06:16:32,761 --> 06:16:37,432
And this model we developed
using a U.K.-based cohort,

7813
06:16:37,432 --> 06:16:40,836
so the UK Biobank, which is
a cohort of healthy individuals.

7814
06:16:41,770 --> 06:16:45,107
And the model performed
very well in the UK Biobank,

7815
06:16:45,107 --> 06:16:48,277
and we validated it using
clinical data from Dana-Farber.

7816
06:16:48,977 --> 06:16:51,413
One of the challenges
that we have with the CHRS

7817
06:16:51,413 --> 06:16:54,383
is that we did develop it
in a very healthy population --

7818
06:16:54,383 --> 06:16:55,584
because those are --

7819
06:16:55,584 --> 06:16:58,153
that’s where our large
clinical genomic databases

7820
06:16:58,153 --> 06:16:59,655
are sort of focused --

7821
06:16:59,655 --> 06:17:03,892
and in specific contexts
such as sickle cell disease,

7822
06:17:03,892 --> 06:17:07,162
such as individuals who are
undergoing cellular therapies

7823
06:17:07,162 --> 06:17:10,399
or receiving chemotherapy
for other indications,

7824
06:17:10,399 --> 06:17:12,634
or people who happen to be
in both categories,

7825
06:17:12,634 --> 06:17:14,469
such as sickle cell
disease patients

7826
06:17:14,469 --> 06:17:18,573
undergoing cellular therapies,
this sort of risk stratification

7827
06:17:18,573 --> 06:17:20,909
doesn’t really take
those patients into account.

7828
06:17:20,909 --> 06:17:23,211
So, a lot of the work
that I’ll talk about in a second

7829
06:17:23,211 --> 06:17:24,980
is really focused
on understanding,

7830
06:17:24,980 --> 06:17:27,883
one, what’s the prevalence
of clonal hematopoiesis

7831
06:17:27,883 --> 06:17:30,786
in a population of individuals
with sickle cell disease?

7832
06:17:30,786 --> 06:17:33,622
And can we take the data
that we’re starting to generate

7833
06:17:33,622 --> 06:17:36,692
and develop
risk stratification systems

7834
06:17:36,692 --> 06:17:38,660
that are specific
to this population?

7835
06:17:40,128 --> 06:17:42,731
So, now, in our clinic,
we do a risk-informed management

7836
06:17:42,731 --> 06:17:44,099
of individuals with CH.

7837
06:17:44,099 --> 06:17:46,868
So, people who are high risk
for transformation

7838
06:17:46,868 --> 06:17:48,704
are people we’re following
pretty often.

7839
06:17:48,704 --> 06:17:51,940
We’re doing repeat
next-generation sequencing.

7840
06:17:51,940 --> 06:17:53,809
These are our patients
that we’re referring

7841
06:17:53,809 --> 06:17:55,544
for clinical trial
participation,

7842
06:17:56,211 --> 06:18:00,349
and we’re -- these are patients
with whom we’re doing more

7843
06:18:00,349 --> 06:18:01,950
than one bone marrow biopsy.

7844
06:18:02,684 --> 06:18:04,553
Individuals who are low risk,
however,

7845
06:18:04,553 --> 06:18:07,789
typically are returned back
to their primary care doctor

7846
06:18:07,789 --> 06:18:09,658
or whoever their primary
provider is.

7847
06:18:10,359 --> 06:18:13,428
And they have their annual
blood counts with that person

7848
06:18:13,428 --> 06:18:15,931
and only come back if there’s
some sort of clinical change

7849
06:18:15,931 --> 06:18:17,599
that’s indicative
of progression.

7850
06:18:19,134 --> 06:18:20,869
So, we have the ability
to detect

7851
06:18:20,869 --> 06:18:23,672
an asymptomatic precursor state,
which is CH.

7852
06:18:23,672 --> 06:18:25,974
We can identify
populations at risk,

7853
06:18:26,842 --> 06:18:29,111
with the caveat
that it’s not specific

7854
06:18:29,111 --> 06:18:31,613
to highly specified situations

7855
06:18:31,613 --> 06:18:34,149
such as chemotherapy
or individuals

7856
06:18:34,149 --> 06:18:37,285
with chronic illnesses
using the CHRS.

7857
06:18:37,285 --> 06:18:39,021
But we don’t yet have
an intervention

7858
06:18:39,021 --> 06:18:42,791
that is available
to block progression from CH

7859
06:18:42,791 --> 06:18:45,193
to blood cancer
and improve survival.

7860
06:18:45,193 --> 06:18:47,829
And many therapeutic
intervention studies for CH

7861
06:18:47,829 --> 06:18:49,498
are in their early stages,

7862
06:18:49,498 --> 06:18:53,168
and it would be lovely to think
about cancer prevention

7863
06:18:53,168 --> 06:18:56,638
in this way for individuals
who have sickle cell disease

7864
06:18:56,638 --> 06:18:59,374
and think about how we might
include this population

7865
06:18:59,374 --> 06:19:01,610
in some of these
early observational

7866
06:19:01,610 --> 06:19:03,311
as well as
interventional studies.

7867
06:19:04,613 --> 06:19:07,883
So, thinking about sickle cell
disease and clonal hematopoiesis

7868
06:19:07,883 --> 06:19:10,752
together, how are these
two things related?

7869
06:19:10,752 --> 06:19:12,687
What’s the underlying biology?

7870
06:19:12,687 --> 06:19:14,589
Can we actually
identify individuals

7871
06:19:14,589 --> 06:19:16,958
who are at risk
for developing MDS

7872
06:19:16,958 --> 06:19:20,262
and AML as patients
with sickle cell disease?

7873
06:19:21,329 --> 06:19:23,031
And can we prevent it
from happening really

7874
06:19:23,031 --> 06:19:24,900
is the sort of crux
of the issue.

7875
06:19:26,635 --> 06:19:32,340
This idea sort of came about
for me and for my colleagues

7876
06:19:32,941 --> 06:19:35,210
in thinking about
the lentiviral gene

7877
06:19:35,210 --> 06:19:40,715
therapy studies and their pause
during the clinical trials

7878
06:19:40,715 --> 06:19:43,785
because of the development
of MDS and AML and CCUS,

7879
06:19:43,785 --> 06:19:45,454
a precursor of MDS and AML,

7880
06:19:46,054 --> 06:19:48,590
in several patients
who are on these trials.

7881
06:19:49,191 --> 06:19:52,060
And this really raised
the question whether or not

7882
06:19:52,060 --> 06:19:56,331
this was a side effect
of the gene therapy itself,

7883
06:19:56,331 --> 06:20:00,102
was a consequence
of an increased risk of MDS

7884
06:20:00,102 --> 06:20:03,105
and AML in the context
of sickle cell disease.

7885
06:20:03,105 --> 06:20:05,273
So, what was really
causing this?

7886
06:20:06,108 --> 06:20:08,310
And so, there are two real
hypothesis that are --

7887
06:20:08,310 --> 06:20:11,346
hypotheses that are floating
around this issue

7888
06:20:11,346 --> 06:20:14,916
that are pretty viable.
One is that the MDS and AML

7889
06:20:14,916 --> 06:20:17,185
that we’re seeing
in sickle cell disease,

7890
06:20:17,185 --> 06:20:19,688
particularly in patients
who are undergoing gene therapy,

7891
06:20:19,688 --> 06:20:21,590
is perhaps therapy related.

7892
06:20:21,590 --> 06:20:25,160
So, we know from individuals who
are undergoing autologous stem

7893
06:20:25,160 --> 06:20:28,396
cell transplantation
for myeloma or lymphoma,

7894
06:20:29,064 --> 06:20:31,166
that there’s about
a 15 percent cumulative

7895
06:20:31,166 --> 06:20:34,169
risk of therapy-related myeloid
malignancy in that setting.

7896
06:20:35,170 --> 06:20:39,040
There’s also donor-derived
MDS and AML

7897
06:20:39,040 --> 06:20:41,143
that arises in individuals

7898
06:20:41,143 --> 06:20:43,912
who receive allogeneic
stem cell transplantation,

7899
06:20:43,912 --> 06:20:46,114
if their donor
happens to have CH.

7900
06:20:46,848 --> 06:20:49,751
And we know as well
that these premalignant lesions

7901
06:20:49,751 --> 06:20:53,321
could be present years
preceding the diagnosis of CH.

7902
06:20:53,321 --> 06:20:58,927
And so -- and that the selection
pressures of chemotherapy,

7903
06:20:58,927 --> 06:21:03,165
of requiring a cell
to undergo engraftment,

7904
06:21:03,165 --> 06:21:05,200
could potentially select
for clones

7905
06:21:05,200 --> 06:21:09,971
that are better at proliferation
or are better at self-renewal.

7906
06:21:11,406 --> 06:21:12,707
The second hypothesis,

7907
06:21:12,707 --> 06:21:16,444
which I find a bit more
biologically interesting,

7908
06:21:16,444 --> 06:21:18,880
is that perhaps
there’s something intrinsic

7909
06:21:18,880 --> 06:21:20,849
to the sickle cell
disease environment.

7910
06:21:21,416 --> 06:21:25,253
So, perhaps in the context
of chronic hemolysis,

7911
06:21:25,253 --> 06:21:27,022
there’s this
erythropoietic stress --

7912
06:21:27,022 --> 06:21:31,059
and we heard a little bit about
hematopoietic aging earlier.

7913
06:21:31,059 --> 06:21:33,895
Perhaps there’s some degree
of bone marrow stress

7914
06:21:33,895 --> 06:21:36,031
from high output
erythropoiesis,

7915
06:21:36,031 --> 06:21:39,000
the inflammation that’s
associated with hemolysis,

7916
06:21:39,000 --> 06:21:40,569
that might increase
the likelihood

7917
06:21:40,569 --> 06:21:43,371
that you develop somatic
alterations in the first place,

7918
06:21:43,371 --> 06:21:44,973
or you develop
clonal hematopoiesis

7919
06:21:44,973 --> 06:21:46,208
in the first place.

7920
06:21:46,208 --> 06:21:49,077
And this, in turn,
leads to an increased risk

7921
06:21:49,077 --> 06:21:51,746
of developing hematologic
malignancy in patients

7922
06:21:51,746 --> 06:21:53,582
with sickle cell disease.

7923
06:21:53,582 --> 06:21:57,352
Now, if this hypothesis is true,
then one would expect

7924
06:21:57,352 --> 06:22:00,088
that irrespective
of cellular therapy,

7925
06:22:00,088 --> 06:22:01,923
there would be
an increased rate

7926
06:22:01,923 --> 06:22:04,793
or increased incidence
of blood cancers

7927
06:22:04,793 --> 06:22:07,929
in a population of patients
with sickle cell disease.

7928
06:22:07,929 --> 06:22:10,031
And looking through
the epidemiologic data,

7929
06:22:10,031 --> 06:22:12,334
this is actually
indeed the case.

7930
06:22:12,334 --> 06:22:15,337
So, this is data from Ted Wun
out of California,

7931
06:22:15,937 --> 06:22:18,340
and he basically showed
that individuals

7932
06:22:18,340 --> 06:22:20,208
with sickle cell disease
in California

7933
06:22:20,208 --> 06:22:23,745
registries had an increased
risk of leukemias

7934
06:22:23,745 --> 06:22:26,181
and particularly,
myeloid leukemias,

7935
06:22:26,181 --> 06:22:29,951
relative to individuals
without sickle cell disease.

7936
06:22:31,987 --> 06:22:34,823
We looked through the literature
and noted

7937
06:22:34,823 --> 06:22:38,059
that there were
a number of case reports

7938
06:22:38,059 --> 06:22:40,829
about blood cancers
in sickle cell disease patients,

7939
06:22:41,529 --> 06:22:44,399
and a lot of these blood cancers

7940
06:22:44,399 --> 06:22:47,469
that were reported had
pretty high-risk features,

7941
06:22:47,469 --> 06:22:50,138
including multiple
genetic abnormalities,

7942
06:22:50,138 --> 06:22:52,540
including multiple
chromosomal abnormalities,

7943
06:22:52,540 --> 06:22:55,176
mutation in a gene
called TP53,

7944
06:22:55,176 --> 06:22:58,213
which confers resistance
to a lot of the therapies

7945
06:22:58,213 --> 06:23:00,382
we like to use to treat MDS
And AML.

7946
06:23:00,949 --> 06:23:04,686
And so, these were
really high-risk scenarios.

7947
06:23:04,686 --> 06:23:05,920
And we wanted to know,

7948
06:23:05,920 --> 06:23:08,690
is this sort of just the cases
that people are reporting

7949
06:23:08,690 --> 06:23:10,759
because they happen
to be particularly bad?

7950
06:23:10,759 --> 06:23:13,561
Or is this sort of
a universal experience

7951
06:23:13,561 --> 06:23:15,797
of a patient
with sickle cell disease?

7952
06:23:15,797 --> 06:23:18,433
Do they all have this sort
of high-risk phenotype?

7953
06:23:19,668 --> 06:23:22,537
And so, Miriam Osei,
who’s in my lab,

7954
06:23:23,138 --> 06:23:24,839
looked through our database,

7955
06:23:24,839 --> 06:23:29,110
and we pulled together
some patients from Duke,

7956
06:23:29,110 --> 06:23:30,345
courtesy of Marilyn,

7957
06:23:30,345 --> 06:23:35,250
who’s here, as well as
MD Anderson, from Guillermo.

7958
06:23:35,250 --> 06:23:39,421
And what we noted first
was that, fortunately,

7959
06:23:39,421 --> 06:23:41,656
the incidence
of myeloid neoplasia

7960
06:23:41,656 --> 06:23:44,192
in sickle cell disease
is not very high.

7961
06:23:44,759 --> 06:23:48,730
And so, we were only really able
to find about 12 cases

7962
06:23:48,730 --> 06:23:51,433
from these three
databases of individuals

7963
06:23:51,433 --> 06:23:57,105
who had myeloid malignancies,
so MDS, AML, or Ph-MPNs,

7964
06:23:58,440 --> 06:24:00,909
in a diagnosis
of sickle cell disease.

7965
06:24:01,509 --> 06:24:03,178
The other thing that we noted

7966
06:24:03,178 --> 06:24:05,280
was that there were
a couple of features

7967
06:24:05,280 --> 06:24:08,683
that were characteristic of
this population that stood out.

7968
06:24:08,683 --> 06:24:11,486
One is that the age of diagnosis
was much lower

7969
06:24:11,486 --> 06:24:15,990
than one would expect
for these blood cancers

7970
06:24:15,990 --> 06:24:17,359
that typically occur in adults

7971
06:24:17,359 --> 06:24:19,060
in their sixth
and seventh decade.

7972
06:24:19,561 --> 06:24:22,630
So, the median age
of diagnosis for AML,

7973
06:24:22,630 --> 06:24:24,599
for instance, was around 35.

7974
06:24:24,599 --> 06:24:28,136
The median age of diagnosis
altogether was around 50.

7975
06:24:28,903 --> 06:24:30,605
And then the other thing
that we noted

7976
06:24:30,605 --> 06:24:34,743
was that there was an increased
rate of complex karyotype

7977
06:24:34,743 --> 06:24:37,879
as well as TP53 mutations
in this population.

7978
06:24:37,879 --> 06:24:41,750
And again, these --
having these genetic features

7979
06:24:41,750 --> 06:24:44,819
confer a sort of resistance
to the kinds of therapies

7980
06:24:44,819 --> 06:24:47,689
that we have available
to treat MDS and AML.

7981
06:24:48,390 --> 06:24:52,193
And so, you know,
this was associated then

7982
06:24:52,193 --> 06:24:54,195
with a pretty poor survival,

7983
06:24:54,195 --> 06:24:57,132
particularly for the MDS
and AML populations,

7984
06:24:57,132 --> 06:25:00,268
with a median overall survival
of around 11 months

7985
06:25:00,835 --> 06:25:02,437
for these folks.

7986
06:25:04,072 --> 06:25:06,875
So, thinking about
and wrapping up this idea

7987
06:25:06,875 --> 06:25:08,910
of myeloid malignancy
and sickle cell disease,

7988
06:25:08,910 --> 06:25:10,745
there’s an earlier age
at diagnosis.

7989
06:25:10,745 --> 06:25:12,714
So, we’re looking at the third
to fifth decade

7990
06:25:12,714 --> 06:25:15,016
instead of the sixth
to seventh decade.

7991
06:25:15,016 --> 06:25:17,886
There’s a high mortality
when folks are diagnosed

7992
06:25:17,886 --> 06:25:19,587
with MDS and AML

7993
06:25:19,587 --> 06:25:22,023
and they happen
to have sickle cell disease.

7994
06:25:22,023 --> 06:25:25,994
And there are high-risk features
associated with the diagnosis

7995
06:25:25,994 --> 06:25:29,664
of at least MDS and AML
in sickle cell disease.

7996
06:25:31,132 --> 06:25:33,201
And so, then we ask
the question,

7997
06:25:33,201 --> 06:25:36,504
should patients with sickle cell
disease or thalassemia

7998
06:25:36,504 --> 06:25:39,674
who are being considered
for curative therapies

7999
06:25:39,674 --> 06:25:42,544
be screened in some way
for clonal hematopoiesis?

8000
06:25:42,544 --> 06:25:46,114
If this outcome is so bad,
should we be making sure

8001
06:25:46,114 --> 06:25:48,249
that we’re at least
detecting individuals who have

8002
06:25:48,249 --> 06:25:52,220
low-level clonal hematopoiesis
involving TP53?

8003
06:25:52,821 --> 06:25:55,123
And the answer here is that
we still kind of don’t know,

8004
06:25:55,123 --> 06:25:56,724
and we’re gathering the data.

8005
06:25:57,559 --> 06:26:00,929
We know that in data that has
been published

8006
06:26:00,929 --> 06:26:04,032
using whole exome
and whole genome studies

8007
06:26:04,032 --> 06:26:05,567
looking at clonal hematopoiesis

8008
06:26:05,567 --> 06:26:07,769
prevalence in sickle
cell disease

8009
06:26:07,769 --> 06:26:09,871
have actually yielded
conflicting results.

8010
06:26:10,405 --> 06:26:14,108
And so, in a whole
exome situation,

8011
06:26:14,108 --> 06:26:15,310
clonal hematopoiesis

8012
06:26:15,310 --> 06:26:17,846
was more prevalent
in sickle cell disease.

8013
06:26:17,846 --> 06:26:20,548
And in a whole genome analysis,

8014
06:26:20,548 --> 06:26:22,283
the report was exactly
the opposite,

8015
06:26:22,283 --> 06:26:24,886
that clonal hematopoiesis
was not more prevalent

8016
06:26:24,886 --> 06:26:26,287
in sickle cell disease.

8017
06:26:26,287 --> 06:26:28,056
And so, what we’re doing
right now,

8018
06:26:28,923 --> 06:26:32,527
under the leadership of Coleman
Lindsley at Dana-Farber,

8019
06:26:32,527 --> 06:26:35,063
is doing what we’re calling
sickle cell disease

8020
06:26:35,063 --> 06:26:37,065
clonal hematopoiesis
prevalence study,

8021
06:26:37,065 --> 06:26:40,068
a very simple study
that has just two objectives.

8022
06:26:40,068 --> 06:26:42,871
One is to define the frequency
and gene distribution

8023
06:26:42,871 --> 06:26:45,340
of clonal hematopoiesis
in sickle cell disease

8024
06:26:45,340 --> 06:26:48,276
and to do this definitively
using a large cohort

8025
06:26:48,276 --> 06:26:50,678
of individuals
with sickle cell disease.

8026
06:26:50,678 --> 06:26:53,581
And then secondly, to determine
what the incidence

8027
06:26:53,581 --> 06:26:56,284
and longitudinal
progression of CH

8028
06:26:56,284 --> 06:26:59,120
is in patients who have
undergone cellular therapy.

8029
06:27:00,221 --> 06:27:01,589
So, we’re currently --

8030
06:27:01,589 --> 06:27:03,791
have sequenced
about 5,000 individuals

8031
06:27:04,526 --> 06:27:07,128
and have some very early data,

8032
06:27:07,128 --> 06:27:10,198
and hopefully we’ll have some
data to present at ASH as well.

8033
06:27:11,032 --> 06:27:13,234
And this sequencing
that we’re using

8034
06:27:13,234 --> 06:27:16,070
is targeted duplex
error-corrected sequencing.

8035
06:27:16,671 --> 06:27:18,606
And the difference here,

8036
06:27:18,606 --> 06:27:20,909
for those who don’t think
about sequencing all the time,

8037
06:27:20,909 --> 06:27:22,944
is that your standard
next-generation sequencing

8038
06:27:22,944 --> 06:27:24,612
that you use
in hematology clinics

8039
06:27:24,612 --> 06:27:30,218
to kind of screen for MDS
have a limit of detection

8040
06:27:30,218 --> 06:27:33,354
of around a VAF of 1
to 2 percent generally.

8041
06:27:33,922 --> 06:27:37,525
And in this case, we can get
down to point 0.001 percent

8042
06:27:37,525 --> 06:27:41,062
using our custom panel
and our technique.

8043
06:27:41,062 --> 06:27:44,332
And so, we are able to detect
much smaller clones

8044
06:27:44,332 --> 06:27:45,667
of clonal hematopoiesis

8045
06:27:45,667 --> 06:27:49,470
than are typically connected --
detected clinically.

8046
06:27:51,139 --> 06:27:54,375
So, in our very early data
so far -- and again,

8047
06:27:54,375 --> 06:27:56,411
this is data
from about 5,000 patients

8048
06:27:56,411 --> 06:27:57,645
that have been sequenced

8049
06:27:57,645 --> 06:28:01,916
and just sort of summarized
descriptively here --

8050
06:28:01,916 --> 06:28:05,320
clonal hematopoiesis
tends to have what we are seeing

8051
06:28:05,320 --> 06:28:07,622
as a distinct age
and gene distribution

8052
06:28:07,622 --> 06:28:09,223
in sickle cell disease.

8053
06:28:09,924 --> 06:28:12,727
And just to sort of point
your eyes

8054
06:28:12,727 --> 06:28:16,598
to a couple of the bars here,
on the red graph,

8055
06:28:17,165 --> 06:28:19,701
we have the percentage
of individuals

8056
06:28:19,701 --> 06:28:23,271
with clonal hematopoiesis
in red.

8057
06:28:23,271 --> 06:28:25,273
And then in gray is
the individual’s percentage

8058
06:28:25,273 --> 06:28:26,507
in that age group,

8059
06:28:26,507 --> 06:28:29,010
which are negative
for clonal hematopoiesis.

8060
06:28:29,010 --> 06:28:33,514
And you see that
there is red in the zero-

8061
06:28:33,514 --> 06:28:37,585
to 17-year-old group.
And if you look at the same bar,

8062
06:28:37,585 --> 06:28:39,654
the corresponding bar
for the blue graph,

8063
06:28:40,288 --> 06:28:44,959
there was no one in that group
who had clonal hematopoiesis.

8064
06:28:44,959 --> 06:28:46,628
So, we’re seeing
clonal hematopoiesis

8065
06:28:46,628 --> 06:28:48,463
detected at much younger ages

8066
06:28:49,564 --> 06:28:51,466
in the sickle cell
disease population.

8067
06:28:52,166 --> 06:28:54,602
And the other thing
that we’re seeing

8068
06:28:54,602 --> 06:28:57,305
is that the gene distribution
is a little bit different.

8069
06:28:57,305 --> 06:28:59,107
So, there are a couple
of different genes --

8070
06:28:59,107 --> 06:29:00,742
genotypes of
clonal hematopoiesis

8071
06:29:00,742 --> 06:29:02,343
that we typically see.

8072
06:29:02,910 --> 06:29:05,647
One is mutations in these
epigenetic regulators,

8073
06:29:05,647 --> 06:29:08,049
and that’s mostly
associated with age.

8074
06:29:08,650 --> 06:29:13,321
And so, individuals
who are 50, 60,

8075
06:29:13,321 --> 06:29:15,890
tend to have mutations
in DNMT3A,

8076
06:29:16,724 --> 06:29:19,661
which is involved
in methylation,

8077
06:29:19,661 --> 06:29:24,399
and TET2,
another epigenetic gene

8078
06:29:24,399 --> 06:29:28,670
that’s involved in methylation,
versus individuals

8079
06:29:28,670 --> 06:29:32,440
who are receiving chemotherapy
or cytotoxic radiation

8080
06:29:32,440 --> 06:29:36,277
tend to have mutations
in TP53 or PPM1D.

8081
06:29:36,277 --> 06:29:37,679
And these are genes
that are involved

8082
06:29:37,679 --> 06:29:40,481
in the DNA damage response
and repair pathway.

8083
06:29:41,215 --> 06:29:44,986
And so, what we’re seeing in the
sickle cell disease population

8084
06:29:44,986 --> 06:29:46,421
is that, yes,
we are seeing

8085
06:29:46,421 --> 06:29:49,290
the sort of age-related
clonal hematopoiesis,

8086
06:29:49,290 --> 06:29:51,059
but we’re also
seeing in young --

8087
06:29:51,592 --> 06:29:53,728
individuals who
are young in age,

8088
06:29:53,728 --> 06:29:56,631
and most of these individuals
did not have any sort of gene

8089
06:29:56,631 --> 06:30:00,201
therapy or cellular therapy
or chemotherapy exposure.

8090
06:30:00,201 --> 06:30:02,770
We’re seeing that they’re
having the development

8091
06:30:02,770 --> 06:30:04,038
of clonal hematopoiesis

8092
06:30:04,038 --> 06:30:08,576
involving TP53 and ppm1d
at these younger ages.

8093
06:30:09,377 --> 06:30:11,679
And so, altogether, what this
is saying

8094
06:30:11,679 --> 06:30:14,348
is that it appears
that something different

8095
06:30:14,348 --> 06:30:16,350
is happening in the bone marrow
of individuals

8096
06:30:16,350 --> 06:30:17,752
with sickle cell disease,

8097
06:30:17,752 --> 06:30:21,556
that clonal hematopoiesis
is occurring at a younger age,

8098
06:30:21,556 --> 06:30:24,392
and is more likely
to be associated

8099
06:30:24,392 --> 06:30:27,562
with these DNA damage repair
and response pathway genes.

8100
06:30:28,930 --> 06:30:31,599
So, in summary,
there’s an increased incidence

8101
06:30:31,599 --> 06:30:33,935
of myeloid neoplasia
in sickle cell disease.

8102
06:30:34,702 --> 06:30:37,405
However, the absolute risk
of this remains very low.

8103
06:30:38,139 --> 06:30:40,742
We see MDs, CMML, and AML

8104
06:30:40,742 --> 06:30:43,044
being associated
with high-risk features,

8105
06:30:43,611 --> 06:30:45,446
particularly high-risk
molecular features

8106
06:30:45,446 --> 06:30:49,951
such as TP53 mutations, complex
cytogenetic abnormalities,

8107
06:30:49,951 --> 06:30:52,720
and this results in poor
survival for this population.

8108
06:30:53,454 --> 06:30:55,890
CH has a distinct age
and gene distribution

8109
06:30:55,890 --> 06:30:57,191
in sickle cell disease.

8110
06:30:57,191 --> 06:31:01,262
And again, we are seeing CH
at a younger age in individuals

8111
06:31:01,262 --> 06:31:02,630
with sickle cell disease,

8112
06:31:02,630 --> 06:31:04,665
and individuals are more likely
to have

8113
06:31:04,665 --> 06:31:08,703
these DNA damage response/repair
pathway mutations,

8114
06:31:08,703 --> 06:31:11,706
even in the absence of cytotoxic
chemotherapy exposure.

8115
06:31:12,573 --> 06:31:15,276
Screening and prevention of
high-risk genotypes of --

8116
06:31:16,010 --> 06:31:18,446
for high-risk
genotypes of MDS and AML,

8117
06:31:18,446 --> 06:31:20,248
could one day save lives.

8118
06:31:20,248 --> 06:31:23,451
And that’s sort of the whole
premise of having a clinic

8119
06:31:23,451 --> 06:31:27,021
and a research lab
that’s focused on development

8120
06:31:27,021 --> 06:31:28,656
of a screening program
for CH.

8121
06:31:29,223 --> 06:31:32,460
And really the populations
that I’m most concerned about

8122
06:31:32,460 --> 06:31:34,128
are these populations
that develop

8123
06:31:34,128 --> 06:31:36,631
these types
of myeloid malignancy,

8124
06:31:36,631 --> 06:31:39,834
MDS and AML, that we are unable
to really treat,

8125
06:31:39,834 --> 06:31:43,137
where the -- that tend to be
invariably, sort of fatal.

8126
06:31:44,338 --> 06:31:46,073
However, in the absence
of proven

8127
06:31:46,073 --> 06:31:48,042
therapeutic
intervention strategies,

8128
06:31:48,042 --> 06:31:49,944
wholesale screening for CH

8129
06:31:49,944 --> 06:31:51,746
currently has
pretty limited benefit.

8130
06:31:51,746 --> 06:31:54,949
Because the question then is,
what do you do with the results?

8131
06:31:54,949 --> 06:31:57,985
And further study evaluating
how the presence

8132
06:31:57,985 --> 06:32:01,489
of CH influences
cellular therapy outcomes

8133
06:32:01,489 --> 06:32:03,224
is going to be
really necessary for us

8134
06:32:03,224 --> 06:32:05,827
to determine
what benefit screening has

8135
06:32:05,827 --> 06:32:09,564
in this very specific context.
And with that, I’d like to say

8136
06:32:09,564 --> 06:32:12,033
thank you to Miriam
and my group,

8137
06:32:12,033 --> 06:32:14,902
who did the myeloid
malignancy work,

8138
06:32:14,902 --> 06:32:17,405
and then to all of our
collaborators in the Sickle Cell

8139
06:32:17,405 --> 06:32:22,376
Disease-CH Collaborative Group
and the NHLBI,

8140
06:32:22,376 --> 06:32:25,780
who is funding that study.
And I’ll take any questions.

8141
06:32:25,780 --> 06:32:30,718
[applause]

8142
06:32:30,718 --> 06:32:31,986
Swee Lay Thein: That was
fabulous.

8143
06:32:31,986 --> 06:32:34,188
You know, I will say one thing

8144
06:32:34,188 --> 06:32:36,390
before anybody
has any questions.

8145
06:32:36,390 --> 06:32:38,626
I’ve heard this word
premature aging.

8146
06:32:39,427 --> 06:32:42,129
This is probably the third
or the fourth time.

8147
06:32:42,129 --> 06:32:45,233
And this is what I say,
sickle cell disease,

8148
06:32:45,233 --> 06:32:47,768
there’s inflammation
occurring in this.

8149
06:32:47,768 --> 06:32:51,005
We heard about premature
aging in the bone marrow,

8150
06:32:51,005 --> 06:32:53,708
when the doctor presenting --

8151
06:32:53,708 --> 06:32:56,110
I forget the name,
excuse me -- on hydroxyurea.

8152
06:32:56,811 --> 06:33:00,781
So, it worries me a little bit,
like, say,

8153
06:33:00,781 --> 06:33:02,783
you know, when they --

8154
06:33:02,783 --> 06:33:05,953
I’m not really the bad girl
for hydroxyurea. Okay?

8155
06:33:06,687 --> 06:33:09,891
What I’m saying is,
if you push the dose too hard

8156
06:33:09,891 --> 06:33:13,427
and, you know,
and they get mild suppressive,

8157
06:33:14,061 --> 06:33:16,731
and then we are
probably accelerating

8158
06:33:16,731 --> 06:33:20,201
this premature aging.
And those [unintelligible]

8159
06:33:20,201 --> 06:33:25,506
clones will appear earlier.
So, anyway, I rest my case.

8160
06:33:26,040 --> 06:33:27,975
[laughter]

8161
06:33:27,975 --> 06:33:29,343
John Tisdale: To be continued.
Right?

8162
06:33:29,343 --> 06:33:30,611
Lachelle Weeks: To that point,

8163
06:33:30,611 --> 06:33:33,748
we are currently
working with Russell Ware

8164
06:33:33,748 --> 06:33:37,051
to gather the blood
specimens from individuals

8165
06:33:37,051 --> 06:33:40,988
who are on many of his
hydroxyurea clinical studies.

8166
06:33:41,689 --> 06:33:44,525
And we’re hopeful that
with those longitudinal samples,

8167
06:33:44,525 --> 06:33:48,863
we’ll be able to tell
if the dose increase associates

8168
06:33:48,863 --> 06:33:53,434
with emergence or expansion
of clonal hematopoiesis.

8169
06:33:53,434 --> 06:33:55,770
That will be really
interesting.

8170
06:33:55,770 --> 06:33:59,707
It is notable, however,
that at least in our populations

8171
06:33:59,707 --> 06:34:02,777
and our cohorts that are coming
from outside of the U.S.,

8172
06:34:02,777 --> 06:34:04,512
many of those individuals
actually don’t --

8173
06:34:04,512 --> 06:34:06,580
are not on hydroxyurea.

8174
06:34:06,580 --> 06:34:08,849
And so, a number
of our younger individuals

8175
06:34:08,849 --> 06:34:10,985
don’t have any hydroxyurea
exposure

8176
06:34:10,985 --> 06:34:15,222
but still have that DNA damage
repair and response pathway, CH,

8177
06:34:15,222 --> 06:34:17,024
which is completely
curious to me.

8178
06:34:17,825 --> 06:34:20,895
And, you know, hopefully
we’ll get some information

8179
06:34:20,895 --> 06:34:22,630
about that
in the next couple of years.

8180
06:34:22,630 --> 06:34:23,965
John Tisdale: So, I’m going to
have a question

8181
06:34:23,965 --> 06:34:25,199
and ask you to speculate.

8182
06:34:25,199 --> 06:34:26,467
Lachelle Weeks: Okay.

8183
06:34:26,467 --> 06:34:28,202
John Tisdale: So, the question
is that,

8184
06:34:28,202 --> 06:34:30,972
given that, you know,
P53 mutations

8185
06:34:30,972 --> 06:34:34,308
are seen more frequently
as a CH event

8186
06:34:34,308 --> 06:34:36,410
in sickle cell disease
at a young age,

8187
06:34:36,410 --> 06:34:39,981
and the malignancies
that have been seen

8188
06:34:39,981 --> 06:34:44,151
at least post-allogeneic
bone marrow transplantation

8189
06:34:44,151 --> 06:34:47,989
have included TP53 mutations
that, in retrospect,

8190
06:34:47,989 --> 06:34:50,458
were found
before the transplant.

8191
06:34:50,458 --> 06:34:55,730
Is that one mutation
that we could at least say

8192
06:34:55,730 --> 06:34:59,700
we should be screening for
until you finish your analysis?

8193
06:34:59,700 --> 06:35:03,437
And then the second question
that I’m going to ask you

8194
06:35:03,437 --> 06:35:05,072
to speculate on is,

8195
06:35:05,072 --> 06:35:07,041
you know, since this is,
you know, proliferation,

8196
06:35:07,041 --> 06:35:09,410
and maybe
there’s hyperproliferation,

8197
06:35:09,410 --> 06:35:12,580
and is causing this that,

8198
06:35:13,214 --> 06:35:15,649
you know,
are these transplants --

8199
06:35:15,649 --> 06:35:17,385
because we’re seeing it
across transplants, right?

8200
06:35:17,385 --> 06:35:20,187
Gene therapy,
allogeneic transplants,

8201
06:35:20,187 --> 06:35:22,990
match sibling, haplo,
what have you.

8202
06:35:25,259 --> 06:35:30,798
Is it because of this, you know,
hyper, sort of, reconstitution,

8203
06:35:30,798 --> 06:35:35,870
that if we do find
this P53 mutation beforehand,

8204
06:35:35,870 --> 06:35:39,073
we might give those cells
an upper hand

8205
06:35:39,073 --> 06:35:43,010
in this whittled down
hematopoietic stem cell number,

8206
06:35:43,010 --> 06:35:45,579
and should we again
be going earlier?

8207
06:35:46,213 --> 06:35:48,082
Lachelle Weeks: Yeah. You know,
to the point of screening,

8208
06:35:48,082 --> 06:35:51,218
I think identification
or screening --

8209
06:35:51,218 --> 06:35:55,556
starting to screen for at least
TP53 now does make some sense.

8210
06:35:56,290 --> 06:35:59,160
You know, I think you would
have to have a conversation

8211
06:35:59,160 --> 06:36:02,430
about what the risks
and benefits are of proceeding

8212
06:36:02,430 --> 06:36:03,964
with any sort
of cellular therapy,

8213
06:36:03,964 --> 06:36:06,300
all things considered, with
the individual in front of you,

8214
06:36:06,300 --> 06:36:07,968
the patient in front of you.

8215
06:36:07,968 --> 06:36:10,538
We do actually have patients,
for instance,

8216
06:36:10,538 --> 06:36:12,840
who are diagnosed
with ovarian cancer,

8217
06:36:12,840 --> 06:36:16,210
who are found to have CH
and have a TP53 mutation.

8218
06:36:16,710 --> 06:36:21,515
It’s not the same since
scenario but similar flavors.

8219
06:36:22,116 --> 06:36:23,851
And the question always
comes up,

8220
06:36:23,851 --> 06:36:25,352
what do we do
with that information?

8221
06:36:25,352 --> 06:36:27,621
And how do we sort of counsel
our patients

8222
06:36:27,621 --> 06:36:31,092
as to what chemo therapies
are safe versus not safe?

8223
06:36:31,092 --> 06:36:33,894
And we always say that you have
to treat the sort of beast

8224
06:36:33,894 --> 06:36:35,763
that’s in front of you,
you know.

8225
06:36:35,763 --> 06:36:37,998
And so, you want to make sure
that you’re doing

8226
06:36:37,998 --> 06:36:40,668
whatever is best
for the patient’s primary issue

8227
06:36:40,668 --> 06:36:42,970
and not trying
to avoid something

8228
06:36:42,970 --> 06:36:45,473
that could potentially
be beneficial in the now

8229
06:36:45,473 --> 06:36:47,308
because of a statistical
risk later.

8230
06:36:47,842 --> 06:36:51,779
But that that whole calculus
might completely change the more

8231
06:36:51,779 --> 06:36:54,115
that we learn
about TP53 mutations.

8232
06:36:56,584 --> 06:36:57,818
Swee Lay Thein: Francine,
I’m sorry.

8233
06:36:57,818 --> 06:36:59,053
Time’s up.

8234
06:36:59,053 --> 06:37:00,421
Francine Baker: Really?
Well, that’s okay.

8235
06:37:00,421 --> 06:37:02,890
Because you and Dr. Tisdale
asked my question [laughs].

8236
06:37:02,890 --> 06:37:04,191
Swee Lay Thein: All right,
quick one then. Quick one.

8237
06:37:04,191 --> 06:37:06,260
Francine Baker: Okay.
So, I was going to --

8238
06:37:06,260 --> 06:37:09,663
quick comment though.
I really do think that,

8239
06:37:09,663 --> 06:37:11,799
just given your talk
and your presentation,

8240
06:37:12,600 --> 06:37:16,036
jumping on Dr. Little’s
soapbox here,

8241
06:37:16,036 --> 06:37:19,206
this is really why we need
to have a global registry

8242
06:37:19,206 --> 06:37:21,242
to have all this data
and all this information.

8243
06:37:21,242 --> 06:37:25,279
Because I’m pretty sure --
okay, I am speculating now --

8244
06:37:25,279 --> 06:37:29,016
that there might be more
to the story than what we’ve --

8245
06:37:29,016 --> 06:37:30,484
what you’ve shared today.
Thank you.

8246
06:37:30,484 --> 06:37:32,486
Lachelle Weeks: Absolutely.

8247
06:37:32,486 --> 06:37:35,322
Swee Lay Thein: And as to what
we’re going to do for now,

8248
06:37:35,322 --> 06:37:37,992
I think it’s very important
to collect the dots

8249
06:37:37,992 --> 06:37:39,693
and then you can
join them later.

8250
06:37:40,261 --> 06:37:41,529
Okay?
It’s very important.

8251
06:37:41,529 --> 06:37:42,730
Lachelle Weeks: Absolutely.

8252
06:37:42,730 --> 06:37:44,031
Swee Lay Thein: So, we should
do that.

8253
06:37:44,031 --> 06:37:49,403
Okay. So -- okay [laughs].

8254
06:37:49,403 --> 06:37:51,539
[applause]

8255
06:37:51,539 --> 06:37:55,609
I also like to say that I do
tell all my patients

8256
06:37:55,609 --> 06:37:58,279
that you have to start
with hydroxyurea.

8257
06:37:58,279 --> 06:38:02,316
Okay? It’s not because
I don’t give them hydroxyurea.

8258
06:38:03,083 --> 06:38:07,821
Anyway, it’s my pleasure
to introduce our next speaker,

8259
06:38:08,522 --> 06:38:10,558
who is John Pierciey,

8260
06:38:11,992 --> 06:38:14,995
and he’s going to talk about
screening for alpha thalassemia

8261
06:38:15,529 --> 06:38:19,733
in gene therapy.
Okay. Thank you so much.

8262
06:38:19,733 --> 06:38:24,205
Oh, John is currently the vice
president of bluebird bio.

8263
06:38:24,205 --> 06:38:29,009
[applause]

8264
06:38:29,009 --> 06:38:30,945
John Pierciey: All right.
Thank you, everybody.

8265
06:38:30,945 --> 06:38:33,113
So, you know, my name --

8266
06:38:33,113 --> 06:38:35,349
as Swee Lay said,
my name is John Pierciey,

8267
06:38:35,349 --> 06:38:37,918
and I’m currently the head
of Research at bluebird.

8268
06:38:37,918 --> 06:38:40,654
And we’ve had some wonderful
talks talking about,

8269
06:38:41,222 --> 06:38:43,157
you know, some exciting data
from gene therapy.

8270
06:38:43,157 --> 06:38:45,259
And I’ve been tasked
with talking

8271
06:38:45,259 --> 06:38:46,694
about a particular situation

8272
06:38:46,694 --> 06:38:49,096
where the data may not be
as we expected it to,

8273
06:38:49,096 --> 06:38:52,366
in particular, the impact
of alpha-globin gene deletions

8274
06:38:52,366 --> 06:38:54,201
on gene therapy
outcomes in sickle.

8275
06:38:54,868 --> 06:38:56,170
First, disclosures.

8276
06:38:56,170 --> 06:38:58,606
I am an employee
and shareholder of bluebird bio.

8277
06:39:00,241 --> 06:39:02,076
So, first couple
of slides are about,

8278
06:39:02,076 --> 06:39:04,378
just a background to make sure
we’re all on the same page.

8279
06:39:04,378 --> 06:39:05,946
We know gamma-globin well,

8280
06:39:05,946 --> 06:39:07,615
but the other major
genetic modifier

8281
06:39:07,615 --> 06:39:09,516
of sickle cell disease severity

8282
06:39:09,516 --> 06:39:12,353
are variants that regulate
alpha-globin protein levels.

8283
06:39:12,353 --> 06:39:15,222
And when we talk about
alpha-globin gene deletions,

8284
06:39:15,222 --> 06:39:18,492
the most common in individuals
of Sub-Saharan African descent

8285
06:39:18,492 --> 06:39:21,362
is the 3.7 kb
rightward deletion,

8286
06:39:21,862 --> 06:39:23,731
which actually deletes
a part of α1

8287
06:39:23,731 --> 06:39:26,033
and α2 to create
a single hybrid gene.

8288
06:39:26,600 --> 06:39:28,335
In sickle patients,
it’s estimated

8289
06:39:28,335 --> 06:39:30,771
that about 30 to 35 percent
have a single deletion,

8290
06:39:30,771 --> 06:39:33,607
and about 3 to 5 percent
have two-gene deletions.

8291
06:39:34,575 --> 06:39:37,811
And this figure will become
more relevant later.

8292
06:39:37,811 --> 06:39:39,780
And this is not
in sickle cell disease,

8293
06:39:40,681 --> 06:39:43,083
but this is the
alpha/beta-globin mRNA ratios.

8294
06:39:43,083 --> 06:39:44,318
And unsurprisingly,

8295
06:39:44,318 --> 06:39:46,854
with an increasing
number of gene deletions,

8296
06:39:46,854 --> 06:39:49,123
you have a decrease
in that alpha/beta ratio.

8297
06:39:51,258 --> 06:39:52,459
There we go.

8298
06:39:52,459 --> 06:39:55,562
When it comes to the impact
of alpha-globin gene deletions

8299
06:39:55,562 --> 06:39:57,631
on biologic
and clinical outcomes,

8300
06:39:57,631 --> 06:39:59,500
I want to cover
a paper on the left.

8301
06:39:59,500 --> 06:40:02,436
But before I do that, I want to
talk about the color scheme.

8302
06:40:02,436 --> 06:40:04,004
So, I kept the color scheme

8303
06:40:04,004 --> 06:40:05,906
consistent through the rest
of the presentation

8304
06:40:05,906 --> 06:40:08,142
to keep it manageable
and digestible.

8305
06:40:08,142 --> 06:40:11,078
And red are patients with
no alpha-globin gene deletions.

8306
06:40:11,078 --> 06:40:13,514
Blue is single alpha-globin
gene deletion.

8307
06:40:13,514 --> 06:40:15,949
And that yellow gold color --
can we see? Yeah, we can see it.

8308
06:40:15,949 --> 06:40:18,819
Good -- is two alpha-globin
gene deletions.

8309
06:40:18,819 --> 06:40:20,054
And so, you know,

8310
06:40:20,054 --> 06:40:22,122
with an increasing number
of gene deletions,

8311
06:40:22,122 --> 06:40:24,525
we have a corresponding increase
in hemoglobin,

8312
06:40:24,525 --> 06:40:26,460
hematocrit,
and RBC count,

8313
06:40:26,460 --> 06:40:30,631
but we also have a corresponding
decrease in MCHC, MCV and MCH.

8314
06:40:30,631 --> 06:40:32,466
And I definitely do want
to point out

8315
06:40:32,466 --> 06:40:34,501
the significant reduction
in the two-gene

8316
06:40:34,501 --> 06:40:36,704
deletion patients,
in MCV and MCH.

8317
06:40:37,338 --> 06:40:39,306
As it relates to
clinical complications

8318
06:40:39,306 --> 06:40:40,941
of alpha-thalassemia,

8319
06:40:40,941 --> 06:40:43,177
there was a nice meta-analysis
published last year

8320
06:40:43,177 --> 06:40:44,778
by the group at St. Jude.

8321
06:40:44,778 --> 06:40:46,647
And what we know, right,
the co-inheritance

8322
06:40:46,647 --> 06:40:48,949
of alpha-globin gene
deletions confers protection

8323
06:40:48,949 --> 06:40:50,551
against cerebral
vascular disease,

8324
06:40:50,551 --> 06:40:53,187
which is stroke on the bottom
there in gold,

8325
06:40:53,187 --> 06:40:55,022
also silent cerebral infarcts,

8326
06:40:55,022 --> 06:40:57,358
vasculopathy,
and also, prefer --

8327
06:40:57,358 --> 06:40:59,259
you know, confers
some protection against priapism

8328
06:40:59,259 --> 06:41:01,261
and renal complications,
et cetera.

8329
06:41:01,261 --> 06:41:04,198
There is some, you know,
mixture in the data here,

8330
06:41:04,198 --> 06:41:06,667
but several studies
have shown some increases

8331
06:41:06,667 --> 06:41:08,068
in the frequency of VOCs,

8332
06:41:08,068 --> 06:41:10,471
particularly in two-gene
deletion patients.

8333
06:41:10,471 --> 06:41:12,373
This is thought to be
due to the fact

8334
06:41:12,373 --> 06:41:13,941
that although
in these patients,

8335
06:41:13,941 --> 06:41:15,809
the red blood cells
are more deformable,

8336
06:41:15,809 --> 06:41:17,010
they’re more adhesive.

8337
06:41:17,010 --> 06:41:18,746
And so, that increased
aggregation,

8338
06:41:18,746 --> 06:41:20,180
coupled with
the higher hematocrits,

8339
06:41:20,180 --> 06:41:21,749
the proposed mechanism
for that.

8340
06:41:22,349 --> 06:41:24,885
Regardless of the protective
effects though,

8341
06:41:24,885 --> 06:41:28,122
the figure on the right is from
the same paper on the left,

8342
06:41:28,122 --> 06:41:30,257
where the authors didn’t see
any difference in admissions

8343
06:41:30,257 --> 06:41:32,092
or inpatient days per year.

8344
06:41:32,092 --> 06:41:34,661
Now, the focus of this talk
is on gene therapy,

8345
06:41:34,661 --> 06:41:37,264
but I did want to do
a single slide for hydroxyurea.

8346
06:41:37,798 --> 06:41:40,033
Now, as you all know
way better than me,

8347
06:41:40,033 --> 06:41:41,635
you know there is
some conflicting data here.

8348
06:41:41,635 --> 06:41:45,539
But I think what it all sort
of aligns around is that in --

8349
06:41:45,539 --> 06:41:46,974
with alpha-globin genotypes --

8350
06:41:46,974 --> 06:41:49,209
sorry,
alpha-globin gene deletions,

8351
06:41:49,209 --> 06:41:52,546
there is not as a significant
of increase in total hemoglobin,

8352
06:41:52,546 --> 06:41:54,047
but that is thought to be due
to the fact

8353
06:41:54,047 --> 06:41:56,817
that they have higher baseline
total hemoglobins.

8354
06:41:56,817 --> 06:41:58,685
And so, in terms of a comparison
of two papers,

8355
06:41:58,685 --> 06:42:00,087
and the main point
I want to make,

8356
06:42:00,087 --> 06:42:02,423
I have the Darbari 2014 paper
on the left.

8357
06:42:02,990 --> 06:42:04,925
Please don’t criticize [laughs].

8358
06:42:04,925 --> 06:42:07,728
And on the right
is the same paper,

8359
06:42:08,328 --> 06:42:11,932
the Brewin 2022 paper
on the previous slide.

8360
06:42:11,932 --> 06:42:13,667
And what I want to point out
the discrepancy of

8361
06:42:13,667 --> 06:42:15,469
is the Brewin paper

8362
06:42:15,469 --> 06:42:18,071
did highlight
in the alpha-thal group --

8363
06:42:18,071 --> 06:42:21,442
so, combining the single and
two-gene deletion patients --

8364
06:42:21,442 --> 06:42:25,979
that there was a reduction
in the increase in MCV and MCH.

8365
06:42:25,979 --> 06:42:28,449
While the Darbari paper
didn’t see a difference

8366
06:42:28,449 --> 06:42:29,883
between the no deletions

8367
06:42:29,883 --> 06:42:32,619
and the one
and two-gene deletion group.

8368
06:42:32,619 --> 06:42:35,088
I think the main point I want
to make is in the bullets below,

8369
06:42:35,088 --> 06:42:36,623
is that in the Darbari paper,

8370
06:42:36,623 --> 06:42:39,860
only 7.7 percent of patients
had two-gene deletions.

8371
06:42:39,860 --> 06:42:41,161
And in the Brewin paper,

8372
06:42:41,161 --> 06:42:43,030
18 percent
had two-gene deletions.

8373
06:42:43,030 --> 06:42:45,299
And so, I think the theme
from my talk

8374
06:42:45,299 --> 06:42:46,767
throughout this entire --

8375
06:42:46,767 --> 06:42:48,802
you know, next few minutes
is going to be that

8376
06:42:48,802 --> 06:42:51,505
by combining the single
and two-gene deletion patients,

8377
06:42:51,505 --> 06:42:53,140
we may be masking
some of the effect

8378
06:42:53,140 --> 06:42:54,741
in the two-gene deletions.

8379
06:42:55,442 --> 06:42:57,110
Now, onto gene therapy.

8380
06:42:57,110 --> 06:43:00,013
Now, the great thing here is
that Dr. Frangoul, Dr. Tisdale,

8381
06:43:00,013 --> 06:43:01,849
and Dr. Kanter did all
the heavy lifting for me.

8382
06:43:01,849 --> 06:43:04,551
So, I’m going to shave time off
and not really go through this.

8383
06:43:04,551 --> 06:43:06,119
I’ll make a couple
of points though.

8384
06:43:06,119 --> 06:43:08,522
So, these are
the three protocols

8385
06:43:08,522 --> 06:43:11,458
where data specifically
on alpha-globin genotype

8386
06:43:11,458 --> 06:43:12,926
are available,
and I’m going to talk about

8387
06:43:12,926 --> 06:43:15,262
some of the outcomes
on the following slides.

8388
06:43:15,896 --> 06:43:17,831
So, lovo-cel, Lyfgenia,
which is gene edition,

8389
06:43:17,831 --> 06:43:19,099
which was covered --

8390
06:43:19,099 --> 06:43:21,969
I do want to reiterate the point
that Dr. Kanter made.

8391
06:43:21,969 --> 06:43:25,272
There -- we are inserting a
functioning copy of beta-globin,

8392
06:43:25,272 --> 06:43:28,575
but we do not impact
the gene expression of βS.

8393
06:43:28,575 --> 06:43:31,144
We reduce
intracellular βS protein --

8394
06:43:31,144 --> 06:43:34,081
which I’ll cover later --
but not mRNA levels.

8395
06:43:34,081 --> 06:43:36,149
And then as Dr. Frangoul
gave a nice overview

8396
06:43:36,149 --> 06:43:38,585
of these BCL11A
approaches, right,

8397
06:43:38,585 --> 06:43:42,823
reduce the transcription of βS
while they up-regulate

8398
06:43:42,823 --> 06:43:44,725
the transcription
of fetal hemoglobin.

8399
06:43:45,492 --> 06:43:47,060
Onto the data.

8400
06:43:47,060 --> 06:43:49,796
So, on the left, we have
some demographic information

8401
06:43:49,796 --> 06:43:53,367
from lovo-cel data as of,
I believe, February of 2023.

8402
06:43:54,334 --> 06:43:57,237
And ultimately, right, we see
about 30 percent of patients

8403
06:43:57,871 --> 06:44:00,407
have a single alpha-globin
gene deletion,

8404
06:44:00,407 --> 06:44:03,744
and 2 or 5 percent have
two alpha-globin gene deletions.

8405
06:44:03,744 --> 06:44:06,113
I wrote the 3.7 kb
at the bottom here

8406
06:44:06,113 --> 06:44:08,815
because every single
alpha-globin gene deletion

8407
06:44:08,815 --> 06:44:11,285
identified
was the 3.7 kb deletion.

8408
06:44:12,119 --> 06:44:15,022
Now, if we look at total
hemoglobin in the middle,

8409
06:44:15,022 --> 06:44:16,490
you know, if you look
at the no deletion

8410
06:44:16,490 --> 06:44:17,791
and the single deletion,
you’re like,

8411
06:44:17,791 --> 06:44:19,259
"Oh, actually, maybe
the single deletion

8412
06:44:19,259 --> 06:44:20,894
looks a little bit lower."

8413
06:44:20,894 --> 06:44:22,729
But when you dive
into the details,

8414
06:44:22,729 --> 06:44:26,466
in the no deletion group,
only 25 percent are female.

8415
06:44:26,466 --> 06:44:29,570
And in the single deletion
group, 64 percent are female.

8416
06:44:30,103 --> 06:44:32,439
Additionally, right,
as we know from hydroxyurea --

8417
06:44:32,439 --> 06:44:33,740
we’ve published on this --

8418
06:44:33,740 --> 06:44:35,842
you know, as your
therapeutic protein goes up,

8419
06:44:35,842 --> 06:44:37,244
your total hemoglobin
is going to go up.

8420
06:44:37,244 --> 06:44:39,746
And you can see on the right,
all gene therapy protocols

8421
06:44:39,746 --> 06:44:41,782
currently have
a pretty decent range

8422
06:44:41,782 --> 06:44:43,283
in the therapeutic protein.

8423
06:44:43,283 --> 06:44:46,019
So, when you actually
attempt to control for sex

8424
06:44:46,019 --> 06:44:48,088
as well as the amount
of therapeutic protein,

8425
06:44:48,088 --> 06:44:50,090
we don’t actually see
a significant difference

8426
06:44:50,090 --> 06:44:52,225
between total hemoglobin.
Numbers are small.

8427
06:44:52,225 --> 06:44:55,128
We’re still looking at it
but thus far, no difference.

8428
06:44:55,796 --> 06:44:58,398
As it relates to the shmiR data,
now, Dr Frangoul did give us

8429
06:44:58,398 --> 06:45:03,103
a nice overview of the 2021
New England Journal paper.

8430
06:45:03,103 --> 06:45:06,907
The authors did provide
a nice update at ASH of 2022,

8431
06:45:06,907 --> 06:45:08,475
where they gave us
patient-specific

8432
06:45:08,475 --> 06:45:11,044
level information,
including a single patient

8433
06:45:11,044 --> 06:45:13,647
with two alpha-globin gene
deletions, Patient 11.

8434
06:45:13,647 --> 06:45:16,583
For ease in digesting the data,
we’ve graphed it down below.

8435
06:45:16,583 --> 06:45:18,919
So, on the bottom left,
you have total hemoglobin.

8436
06:45:18,919 --> 06:45:20,821
And on the bottom right,
we have HbF levels.

8437
06:45:20,821 --> 06:45:23,857
And similarly --
actually, I am just --

8438
06:45:23,857 --> 06:45:26,159
I completely realized
that I forgot to talk

8439
06:45:26,159 --> 06:45:27,594
about the two-gene
deletion patients.

8440
06:45:27,594 --> 06:45:30,130
Wow. I was just in the zone.
Sorry about that.

8441
06:45:31,832 --> 06:45:34,167
So, yeah, two-gene
deletion patients, right,

8442
06:45:34,167 --> 06:45:38,872
so the pediatric patient in gold
and the adult patient in gray.

8443
06:45:38,872 --> 06:45:41,174
And so, ultimately, they had
lower total hemoglobins.

8444
06:45:41,174 --> 06:45:44,811
The adult patient was not
transfusion dependent

8445
06:45:44,811 --> 06:45:46,313
before coming on study

8446
06:45:46,313 --> 06:45:49,349
and became transfusion
dependent, post-lovo-cel.

8447
06:45:49,349 --> 06:45:51,351
And I’m going to talk
about that a bit more.

8448
06:45:51,351 --> 06:45:53,920
And then additionally,
the pediatric patient,

8449
06:45:53,920 --> 06:45:56,523
you know, the hemoglobin
was between 8.8 and 10,

8450
06:45:56,523 --> 06:45:59,026
between six months
and 24 months, post-transplant.

8451
06:45:59,559 --> 06:46:00,961
The other thing
I will comment on

8452
06:46:00,961 --> 06:46:04,998
is that both participants
had erythroid-specific dysplasia

8453
06:46:04,998 --> 06:46:06,566
in their marrow,
post-lovo-cel.

8454
06:46:06,566 --> 06:46:08,168
And I’m going to go
into that into a lot more.

8455
06:46:08,168 --> 06:46:10,037
Wow, I bungled that.
Geez.

8456
06:46:10,037 --> 06:46:11,304
All right.

8457
06:46:11,304 --> 06:46:13,540
So, now, we’re going to go back
to total hemoglobin levels

8458
06:46:13,540 --> 06:46:15,509
for the shmiR protocol.

8459
06:46:15,509 --> 06:46:18,578
And so, although there was
a pretty decent increase

8460
06:46:18,578 --> 06:46:22,616
in total hemoglobin
in the two-deletion patient,

8461
06:46:23,350 --> 06:46:26,019
the total hemoglobin
was lower at 8.8

8462
06:46:26,019 --> 06:46:28,388
compared to the no deletion
in single deletion patients

8463
06:46:28,388 --> 06:46:30,357
with similar levels of HbF.

8464
06:46:30,357 --> 06:46:32,292
I will comment
on that single gray dot

8465
06:46:32,292 --> 06:46:35,262
and the no deletion who did have
a lower total hemoglobin.

8466
06:46:35,262 --> 06:46:36,763
But you can also see
in the right figure,

8467
06:46:36,763 --> 06:46:38,732
they had a lower percent F
because they had

8468
06:46:38,732 --> 06:46:40,934
a lower peripheral
blood vector copy number.

8469
06:46:41,868 --> 06:46:45,172
Last but not least, we have
the exa-cel or Casgevy data.

8470
06:46:45,172 --> 06:46:46,673
And I do want
to thank the authors

8471
06:46:46,673 --> 06:46:48,341
for including
this supplemental table

8472
06:46:48,341 --> 06:46:50,444
in the most recent
New England Journal article,

8473
06:46:50,444 --> 06:46:53,680
where they did break out data
between no deletions

8474
06:46:53,680 --> 06:46:56,950
and that they combined
single and two-gene deletions.

8475
06:46:56,950 --> 06:46:59,286
Looking at the demographics
on the top,

8476
06:46:59,286 --> 06:47:01,121
34 percent
had a single deletion.

8477
06:47:01,121 --> 06:47:03,990
And similarly, there was
two patients, or 5 percent,

8478
06:47:03,990 --> 06:47:06,059
that had two alpha-globin
gene deletions.

8479
06:47:06,593 --> 06:47:09,129
Unfortunately for me,
in the purposes of this talk,

8480
06:47:09,129 --> 06:47:12,766
they combined the two, single
deletion and two deletion.

8481
06:47:12,766 --> 06:47:14,768
So, we can’t make
any more conclusions,

8482
06:47:14,768 --> 06:47:16,536
except that the --
what the authors made

8483
06:47:16,536 --> 06:47:19,039
that there was no difference
between the two groups.

8484
06:47:19,039 --> 06:47:21,174
But we don’t know, right,
whether or not this --

8485
06:47:21,174 --> 06:47:23,510
the two-deletion patients
were on the low end.

8486
06:47:23,510 --> 06:47:25,145
Did they have low F, high F?

8487
06:47:25,145 --> 06:47:27,481
So, unfortunately,
we can’t, you know,

8488
06:47:27,481 --> 06:47:30,283
distinguish anything more
from this particular data set.

8489
06:47:31,518 --> 06:47:33,019
In the interest of time,

8490
06:47:33,019 --> 06:47:34,855
I’m not going to go through
the full slide here.

8491
06:47:34,855 --> 06:47:36,289
But for --
as an interim summary,

8492
06:47:36,289 --> 06:47:39,159
before we dive deep into
some of the lovo-cel data,

8493
06:47:39,159 --> 06:47:41,361
out of the 89 patients
that I just summarized

8494
06:47:41,361 --> 06:47:42,996
on the previous
three slides,

8495
06:47:42,996 --> 06:47:45,198
where alpha-globin genotype
is available,

8496
06:47:45,198 --> 06:47:47,934
33.7 percent had
a single deletion,

8497
06:47:47,934 --> 06:47:50,771
and 5.6 percent
had two deletions,

8498
06:47:50,771 --> 06:47:52,672
which aligns with
the accepted prevalence,

8499
06:47:52,672 --> 06:47:55,008
which I think is really cool
when we consider the fact

8500
06:47:55,008 --> 06:47:56,777
that these gene
therapy protocols

8501
06:47:56,777 --> 06:47:58,545
have pretty stringent
inclusion criteria

8502
06:47:58,545 --> 06:48:00,447
as we think
about disease severity

8503
06:48:00,447 --> 06:48:01,848
in these different genotypes.

8504
06:48:01,848 --> 06:48:03,083
The last two.

8505
06:48:03,083 --> 06:48:05,819
The next bullet, right,
from what we can see so far,

8506
06:48:05,819 --> 06:48:08,488
is total hemoglobin
is generally not different

8507
06:48:08,488 --> 06:48:11,691
between the no deletions and
the single deletion patients.

8508
06:48:11,691 --> 06:48:15,695
And then, you know, sometimes
we can mask differences

8509
06:48:15,695 --> 06:48:17,364
or not be able
to determine differences

8510
06:48:17,364 --> 06:48:18,732
between the single
and two-deletion

8511
06:48:18,732 --> 06:48:20,700
by combining them.
The rest of it --

8512
06:48:20,700 --> 06:48:22,736
I’ll skip to my last slide.
Because I do want to make sure

8513
06:48:22,736 --> 06:48:24,538
that we get into
the lovo-cel data.

8514
06:48:24,538 --> 06:48:26,773
And so, as we think about
the impact

8515
06:48:26,773 --> 06:48:28,141
of alpha-globin gene deletions,

8516
06:48:28,141 --> 06:48:30,510
what has our experience
with lovo-cel taught us?

8517
06:48:32,012 --> 06:48:33,780
Before I really get
into our data,

8518
06:48:33,780 --> 06:48:35,148
I do just want to do a quick,

8519
06:48:35,148 --> 06:48:37,050
high-level overview
of the literature.

8520
06:48:37,050 --> 06:48:39,452
You know, over the past,
you know, 40 years now,

8521
06:48:40,220 --> 06:48:43,089
there have been sporadic reports
-- and the data is evolving --

8522
06:48:43,089 --> 06:48:45,959
that ineffective
erythropoiesis occurs

8523
06:48:45,959 --> 06:48:47,394
in baseline
sickle cell disease.

8524
06:48:47,394 --> 06:48:49,963
And I’ll sort of summarize
all of these papers to say

8525
06:48:49,963 --> 06:48:52,332
that we know that
the bone marrow is hypoxic.

8526
06:48:52,332 --> 06:48:54,734
Developing erythroblasts
can sickle.

8527
06:48:54,734 --> 06:48:57,337
And there was a nice paper
by Sara El Hoss and colleagues,

8528
06:48:57,337 --> 06:49:00,907
recently showing that cell death
occurs between the polychromatic

8529
06:49:00,907 --> 06:49:03,243
and orthochromatic
erythroblast stages.

8530
06:49:03,910 --> 06:49:05,512
So, what do we see in our data?

8531
06:49:06,179 --> 06:49:08,181
So, well, we’ve been
collecting baseline

8532
06:49:08,181 --> 06:49:09,916
bone marrows at screening

8533
06:49:09,916 --> 06:49:11,585
and sometimes
prior to transplant,

8534
06:49:11,585 --> 06:49:13,520
and we’ve been having them
centrally red.

8535
06:49:13,520 --> 06:49:15,989
And so, here in the bars
on the bottom left,

8536
06:49:16,590 --> 06:49:18,425
at the top, we have
a healthy reference

8537
06:49:18,425 --> 06:49:21,628
from a recent paper
in 2020 with, like, 200 --

8538
06:49:21,628 --> 06:49:23,597
with 240 healthy marrows.

8539
06:49:23,597 --> 06:49:29,135
And then below that, each bar
is an individual participant

8540
06:49:29,135 --> 06:49:30,537
coming onto our study.

8541
06:49:30,537 --> 06:49:33,473
You can see on the left,
it’s broken up by genotype,

8542
06:49:33,473 --> 06:49:35,041
and you can see
the cumulative proportions

8543
06:49:35,041 --> 06:49:37,143
of the different
erythroblast types.

8544
06:49:37,143 --> 06:49:40,213
If we put all the data together
and the figure on the right,

8545
06:49:40,213 --> 06:49:42,249
you can see that there doesn’t
appear to be a difference

8546
06:49:42,249 --> 06:49:43,917
between the single deletion
and the no deletion.

8547
06:49:43,917 --> 06:49:45,185
Numbers are small.

8548
06:49:45,185 --> 06:49:47,053
We’re continuing
to gather this data.

8549
06:49:47,988 --> 06:49:49,322
And you know, there’s a --

8550
06:49:49,322 --> 06:49:51,291
and compared to
the healthy volunteers,

8551
06:49:51,291 --> 06:49:54,227
there was a lower polychromatic
erythroblast population.

8552
06:49:54,227 --> 06:49:57,030
And because this is proportion,
there was a corresponding

8553
06:49:57,030 --> 06:49:59,099
increase
in the other populations.

8554
06:49:59,099 --> 06:50:01,201
I’ll also point out
on the bottom left,

8555
06:50:01,201 --> 06:50:03,770
the adult participant
with two gene deletions.

8556
06:50:03,770 --> 06:50:05,639
We did have a baseline
bone marrow available.

8557
06:50:05,639 --> 06:50:08,408
And you can see the quite
significant orthochromatic

8558
06:50:08,408 --> 06:50:09,643
erythroblast population.

8559
06:50:09,643 --> 06:50:11,578
But I think
the most important finding,

8560
06:50:11,578 --> 06:50:13,647
at least for me,
on this slide,

8561
06:50:13,647 --> 06:50:17,217
is the fact that we do see
low-level stress erythropoiesis

8562
06:50:17,217 --> 06:50:19,653
in the bone marrow
of sickle patients at baseline.

8563
06:50:19,653 --> 06:50:21,755
And we had five patients
with dysplasia

8564
06:50:21,755 --> 06:50:25,258
with a median of 5 percent
and a max of 10 percent,

8565
06:50:25,258 --> 06:50:26,860
with some of those
dyspoietic findings

8566
06:50:26,860 --> 06:50:29,362
being nuclear karyorrhexis,
multinucleation,

8567
06:50:30,430 --> 06:50:32,032
you know,
cytoplasmic asynchrony,

8568
06:50:32,032 --> 06:50:33,333
and basophilic stippling.

8569
06:50:33,333 --> 06:50:35,735
And you know, if you talk
to any hematopathologist

8570
06:50:35,735 --> 06:50:37,570
with experience,
here, they’ll go, "Yeah. Duh."

8571
06:50:37,570 --> 06:50:38,805
Right?
You know, these are --

8572
06:50:38,805 --> 06:50:41,775
these dyspoietic findings
are quite common in sickle cell.

8573
06:50:41,775 --> 06:50:45,145
Unfortunately, most of these
data are largely unpublished.

8574
06:50:45,979 --> 06:50:49,049
And that has been challenging,
specifically for us,

8575
06:50:49,049 --> 06:50:51,551
because
distinguishing dyserythropoiesis

8576
06:50:51,551 --> 06:50:52,953
from myelodysplastic syndrome

8577
06:50:52,953 --> 06:50:55,288
can be quite challenging.
And as we have --

8578
06:50:56,222 --> 06:50:58,458
actually, I think
the previous presentation

8579
06:50:58,458 --> 06:51:01,294
mentioned it as we presented --
and is also on our label,

8580
06:51:01,928 --> 06:51:04,164
the pediatric participant
with two-gene deletions

8581
06:51:04,164 --> 06:51:07,600
was diagnosed with MDS
in January of 2023,

8582
06:51:07,600 --> 06:51:09,202
30 months, post-lovo-cel.

8583
06:51:09,836 --> 06:51:13,173
This was done by a local tumor
board, based on the anemia,

8584
06:51:13,173 --> 06:51:15,475
the erythroid-restricted
dysplasia,

8585
06:51:15,475 --> 06:51:17,911
as well as clonality
identified by karyotype.

8586
06:51:17,911 --> 06:51:20,480
And ultimately, what I wanted
to do on this slide

8587
06:51:20,480 --> 06:51:23,350
was talk about the diagnostic
and clinical features of MDS

8588
06:51:23,350 --> 06:51:26,286
on the left
and compare both the pediatric

8589
06:51:26,286 --> 06:51:28,955
and the adult participant
on the right.

8590
06:51:28,955 --> 06:51:31,124
As it relates
to clinical symptoms,

8591
06:51:31,124 --> 06:51:33,360
the pediatric participant
is doing well.

8592
06:51:33,360 --> 06:51:35,161
They are transfusion
independent.

8593
06:51:35,161 --> 06:51:37,397
They have not had any VOEs,
post-transplant.

8594
06:51:38,331 --> 06:51:40,934
The treating physician
as well as the family

8595
06:51:40,934 --> 06:51:42,335
are actively monitoring

8596
06:51:42,335 --> 06:51:44,771
and are currently not
pursuing treatment for MDS.

8597
06:51:45,638 --> 06:51:46,906
However, as I mentioned,

8598
06:51:46,906 --> 06:51:49,275
the adult participant
is transfusion dependent

8599
06:51:49,275 --> 06:51:51,511
and does continue
to experience chronic pain.

8600
06:51:52,145 --> 06:51:55,081
As it relates to blood counts,
there are no other cytopenias

8601
06:51:55,081 --> 06:51:56,850
aside from the anemias
discussed.

8602
06:51:57,550 --> 06:51:59,652
The pediatric participant,
since diagnosis,

8603
06:51:59,652 --> 06:52:01,721
has had a stable
hemoglobin greater than 10.

8604
06:52:01,721 --> 06:52:03,690
It’s actually been
slowly going up,

8605
06:52:03,690 --> 06:52:06,626
and it’s been hovering between
11 and 12 for the past year.

8606
06:52:07,227 --> 06:52:09,229
As it relates to the dysplasia
that I mentioned,

8607
06:52:09,229 --> 06:52:11,431
in the marrow,
the dyspoietic findings

8608
06:52:11,431 --> 06:52:12,932
are similar
to the previous slide.

8609
06:52:12,932 --> 06:52:15,001
It is bi-
and tri-nucleation.

8610
06:52:15,001 --> 06:52:17,270
It is basophilic stippling,
et cetera.

8611
06:52:17,270 --> 06:52:19,506
And I’ll show you some pictures
in a couple of slides.

8612
06:52:19,506 --> 06:52:21,341
We’ve had all available
bone marrows

8613
06:52:21,341 --> 06:52:23,410
reviewed by two
independent pathologists,

8614
06:52:23,410 --> 06:52:25,612
and they have both concluded
that these findings

8615
06:52:25,612 --> 06:52:27,714
are consistent
with stress erythropoiesis.

8616
06:52:28,214 --> 06:52:29,816
As it relates to clonality,

8617
06:52:30,383 --> 06:52:33,386
the adult participant has had
no clonal process identified,

8618
06:52:33,386 --> 06:52:35,789
whether or not
by insertion site analysis,

8619
06:52:35,789 --> 06:52:37,557
looking for a vector clone,

8620
06:52:38,124 --> 06:52:39,959
or by karyotype
or any other measure.

8621
06:52:40,660 --> 06:52:43,930
And the pediatric participant
did have two clones

8622
06:52:43,930 --> 06:52:45,865
identified at their Month 30,

8623
06:52:45,865 --> 06:52:48,535
but there was significant
discrepancy in the frequency

8624
06:52:48,535 --> 06:52:51,271
between the local
and the central labs.

8625
06:52:51,271 --> 06:52:53,573
And when there was a subsequent
bone marrow at month --

8626
06:52:53,573 --> 06:52:56,910
the Month 36 assessment,
we did bilateral bone marrows.

8627
06:52:57,644 --> 06:53:01,514
And there was essentially --
only one iliac crest

8628
06:53:01,514 --> 06:53:04,951
showed one of the clones
at a frequency of 1.4 percent.

8629
06:53:05,718 --> 06:53:07,353
And then as we look for,
you know,

8630
06:53:07,353 --> 06:53:09,856
driver mutations
consistent with MDS,

8631
06:53:10,457 --> 06:53:12,959
there have been no mutations
identified by,

8632
06:53:12,959 --> 06:53:15,161
you know,
next-generation sequencing

8633
06:53:15,161 --> 06:53:16,963
or multiple other assays.

8634
06:53:16,963 --> 06:53:18,598
And there have been
no aneuploidy

8635
06:53:19,399 --> 06:53:21,101
identified by karyotype.

8636
06:53:21,101 --> 06:53:23,970
I will comment that
the adult participant

8637
06:53:23,970 --> 06:53:26,539
did have trisomy
8 identified by FISH

8638
06:53:26,539 --> 06:53:28,441
in a single bone
marrow aspirate,

8639
06:53:28,441 --> 06:53:30,210
six months
post-transplant.

8640
06:53:30,210 --> 06:53:32,879
However, six weeks later,
when we came back

8641
06:53:32,879 --> 06:53:34,280
and did another bone
marrow aspirate,

8642
06:53:34,280 --> 06:53:37,117
the trisomy was not found,
and it was never seen again.

8643
06:53:37,117 --> 06:53:39,352
They did originally
have an MDS diagnosis

8644
06:53:39,352 --> 06:53:41,488
at that original
trisomy 8 finding.

8645
06:53:41,488 --> 06:53:44,224
But that diagnosis was changed
a few weeks later.

8646
06:53:44,224 --> 06:53:46,593
As it relates to
the pediatric participant,

8647
06:53:46,593 --> 06:53:48,428
the FISH has been
all over the place.

8648
06:53:48,428 --> 06:53:51,431
We’ve seen low-level aneuploidy,
specifically trisomies,

8649
06:53:52,232 --> 06:53:54,033
which has been
significant discrepancy

8650
06:53:54,033 --> 06:53:56,202
between the local lab
and central lab.

8651
06:53:56,202 --> 06:53:57,704
In one particular assessment,

8652
06:53:57,704 --> 06:54:00,340
I think every single chromosome
tested on the --

8653
06:54:00,340 --> 06:54:02,775
by the local lab
had trisomies,

8654
06:54:02,775 --> 06:54:04,944
and the central lab
had no trisomies.

8655
06:54:04,944 --> 06:54:06,246
So, there’s definitely been

8656
06:54:06,246 --> 06:54:08,348
a significant amount
of discrepancy there.

8657
06:54:08,348 --> 06:54:10,717
So, if we put all of the data
together,

8658
06:54:10,717 --> 06:54:13,253
the data do look consistent
with dyserythropoiesis,

8659
06:54:13,887 --> 06:54:16,256
likely due to the globin chain
imbalance

8660
06:54:16,256 --> 06:54:17,457
from two-gene deletions

8661
06:54:17,457 --> 06:54:19,592
and exacerbated
from gene addition therapy.

8662
06:54:20,226 --> 06:54:23,229
The challenge though is,
how do you definitively prove

8663
06:54:23,229 --> 06:54:26,065
that this is dyserythropoiesis
from globin chain imbalance

8664
06:54:26,065 --> 06:54:27,333
and not MDS?

8665
06:54:27,333 --> 06:54:29,068
So, in the last couple
of data slides,

8666
06:54:29,068 --> 06:54:30,370
I’d like to show some things

8667
06:54:30,370 --> 06:54:32,205
that the research and
clinical teams have been doing.

8668
06:54:32,205 --> 06:54:33,540
And so -- and how --

8669
06:54:33,540 --> 06:54:34,807
I don’t know how
I’m doing on time,

8670
06:54:34,807 --> 06:54:36,643
but I’m going to try to rush.

8671
06:54:36,643 --> 06:54:40,046
So, we have been getting blood
and bone marrow samples

8672
06:54:40,046 --> 06:54:44,784
at the lab at bluebird,
and we have been enriching CD71

8673
06:54:44,784 --> 06:54:46,753
reticulocytes out
of peripheral blood

8674
06:54:46,753 --> 06:54:48,621
and erythroblasts
out of the bone marrow.

8675
06:54:48,621 --> 06:54:49,923
And one of the things
that we did

8676
06:54:49,923 --> 06:54:51,157
in the figure on the left here

8677
06:54:51,157 --> 06:54:56,229
is look at the proportion
of T87Q mRNA of total beta mRNA

8678
06:54:56,229 --> 06:54:57,897
and comparing that
to the proportion

8679
06:54:57,897 --> 06:54:59,632
of transduced
erythroid colonies.

8680
06:54:59,632 --> 06:55:01,734
And this is a shockingly
straight line.

8681
06:55:02,535 --> 06:55:04,204
And you know, I like this

8682
06:55:04,204 --> 06:55:06,339
because it tells us a little bit
about cell biology.

8683
06:55:06,339 --> 06:55:09,142
If we look in the literature
for globin chain imbalance,

8684
06:55:09,142 --> 06:55:11,411
when the cell tries to deal
with that stress,

8685
06:55:12,345 --> 06:55:13,980
it shuts down translation.

8686
06:55:13,980 --> 06:55:17,116
But because an erythroblast
needs so much hemoglobin,

8687
06:55:17,116 --> 06:55:19,219
it does not down-regulate
transcription.

8688
06:55:19,219 --> 06:55:20,787
And I think that’s
what we’re seeing here.

8689
06:55:20,787 --> 06:55:24,090
You can see in the top right,
the two-gene deletion patients

8690
06:55:24,090 --> 06:55:26,326
have very high
transduction efficiency.

8691
06:55:26,326 --> 06:55:27,927
So, even in the context
when you have only

8692
06:55:27,927 --> 06:55:29,796
two alpha-globin
gene deletions

8693
06:55:29,796 --> 06:55:31,331
and a high number
of vector copies

8694
06:55:31,331 --> 06:55:34,767
or a high number
of beta-globin mRNA,

8695
06:55:34,767 --> 06:55:36,569
we’re not seeing
the transcription

8696
06:55:36,569 --> 06:55:38,171
deviate from that line.

8697
06:55:38,938 --> 06:55:41,708
Then when we look
at the alpha/beta mRNA ratios,

8698
06:55:41,708 --> 06:55:43,309
we’ve got the reference range
that I showed you

8699
06:55:43,309 --> 06:55:45,411
on that first slide
in blue in the back,

8700
06:55:45,411 --> 06:55:47,347
but we don’t actually see
the alpha/beta ratio

8701
06:55:47,347 --> 06:55:50,316
in the peripheral blood be
outside of the reference ranges.

8702
06:55:51,184 --> 06:55:53,553
And the lovo-cel data
is in black here.

8703
06:55:54,320 --> 06:55:56,723
When we look at
the protein ratios, however,

8704
06:55:57,257 --> 06:55:59,425
both in the peripheral blood
and the bone marrow,

8705
06:55:59,425 --> 06:56:01,494
we don’t see a statistically
significant difference

8706
06:56:01,494 --> 06:56:03,963
in the protein ratio
between the no deletion

8707
06:56:03,963 --> 06:56:05,965
and the single deletion,
which makes sense.

8708
06:56:05,965 --> 06:56:08,001
We’re not really seeing
a difference in hemoglobin

8709
06:56:08,001 --> 06:56:09,569
or any other outcome.

8710
06:56:09,569 --> 06:56:12,372
But we are starting to see
a drop in the protein ratio.

8711
06:56:12,372 --> 06:56:14,440
Now, in the interest
of time and space,

8712
06:56:14,440 --> 06:56:17,010
I didn’t show the bone
marrow alpha/beta ratio.

8713
06:56:17,010 --> 06:56:18,611
But I did want
to specifically point

8714
06:56:18,611 --> 06:56:21,414
out the average ratio
of the adult participant

8715
06:56:21,414 --> 06:56:24,117
that was 0.29,
which comparing that to,

8716
06:56:24,117 --> 06:56:27,153
you know, the ratios
of HbH disease, which are 0.2.

8717
06:56:27,153 --> 06:56:29,389
But remember, this is
Gene addition therapy,

8718
06:56:29,389 --> 06:56:31,791
meaning that there is
a distribution of vector

8719
06:56:31,791 --> 06:56:34,294
copy number or vector copies.

8720
06:56:34,294 --> 06:56:35,795
And so,
this particular participant

8721
06:56:35,795 --> 06:56:38,631
had a peripheral blood vector
copy number of almost 5.

8722
06:56:38,631 --> 06:56:41,401
And so, when you think
about that 0.29 average,

8723
06:56:41,401 --> 06:56:42,769
half of the cells
are going to have

8724
06:56:42,769 --> 06:56:44,837
an alpha/beta ratio below that.

8725
06:56:44,837 --> 06:56:47,106
And when we think about the
very high vector copy numbers,

8726
06:56:47,106 --> 06:56:50,743
we’re probably getting very,
very low alpha/beta ratios.

8727
06:56:51,544 --> 06:56:52,745
Become even
more apparent here --

8728
06:56:52,745 --> 06:56:54,347
and this is my last data slide.

8729
06:56:54,947 --> 06:56:57,417
The other thing that we started
to do was to pick individual

8730
06:56:57,417 --> 06:56:59,519
BFU-e colonies grown
from the blood

8731
06:56:59,519 --> 06:57:03,323
and bone marrow of some patients
with no alpha-globin single

8732
06:57:03,323 --> 06:57:06,192
and two alpha-globin gene
deletions, post-gene therapy.

8733
06:57:06,192 --> 06:57:07,827
And you know,
I like picking BFU-es

8734
06:57:07,827 --> 06:57:09,162
because BFU-es are clonal,

8735
06:57:09,162 --> 06:57:11,531
meaning all cells
will have the same VCN.

8736
06:57:11,531 --> 06:57:12,932
And so, we can pick a BFU-e,

8737
06:57:12,932 --> 06:57:14,834
and we can measure
the vector copy number,

8738
06:57:14,834 --> 06:57:17,070
and we can separate the protein
by globin chains.

8739
06:57:17,070 --> 06:57:18,304
And that’s what we have here.

8740
06:57:18,304 --> 06:57:21,641
On the y axis, we have the
proportion of globin fractions.

8741
06:57:21,641 --> 06:57:24,310
On the x axis,
we have the different VCNs.

8742
06:57:24,310 --> 06:57:28,715
And in white, you have the T87Q.
In blue, you have the HBS.

8743
06:57:28,715 --> 06:57:31,484
And because this is
in vitro culture of colonies,

8744
06:57:31,484 --> 06:57:34,087
it does up-regulate fetal
hemoglobin expression.

8745
06:57:34,087 --> 06:57:36,189
But it’s actually pretty cool
that it’s here.

8746
06:57:36,189 --> 06:57:39,425
Because we can think about
affinities for alpha-globin.

8747
06:57:39,425 --> 06:57:42,295
And so, if we look,
I grouped the no deletion

8748
06:57:42,295 --> 06:57:45,064
and single deletion together,
largely due to space,

8749
06:57:45,064 --> 06:57:47,433
but also because
the results looked so similar.

8750
06:57:48,067 --> 06:57:50,169
And we can see
just how different

8751
06:57:50,169 --> 06:57:52,305
the two alpha-globin gene
deletion data looks.

8752
06:57:52,305 --> 06:57:55,341
And when I look at these,
I think one of the big things

8753
06:57:55,341 --> 06:57:57,510
that really stands out to me
is that,

8754
06:57:57,510 --> 06:57:59,879
one, I like to say that HBS
is the biggest loser, right?

8755
06:57:59,879 --> 06:58:03,182
When we think about the affinity
for alpha-globin

8756
06:58:03,182 --> 06:58:06,386
and how the T87Q, the βS,

8757
06:58:06,386 --> 06:58:08,354
and the HbF
are going to compete,

8758
06:58:08,354 --> 06:58:09,655
it does look here

8759
06:58:09,655 --> 06:58:14,794
that the βS is ultimately
not pairing for alpha.

8760
06:58:14,794 --> 06:58:17,063
The other thing that we can
get out of this too

8761
06:58:17,063 --> 06:58:20,700
is that it does look like
the protein levels

8762
06:58:20,700 --> 06:58:23,035
with two-gene deletions
are significantly different,

8763
06:58:23,035 --> 06:58:26,038
which is really throwing off the
ratios that we’re seeing here.

8764
06:58:26,038 --> 06:58:27,340
The other thing I’ll comment,

8765
06:58:27,340 --> 06:58:29,208
which I mentioned
on the previous slide,

8766
06:58:29,208 --> 06:58:31,644
is the adult participant
that has the high VCN.

8767
06:58:32,412 --> 06:58:34,881
Over 50 percent of their
colonies measured here

8768
06:58:34,881 --> 06:58:37,049
had that VCN a 4-plus,

8769
06:58:37,049 --> 06:58:38,518
so that all the way
on the right there,

8770
06:58:38,518 --> 06:58:41,254
I mean, we have almost
undetectable amounts of βS.

8771
06:58:41,254 --> 06:58:43,689
So, the question becomes,
we can’t speak to the amount

8772
06:58:43,689 --> 06:58:46,058
of translation, right,
the competition between,

8773
06:58:46,926 --> 06:58:50,930
you know, mRNAs at the ribosome,
but what it does look like here

8774
06:58:50,930 --> 06:58:53,032
is that when alpha-globin
is limiting,

8775
06:58:53,032 --> 06:58:55,268
βS is not pairing
to alpha-globin.

8776
06:58:55,268 --> 06:58:56,602
And so, therefore, you know,

8777
06:58:56,602 --> 06:58:58,404
is that accumulating
in the cytoplasm,

8778
06:58:58,404 --> 06:59:00,807
and what does that stability
of that protein do?

8779
06:59:01,874 --> 06:59:04,477
Well, what do we know about
how the cell deals with this?

8780
06:59:04,477 --> 06:59:07,814
This is a nice review
from Mitch Weiss

8781
06:59:07,814 --> 06:59:10,817
and Eugene Khandros from 2010.

8782
06:59:10,817 --> 06:59:13,486
So, we know that irreversibly
folded proteins

8783
06:59:13,486 --> 06:59:16,155
will be ubiquitin tagged
and sent to the proteasome.

8784
06:59:16,155 --> 06:59:17,824
But when they accumulate
too much,

8785
06:59:17,824 --> 06:59:19,792
chaperone proteins
will get them

8786
06:59:19,792 --> 06:59:22,462
together into an aggresome
in an attempt to deal with that.

8787
06:59:22,462 --> 06:59:24,630
And if the proteasome
can’t deal with it,

8788
06:59:24,630 --> 06:59:26,432
then autophagy kicks in.

8789
06:59:26,432 --> 06:59:31,370
And so, we’ll end in the context
of, sorry, denatured hemoglobin.

8790
06:59:31,370 --> 06:59:33,506
In this context,
we call them Heinz bodies.

8791
06:59:33,506 --> 06:59:36,275
So, you know, are we seeing
these aggresomes

8792
06:59:36,275 --> 06:59:37,510
in these bone marrows?

8793
06:59:37,510 --> 06:59:40,746
So, here are some pictures
that I’ve cropped out

8794
06:59:40,746 --> 06:59:42,448
of some of
the bone marrow images,

8795
06:59:42,448 --> 06:59:45,184
the pediatric participant
on the top, adult in the middle,

8796
06:59:45,184 --> 06:59:47,587
and some baseline
beta thalassemia marrows

8797
06:59:47,587 --> 06:59:48,921
from our 207 study.

8798
06:59:48,921 --> 06:59:51,657
And you can see
these eosinophilic cytoplasmic

8799
06:59:51,657 --> 06:59:54,894
inclusions that look
quite similar to the inclusions

8800
06:59:54,894 --> 06:59:56,929
that you do see
in the thal patients.

8801
06:59:56,929 --> 06:59:59,599
Now, I will say --
and I’m running out of time –

8802
06:59:59,599 --> 07:00:01,501
but we had a very difficult time

8803
07:00:01,501 --> 07:00:03,803
actually proving
these are globin chains.

8804
07:00:03,803 --> 07:00:06,305
Some of the techniques just
aren’t really around anymore.

8805
07:00:06,305 --> 07:00:07,773
No one’s doing it.

8806
07:00:07,773 --> 07:00:09,709
And some of the logistics
associated with doing this

8807
07:00:09,709 --> 07:00:11,143
with fresh
have been quite difficult.

8808
07:00:11,143 --> 07:00:12,812
But I’ve had some
great conversations

8809
07:00:12,812 --> 07:00:14,447
with all of you for some ideas.

8810
07:00:14,447 --> 07:00:17,416
So, I’m going to wrap up and say
that, you know, ultimately,

8811
07:00:17,416 --> 07:00:20,786
in the context of two
alpha-globin gene deletions,

8812
07:00:20,786 --> 07:00:23,222
regardless of the gene
therapy approach,

8813
07:00:23,222 --> 07:00:25,758
there will always be
a thalassemic phenotype.

8814
07:00:25,758 --> 07:00:28,261
And so, you know,
even if you’re not,

8815
07:00:28,261 --> 07:00:31,597
even if you are reducing ßS,
you are still likely going

8816
07:00:31,597 --> 07:00:33,599
to have some level
of globin chain imbalance.

8817
07:00:33,599 --> 07:00:35,234
And so, ultimately, you know,

8818
07:00:35,234 --> 07:00:36,769
we should continue
to characterize

8819
07:00:36,769 --> 07:00:38,971
what those outcomes
are going to look like.

8820
07:00:38,971 --> 07:00:40,973
And so, I’m just going to kind
of skip through the conclusions

8821
07:00:40,973 --> 07:00:43,075
a little bit.
Right, with the lovo-cel data,

8822
07:00:43,075 --> 07:00:44,877
we did see
some unexpected results

8823
07:00:44,877 --> 07:00:47,480
in the two patients
treated in the lovo-cel studies.

8824
07:00:48,080 --> 07:00:50,216
And because of this, you know,

8825
07:00:50,216 --> 07:00:52,051
patients with
alpha-thalassemia trait

8826
07:00:52,051 --> 07:00:54,020
are included in the limitation
of use section

8827
07:00:54,020 --> 07:00:55,788
on the Lyfgenia label.

8828
07:00:55,788 --> 07:00:58,591
But I do expect that
all gene addition approaches

8829
07:00:58,591 --> 07:01:00,326
are likely to exacerbate
globin chain

8830
07:01:00,326 --> 07:01:02,628
imbalance
in two-gene deletion patients.

8831
07:01:02,628 --> 07:01:04,664
And as we saw
in the shmiR trial,

8832
07:01:05,331 --> 07:01:06,799
the single two-gene
deletion patient

8833
07:01:06,799 --> 07:01:08,000
did have lower hemoglobin.

8834
07:01:08,000 --> 07:01:11,437
And unfortunately, at this time,
we can’t actually figure out

8835
07:01:11,437 --> 07:01:13,940
how the two patients did
in the exa-cel study.

8836
07:01:13,940 --> 07:01:15,508
But, right,
the purpose of this session,

8837
07:01:15,508 --> 07:01:17,543
I would propose
that all patients

8838
07:01:17,543 --> 07:01:20,546
that are undergoing gene therapy
do get alpha-globin genotypes.

8839
07:01:20,546 --> 07:01:23,516
I would also kindly request
that all -- you know,

8840
07:01:23,516 --> 07:01:26,285
until we have a registry,
all future manuscripts

8841
07:01:26,285 --> 07:01:29,021
publish the two-gene
deletion data separately,

8842
07:01:29,021 --> 07:01:30,656
even if it’s in the supplement.

8843
07:01:30,656 --> 07:01:33,259
Because I would love to get
invited back in maybe five years

8844
07:01:33,259 --> 07:01:34,460
and I can talk
about the results.

8845
07:01:34,460 --> 07:01:35,661
[laughter]

8846
07:01:35,661 --> 07:01:38,331
So, with that, I want
to thank the study sites.

8847
07:01:38,331 --> 07:01:40,933
I want to thank the patients,
their families,

8848
07:01:40,933 --> 07:01:42,268
the bluebird research team,

8849
07:01:42,268 --> 07:01:44,370
and especially Melissa Kinney,
who made all the figures,

8850
07:01:44,370 --> 07:01:47,873
because I am not that good
at PowerPoint and GraphPad.

8851
07:01:48,441 --> 07:01:52,345
All right. Any questions
[laughs].

8852
07:01:52,345 --> 07:01:54,413
[applause]

8853
07:01:54,413 --> 07:01:56,248
Swee Lay Thein: John,
this is great.

8854
07:01:56,248 --> 07:01:58,284
I mean, you can --

8855
07:01:58,284 --> 07:02:01,654
sometimes you can get
very severe myelodysplasia.

8856
07:02:02,221 --> 07:02:04,590
I remember looking
at a patient

8857
07:02:04,590 --> 07:02:07,360
coming in with a hemoglobin
of like 4,

8858
07:02:08,260 --> 07:02:10,496
and she turned out to be
very iron deficient

8859
07:02:10,496 --> 07:02:14,066
and turned out
to have [unintelligible].

8860
07:02:14,066 --> 07:02:16,202
And when we treated her,

8861
07:02:16,202 --> 07:02:18,037
the bone marrow became
completely normal.

8862
07:02:18,037 --> 07:02:19,672
We thought initially,
it was MDS.

8863
07:02:20,272 --> 07:02:22,875
Second point, John --
comment I’d like to make is,

8864
07:02:23,376 --> 07:02:25,378
can I suggest something
very simple?

8865
07:02:25,378 --> 07:02:27,246
I mean, you may laugh.
Okay?

8866
07:02:27,246 --> 07:02:29,315
When you have gene therapy,

8867
07:02:30,316 --> 07:02:33,352
they all undergo myeloablation,
right, or some form or other.

8868
07:02:34,020 --> 07:02:36,689
And even -- if you already
have two-gene deletion,

8869
07:02:37,556 --> 07:02:41,293
then you’re wiping out
the beta component

8870
07:02:41,293 --> 07:02:43,029
as well as
the alpha component.

8871
07:02:43,029 --> 07:02:46,832
But you’re only replacing
the beta subcomponent, right?

8872
07:02:46,832 --> 07:02:49,035
And so, the chain
imbalance gets worse.

8873
07:02:49,568 --> 07:02:51,971
So, it’s like
a hemoglobin H disease.

8874
07:02:52,805 --> 07:02:55,207
And have you tried staining
the peripheral blood

8875
07:02:55,207 --> 07:02:56,509
with brilliant cresyl blue --

8876
07:02:56,509 --> 07:02:57,710
John Pierciey: Yeah.

8877
07:02:57,710 --> 07:02:59,111
-- to see whether they have
H bodies?

8878
07:02:59,111 --> 07:03:00,346
John Pierciey: Yeah.

8879
07:03:00,346 --> 07:03:03,049
So, yeah, the question
was around whether --

8880
07:03:03,049 --> 07:03:05,384
oh, did I just break it?
I think maybe I did --

8881
07:03:06,252 --> 07:03:09,689
about finding HbH bodies
in the peripheral blood.

8882
07:03:10,256 --> 07:03:13,325
So, that was done,
and we didn’t see any HbH,

8883
07:03:13,325 --> 07:03:14,927
maybe a couple of percent.

8884
07:03:15,461 --> 07:03:17,029
And that was ultimately
why we started

8885
07:03:17,029 --> 07:03:18,497
looking at peripheral blood.

8886
07:03:18,497 --> 07:03:20,900
But then we shifted our focus
to investigating the marrow.

8887
07:03:20,900 --> 07:03:23,636
Because if we weren’t seeing HbH
in the peripheral blood,

8888
07:03:23,636 --> 07:03:24,970
is that because these cells

8889
07:03:24,970 --> 07:03:26,706
weren’t making it
out of the marrow?

8890
07:03:26,706 --> 07:03:30,242
Or that second to last slide,
you know, are --

8891
07:03:30,242 --> 07:03:31,911
is the globin chain imbalance

8892
07:03:31,911 --> 07:03:35,347
because we’re getting that
accumulated ßS in the cytoplasm,

8893
07:03:35,347 --> 07:03:38,451
and it’s just aggregating
or denaturing faster

8894
07:03:38,451 --> 07:03:40,419
than it can form?
We don’t have the answer,

8895
07:03:40,419 --> 07:03:42,555
but those are some of the things
we’re trying to figure out.

8896
07:03:42,555 --> 07:03:46,092
But the answer is, no, we didn’t
see any broad amount of HbH

8897
07:03:46,092 --> 07:03:48,060
in the periphery.
No.

8898
07:03:48,060 --> 07:03:49,361
Jane Little: This is
Jane Little.

8899
07:03:49,361 --> 07:03:51,363
I just had --
I’m just a little curious.

8900
07:03:52,398 --> 07:03:53,933
Julie Cantor
is looking at trait,

8901
07:03:53,933 --> 07:03:56,802
and she’s seen a -- to me,
a surprising number of patients

8902
07:03:56,802 --> 07:04:00,039
who had alpha chain
triplicates or more.

8903
07:04:01,207 --> 07:04:04,376
It was not a small number.
I think was more than 5 percent.

8904
07:04:04,376 --> 07:04:06,445
And when I think about it,
I’m a little surprised

8905
07:04:06,445 --> 07:04:10,049
that this isn’t coming up
in any of these gene therapy.

8906
07:04:10,049 --> 07:04:11,517
There was one triplication
in your --

8907
07:04:11,517 --> 07:04:12,785
John Pierciey: Yeah.

8908
07:04:12,785 --> 07:04:14,920
There were triplication
in the exa-cel study.

8909
07:04:14,920 --> 07:04:17,790
We did -- our lovo-cel studies
have not seen any triplication.

8910
07:04:17,790 --> 07:04:19,458
Jane Little: Because I wonder
if it might not be enriched.

8911
07:04:19,458 --> 07:04:21,627
Because it will make
the disease worse,

8912
07:04:21,627 --> 07:04:23,496
I have always thought,
more hemolytic.

8913
07:04:23,496 --> 07:04:24,730
Right, Swee Lay?

8914
07:04:24,730 --> 07:04:25,965
John Pierciey: Right.
I mean, that’s --

8915
07:04:25,965 --> 07:04:27,233
Swee Lay Thein: There was one.

8916
07:04:27,233 --> 07:04:29,969
It was a little bit higher
than your two-gene deletion,

8917
07:04:30,569 --> 07:04:33,005
the triplicated one,
in your slides. I saw it.

8918
07:04:33,005 --> 07:04:34,373
John Pierciey: So, there was --

8919
07:04:34,373 --> 07:04:37,843
so, the only triplicate
was in the exa-cel study.

8920
07:04:37,843 --> 07:04:40,112
And I actually thought
that the triplicate

8921
07:04:40,112 --> 07:04:42,248
was excluded
from that supplemental analysis.

8922
07:04:42,248 --> 07:04:45,818
But I mean, to your point,
right, the reason why, right,

8923
07:04:45,818 --> 07:04:47,019
we --
you know, the --

8924
07:04:47,019 --> 07:04:50,756
it’s thought that there is
a higher RBC count

8925
07:04:50,756 --> 07:04:52,858
in patients
with alpha-globin gene deletions

8926
07:04:52,858 --> 07:04:54,894
is because the MCHC
is lower, right?

8927
07:04:54,894 --> 07:04:57,229
And when your concentration
of hemoglobin in the cell

8928
07:04:57,229 --> 07:04:59,298
is lower, you’re going
to have less sickling.

8929
07:04:59,298 --> 07:05:02,001
So, to your, comment, right,
with triplication,

8930
07:05:02,001 --> 07:05:04,003
if there’s more alpha globin,
does that mean that

8931
07:05:04,003 --> 07:05:06,438
you’ll actually
get more intracellular ßS?

8932
07:05:06,438 --> 07:05:09,175
I mean, maybe.
Unless, of course, right,

8933
07:05:09,175 --> 07:05:11,277
you’re just not having enough
translation of beta globin

8934
07:05:11,277 --> 07:05:12,978
to actually
compensate for that.

8935
07:05:12,978 --> 07:05:16,148
But we didn’t see
any triplication in lovo-cel.

8936
07:05:16,148 --> 07:05:17,950
Yeah.

8937
07:05:17,950 --> 07:05:19,251
Swee Lay Thein: Your numbers
are so small.

8938
07:05:19,251 --> 07:05:20,486
You’re only one.
John Pierciey: Yes.

8939
07:05:20,486 --> 07:05:21,687
Swee Lay Thein: And it was
actually higher

8940
07:05:21,687 --> 07:05:23,756
than the two genomes, you know.

8941
07:05:23,756 --> 07:05:26,458
It was equivalent
to the one single gene.

8942
07:05:27,726 --> 07:05:31,163
I think you cannot base it
on one case of [unintelligible].

8943
07:05:31,163 --> 07:05:32,765
John Pierciey: Yeah.

8944
07:05:33,532 --> 07:05:34,934
Male Speaker: Great talk, John.

8945
07:05:34,934 --> 07:05:36,802
I -- as, you know,
somebody who thinks

8946
07:05:36,802 --> 07:05:38,537
about global biology a lot,
I agree with you.

8947
07:05:38,537 --> 07:05:40,472
This looks like these were
the voices from an induced

8948
07:05:40,472 --> 07:05:42,308
thalassemia
versus anything MDS related.

8949
07:05:42,308 --> 07:05:43,776
So, I just want to say
that as well.

8950
07:05:43,776 --> 07:05:45,444
I think that -- I just wonder

8951
07:05:45,444 --> 07:05:48,013
if you looked at zeta-globin
expression at all,

8952
07:05:48,981 --> 07:05:50,616
just in terms
of compensatory aspects.

8953
07:05:50,616 --> 07:05:51,817
I mean, it’d be very small.

8954
07:05:51,817 --> 07:05:53,552
We don’t really have
great inducers

8955
07:05:54,086 --> 07:05:55,921
or any, you know, knowledge
of how to switch this.

8956
07:05:55,921 --> 07:05:57,756
But I’m just curious
if you’ve seen increases in that

8957
07:05:57,756 --> 07:05:59,124
or compensation in anyway.

8958
07:05:59,124 --> 07:06:01,527
John Pierciey: Yeah.
You know, by HPLC,

8959
07:06:02,294 --> 07:06:04,530
whether or not
it’s delta-globin, gamma-globin,

8960
07:06:04,530 --> 07:06:06,065
et cetera, no.

8961
07:06:06,065 --> 07:06:08,934
The -- all the globin chains
remained pretty consistent.

8962
07:06:08,934 --> 07:06:10,736
Now, the globin --
looking at the globin chain

8963
07:06:10,736 --> 07:06:12,071
in the adult participants
is hard.

8964
07:06:12,071 --> 07:06:13,272
They were on transfusions.

8965
07:06:13,272 --> 07:06:14,773
But if you look
at the pediatric participant,

8966
07:06:14,773 --> 07:06:19,511
yeah, the, you know, T87Q,
ßS, all very consistent.

8967
07:06:19,511 --> 07:06:21,347
And all the other globin chains
were quite low.

8968
07:06:21,347 --> 07:06:22,882
Male Speaker: I’d just be
interested in looking at HbZ

8969
07:06:22,882 --> 07:06:24,183
in RNA-seq as well.

8970
07:06:24,183 --> 07:06:25,618
Because even with
3.7 kb deletion,

8971
07:06:25,618 --> 07:06:26,852
you still have that region

8972
07:06:26,852 --> 07:06:28,254
that’s present
in the alpha-globin locus.

8973
07:06:28,254 --> 07:06:29,488
John Pierciey: Did my team
hear that

8974
07:06:29,488 --> 07:06:31,490
about looking
at the RNA-seq data?

8975
07:06:31,490 --> 07:06:32,858
Male Speaker: That’s true.
Yeah, that’s true.

8976
07:06:32,858 --> 07:06:34,059
John Pierciey: Yeah.

8977
07:06:34,059 --> 07:06:35,361
No, good suggestion.
We can look.

8978
07:06:35,361 --> 07:06:37,463
Because we did --
at the same time

8979
07:06:37,463 --> 07:06:39,231
that we did the enrichment
of the erythroblast

8980
07:06:39,231 --> 07:06:40,499
for looking at protein ratios

8981
07:06:40,499 --> 07:06:41,867
and other things,
we have done RNA-seq.

8982
07:06:41,867 --> 07:06:43,502
And we’re still
analyzing the data,

8983
07:06:43,502 --> 07:06:46,338
trying to see if we can get
anything else out of that.

8984
07:06:46,338 --> 07:06:47,840
Male Speaker: Yeah.

8985
07:06:47,840 --> 07:06:49,441
John Pierciey: Okay? Thank you.

8986
07:06:50,276 --> 07:06:52,511
Swee Lay Thein: I think that was
excellent though.

8987
07:06:52,511 --> 07:06:53,779
Excellent.

8988
07:06:53,779 --> 07:06:56,148
[applause]

8989
07:06:56,148 --> 07:06:57,883
Jennifer
is dying to get here.

8990
07:06:59,018 --> 07:07:00,953
I just want to read
a message here.

8991
07:07:00,953 --> 07:07:04,423
It says, "From Lisa Wisdom
to the meeting group,

8992
07:07:04,423 --> 07:07:06,125
kudos to the organization --

8993
07:07:06,125 --> 07:07:09,261
organizing team
and all the presenters.

8994
07:07:09,261 --> 07:07:13,032
This has been an excellent
seminar, has instilled hope,

8995
07:07:13,032 --> 07:07:15,734
even as it’s raised more
questions for future efforts.

8996
07:07:16,335 --> 07:07:18,971
Keep up the great work.
Thank you to all of you."

8997
07:07:20,472 --> 07:07:29,748
[applause]

8998
07:07:29,748 --> 07:07:31,183
Jennifer Knight-Madden: Okay.

8999
07:07:31,183 --> 07:07:33,652
Thank you for making it
to the final session.

9000
07:07:33,652 --> 07:07:38,791
And we have some interesting
discussions ahead.

9001
07:07:39,825 --> 07:07:44,096
So, I’m going to go ahead and
invite Dr. Mark Walters to come.

9002
07:07:44,697 --> 07:07:48,434
He’s going -- Dr. Walters
is the Jordan Family director

9003
07:07:48,434 --> 07:07:53,072
of the Blood and Marrow
Transplantation Program at UCSF

9004
07:07:53,072 --> 07:07:56,809
Benioff Children’s Hospital
and professor and chief,

9005
07:07:57,810 --> 07:08:00,579
Hematology Division
in the Department of Pediatrics

9006
07:08:01,080 --> 07:08:02,848
at the University
of California,

9007
07:08:02,848 --> 07:08:05,084
San Francisco School
of Medicine.

9008
07:08:05,784 --> 07:08:08,654
And he’ll be talking
about curative therapies.

9009
07:08:08,654 --> 07:08:10,255
Dr. Walters.

9010
07:08:11,056 --> 07:08:17,096
[applause]

9011
07:08:17,096 --> 07:08:18,697
Mark Walters: Thanks, Jennifer.

9012
07:08:19,465 --> 07:08:23,635
And thank you, Monika, John,
and Swee Lay

9013
07:08:23,635 --> 07:08:27,539
for giving me this topic
at the end of the meeting.

9014
07:08:27,539 --> 07:08:30,576
[laughter]

9015
07:08:30,576 --> 07:08:34,179
So, I surmise that I would be
in this situation.

9016
07:08:35,314 --> 07:08:39,718
And so, I thought, why don’t I
show something that’s so old,

9017
07:08:39,718 --> 07:08:41,987
I’m hoping most of you
will have forgotten about it,

9018
07:08:41,987 --> 07:08:44,523
and it’ll look new again?
So, I’ll start there.

9019
07:08:44,523 --> 07:08:45,891
[laughter]

9020
07:08:45,891 --> 07:08:48,160
And then I’ll share some data

9021
07:08:48,160 --> 07:08:50,529
that have been submitted
for publication

9022
07:08:50,529 --> 07:08:51,930
but not published yet.

9023
07:08:51,930 --> 07:08:55,467
And I’m going to focus
on bone marrow transplantation.

9024
07:08:57,936 --> 07:09:00,039
The disclosures I have,
the conflicts,

9025
07:09:00,039 --> 07:09:02,541
have nothing to do with
what I’m about to present.

9026
07:09:03,142 --> 07:09:08,847
So, we started this over
30 years ago, 1991.

9027
07:09:10,082 --> 07:09:12,051
Keith Sullivan and I
led an international

9028
07:09:12,051 --> 07:09:14,420
multi-center
collaborative investigation,

9029
07:09:14,420 --> 07:09:18,023
27 transplant centers,
and we completed enrollment

9030
07:09:18,624 --> 07:09:20,559
almost 25 years ago.

9031
07:09:20,559 --> 07:09:23,662
So, 59 children with severe
sickle cell disease

9032
07:09:23,662 --> 07:09:26,865
received HLA-identical bone
marrow transplants.

9033
07:09:27,833 --> 07:09:29,234
And these were the results.

9034
07:09:29,234 --> 07:09:31,236
They -- I’m sure
they look very similar.

9035
07:09:32,638 --> 07:09:34,540
Overall
survival, event-free survival,

9036
07:09:34,540 --> 07:09:37,376
has shown 3 percent
rejected their grafts.

9037
07:09:37,376 --> 07:09:42,047
So, you know, 20-some years ago,
this looked really pretty good,

9038
07:09:42,848 --> 07:09:44,750
so 85 percent
of event-free survival.

9039
07:09:44,750 --> 07:09:46,418
But there are two problems.

9040
07:09:46,418 --> 07:09:49,021
Patients were dying
of the conditioning regimen

9041
07:09:49,021 --> 07:09:50,456
or graft versus host disease

9042
07:09:50,456 --> 07:09:52,891
or something related
to transplant too often.

9043
07:09:53,425 --> 07:09:56,528
And too often, the sickle cell
disease was coming back.

9044
07:09:57,096 --> 07:10:01,033
So, all of the current
iterations really come back

9045
07:10:01,033 --> 07:10:04,903
to what do we do to mitigate
those two risk factors?

9046
07:10:05,437 --> 07:10:08,474
And so, today, I think

9047
07:10:08,474 --> 07:10:11,343
HLA-identical sibling bone
marrow transplantation

9048
07:10:11,343 --> 07:10:12,945
is still a standard

9049
07:10:13,445 --> 07:10:15,347
because it has very good
event-free survival,

9050
07:10:15,347 --> 07:10:18,584
particularly in children
less than 12 years of age.

9051
07:10:18,584 --> 07:10:22,654
And we understand pretty well
the late effects

9052
07:10:22,654 --> 07:10:26,291
of myeloablation with busulfan
in that population.

9053
07:10:26,291 --> 07:10:30,729
And the risk of graft rejection
in graft versus host disease

9054
07:10:30,729 --> 07:10:33,432
have largely been manageable.
So, the question though is,

9055
07:10:33,432 --> 07:10:36,435
because very few
of our patients have --

9056
07:10:36,435 --> 07:10:38,604
are young and have
a sibling donor,

9057
07:10:38,604 --> 07:10:40,873
what about HLA-haplo-identical
transplantation?

9058
07:10:40,873 --> 07:10:42,774
What about unrelated
donor transplantation

9059
07:10:42,774 --> 07:10:45,677
from well-matched donor, donor?

9060
07:10:45,677 --> 07:10:48,780
And then you already know
the answer to the last one.

9061
07:10:48,780 --> 07:10:51,650
So, let’s -- I thought
I would share

9062
07:10:51,650 --> 07:10:55,287
then some fairly contemporary
bone marrow transplant

9063
07:10:55,287 --> 07:10:57,990
trials like this first one,

9064
07:10:57,990 --> 07:11:00,692
all multi-center,
NIH-supported --

9065
07:11:00,692 --> 07:11:02,327
and let me tell you,

9066
07:11:02,327 --> 07:11:05,030
these are really hard
to do properly, and --

9067
07:11:06,064 --> 07:11:07,833
but we learn a lot
from them and --

9068
07:11:08,467 --> 07:11:11,470
but often, the information
is not spectacular

9069
07:11:11,470 --> 07:11:14,940
because it takes so darn
long to finish the studies.

9070
07:11:14,940 --> 07:11:16,175
But anyway, let’s go through it

9071
07:11:16,175 --> 07:11:18,177
because I think
they’re instructive.

9072
07:11:20,479 --> 07:11:25,050
So, the first one is --
goes under the acronym STRIDE2.

9073
07:11:25,050 --> 07:11:28,720
Lakshmanan Krishnamurthi at Yale
led the study with me.

9074
07:11:28,720 --> 07:11:30,189
And Mary Eapen, Donna Neuberg,

9075
07:11:30,189 --> 07:11:32,758
and Keith Sullivan
were key leaders.

9076
07:11:32,758 --> 07:11:34,793
This has been submitted
to Blood Advances,

9077
07:11:34,793 --> 07:11:36,395
where it’s under review.

9078
07:11:37,095 --> 07:11:39,665
So, this was a bone
marrow transplant trial

9079
07:11:39,665 --> 07:11:43,468
that compared transplantation
to best available therapy.

9080
07:11:43,468 --> 07:11:44,770
And to be eligible,

9081
07:11:44,770 --> 07:11:47,306
patients had to be
adolescents or young adults

9082
07:11:47,306 --> 07:11:50,008
with severe sickle cell disease,
as defined here.

9083
07:11:50,008 --> 07:11:53,712
And this is a fairly
familiar list of indications

9084
07:11:53,712 --> 07:11:55,814
of severe disease
that we followed.

9085
07:11:56,415 --> 07:12:02,120
And the idea though was to
perform a biologic assignment,

9086
07:12:02,721 --> 07:12:05,324
which was our best attempt

9087
07:12:05,324 --> 07:12:07,993
at doing a randomization
comparison.

9088
07:12:07,993 --> 07:12:11,330
So, if a person had
a well-matched donor,

9089
07:12:11,330 --> 07:12:13,365
either a sibling
or unrelated donor,

9090
07:12:13,365 --> 07:12:15,601
they were assigned
to a donor arm.

9091
07:12:15,601 --> 07:12:17,903
And for the majority
who lacked a donor,

9092
07:12:17,903 --> 07:12:20,205
they were assigned
to the new donor arm.

9093
07:12:20,205 --> 07:12:22,040
And at the time
of biological assignment,

9094
07:12:22,040 --> 07:12:25,944
that’s when we began
to track for endpoints.

9095
07:12:25,944 --> 07:12:27,279
And the primary endpoint

9096
07:12:27,279 --> 07:12:29,514
was just to compare
overall survival.

9097
07:12:29,514 --> 07:12:32,517
And what we reasoned was that
two years after a transplant,

9098
07:12:32,517 --> 07:12:35,053
survival would be worse
after a transplant

9099
07:12:35,053 --> 07:12:36,388
than best available therapy.

9100
07:12:36,388 --> 07:12:38,390
But because most of
the mortality

9101
07:12:38,390 --> 07:12:41,193
after an allotransplant occurs
in those first two years,

9102
07:12:41,193 --> 07:12:43,962
their survival should be
static at that point,

9103
07:12:43,962 --> 07:12:46,932
while it would continue
to accrue on the no donor arm.

9104
07:12:46,932 --> 07:12:49,067
And the idea was
that over 10 years,

9105
07:12:49,067 --> 07:12:51,270
we would begin to show a benefit

9106
07:12:51,270 --> 07:12:53,572
in terms of survival
after transplant.

9107
07:12:53,572 --> 07:12:56,642
But for us to be able
to make that comparison,

9108
07:12:56,642 --> 07:12:57,843
we actually need not 40,

9109
07:12:57,843 --> 07:13:01,313
but 60 in the donor arm
and 140 in the new donor.

9110
07:13:03,482 --> 07:13:07,552
So, it was a myeloablative
regimen that was in 2015.

9111
07:13:08,220 --> 07:13:11,857
The standard approach, busulfan,
fludarabine, rapid-ATG,

9112
07:13:11,857 --> 07:13:13,091
and then tacrolimus,

9113
07:13:13,091 --> 07:13:15,794
methotrexate for
GVHD prophylaxis.

9114
07:13:15,794 --> 07:13:18,530
And the consort diagram
indicates problems.

9115
07:13:18,530 --> 07:13:19,798
So, we didn’t enroll 200.

9116
07:13:19,798 --> 07:13:24,870
We only enrolled 142 largely
because of the pandemic.

9117
07:13:24,870 --> 07:13:27,339
And of that group,
138 were eligible;

9118
07:13:27,339 --> 07:13:30,309
28 were assigned
to the donor arm

9119
07:13:30,309 --> 07:13:32,411
and 95 to the no donor arm.

9120
07:13:32,411 --> 07:13:35,714
And because we didn’t meet
the accrual goals, this --

9121
07:13:36,581 --> 07:13:39,685
the study was suspended
before it was completed

9122
07:13:39,685 --> 07:13:42,154
because of
the accrual challenges.

9123
07:13:42,154 --> 07:13:45,357
We lack power
to compare overall survival.

9124
07:13:45,357 --> 07:13:50,429
So, Donna Neuberg insisted
that I emphasize

9125
07:13:50,429 --> 07:13:53,231
that this is
a descriptive comparison.

9126
07:13:53,231 --> 07:13:56,301
This is not --
didn’t have statistical power.

9127
07:13:56,301 --> 07:13:58,303
So, please keep that in mind.

9128
07:13:58,303 --> 07:13:59,838
But I think the results
are interesting.

9129
07:13:59,838 --> 07:14:01,506
So, the two-year
overall survival

9130
07:14:02,040 --> 07:14:04,609
indicated by the red curve
on the donor arm

9131
07:14:04,609 --> 07:14:08,647
was 89 percent and 93 percent
on the no donor arm.

9132
07:14:08,647 --> 07:14:13,251
So, I thought
the mortality in adults --

9133
07:14:13,251 --> 07:14:15,520
in adolescents getting
best available therapy

9134
07:14:15,520 --> 07:14:19,391
was quite eye opening,
over a two-year period.

9135
07:14:19,391 --> 07:14:23,228
And you can imagine
that if that overall survival

9136
07:14:23,228 --> 07:14:27,899
after a transplant is stable,
that would -- it would,

9137
07:14:27,899 --> 07:14:30,836
over a much shorter period
of time than six to eight years,

9138
07:14:30,836 --> 07:14:32,471
probably in the next
several years,

9139
07:14:32,471 --> 07:14:35,907
show benefit in terms
of survival of transplant.

9140
07:14:35,907 --> 07:14:37,609
But, of course,
I’m not allowed to say that

9141
07:14:37,609 --> 07:14:40,112
because Donna would
slap my wrist.

9142
07:14:40,112 --> 07:14:41,580
[laughter]

9143
07:14:41,580 --> 07:14:46,118
But it’s more complicated
because the current way

9144
07:14:46,118 --> 07:14:49,121
that we look at
event-free survival is --

9145
07:14:49,121 --> 07:14:50,822
particularly in sickle cell
disease, is this is --

9146
07:14:50,822 --> 07:14:52,724
to do graft versus host
disease-free

9147
07:14:52,724 --> 07:14:54,059
event-free survival.

9148
07:14:54,059 --> 07:14:56,061
And that declined
all the way to 38 percent.

9149
07:14:56,061 --> 07:14:57,462
So, that’s sobering.

9150
07:14:57,462 --> 07:15:00,932
So, graft versus host disease
was a significant problem,

9151
07:15:00,932 --> 07:15:03,268
both acute and chronic GVHD,

9152
07:15:03,869 --> 07:15:06,772
and particularly after unrelated
donor transplantation.

9153
07:15:06,772 --> 07:15:09,508
So, that’s the green curve
where acute

9154
07:15:09,508 --> 07:15:14,880
GVHD approached 50 percent
in both acute and chronic GVHD.

9155
07:15:15,480 --> 07:15:18,550
So, why didn’t that cause
significant

9156
07:15:19,918 --> 07:15:21,286
transplant-related mortality?

9157
07:15:21,286 --> 07:15:23,121
I think it’s because
our treatment of graft

9158
07:15:23,121 --> 07:15:25,290
versus host disease
has gotten quite a bit better.

9159
07:15:25,290 --> 07:15:28,693
We have a broader
armamentarium of drugs.

9160
07:15:28,693 --> 07:15:30,962
And it comes.
It’s treated.

9161
07:15:30,962 --> 07:15:33,031
And generally,
it goes away these days.

9162
07:15:33,031 --> 07:15:36,268
So, that’s my interpretation
of why

9163
07:15:36,268 --> 07:15:39,171
this didn’t impact
overall mortality.

9164
07:15:41,173 --> 07:15:43,408
So, what about the secondary
endpoint comparison?

9165
07:15:43,408 --> 07:15:46,211
So, this is --
you’ve been accustomed

9166
07:15:46,211 --> 07:15:48,246
to looking at these now.

9167
07:15:48,246 --> 07:15:50,849
John showed
Alexis Leonard’s data

9168
07:15:50,849 --> 07:15:53,518
after his allogeneic
transplantation cohort.

9169
07:15:53,518 --> 07:15:55,287
You’ve seen all of the swim
lane plots --

9170
07:15:55,287 --> 07:15:57,689
swimmer plots
after gene therapy.

9171
07:15:57,689 --> 07:16:00,125
Well, so, this is what
happened in this study.

9172
07:16:00,125 --> 07:16:01,560
And so, the point of comparison

9173
07:16:01,560 --> 07:16:04,062
is at the time
of biological randomization.

9174
07:16:04,729 --> 07:16:07,766
And the donor arm
is on the top panel

9175
07:16:07,766 --> 07:16:11,670
and the no donor best available
care in the bottom panel.

9176
07:16:11,670 --> 07:16:15,440
And in some cases, there was a
long delay between randomization

9177
07:16:15,440 --> 07:16:16,775
and actually getting
the transplant.

9178
07:16:16,775 --> 07:16:19,177
So, it’s the period of time
in dark blue

9179
07:16:19,177 --> 07:16:20,979
that’s the pain rate --

9180
07:16:20,979 --> 07:16:23,682
pain event rate
after transplantation.

9181
07:16:23,682 --> 07:16:26,051
And there was
a significant improvement

9182
07:16:26,051 --> 07:16:27,719
in vaso-occlusive pain.

9183
07:16:27,719 --> 07:16:31,490
These are contemporary
prospective populations

9184
07:16:31,490 --> 07:16:33,758
that we studied
in the study.

9185
07:16:33,758 --> 07:16:36,127
So, that’s, I think,
an important finding.

9186
07:16:36,127 --> 07:16:38,296
It’s not surprising,
but this is the first time

9187
07:16:38,296 --> 07:16:41,066
that someone showed this
in an intent-to-treat way.

9188
07:16:42,367 --> 07:16:45,871
The other important finding
was that these domains,

9189
07:16:46,605 --> 07:16:50,075
in particular fatigue and
participation in social roles,

9190
07:16:50,809 --> 07:16:53,812
got better after a transplant.
So, fatigue went down.

9191
07:16:54,713 --> 07:16:57,115
Participation in social roles
got better.

9192
07:16:57,716 --> 07:17:00,051
We’re comparing red
after a transplant,

9193
07:17:00,051 --> 07:17:03,255
to blue, no donor
standard of care.

9194
07:17:03,255 --> 07:17:06,491
And then pain interference
also got significantly better

9195
07:17:06,491 --> 07:17:09,728
following
the PROMIS 57 PRO domains.

9196
07:17:10,262 --> 07:17:13,899
Although in the others, there
was no significant difference.

9197
07:17:13,899 --> 07:17:15,400
And again,
I think some of these,

9198
07:17:15,400 --> 07:17:18,703
like physical function
and depression, anxiety,

9199
07:17:18,703 --> 07:17:22,374
were probably affected by
the high rate of chronic GVHD.

9200
07:17:22,374 --> 07:17:25,076
So, that’s an important
difference between gene therapy,

9201
07:17:25,076 --> 07:17:28,179
where those things got better,
and this type of transplant.

9202
07:17:29,948 --> 07:17:33,685
So, that’s a summary
of the of the STRIDE2 data.

9203
07:17:33,685 --> 07:17:38,490
I’m going to go now
to the BMT CTN haploidentical

9204
07:17:38,490 --> 07:17:40,659
bone marrow transplant study.

9205
07:17:40,659 --> 07:17:42,761
This has been submitted
and is under review

9206
07:17:42,761 --> 07:17:45,430
in the New England Journal
of Medicine Evidence.

9207
07:17:45,430 --> 07:17:47,032
And the leader of this study --

9208
07:17:47,766 --> 07:17:49,634
well, the co-chairs
are shown here.

9209
07:17:49,634 --> 07:17:52,771
Adetola Kassim
led the adult study,

9210
07:17:53,471 --> 07:17:56,641
and this was sponsored by NHLBI.

9211
07:17:58,376 --> 07:18:01,446
So, there are two strata for
this study, children and adults.

9212
07:18:02,314 --> 07:18:07,552
The adults followed
the same eligibility criteria

9213
07:18:07,552 --> 07:18:09,554
that we used
in the previous study.

9214
07:18:10,488 --> 07:18:13,758
And in children,
it initially focused on stroke,

9215
07:18:13,758 --> 07:18:15,660
either silent or avert,

9216
07:18:15,660 --> 07:18:19,297
and then was later expanded
once safety was observed

9217
07:18:19,297 --> 07:18:21,833
in the early patients
to include recurrent pain,

9218
07:18:21,833 --> 07:18:24,603
life-threatening acute chest,
and priapism.

9219
07:18:24,603 --> 07:18:27,305
So, the data are mature
and have been analyzed

9220
07:18:27,305 --> 07:18:28,607
in the adult stratum.

9221
07:18:28,607 --> 07:18:31,309
The pediatric stratum
will be available

9222
07:18:31,309 --> 07:18:32,844
at the end
of this calendar year.

9223
07:18:32,844 --> 07:18:35,880
So, we’ll be able to see more
about that at next meeting.

9224
07:18:37,082 --> 07:18:38,817
So, here’s the regimen
that was utilized.

9225
07:18:38,817 --> 07:18:40,418
It follows the Johns Hopkins

9226
07:18:41,252 --> 07:18:43,388
post-transplantation
cyclophosphamide,

9227
07:18:43,922 --> 07:18:45,423
and it aimed to improve

9228
07:18:45,423 --> 07:18:48,026
on the initial
Johns Hopkins experience,

9229
07:18:48,026 --> 07:18:52,397
which had a 43 percent
incidence of graft rejection

9230
07:18:52,397 --> 07:18:54,399
with return
of sickle cell disease

9231
07:18:54,399 --> 07:18:56,201
by augmenting with thiotepa

9232
07:18:56,768 --> 07:18:59,270
that’s administered
on day minus seven.

9233
07:18:59,270 --> 07:19:03,174
Otherwise, it’s identical
to the Javier Bollon

9234
07:19:03,174 --> 07:19:04,509
[phonetic sp]

9235
07:19:04,509 --> 07:19:06,378
study that was published
previously.

9236
07:19:06,911 --> 07:19:08,713
And then
post-transplantation Cy

9237
07:19:08,713 --> 07:19:11,950
to accomplish in vivo
T-cell depletion

9238
07:19:11,950 --> 07:19:14,152
for post-grafting
immunosuppression

9239
07:19:14,152 --> 07:19:16,021
and then sirolimus and MMF

9240
07:19:16,021 --> 07:19:18,723
to augment engraftment
of the donor cells.

9241
07:19:20,492 --> 07:19:22,894
So, the event-free survival
in the adult cohort

9242
07:19:22,894 --> 07:19:26,398
is shown here, quite good,
88 percent at two years.

9243
07:19:27,165 --> 07:19:29,401
And while there were
graft failures,

9244
07:19:30,635 --> 07:19:34,172
ended up at a total
of 7.1 percent,

9245
07:19:34,172 --> 07:19:36,841
so a decline,
after adding thiotepa

9246
07:19:36,841 --> 07:19:40,845
to the conditioning regimen,
from 43 percent to 7 percent.

9247
07:19:40,845 --> 07:19:44,315
So, I think that was
this principal improvement

9248
07:19:44,315 --> 07:19:46,851
that was shown
in this study.

9249
07:19:48,620 --> 07:19:52,257
Graft versus host disease
though was still a problem,

9250
07:19:52,257 --> 07:19:53,892
both acute and chronic.

9251
07:19:53,892 --> 07:19:58,296
And so, Grade 3,
5 percent acute GVHD

9252
07:19:58,296 --> 07:20:00,131
and quite severe, moderate,

9253
07:20:00,131 --> 07:20:03,001
or severe chronic GVHD
is 7 percent.

9254
07:20:03,001 --> 07:20:08,640
So, the overall cumulative
incidence rate was 22.4 percent.

9255
07:20:08,640 --> 07:20:12,677
So, still HLA-haploidentical
half-matched donors,

9256
07:20:13,478 --> 07:20:14,779
very good event for survival,

9257
07:20:14,779 --> 07:20:18,049
but still problems with
graft versus host disease.

9258
07:20:18,049 --> 07:20:19,250
Again,

9259
07:20:19,250 --> 07:20:22,721
after post-transplantation
cyclophosphamide, it happens.

9260
07:20:22,721 --> 07:20:25,957
You can treat it,
and it tends to get better.

9261
07:20:29,160 --> 07:20:32,363
So, here we are.
Should we pursue genome therapy

9262
07:20:32,363 --> 07:20:34,532
or an HLA-haploidentical
transplant?

9263
07:20:35,366 --> 07:20:37,635
Both will have
a readily available donor,

9264
07:20:38,937 --> 07:20:41,172
but in one instance, no --
graft versus host disease,

9265
07:20:41,172 --> 07:20:43,541
no graft rejection
while, on the other hand,

9266
07:20:43,541 --> 07:20:46,978
still risks of GVHD
and graft rejection.

9267
07:20:46,978 --> 07:20:49,781
One relies on ex-vivo
manufacturing,

9268
07:20:49,781 --> 07:20:55,386
high-dose busulfan with perhaps
a risk of myeloid leukemia.

9269
07:20:56,187 --> 07:20:59,124
Reduced intensity
conditioning regimen

9270
07:20:59,124 --> 07:21:03,027
probably causes fewer toxicities
in the short term.

9271
07:21:03,027 --> 07:21:05,063
It’s less expensive.

9272
07:21:05,063 --> 07:21:09,134
But both will still be
inaccessible to some patients,

9273
07:21:09,868 --> 07:21:11,469
maybe to most patients.

9274
07:21:13,238 --> 07:21:16,808
So -- and here are what
we’ve been listening to

9275
07:21:16,808 --> 07:21:19,444
for the last two days.
Here’s what they cost.

9276
07:21:19,444 --> 07:21:21,346
So, let’s face it;

9277
07:21:21,346 --> 07:21:24,849
this is not going to have the
impact that we dreamed about.

9278
07:21:24,849 --> 07:21:26,818
So, this is a steppingstone,

9279
07:21:26,818 --> 07:21:29,220
and we’re going
to make improvements.

9280
07:21:29,954 --> 07:21:31,256
But I thought it would finish

9281
07:21:31,256 --> 07:21:34,225
with these
provocative conclusions

9282
07:21:34,225 --> 07:21:37,328
that Jane and Marilyn
will shoot me down for.

9283
07:21:37,328 --> 07:21:41,366
[laughter]

9284
07:21:41,366 --> 07:21:43,434
So, risks associated
with chemotherapy

9285
07:21:43,434 --> 07:21:45,036
and radiation conditioning,

9286
07:21:45,670 --> 07:21:47,705
graft versus host disease
graft rejection

9287
07:21:47,705 --> 07:21:51,810
have tempered enthusiasm
for allogeneic transplantation

9288
07:21:51,810 --> 07:21:53,778
in this era of gene therapy.

9289
07:21:54,779 --> 07:21:57,649
But I would argue that the data,
at least in adults,

9290
07:21:57,649 --> 07:22:00,685
argues that HLA-haploidentical
transplantation

9291
07:22:00,685 --> 07:22:01,953
is an alternative,

9292
07:22:01,953 --> 07:22:04,355
particularly in young adults
with sickle cell disease,

9293
07:22:04,355 --> 07:22:07,258
and should have broad
accessibility, theoretically.

9294
07:22:07,959 --> 07:22:09,928
So -- but still
my prediction --

9295
07:22:10,795 --> 07:22:12,130
and this is just
my prediction --

9296
07:22:12,130 --> 07:22:13,665
it’s very likely
that improvements

9297
07:22:13,665 --> 07:22:16,835
in the safety/efficacy of
genomic modification therapies,

9298
07:22:17,535 --> 07:22:20,405
autologous cells will supplant
allogeneic transplant.

9299
07:22:20,972 --> 07:22:22,841
But in the immediate future,

9300
07:22:22,841 --> 07:22:25,944
I think disease-modifying
therapy options

9301
07:22:25,944 --> 07:22:29,280
will continue to dominate care
and sickle cell disease.

9302
07:22:29,280 --> 07:22:33,117
But curative therapy should
eventually be transformative.

9303
07:22:33,785 --> 07:22:37,555
So, I leave you with, I think,
what John shared,

9304
07:22:37,555 --> 07:22:38,756
what others have shared,

9305
07:22:38,756 --> 07:22:41,726
is that that where we are now
is a steppingstone,

9306
07:22:41,726 --> 07:22:43,895
and we will continue
to make improvements.

9307
07:22:43,895 --> 07:22:47,765
So, we should invest in them
and encourage our families

9308
07:22:47,765 --> 07:22:50,535
to participate
in the clinical trials

9309
07:22:50,535 --> 07:22:52,403
as we get better
and better At this.

9310
07:22:52,403 --> 07:22:54,906
So, thanks very much
for your attention.

9311
07:22:54,906 --> 07:22:57,275
[applause]

9312
07:22:57,275 --> 07:23:00,144
Jennifer Knight-Madden:
[unintelligible]

9313
07:23:00,144 --> 07:23:01,746
Mark Walters: Okay.

9314
07:23:02,313 --> 07:23:03,514
Jennifer Knight-Madden: Okay.

9315
07:23:03,514 --> 07:23:05,950
Thank you very much,
Dr. Walters.

9316
07:23:05,950 --> 07:23:08,853
We’re going to have questions
at the end of the session.

9317
07:23:09,387 --> 07:23:12,724
So, we’re now going to move
to our illustrious tag team.

9318
07:23:14,292 --> 07:23:17,161
First, we’ll have
Dr. Marilyn Telen,

9319
07:23:17,161 --> 07:23:19,998
who’s a Wellcome Clinical
Distinguished Professor

9320
07:23:19,998 --> 07:23:23,268
of Medicine at Duke University
School of Medicine.

9321
07:23:23,835 --> 07:23:25,603
And after her will be
Dr. Jane

9322
07:23:25,603 --> 07:23:29,807
Little, who’s a professor
of Medicine at UNC Chapel Hill.

9323
07:23:30,341 --> 07:23:32,210
I don’t know how these two
get together.

9324
07:23:32,210 --> 07:23:35,280
You know, they --
with the two different blues.

9325
07:23:35,280 --> 07:23:40,385
But Dr. Telen is definitely
from the better blue.

9326
07:23:40,385 --> 07:23:45,690
Oh, okay.
I’m a Duke alum, okay?

9327
07:23:45,690 --> 07:23:48,760
Okay. All right, right.
And so, Dr. Jane Little

9328
07:23:48,760 --> 07:23:51,262
is a professor of Medicine
at UNC Chapel Hill

9329
07:23:51,262 --> 07:23:54,866
and directs the adult sickle
cell disease program there.

9330
07:23:54,866 --> 07:23:56,200
And they’re both
going to talk

9331
07:23:56,200 --> 07:23:59,637
about more disease-modifying
small molecules.

9332
07:24:00,171 --> 07:24:05,176
[applause]

9333
07:24:05,176 --> 07:24:06,778
Marilyn Telen: Thank you.

9334
07:24:08,980 --> 07:24:13,084
I’m trying to make sure I know
how to advance the slides.

9335
07:24:13,084 --> 07:24:14,886
Jennifer Knight-Madden: It’s the
other one.

9336
07:24:14,886 --> 07:24:16,154
Marilyn Telen: It’s this one?

9337
07:24:16,154 --> 07:24:17,388
Jennifer Knight-Madden: Yeah.

9338
07:24:17,388 --> 07:24:21,626
Marilyn Telen: Okay.
Oh, if I were tall,

9339
07:24:21,626 --> 07:24:24,228
I could actually see
what the slide says.

9340
07:24:24,228 --> 07:24:25,930
[laughter]

9341
07:24:25,930 --> 07:24:30,868
Well, as we heard
Mark Walter’s comment,

9342
07:24:30,868 --> 07:24:34,339
we need to still think
about modifying --

9343
07:24:35,139 --> 07:24:36,808
disease-modifying therapies.

9344
07:24:36,808 --> 07:24:39,077
And the reason is
that curative therapies,

9345
07:24:39,077 --> 07:24:42,080
such as we’ve heard a lot
about yesterday and today,

9346
07:24:42,080 --> 07:24:44,816
will not reach
most of the patients

9347
07:24:44,816 --> 07:24:48,453
with sickle cell disease in
the world who are alive today.

9348
07:24:48,453 --> 07:24:51,189
And so, it behooves us
to provide

9349
07:24:51,189 --> 07:24:53,424
those patients with alternative

9350
07:24:53,424 --> 07:24:57,228
therapies to improve both
survival and quality of life.

9351
07:24:59,998 --> 07:25:05,169
So, there’s no question
that hemoglobin S

9352
07:25:05,169 --> 07:25:06,671
and the sickle red cell --

9353
07:25:06,671 --> 07:25:09,207
sickle, not sick old,
necessarily --

9354
07:25:09,207 --> 07:25:13,611
is the root of the problem.
Hemoglobin S is not stable.

9355
07:25:13,611 --> 07:25:16,681
It polymerizes
with deoxygenation.

9356
07:25:16,681 --> 07:25:19,450
It causes sickling.
It causes hemolysis.

9357
07:25:19,450 --> 07:25:21,686
But if you actually start
listing what’s wrong

9358
07:25:21,686 --> 07:25:25,056
with sickle red cells,
it goes beyond there,

9359
07:25:25,056 --> 07:25:27,925
and there is a lot wrong
with sickle red cells.

9360
07:25:27,925 --> 07:25:31,963
I think Swee Lay
talked about it a little.

9361
07:25:31,963 --> 07:25:33,931
We’ve heard other things
about it.

9362
07:25:33,931 --> 07:25:36,134
And I probably don’t have,
in my 10 minutes,

9363
07:25:36,134 --> 07:25:38,536
the time to actually
go through all of this.

9364
07:25:38,536 --> 07:25:41,672
But if you read through it,
you can see that every place

9365
07:25:41,672 --> 07:25:44,442
you look in the red cell
of a sickle red cell

9366
07:25:44,442 --> 07:25:47,645
has something wrong with it,
the membrane,

9367
07:25:47,645 --> 07:25:51,215
distribution of things
like phosphatidylserine,

9368
07:25:51,215 --> 07:25:53,351
metabolic components,

9369
07:25:53,951 --> 07:25:56,654
the deformability and rheology
of the cells.

9370
07:25:57,355 --> 07:26:01,359
They’re much younger
than normal red cells,

9371
07:26:02,026 --> 07:26:05,296
and they have abnormal
adhesive properties,

9372
07:26:05,296 --> 07:26:08,466
interesting but abnormal
signaling properties,

9373
07:26:09,400 --> 07:26:12,904
and deficient expression
of antioxidant compounds.

9374
07:26:15,239 --> 07:26:18,509
And so, while the red cell

9375
07:26:18,509 --> 07:26:21,712
is the center
of sickle cell disease

9376
07:26:21,712 --> 07:26:25,349
and causes anemia
and vaso-occlusion

9377
07:26:25,349 --> 07:26:28,653
and then chronic tissue
damage and organ damage,

9378
07:26:28,653 --> 07:26:33,624
the Pathways leading to these
are multiple and complex.

9379
07:26:33,624 --> 07:26:38,129
And this is one incomplete
illustration of things

9380
07:26:38,129 --> 07:26:40,531
that are going on
in sickle cell disease.

9381
07:26:40,531 --> 07:26:43,501
And while, as you see this,
the red cell

9382
07:26:43,501 --> 07:26:47,672
is in the center of it,
the arrows go all over,

9383
07:26:47,672 --> 07:26:50,575
and they involve
adhesive interactions.

9384
07:26:50,575 --> 07:26:53,945
They involve
inflammatory effects.

9385
07:26:53,945 --> 07:26:56,247
They involve activation
of coagulation,

9386
07:26:57,949 --> 07:27:01,519
free hemoglobin,
and oxidative damage,

9387
07:27:01,519 --> 07:27:04,288
endothelial dysfunction,
and it goes on and on.

9388
07:27:04,922 --> 07:27:08,893
And I’ll spend about --
and the blue here

9389
07:27:08,893 --> 07:27:11,996
is some of the possible ways
we can intervene

9390
07:27:11,996 --> 07:27:15,299
with this vicious cycle
of effects

9391
07:27:15,299 --> 07:27:18,002
of having hemoglobin S
in the red cell.

9392
07:27:19,003 --> 07:27:21,939
But while there are
a lot of targets,

9393
07:27:21,939 --> 07:27:24,709
or therapeutic targets,
in sickle cell disease,

9394
07:27:25,309 --> 07:27:29,714
the critical issue is, what --
which ones are most important?

9395
07:27:29,714 --> 07:27:33,818
Which ones will have most effect
on the patient’s well-being,

9396
07:27:33,818 --> 07:27:35,419
the patient’s survival?

9397
07:27:37,421 --> 07:27:42,560
And in the old days,
Durham was the center

9398
07:27:42,560 --> 07:27:48,099
for making cigarettes.
And at one point, the supervisor

9399
07:27:48,099 --> 07:27:51,836
of the cigarette-making
factory retired.

9400
07:27:52,570 --> 07:27:54,338
And a few months later,

9401
07:27:54,338 --> 07:27:57,108
the head of the company
called him up and said,

9402
07:27:57,108 --> 07:27:58,342
"You’ve got to come and help us.

9403
07:27:58,342 --> 07:28:01,379
The cigarette-making machine
stopped working,

9404
07:28:01,379 --> 07:28:03,314
and we can’t figure out
how to fix it."

9405
07:28:04,081 --> 07:28:07,251
And the guy said,
"Well, if you give me $500,

9406
07:28:07,785 --> 07:28:09,453
I’ll come and fix it for you."

9407
07:28:09,453 --> 07:28:11,522
So, that was a lot of money
at the time,

9408
07:28:11,522 --> 07:28:15,059
but they said okay because
their factory was stopped.

9409
07:28:15,059 --> 07:28:17,628
And the guy came in,
walked around the machine,

9410
07:28:17,628 --> 07:28:19,931
gave it a good kick,
it started working.

9411
07:28:20,765 --> 07:28:23,868
And the guy said,
"Why should I pay you $500.

9412
07:28:23,868 --> 07:28:26,470
All you did was
kick the machine."

9413
07:28:27,004 --> 07:28:30,341
And the guy,
the retired supervisor, said,

9414
07:28:30,341 --> 07:28:32,109
"But I knew where to kick it."

9415
07:28:32,109 --> 07:28:33,544
[laughter]

9416
07:28:33,544 --> 07:28:37,748
So, it’s the same thing here.
It may take multi-agent therapy.

9417
07:28:37,748 --> 07:28:39,884
That’s my particular bias.

9418
07:28:39,884 --> 07:28:42,520
But there are a lot
of things going on.

9419
07:28:42,520 --> 07:28:44,288
And if we just aim at one,

9420
07:28:44,288 --> 07:28:46,991
we may not accomplish
what we’re trying to accomplish.

9421
07:28:48,326 --> 07:28:52,363
So, we do have three relatively
new FDA approved medications

9422
07:28:52,363 --> 07:28:54,265
that you haven’t heard
much about.

9423
07:28:55,233 --> 07:28:59,537
Voxelotor increased hemoglobin
levels in its trials.

9424
07:29:00,204 --> 07:29:03,808
It decreased sickled
red cell levels,

9425
07:29:03,808 --> 07:29:05,643
increased oxygen affinity,

9426
07:29:06,811 --> 07:29:12,817
didn’t have any notable effect
on vaso-occlusive events.

9427
07:29:13,851 --> 07:29:16,921
L-glutamine decreased
hospitalization for VOC,

9428
07:29:16,921 --> 07:29:20,391
decreased number
of hospital days,

9429
07:29:20,391 --> 07:29:25,863
decreased acute chest syndrome.
Crizanlizumab reduced VOC,

9430
07:29:25,863 --> 07:29:29,533
although the European agency
in charge of drug approval

9431
07:29:29,533 --> 07:29:32,003
has decided maybe not enough
to make it worth it.

9432
07:29:33,638 --> 07:29:35,840
And so, that’s where
we stand now,

9433
07:29:35,840 --> 07:29:37,541
but there’s a lot
in the pipeline.

9434
07:29:38,676 --> 07:29:41,245
So, some of these things
target adhesion.

9435
07:29:42,580 --> 07:29:46,884
Crizanlizumab is the one
that’s in -- on the market.

9436
07:29:46,884 --> 07:29:50,321
Rivipansel was tested
in a phase 3 study.

9437
07:29:51,689 --> 07:29:54,725
Those two drugs
are anti-selectin agents,

9438
07:29:54,725 --> 07:29:57,962
crizanlizumab directed
against P-selectin,

9439
07:29:57,962 --> 07:29:59,930
rivipansel against E-selectin --

9440
07:30:01,098 --> 07:30:06,904
rivipansel did a great job
in mice -- and then inclacumab,

9441
07:30:06,904 --> 07:30:11,075
which is currently
in phase 3 study,

9442
07:30:11,742 --> 07:30:15,513
is simply --
it’s similar to crizanlizumab,

9443
07:30:15,513 --> 07:30:18,182
but it has a three-month
half-life,

9444
07:30:19,216 --> 07:30:23,387
instead of requiring doses
every four weeks.

9445
07:30:25,756 --> 07:30:29,026
And the other way
of approaching adhesion

9446
07:30:29,894 --> 07:30:33,764
is that you can inhibit
the signaling pathways

9447
07:30:33,764 --> 07:30:36,133
and the metabolic pathways
inside the cell,

9448
07:30:36,133 --> 07:30:38,102
which actually
increase activation

9449
07:30:38,102 --> 07:30:41,038
of the adhesion receptors
that there --

9450
07:30:41,038 --> 07:30:42,840
exist on the outside
of the red cell,

9451
07:30:42,840 --> 07:30:44,275
of which there are a number.

9452
07:30:44,275 --> 07:30:45,543
And I’m not going
to get into that.

9453
07:30:45,543 --> 07:30:47,244
Because then it’ll be
a long talk.

9454
07:30:48,946 --> 07:30:52,750
So, crizanlizumab is a humanized
monoclonal antibody

9455
07:30:52,750 --> 07:30:57,855
that binds to P-selectin.
And inclacumab, as I said,

9456
07:30:57,855 --> 07:31:01,359
is simply engineered
to have a longer lifespan.

9457
07:31:03,728 --> 07:31:06,664
And the value of crizanlizumab,
I think,

9458
07:31:06,664 --> 07:31:08,032
depends on who you talk to.

9459
07:31:08,032 --> 07:31:10,067
Although there’s
some evidence now

9460
07:31:10,067 --> 07:31:14,472
also that it may help
with priapism,

9461
07:31:14,472 --> 07:31:19,844
which is a very interesting --
actually interesting idea,

9462
07:31:20,811 --> 07:31:22,780
but I don’t have time for it.

9463
07:31:23,414 --> 07:31:28,552
So, in any case, even if
it does reduce vaso-occlusion,

9464
07:31:28,552 --> 07:31:30,921
all the other things
listed here,

9465
07:31:30,921 --> 07:31:33,724
so far, there’s no evidence.

9466
07:31:33,724 --> 07:31:35,693
And I don’t think
there’s much coming

9467
07:31:35,693 --> 07:31:37,828
that it has much effect
on any of the other things

9468
07:31:37,828 --> 07:31:40,164
that we know goes wrong
in sickle cell disease.

9469
07:31:41,632 --> 07:31:46,203
Other potential anti-adhesive
medications include things

9470
07:31:46,203 --> 07:31:50,274
like beta-adrenergic receptor
inhibitors like propranolol,

9471
07:31:50,274 --> 07:31:53,310
which reduce the activation

9472
07:31:53,310 --> 07:31:55,579
of several
of the adhesion molecules,

9473
07:31:55,579 --> 07:31:58,549
including the laminin receptor
and the integrin receptor

9474
07:31:58,549 --> 07:32:01,218
on red cells.
Again, it works in mice.

9475
07:32:02,353 --> 07:32:06,290
It works with the surrogate
endpoint in people,

9476
07:32:06,290 --> 07:32:09,860
but it’s never gone through
a real clinical trial.

9477
07:32:10,828 --> 07:32:12,263
Inhibition of MEK and ERK --

9478
07:32:12,263 --> 07:32:16,100
and there’s MEK inhibitors
on the market in oncology --

9479
07:32:16,634 --> 07:32:19,870
also inhibit activation of
these same adhesion receptors.

9480
07:32:19,870 --> 07:32:22,907
That’s never been tried
in sickle cell disease.

9481
07:32:22,907 --> 07:32:25,443
Although we tried to get
some companies interested.

9482
07:32:26,811 --> 07:32:31,248
Prevention of oxidative stress
has been shown

9483
07:32:31,248 --> 07:32:32,750
by Dr. Zennadi’s group

9484
07:32:32,750 --> 07:32:35,886
to reduce red cell adhesion
to the endothelium.

9485
07:32:36,420 --> 07:32:39,757
And then rivipansel,
which failed its primary

9486
07:32:39,757 --> 07:32:42,893
endpoint
of shortening acute VOC,

9487
07:32:42,893 --> 07:32:44,462
nonetheless looked like it

9488
07:32:44,462 --> 07:32:50,134
was probably at least somewhat
effective if it was given early.

9489
07:32:51,135 --> 07:32:53,103
But at least
in the United States,

9490
07:32:53,103 --> 07:32:55,840
our standard practice
is to tell patients to stay home

9491
07:32:55,840 --> 07:32:59,443
and take pain medicine until
they can’t manage their pain,

9492
07:32:59,443 --> 07:33:02,379
and only then to come
to the hospital.

9493
07:33:02,379 --> 07:33:05,583
And if we were to use
a drug like rivipansel

9494
07:33:05,583 --> 07:33:06,851
or even retest it,

9495
07:33:06,851 --> 07:33:09,353
we would probably have
to change that practice.

9496
07:33:10,488 --> 07:33:11,822
Okay.

9497
07:33:11,822 --> 07:33:17,194
So, another source of problems
is oxidative damage.

9498
07:33:19,096 --> 07:33:24,268
And we’ve heard a few things
about oxidative damage,

9499
07:33:24,268 --> 07:33:27,171
especially to the membrane
and membrane components,

9500
07:33:27,171 --> 07:33:31,108
but also possibly to submembrane
and cytoskeletal components.

9501
07:33:34,512 --> 07:33:39,884
And we know that one way
that hemoglobin F may be helpful

9502
07:33:39,884 --> 07:33:42,386
is because it actually
reduces oxidative damage.

9503
07:33:43,621 --> 07:33:47,525
So, L-glutamine or Endari
was FDA-approved

9504
07:33:47,525 --> 07:33:50,561
to reduce VOC frequency
in sickle cell disease.

9505
07:33:51,095 --> 07:33:54,698
L-glutamine
has antioxidant activity.

9506
07:33:54,698 --> 07:33:59,036
Although initial studies
did not try very hard

9507
07:33:59,036 --> 07:34:02,039
to demonstrate how the drug
worked, only that it worked.

9508
07:34:02,773 --> 07:34:06,610
But a recent study has had some
really interesting results,

9509
07:34:06,610 --> 07:34:08,779
showing that, on the one hand,

9510
07:34:08,779 --> 07:34:11,982
it actually worsens
cell dehydration.

9511
07:34:11,982 --> 07:34:14,118
It increases blood viscosity.

9512
07:34:15,252 --> 07:34:21,325
So, there are at least some,
probably, cation transport

9513
07:34:21,325 --> 07:34:23,661
processes
that are not made better.

9514
07:34:24,194 --> 07:34:29,633
But anyway, it does decrease
point of sickling

9515
07:34:29,633 --> 07:34:33,103
and reduce reactive
oxygen species.

9516
07:34:35,372 --> 07:34:39,310
Voxelotor, osivelotor,
which is now undergoing trial,

9517
07:34:39,310 --> 07:34:43,247
decreases sickling.
And I’m going to skip this.

9518
07:34:43,247 --> 07:34:45,816
And we heard a lot about
PKR activators,

9519
07:34:45,816 --> 07:34:49,620
which have probably,
as Dr. Thein said,

9520
07:34:49,620 --> 07:34:52,423
multiple effects on the
red cells that could be helpful.

9521
07:34:54,858 --> 07:34:57,094
So, since I’m told
my time is up,

9522
07:34:57,094 --> 07:35:01,098
I’m actually going to go --
to skip all this.

9523
07:35:03,133 --> 07:35:06,337
But in any case,
as the old saying goes,

9524
07:35:06,337 --> 07:35:08,305
many roads lead to Rome.

9525
07:35:08,305 --> 07:35:11,408
Other drugs out there include
anti-inflammatory drugs,

9526
07:35:11,408 --> 07:35:13,344
down-modulation of
free hemoglobin,

9527
07:35:13,344 --> 07:35:14,778
like with hemopexin,

9528
07:35:14,778 --> 07:35:19,216
upregulation of hemoglobin F,
either by gene therapy,

9529
07:35:19,216 --> 07:35:21,552
but even potentially
and certainly

9530
07:35:21,552 --> 07:35:26,857
by a number of pharmacologic
agents, anti-sickling compounds,

9531
07:35:26,857 --> 07:35:30,961
and other hemoglobin modifiers
that we’ve probably yet to see.

9532
07:35:33,364 --> 07:35:35,866
Nonetheless, the health
of millions of patients

9533
07:35:35,866 --> 07:35:37,801
living with sickle
cell disease today

9534
07:35:37,801 --> 07:35:41,138
depend upon the success
of both developing

9535
07:35:41,138 --> 07:35:43,007
and delivering targeted

9536
07:35:43,007 --> 07:35:46,343
and probably multi-agent
therapeutic approaches

9537
07:35:46,343 --> 07:35:48,479
to the sequelae
of having red cells

9538
07:35:48,479 --> 07:35:50,614
with predominantly
hemoglobin S.

9539
07:35:50,614 --> 07:35:53,951
And I’m going to turn it over
to Dr. Little.

9540
07:35:54,718 --> 07:36:00,891
[applause]

9541
07:36:00,891 --> 07:36:02,726
Jane Little: Yeah.
It’s really fun to follow

9542
07:36:02,726 --> 07:36:05,729
right after Marilyn and Mark.
That’s okay.

9543
07:36:07,564 --> 07:36:12,036
So, I’m also glad we ended up
in Rome, in Marilyn’s talk,

9544
07:36:12,036 --> 07:36:18,375
because that’s kind of
where I’m going to -- thank you.

9545
07:36:18,375 --> 07:36:20,577
I knew there was
a lot of trouble with this.

9546
07:36:21,445 --> 07:36:26,083
I mean, sickle cell is quite
amazing because it is really --

9547
07:36:26,083 --> 07:36:34,491
started when a gene sort of
talked to an endemic infection,

9548
07:36:34,491 --> 07:36:35,826
9,000 years ago, right?

9549
07:36:35,826 --> 07:36:38,529
Like the sickle gene mutated.

9550
07:36:38,529 --> 07:36:40,831
And then it spread across
Sub-Saharan Africa.

9551
07:36:40,831 --> 07:36:44,268
Because it saved millions
and millions of people’s lives.

9552
07:36:44,268 --> 07:36:47,071
And now, I feel like
it’s time for the globe

9553
07:36:47,071 --> 07:36:48,706
to talk back to sickle cell.

9554
07:36:48,706 --> 07:36:50,607
And I’ll go through
that very briefly.

9555
07:36:51,308 --> 07:36:53,977
The Blue Devil is
in the details, as you know.

9556
07:36:53,977 --> 07:36:55,479
[laughter]

9557
07:36:55,479 --> 07:36:57,081
And I’m just going to
sort of summarize

9558
07:36:57,081 --> 07:36:58,515
a little bit about
the newborn screening

9559
07:36:58,515 --> 07:37:00,050
that we’ve learned yesterday,

9560
07:37:00,050 --> 07:37:02,519
which was lovely,
access to medications,

9561
07:37:02,519 --> 07:37:03,954
adherence to medications,

9562
07:37:03,954 --> 07:37:06,056
and assessing impact
of medications.

9563
07:37:06,056 --> 07:37:09,393
I can follow it [laughs].

9564
07:37:09,393 --> 07:37:12,429
So, you know, you can’t give
new therapies

9565
07:37:12,429 --> 07:37:14,031
these wonderful things
we’ve heard about,

9566
07:37:14,031 --> 07:37:17,434
if we don’t know
where the patients are.

9567
07:37:17,434 --> 07:37:19,470
So, you need
resources for that.

9568
07:37:19,470 --> 07:37:21,672
Access to medications
is also a struggle.

9569
07:37:22,639 --> 07:37:24,808
Adherence to medications
is a struggle

9570
07:37:24,808 --> 07:37:26,410
in human beings everywhere.

9571
07:37:27,211 --> 07:37:30,948
And I think, finally, learning
from high-prevalence sites

9572
07:37:30,948 --> 07:37:32,816
will be a benefit to all of us.

9573
07:37:33,851 --> 07:37:36,420
And then assessing
the impact of medications,

9574
07:37:36,420 --> 07:37:38,288
I don’t know if you guys
have heard of registries,

9575
07:37:38,288 --> 07:37:41,358
but I thought I might just
mention a few things at the end.

9576
07:37:41,358 --> 07:37:42,893
[laughter]

9577
07:37:42,893 --> 07:37:46,396
So, you know, targeted therapies
are not valuable

9578
07:37:46,396 --> 07:37:47,998
if the target is hidden.

9579
07:37:48,699 --> 07:37:51,468
And so, again,
you heard some of this.

9580
07:37:51,468 --> 07:37:53,737
And in the U.S.,
it is 100 percent,

9581
07:37:53,737 --> 07:37:56,807
but it’s state by state.
And I asked Mary Hulihan

9582
07:37:56,807 --> 07:38:00,611
if we knew how many babies
are born every year

9583
07:38:00,611 --> 07:38:02,079
in the U.S.
with sickle cell disease.

9584
07:38:02,079 --> 07:38:03,947
And she said,
"You know, it’s like --

9585
07:38:04,548 --> 07:38:08,385
this is the most resourced
country in the world,

9586
07:38:08,385 --> 07:38:11,722
and we still do not know how
many babies actually are born

9587
07:38:11,722 --> 07:38:14,391
in the U.S. every year
with sickle cell disease."

9588
07:38:14,391 --> 07:38:16,260
In Jamaica, you have 100 percent
newborn screening,

9589
07:38:16,260 --> 07:38:19,163
which is lovely.
I know COVID took its toll,

9590
07:38:19,163 --> 07:38:21,198
but you’re recovering
beautifully.

9591
07:38:21,198 --> 07:38:23,133
And in Sub-Saharan Africa,
as you heard,

9592
07:38:23,133 --> 07:38:25,803
it’s starting out strong,
but it’s very patchy.

9593
07:38:26,603 --> 07:38:28,739
CONSA was described,
the ASH effort,

9594
07:38:28,739 --> 07:38:30,440
Ghana, Uganda, and Tanzania.

9595
07:38:31,041 --> 07:38:32,576
This was very interesting to me.

9596
07:38:32,576 --> 07:38:35,012
You know, the gross domestic
product in the U.S.,

9597
07:38:35,012 --> 07:38:36,680
where we have 100 percent
newborn screening,

9598
07:38:36,680 --> 07:38:38,282
is $26 trillion.

9599
07:38:39,316 --> 07:38:42,419
The Mexican GDP is --
sorry, Mexican --

9600
07:38:42,419 --> 07:38:45,455
Jamaican GDP
is about 20 billion.

9601
07:38:45,455 --> 07:38:48,392
And in Sub-Saharan Africa,
the whole country, it’s --

9602
07:38:48,392 --> 07:38:49,660
Female Speaker: The
whole country?

9603
07:38:49,660 --> 07:38:50,894
Jane Little: Yes. Thank you.

9604
07:38:50,894 --> 07:38:53,764
The whole Sub-Saharan,
I meant -- yes, thank you --

9605
07:38:53,764 --> 07:38:57,801
the whole series of countries.
It’s 2 trillion.

9606
07:38:57,801 --> 07:38:59,403
But you can see the ones
where they have

9607
07:38:59,403 --> 07:39:03,640
the most newborn screening
are the more wealthy countries.

9608
07:39:03,640 --> 07:39:07,878
So, Ghana is 76 billion.
Uganda is 50 billion.

9609
07:39:07,878 --> 07:39:10,614
Tanzania is almost 80 billion.

9610
07:39:10,614 --> 07:39:12,516
So, even though it has
cumulatively --

9611
07:39:14,451 --> 07:39:18,088
a median of 16 billion
and cumulatively, 2 trillion,

9612
07:39:18,088 --> 07:39:19,356
there are places like Malawi

9613
07:39:19,356 --> 07:39:21,525
where it’s, you know,
way, way down.

9614
07:39:22,025 --> 07:39:23,894
And those places do not have
newborn screening.

9615
07:39:23,894 --> 07:39:26,930
So, I think the places where
they have resources, as usual,

9616
07:39:26,930 --> 07:39:28,832
we’re starting to get
newborn screening.

9617
07:39:28,832 --> 07:39:30,634
And as you know,
without newborn screening,

9618
07:39:30,634 --> 07:39:33,270
we won’t have that
beautiful uptick in survival.

9619
07:39:35,205 --> 07:39:36,540
You’ve heard this as well.

9620
07:39:36,540 --> 07:39:38,575
It’s always amazing to me
to realize

9621
07:39:38,575 --> 07:39:39,943
that there are half
a million babies

9622
07:39:39,943 --> 07:39:42,012
born every year
with sickle cell disease.

9623
07:39:42,546 --> 07:39:46,049
Mary Hulihan estimated
that 1600 babies per year

9624
07:39:46,049 --> 07:39:48,385
are born with sickle cell
disease in the U.S.,

9625
07:39:49,519 --> 07:39:53,223
and the prevalence overall
is about point 0.035 percent.

9626
07:39:53,223 --> 07:39:55,959
In Jamaica, as you’ve heard
those lovely talks yesterday,

9627
07:39:55,959 --> 07:40:00,530
it’s 0.67, with about 180
to 200, is what I understood.

9628
07:40:01,932 --> 07:40:06,770
And in Sub-Saharan Africa,
the countries -- continent --

9629
07:40:06,770 --> 07:40:10,140
it’s greater than
290,000 babies per year.

9630
07:40:12,109 --> 07:40:16,380
So, it’s estimated that there
are 7 to 8 million people

9631
07:40:16,380 --> 07:40:18,916
around the world
living with sickle cell disease.

9632
07:40:18,916 --> 07:40:21,885
And again, that’s probably
very distorted

9633
07:40:21,885 --> 07:40:23,320
by our lack of newborn screening

9634
07:40:23,320 --> 07:40:25,188
and all the deaths
that happen in kids.

9635
07:40:25,188 --> 07:40:27,357
So, I think if we did
newborn screening

9636
07:40:27,357 --> 07:40:29,426
and figured out how to get care
to those children,

9637
07:40:29,426 --> 07:40:31,028
this number would expand.

9638
07:40:31,929 --> 07:40:36,934
In the U.S., it’s 0.1x106
living in the U.S.

9639
07:40:36,934 --> 07:40:38,535
with sickle cell disease
is estimated,

9640
07:40:38,535 --> 07:40:40,871
or 1.3 percent
of the global burden,

9641
07:40:42,272 --> 07:40:44,841
Just as -- I was just trying
to understand the numbers.

9642
07:40:44,841 --> 07:40:46,376
Because you keep hearing
about PEPFAR

9643
07:40:46,376 --> 07:40:50,314
and how great it has been
using high-prevalence sites

9644
07:40:50,314 --> 07:40:54,985
to inform low-prevalence sites.
It’s estimated, in 2023,

9645
07:40:54,985 --> 07:40:56,453
there are about
40 million people

9646
07:40:56,453 --> 07:40:58,956
living with HIV
in Sub-Saharan Africa --

9647
07:40:58,956 --> 07:41:02,659
sorry, in the world,
and most of them are in Africa.

9648
07:41:02,659 --> 07:41:07,397
And again, 1.2 million
are living in the U.S. with HIV.

9649
07:41:07,397 --> 07:41:09,299
So, that’s about 3 percent
of the global burden.

9650
07:41:09,299 --> 07:41:11,268
So, it’s actually not
as different as I thought.

9651
07:41:11,268 --> 07:41:12,869
I was sort of surprised.

9652
07:41:14,304 --> 07:41:17,274
So, I started with Jamaica.
It seemed only right.

9653
07:41:17,274 --> 07:41:19,076
And when you think
about learning

9654
07:41:19,076 --> 07:41:20,410
from high-prevalence settings

9655
07:41:20,410 --> 07:41:22,479
and how that really
teaches us all.

9656
07:41:23,580 --> 07:41:26,650
There was a nice study
from Lesley King

9657
07:41:26,650 --> 07:41:30,354
about giving IM penicillin
and how it was much more --

9658
07:41:31,922 --> 07:41:34,358
they might have much less sepsis
in the children

9659
07:41:34,358 --> 07:41:35,959
who got the monthly injectable.

9660
07:41:36,560 --> 07:41:38,495
And then there are
all the cohort studies

9661
07:41:38,495 --> 07:41:41,531
that have taught us so much
over the decades about --

9662
07:41:42,499 --> 07:41:44,801
I just pulled a few out,
just to sort of show you,

9663
07:41:44,801 --> 07:41:46,837
and of course,
the obligatory picture.

9664
07:41:46,837 --> 07:41:49,773
But it really is important
that they really tracked people

9665
07:41:49,773 --> 07:41:55,479
in a very deep and consistent
way across many decades.

9666
07:41:55,479 --> 07:41:57,114
And people have learned a ton.

9667
07:41:58,382 --> 07:42:02,052
In HIV, the --
they’ve learned things

9668
07:42:02,052 --> 07:42:05,188
like giving HIV medications
more effectively,

9669
07:42:05,188 --> 07:42:07,858
so oral versus monthly prep.

9670
07:42:09,192 --> 07:42:12,262
Turns out, people don’t like
to take pills every day.

9671
07:42:12,262 --> 07:42:16,833
And so, they had 43 percent
plasma levels in daily oral

9672
07:42:16,833 --> 07:42:19,002
and 36 new infections.

9673
07:42:19,002 --> 07:42:23,740
A monthly injection,
93% coverage for infections.

9674
07:42:23,740 --> 07:42:25,709
So, again, you know,
they learned

9675
07:42:25,709 --> 07:42:30,013
this over three years
in 3,000 patients in Malawi.

9676
07:42:30,013 --> 07:42:32,049
There’s a consortium
that did this.

9677
07:42:32,049 --> 07:42:34,251
This is the kind of study,
when Mark talked about his study

9678
07:42:34,251 --> 07:42:37,020
that took 5 million years
to accrue,

9679
07:42:37,020 --> 07:42:39,456
you know, if you’re
in a high-density setting,

9680
07:42:39,456 --> 07:42:42,659
you can answer a lot of really
simple, important questions

9681
07:42:42,659 --> 07:42:44,795
that we’ve been asking
for decades.

9682
07:42:44,795 --> 07:42:46,763
And it’ll really inform care
across the world,

9683
07:42:46,763 --> 07:42:47,998
including in the U.S.

9684
07:42:47,998 --> 07:42:50,000
Because we won’t be able
to do gene therapy

9685
07:42:50,000 --> 07:42:55,639
on everybody for a decade at
least, and transplant likewise.

9686
07:42:56,406 --> 07:42:58,108
And then they did
this study on --

9687
07:42:58,642 --> 07:43:02,779
using one day of IV liposomal

9688
07:43:02,779 --> 07:43:05,415
Amphotericin with seven days
of an oral antifungal,

9689
07:43:05,415 --> 07:43:06,817
and it was equivalent.

9690
07:43:06,817 --> 07:43:08,752
And so, it saved them
a lot of time, a lot of money,

9691
07:43:08,752 --> 07:43:11,922
and this went into the U.S. --
to the worldwide guidelines.

9692
07:43:11,922 --> 07:43:14,424
So, again, we can learn a lot
from places

9693
07:43:14,424 --> 07:43:16,893
where there are people
who have the disease.

9694
07:43:16,893 --> 07:43:19,529
And I think that’s like
pregnancy, you know, like,

9695
07:43:19,529 --> 07:43:21,098
that’s something
where you can learn a ton

9696
07:43:21,098 --> 07:43:23,366
from places where there’s
a lot of pregnancy.

9697
07:43:23,366 --> 07:43:25,602
And we’re just not sharing
that kind of data.

9698
07:43:26,236 --> 07:43:28,538
Okay. I’m sorry.
One of my colleagues said,

9699
07:43:29,072 --> 07:43:30,574
"You know when --
you know that thing, like,

9700
07:43:30,574 --> 07:43:32,742
where everyone rolls their eyes
when Jane says, GRNDaD?’"

9701
07:43:32,742 --> 07:43:33,944
I thought, what?

9702
07:43:33,944 --> 07:43:35,178
[laughter]

9703
07:43:35,178 --> 07:43:36,780
No, I didn’t know that thing.

9704
07:43:36,780 --> 07:43:39,783
But apparently,
I might be obsessed,

9705
07:43:39,783 --> 07:43:41,084
and I’m sorry about that.

9706
07:43:41,084 --> 07:43:43,520
But it was actually
because of this conference

9707
07:43:43,520 --> 07:43:44,754
about five years ago.

9708
07:43:44,754 --> 07:43:46,356
They also asked me
to do this debate thing,

9709
07:43:46,356 --> 07:43:48,191
which I don’t really understand.

9710
07:43:48,191 --> 07:43:50,994
But I was looking at all
the registries and was like,

9711
07:43:51,661 --> 07:43:53,964
CF has had a 50-year registry,

9712
07:43:53,964 --> 07:43:56,566
hemophilia has had
a 60-year registry.

9713
07:43:56,566 --> 07:43:59,369
They’ve really informed
and improved care

9714
07:43:59,369 --> 07:44:01,838
and made it consistent.
They had quality feedback.

9715
07:44:02,372 --> 07:44:05,809
And the sickle cell experience
has been really unfortunate.

9716
07:44:05,809 --> 07:44:08,612
The CSCCD was so valuable,

9717
07:44:08,612 --> 07:44:11,047
but it died
in the sort of late ‘80s.

9718
07:44:11,681 --> 07:44:13,650
But it -- sort of its offspring

9719
07:44:13,650 --> 07:44:16,086
are all those things
like penicillin prophylaxis,

9720
07:44:16,753 --> 07:44:18,755
hydrea, partly, the STOP study.

9721
07:44:18,755 --> 07:44:21,758
They all grew out of problems
that were identified in CSCCD.

9722
07:44:22,325 --> 07:44:23,760
And then we have these little --

9723
07:44:23,760 --> 07:44:26,196
like walk fast,
rush, not little,

9724
07:44:26,196 --> 07:44:30,100
but they’ve never been
consistent or comprehensive.

9725
07:44:30,667 --> 07:44:32,235
And I think there are
two efforts now.

9726
07:44:32,235 --> 07:44:33,703
The one I know best is GRNDaD.

9727
07:44:33,703 --> 07:44:37,641
And we started that in 2010,
I think, ‘13.

9728
07:44:38,608 --> 07:44:43,380
And this is PI driven.
ASH has a more EHR driven one.

9729
07:44:43,380 --> 07:44:45,549
I can just tell you about
GRNDaD, which is --

9730
07:44:45,549 --> 07:44:47,918
it’s a prospective,
longitudinal registry.

9731
07:44:47,918 --> 07:44:49,886
We now have about
4,000 people in it.

9732
07:44:49,886 --> 07:44:51,555
And we’re really starting
to learn things

9733
07:44:51,555 --> 07:44:53,056
about how we’re taking
care of people

9734
07:44:53,056 --> 07:44:55,058
with sickle cell disease
in the U.S.

9735
07:44:55,058 --> 07:44:56,626
that are helping us
take better care.

9736
07:44:56,626 --> 07:44:58,161
Because you learn
from yourself.

9737
07:44:58,161 --> 07:45:01,531
Because it’s amazing how, when
you don’t actually look at data,

9738
07:45:01,531 --> 07:45:03,200
you don’t actually know
what you’re doing.

9739
07:45:03,200 --> 07:45:06,069
And you think, "Oh, I’m doing
so well." And then --

9740
07:45:06,069 --> 07:45:07,370
[laughter]

9741
07:45:07,370 --> 07:45:09,773
-- and then you suddenly see,
"Oh, I didn’t know at Hopkins,

9742
07:45:09,773 --> 07:45:11,074
they did it better."

9743
07:45:11,074 --> 07:45:14,010
I mean, it’s -- I’m a little
embarrassed, actually,

9744
07:45:14,010 --> 07:45:15,579
at how powerful that was.

9745
07:45:15,579 --> 07:45:17,847
So, these are
the three registry efforts

9746
07:45:17,847 --> 07:45:19,449
that we’re undertaking now.

9747
07:45:19,950 --> 07:45:21,751
COUSIN was one we did
with the U.K.,

9748
07:45:21,751 --> 07:45:24,754
where we just looked at symptom
burden in the two populations.

9749
07:45:24,754 --> 07:45:27,257
And they were quite similar,
which was surprising.

9750
07:45:28,658 --> 07:45:31,228
GRNDaD is now --
has more than 4,000 people,

9751
07:45:31,228 --> 07:45:33,296
and it’s really been kept alive
by Sophie Lanzkron

9752
07:45:33,296 --> 07:45:37,000
and Julie Kanter,
to whom I give full credit.

9753
07:45:37,500 --> 07:45:43,907
And GRNDMA is a very small,
SPARCO-aligned effort

9754
07:45:43,907 --> 07:45:45,775
that we’re undertaking
in Malawi,

9755
07:45:45,775 --> 07:45:47,377
where they’re facing
the same thing

9756
07:45:47,377 --> 07:45:49,779
we all faced in the U.S.,
30 years ago.

9757
07:45:49,779 --> 07:45:52,315
They have this whole cohort
of children who are coming up,

9758
07:45:52,315 --> 07:45:53,783
and they’re right at the gate.

9759
07:45:53,783 --> 07:45:56,219
And there’s no one
on the adult side to catch them.

9760
07:45:56,219 --> 07:45:59,823
And so, we’ve started
to collect data on those adults,

9761
07:45:59,823 --> 07:46:02,459
so that we can hopefully
take better care of them.

9762
07:46:02,459 --> 07:46:06,796
So, I really hope, as gene
therapy becomes really --

9763
07:46:07,297 --> 07:46:08,598
and bone marrow transplant,

9764
07:46:08,598 --> 07:46:10,000
they all become part
of our landscape,

9765
07:46:10,000 --> 07:46:12,602
that we’re able to have one
or two registries

9766
07:46:12,602 --> 07:46:13,937
that can talk
to each other,

9767
07:46:13,937 --> 07:46:15,872
so that we can
understand the impact.

9768
07:46:15,872 --> 07:46:18,341
We don’t even know this
about hydrea. It’s shocking.

9769
07:46:18,341 --> 07:46:20,744
Like we don’t really have
30 years of data

9770
07:46:20,744 --> 07:46:25,115
telling us its impact.
So, I think we can do better.

9771
07:46:25,849 --> 07:46:27,217
PepNear --
[laughter]

9772
07:46:27,217 --> 07:46:29,286
-- you know,
sometimes late at night --

9773
07:46:29,286 --> 07:46:30,854
and my computer broke.
So, I couldn’t modify.

9774
07:46:30,854 --> 07:46:32,756
But I don’t think PEPFAR
is the answer.

9775
07:46:32,756 --> 07:46:35,358
But I think investing more
in resource-limited settings

9776
07:46:35,358 --> 07:46:37,861
will really help us
all everywhere,

9777
07:46:37,861 --> 07:46:40,130
to take better care of people
with sickle cell disease.

9778
07:46:40,130 --> 07:46:43,700
I think registries
need to be comprehensive

9779
07:46:43,700 --> 07:46:46,970
and compatible
with the local environment,

9780
07:46:48,004 --> 07:46:50,874
and we really need to get on
top of it with gene therapy,

9781
07:46:50,874 --> 07:46:52,876
so that we’re being
comprehensive about,

9782
07:46:53,476 --> 07:46:56,613
is there clonal hematopoiesis?
Is there an alpha gene?

9783
07:46:56,613 --> 07:46:58,048
How are we reporting
the outcome,

9784
07:46:58,048 --> 07:47:00,950
so that we’re really consistent?
We know what we’re doing.

9785
07:47:01,851 --> 07:47:05,422
I think hydroxyurea, you know,
we need to get it to people.

9786
07:47:05,422 --> 07:47:08,892
Like these other treatments are
terrific, but we haven’t even --

9787
07:47:08,892 --> 07:47:12,095
like, there was --
Jamaica is 10 to 20 percent.

9788
07:47:12,095 --> 07:47:13,530
I’m embarrassed to admit
that we looked

9789
07:47:13,530 --> 07:47:15,198
at the Medicaid data
in North Carolina.

9790
07:47:15,198 --> 07:47:17,534
Across the whole state,
it’s 25 percent.

9791
07:47:18,268 --> 07:47:21,471
So, it’s not even like --
we haven’t solved that problem.

9792
07:47:22,038 --> 07:47:25,742
And I think local interests
in the U.S. absolutely synergize

9793
07:47:25,742 --> 07:47:27,644
with local interests
in international sites,

9794
07:47:27,644 --> 07:47:29,012
and we need to be
more collaborative

9795
07:47:29,012 --> 07:47:30,680
and support each other.
Thanks.

9796
07:47:32,148 --> 07:47:40,023
[applause]

9797
07:47:40,023 --> 07:47:41,358
Jennifer Knight-Madden: I’m
afraid

9798
07:47:41,358 --> 07:47:43,360
to touch this,
but let’s try.

9799
07:47:44,527 --> 07:47:49,099
Okay. So, you know,
one of the highlights

9800
07:47:49,099 --> 07:47:51,468
for me of any conference
that we do

9801
07:47:51,468 --> 07:47:54,738
is actually to hear
some feedback from the patients

9802
07:47:54,738 --> 07:47:57,006
about their lived experiences.

9803
07:47:57,006 --> 07:48:01,578
And today, we have two wonderful
people to share with us.

9804
07:48:02,712 --> 07:48:05,648
We have Miss Morette Wright,
who’s the co-founder

9805
07:48:05,648 --> 07:48:08,184
of the Sickle Cell
Support Foundation of Jamaica

9806
07:48:08,184 --> 07:48:11,321
and a sickle cell patient at the
University of the West Indies.

9807
07:48:11,855 --> 07:48:13,790
She’s going to speak
with us first.

9808
07:48:13,790 --> 07:48:17,660
And then she’ll be followed
by Mr. Patrick Onwuemene,

9809
07:48:18,528 --> 07:48:20,463
who’s a patient of Professor

9810
07:48:20,463 --> 07:48:24,234
Thein’s at NIH Clinical Center,
Bethesda, Maryland.

9811
07:48:24,934 --> 07:48:27,837
And we’re going
to hear from them.

9812
07:48:27,837 --> 07:48:30,740
And then we’re going to have
a short panel discussion,

9813
07:48:30,740 --> 07:48:33,076
where I’ll invite the other
speakers for this session

9814
07:48:33,076 --> 07:48:34,811
to also join us
at the front,

9815
07:48:36,613 --> 07:48:41,284
so that we can have a discussion
back and forth amongst them

9816
07:48:41,284 --> 07:48:44,954
and us as a wider audience.
So, Miss Wright.

9817
07:48:46,456 --> 07:48:56,032
[applause]

9818
07:48:56,599 --> 07:48:58,768
Morette Wright: Good afternoon,
everyone.

9819
07:49:00,637 --> 07:49:02,572
Okay. As you heard,
I’m Morette Wright.

9820
07:49:03,239 --> 07:49:05,642
And it’s my honor
to be here

9821
07:49:06,242 --> 07:49:09,879
as a sickle cell patient,
representing.

9822
07:49:11,080 --> 07:49:16,052
So, I would say
I have been living

9823
07:49:16,052 --> 07:49:19,756
and generally thriving
with sickle cell disease.

9824
07:49:19,756 --> 07:49:24,093
I’ve long passed
the average survival age.

9825
07:49:25,395 --> 07:49:28,832
So, you know,
I’m thankful for that.

9826
07:49:28,832 --> 07:49:34,871
As a young girl, I was told
I would not be employed

9827
07:49:34,871 --> 07:49:36,473
because of my condition.

9828
07:49:37,574 --> 07:49:41,711
But fast forward
over two decades,

9829
07:49:43,079 --> 07:49:47,350
I have built a successful career
in human resources

9830
07:49:48,351 --> 07:49:52,522
and have been recruiting
top talent

9831
07:49:52,522 --> 07:49:55,592
for one of Jamaica’s
leading companies.

9832
07:49:56,125 --> 07:50:00,930
[applause]

9833
07:50:00,930 --> 07:50:04,868
And I say this is possible
because of my faith,

9834
07:50:05,935 --> 07:50:09,772
my resilience,
a supportive family,

9835
07:50:10,440 --> 07:50:14,043
and access to medical care
and treatment.

9836
07:50:15,945 --> 07:50:19,549
So, I’ll start
my presentation today

9837
07:50:20,683 --> 07:50:26,422
with a simple yet profound
Japanese proverb,

9838
07:50:27,257 --> 07:50:31,261
and it says,
"Pain is inevitable.

9839
07:50:31,861 --> 07:50:33,696
Suffering is optional."

9840
07:50:35,231 --> 07:50:38,635
This perfectly captures
the reality of living

9841
07:50:38,635 --> 07:50:40,236
with sickle cell disease.

9842
07:50:41,070 --> 07:50:44,674
So, pain is a constant
companion for most of us.

9843
07:50:45,942 --> 07:50:50,213
This lifelong condition
doesn’t take a break.

9844
07:50:51,714 --> 07:50:54,183
How we manage the pain
and suffering

9845
07:50:55,485 --> 07:51:00,223
that it can bring depends
primarily on the support,

9846
07:51:00,924 --> 07:51:04,460
the treatment,
and choices available to us.

9847
07:51:06,262 --> 07:51:08,898
So, as someone living
with sickle cell,

9848
07:51:08,898 --> 07:51:14,070
I understand the daily
struggles, the physical pain,

9849
07:51:15,071 --> 07:51:19,409
and the emotional weight
that this disease brings.

9850
07:51:19,409 --> 07:51:23,880
Yes, at times, we say life,
hard, and it is.

9851
07:51:25,181 --> 07:51:29,852
I also know the importance
of resilience, of hope,

9852
07:51:29,852 --> 07:51:36,192
and the need for treatments that
can transform our experiences.

9853
07:51:38,294 --> 07:51:41,030
So, where are we headed
in the treatment

9854
07:51:41,030 --> 07:51:42,599
of sickle cell in Jamaica?

9855
07:51:43,266 --> 07:51:46,135
So, much have been shared
about the current state

9856
07:51:46,135 --> 07:51:49,339
of treating sickle cell disease
in Jamaica.

9857
07:51:49,839 --> 07:51:51,608
You’ve heard about the strides

9858
07:51:51,608 --> 07:51:55,345
in the options available
for pain management,

9859
07:51:55,878 --> 07:52:00,483
the comprehensive care
that is available,

9860
07:52:00,483 --> 07:52:02,285
and I must pause to commend

9861
07:52:02,285 --> 07:52:06,155
the excellent work
of the sickle cell unit.

9862
07:52:06,155 --> 07:52:08,925
And I want to acknowledge
the team,

9863
07:52:08,925 --> 07:52:13,696
the past and the present team.
So, yes, let’s acknowledge them.

9864
07:52:14,230 --> 07:52:17,800
[applause]

9865
07:52:17,800 --> 07:52:21,804
And we also heard about
the National Sickle Cell Program

9866
07:52:21,804 --> 07:52:24,307
and the strides
that have been made in Jamaica.

9867
07:52:25,174 --> 07:52:27,543
But with that,
we still have some hurdles.

9868
07:52:28,378 --> 07:52:32,548
So, there’s not equitable
access for all patients.

9869
07:52:32,548 --> 07:52:34,217
Because it depends on,
you know,

9870
07:52:34,217 --> 07:52:37,253
one’s social
and economic status.

9871
07:52:37,820 --> 07:52:41,090
And so, the burden
of the cost of healthcare

9872
07:52:41,090 --> 07:52:46,162
is still prevalent.
There’s also the inconsistent --

9873
07:52:46,162 --> 07:52:50,867
yes, treatment in the
public sector is accessible,

9874
07:52:50,867 --> 07:52:52,468
but it is inconsistent.

9875
07:52:53,169 --> 07:52:58,241
The quality of care,
at times, is not equitable.

9876
07:52:58,841 --> 07:53:03,780
So, there is the long wait,
the inadequate pain management,

9877
07:53:04,647 --> 07:53:07,750
the inadequately trained
health care workers,

9878
07:53:08,251 --> 07:53:10,286
and stigmatization.

9879
07:53:11,721 --> 07:53:15,324
But with that said,
where are we headed?

9880
07:53:16,192 --> 07:53:19,829
What does the future
hold for us here in Jamaica?

9881
07:53:20,630 --> 07:53:22,365
And we’ve heard --
I mean, this has been

9882
07:53:22,365 --> 07:53:26,969
a really rich content,
rich session two days.

9883
07:53:27,970 --> 07:53:32,341
And, you know, it just
reinforces where we want,

9884
07:53:32,341 --> 07:53:34,610
what we want
in terms of treatment.

9885
07:53:35,378 --> 07:53:38,581
And I’ll start with,
yes, we need a cure.

9886
07:53:39,882 --> 07:53:44,754
But hope is on the horizon
with the advent of gene therapy.

9887
07:53:44,754 --> 07:53:46,556
We heard all about that.

9888
07:53:46,556 --> 07:53:49,058
So, we hope to see
this soon in Jamaica.

9889
07:53:49,058 --> 07:53:51,027
Right, Prof?

9890
07:53:51,027 --> 07:53:53,730
So, rather than just treating
the symptoms,

9891
07:53:53,730 --> 07:54:00,069
the possibility of life
without pain is on the horizon.

9892
07:54:02,038 --> 07:54:07,376
We also need more access
to the new medications

9893
07:54:07,910 --> 07:54:10,246
and the treatment
approaches.

9894
07:54:10,246 --> 07:54:13,049
So, in addition
to the curative therapies,

9895
07:54:13,983 --> 07:54:17,987
we hope to benefit
from these new medications

9896
07:54:18,488 --> 07:54:22,091
that we have been hearing
that’s changing the landscape

9897
07:54:22,925 --> 07:54:25,695
for persons living
with sickle cell disease.

9898
07:54:26,496 --> 07:54:32,435
So, we are -- we’ve made
some progress with hydroxyurea,

9899
07:54:33,035 --> 07:54:36,739
but yes, there are
those other drugs

9900
07:54:36,739 --> 07:54:40,409
that are designed to reduce the
frequency of the painful crisis

9901
07:54:41,110 --> 07:54:45,548
and help to improve
our overall quality of life.

9902
07:54:46,849 --> 07:54:48,885
And in fact,
next week, Wednesday,

9903
07:54:49,619 --> 07:54:52,288
during our annual lecture,

9904
07:54:52,288 --> 07:54:54,490
Professor Asnani
will be speaking

9905
07:54:54,490 --> 07:54:59,862
about the innovative approaches
and emerging therapies

9906
07:54:59,862 --> 07:55:02,064
in management
of sickle cell disease.

9907
07:55:02,064 --> 07:55:05,434
So, she’s going to give us,
Jamaican patients,

9908
07:55:05,434 --> 07:55:07,036
a little more hope.

9909
07:55:07,870 --> 07:55:16,179
So, we are also expecting better
pain management options.

9910
07:55:16,179 --> 07:55:20,516
So, managing pain remains
a key focus of treatment

9911
07:55:21,484 --> 07:55:22,952
in Jamaica and all around.

9912
07:55:22,952 --> 07:55:26,722
However, there’s a shift
in how we approach it,

9913
07:55:26,722 --> 07:55:32,361
from non-opioid pain relief
to more holistic methods.

9914
07:55:32,361 --> 07:55:34,997
So, we want to look
at cognitive behavioral

9915
07:55:34,997 --> 07:55:39,068
therapy,
mindfulness, acupuncture,

9916
07:55:39,068 --> 07:55:41,604
whatever else
we can do to support.

9917
07:55:41,604 --> 07:55:43,873
It might not necessarily
remove the pain,

9918
07:55:43,873 --> 07:55:47,977
but it helps the patient
to better manage the process.

9919
07:55:49,579 --> 07:55:54,350
We’re on a path
with policy changes, advocacy,

9920
07:55:54,350 --> 07:55:56,919
strengthening
the healthcare system.

9921
07:55:57,820 --> 07:56:00,356
With the inclusion
of sickle cell disease

9922
07:56:00,356 --> 07:56:03,492
in the Ministry of Health
and Wellness strategic plan,

9923
07:56:04,227 --> 07:56:07,430
there have been
some significant advancement

9924
07:56:07,430 --> 07:56:11,567
in treating persons living with
sickle cell disease in Jamaica.

9925
07:56:12,435 --> 07:56:15,938
And our desire is to have
a system

9926
07:56:15,938 --> 07:56:21,577
where no patient is left behind,
and no one has to suffer.

9927
07:56:22,945 --> 07:56:27,149
So, as I started, you know,
pain is inevitable.

9928
07:56:27,149 --> 07:56:32,355
Suffering is optional.
Pain may still exist,

9929
07:56:32,355 --> 07:56:36,759
but with the medical advances,
with the policy changes,

9930
07:56:37,693 --> 07:56:41,597
and with the --
with comprehensive care,

9931
07:56:41,597 --> 07:56:45,568
suffering does not
have to define our lives.

9932
07:56:46,636 --> 07:56:52,341
So, let’s continue this work,
research, care, advocacy,

9933
07:56:52,341 --> 07:56:57,613
policy, so that future
generations will know a world

9934
07:56:57,613 --> 07:57:01,217
where sickle cell disease
no longer brings suffering

9935
07:57:01,784 --> 07:57:05,621
but instead brings resilience,
hope,

9936
07:57:06,389 --> 07:57:09,558
and a life filled
with possibilities.

9937
07:57:09,558 --> 07:57:10,860
Thank you.

9938
07:57:10,860 --> 07:57:15,231
[applause]

9939
07:57:15,231 --> 07:57:17,366
Did I go down or stay here?

9940
07:57:17,366 --> 07:57:18,968
Jennifer Knight-Madden: You can
stay here.

9941
07:57:23,072 --> 07:57:24,674
Okay.

9942
07:57:25,174 --> 07:57:33,049
[applause]

9943
07:57:33,049 --> 07:57:34,784
Patrick Omwuemene: Good evening,
everyone.

9944
07:57:34,784 --> 07:57:42,892
My name is Patrick Omwuemene,
one of Dr. Thein’s patients.

9945
07:57:43,759 --> 07:57:49,765
I am going to be talking more
on the personal side,

9946
07:57:49,765 --> 07:57:54,303
my side of the story,
when it comes to sickle cell.

9947
07:57:54,303 --> 07:58:02,311
My experience as a child --
I guess I would fall

9948
07:58:02,311 --> 07:58:07,550
under the threshold
of the mild sickle cell,

9949
07:58:07,550 --> 07:58:11,754
according to the presentation
Wynona Coles made.

9950
07:58:13,122 --> 07:58:17,360
And as a child, I really
didn’t experience sickle cell,

9951
07:58:18,294 --> 07:58:20,129
like many other children did,

9952
07:58:21,397 --> 07:58:23,099
because I didn’t have
much crisis.

9953
07:58:23,599 --> 07:58:26,869
I grew up with a lot of boys
on my streets,

9954
07:58:27,536 --> 07:58:33,576
four very athletic brothers,
very athletic neighbors,

9955
07:58:33,576 --> 07:58:35,144
and as a matter of fact,

9956
07:58:35,144 --> 07:58:37,213
one of them had a father
as a coach.

9957
07:58:37,213 --> 07:58:39,882
So, he encouraged us
in whatever sports we played.

9958
07:58:40,750 --> 07:58:44,520
It’ll shock you that
I didn’t just play with them.

9959
07:58:46,455 --> 07:58:48,057
I played in the ring with them.

9960
07:58:48,657 --> 07:58:51,293
So, a lot of people
cringe when they hear,

9961
07:58:51,293 --> 07:58:53,262
"You played in the ring,"
you know.

9962
07:58:53,262 --> 07:58:58,701
But that was life growing up.

9963
07:58:58,701 --> 07:59:03,706
So, my very first crisis
came at about the age of 7, 8.

9964
07:59:04,940 --> 07:59:06,575
I believe my mom
was testing me.

9965
07:59:06,575 --> 07:59:08,978
She told me to --
it was raining.

9966
07:59:08,978 --> 07:59:10,579
It was a cold day.

9967
07:59:11,747 --> 07:59:14,216
I had never experienced crisis
at this point.

9968
07:59:15,317 --> 07:59:18,754
And she asked me
to go get something

9969
07:59:18,754 --> 07:59:21,757
from the neighborhood
grocery store,

9970
07:59:21,757 --> 07:59:23,292
which was just
a couple of streets away.

9971
07:59:23,292 --> 07:59:26,028
So, without the thought
of grabbing an umbrella

9972
07:59:27,630 --> 07:59:29,965
or a raincoat,
I just dashed into the rain.

9973
07:59:31,200 --> 07:59:32,835
And in a few minutes,
I was back,

9974
07:59:33,469 --> 07:59:35,071
ran as fast as I could.

9975
07:59:35,838 --> 07:59:39,642
Getting home, I began to feel
this very funny pain

9976
07:59:39,642 --> 07:59:43,412
emanating from my back.
And before I knew it --

9977
07:59:43,412 --> 07:59:45,881
I mean, it just crawled
from my back

9978
07:59:46,949 --> 07:59:48,484
through the left side
of my chest.

9979
07:59:48,484 --> 07:59:51,787
And before I knew it,
I was engulfed in pain.

9980
07:59:51,787 --> 07:59:55,491
Pain felt like glass --
broken glass,

9981
07:59:55,491 --> 07:59:58,427
just crawling very slowly,

9982
07:59:58,427 --> 08:00:02,331
making its way
from my back to the chest,

9983
08:00:02,331 --> 08:00:05,000
to everywhere else,
and then the joints.

9984
08:00:06,135 --> 08:00:10,106
So, everybody was perplexed.
Everybody was confused.

9985
08:00:10,106 --> 08:00:12,441
Because they had never
seen me that way before.

9986
08:00:13,142 --> 08:00:16,479
All of a sudden,
my mom is getting ointment,

9987
08:00:16,479 --> 08:00:18,080
trying to massage me.

9988
08:00:18,781 --> 08:00:21,584
My cousin eventually
grabbed me some water.

9989
08:00:21,584 --> 08:00:22,818
I drank that.

9990
08:00:22,818 --> 08:00:25,688
And eventually, after
a few hours, I felt better.

9991
08:00:26,722 --> 08:00:31,393
So, after that, they began
watching me closely,

9992
08:00:33,863 --> 08:00:36,398
for fear that --
it shouldn’t happen again.

9993
08:00:37,399 --> 08:00:41,237
And before you knew it,
I was back to my old self,

9994
08:00:41,237 --> 08:00:42,638
just running the streets

9995
08:00:42,638 --> 08:00:44,440
with the rest of the boys,
playing soccer.

9996
08:00:44,440 --> 08:00:46,242
Of course, this was
during the summer break.

9997
08:00:46,242 --> 08:00:49,512
So, we had the time
of our lives, you know [laughs].

9998
08:00:49,512 --> 08:00:50,913
We had the time of our lives.

9999
08:00:50,913 --> 08:00:54,216
And I just can’t tell you
how much fun that was.

10000
08:00:55,584 --> 08:00:59,488
And I really, after that point,

10001
08:00:59,488 --> 08:01:01,924
did not experience
any more crisis.

10002
08:01:03,225 --> 08:01:06,662
My crisis basically
was to myself,

10003
08:01:07,496 --> 08:01:10,065
in the sense
that I was accident prone.

10004
08:01:10,599 --> 08:01:12,401
[laughter]

10005
08:01:12,401 --> 08:01:14,570
Yeah. It was me
and my younger brother,

10006
08:01:14,570 --> 08:01:16,739
and I was the one
getting the injuries.

10007
08:01:16,739 --> 08:01:21,343
He was just only --
he was just having fun with me.

10008
08:01:21,343 --> 08:01:26,916
That’s all, just -- and I --

10009
08:01:27,583 --> 08:01:30,986
it got to the point that
they almost amputated my arm.

10010
08:01:30,986 --> 08:01:33,055
That’s how dangerous
I was to myself.

10011
08:01:34,390 --> 08:01:35,691
But that wasn’t my fault.

10012
08:01:35,691 --> 08:01:39,195
Because that was a whole
different freak accident.

10013
08:01:39,762 --> 08:01:42,765
I wasn’t being dangerous
or anything to myself.

10014
08:01:43,832 --> 08:01:46,669
And then they made up a name
for my brother and me.

10015
08:01:46,669 --> 08:01:48,737
They called us
destructive elements

10016
08:01:48,737 --> 08:01:52,408
because of how bad we were,
ruined the house,

10017
08:01:53,175 --> 08:01:55,110
when there was the one home,
you know,

10018
08:01:55,110 --> 08:01:57,079
destroyed all the electronics
and everything,

10019
08:01:57,079 --> 08:02:01,650
just everything [laughs].
So, growing up in my --

10020
08:02:03,285 --> 08:02:06,789
growing up into my teenage
years, everybody found out,

10021
08:02:06,789 --> 08:02:09,959
yeah, there was
something peculiar with me.

10022
08:02:09,959 --> 08:02:13,696
Because I wasn’t growing as fast
as the rest of the kids.

10023
08:02:14,230 --> 08:02:15,831
So, I had stunted growth.

10024
08:02:17,399 --> 08:02:20,236
No pain, just -- no crisis,
just stunted growth.

10025
08:02:21,670 --> 08:02:24,306
Still played,
walked from school,

10026
08:02:25,174 --> 08:02:27,276
home and back, you know.

10027
08:02:27,276 --> 08:02:30,746
So, I wasn’t challenged
physically.

10028
08:02:32,481 --> 08:02:35,451
On certain occasions though,
during my teenage years,

10029
08:02:35,451 --> 08:02:37,987
I will --
I would get some crisis,

10030
08:02:37,987 --> 08:02:40,489
and they’ll say,
"Okay, that sickle cell is back,

10031
08:02:40,489 --> 08:02:44,393
and we need to pay
a close eye on him."

10032
08:02:44,393 --> 08:02:47,463
And eventually overcome it.

10033
08:02:47,463 --> 08:02:51,734
But, again, that experience
of the sharp pain

10034
08:02:51,734 --> 08:02:55,170
that go through the vein
was very scary.

10035
08:02:55,170 --> 08:02:57,539
You know, it was not just scary

10036
08:02:57,539 --> 08:03:03,145
because you see the concern
in the faces of your loved ones,

10037
08:03:03,145 --> 08:03:05,414
it affects you too, you know.

10038
08:03:05,414 --> 08:03:07,816
It affects you emotionally
that everybody’s --

10039
08:03:07,816 --> 08:03:10,686
that joy that’s there
has disappeared

10040
08:03:10,686 --> 08:03:15,491
just because you are
being worried about.

10041
08:03:16,925 --> 08:03:22,131
So, I felt some kind of way --
I felt very guilty for that.

10042
08:03:22,131 --> 08:03:26,201
And not only that,
I also felt --

10043
08:03:28,070 --> 08:03:32,841
I felt strong mentally after the
whole thing had come and gone.

10044
08:03:32,841 --> 08:03:39,014
It was like, you know, arrows
in the night that pierces you,

10045
08:03:39,014 --> 08:03:42,851
and in the daytime,
you finally overcome that --

10046
08:03:43,419 --> 08:03:46,288
whatever battle you were
going through the night.

10047
08:03:46,288 --> 08:03:49,291
And you be made stronger.

10048
08:03:49,291 --> 08:03:54,229
So, in that sense, mentally,
I got stronger, you know.

10049
08:03:56,465 --> 08:03:58,801
Fast forward to when I came
to this country.

10050
08:03:59,568 --> 08:04:02,905
I got here when I was
in the 11th grade.

10051
08:04:03,939 --> 08:04:05,541
Now, when I got here,

10052
08:04:06,141 --> 08:04:08,644
it looked like I was
still in elementary school.

10053
08:04:08,644 --> 08:04:10,245
I had that stature.

10054
08:04:10,879 --> 08:04:14,149
And then my parents
took me to a doctor

10055
08:04:14,149 --> 08:04:16,185
who diagnosed me
and said, "Okay."

10056
08:04:17,219 --> 08:04:25,994
He gave me ion shots,
and -- oh, God [laughs].

10057
08:04:25,994 --> 08:04:27,396
She’s telling me one minute.

10058
08:04:27,396 --> 08:04:29,832
I have 10 more minutes
left in me.

10059
08:04:29,832 --> 08:04:31,500
But make it quick.

10060
08:04:33,135 --> 08:04:34,737
I had a growth spurt of two --

10061
08:04:35,804 --> 08:04:40,042
for about two years
after I had seen this doctor.

10062
08:04:40,042 --> 08:04:43,212
And come to my mid-20s --

10063
08:04:43,212 --> 08:04:45,247
I mean, I went to school,
went to work.

10064
08:04:46,081 --> 08:04:49,251
It really didn’t affect me
until I got to my mid-20s.

10065
08:04:49,251 --> 08:04:51,153
I got to my mid-20s,

10066
08:04:51,153 --> 08:04:55,724
and I started feeling the
effects of sickle cell again.

10067
08:04:56,392 --> 08:04:57,626
And I thank God --

10068
08:04:57,626 --> 08:05:00,629
I don’t know if you guys know
about the Affordable Care Act.

10069
08:05:01,397 --> 08:05:03,232
Obamacare,
they call it back home.

10070
08:05:03,232 --> 08:05:05,601
And that was a godsend.

10071
08:05:05,601 --> 08:05:07,403
Because sickle cell made me

10072
08:05:08,270 --> 08:05:10,439
broke over and over
and over again

10073
08:05:10,439 --> 08:05:14,710
before that Obamacare
or Affordable Care Act came.

10074
08:05:14,710 --> 08:05:16,478
Because year after year,

10075
08:05:16,478 --> 08:05:19,081
I was spending a weekend
in the hospital.

10076
08:05:19,081 --> 08:05:22,084
That’s easily 15 to $20,000
out of my pocket.

10077
08:05:24,353 --> 08:05:27,656
Okay.
And I’ll round up real quick.

10078
08:05:27,656 --> 08:05:30,459
Fast forward to when
I came to the NIH,

10079
08:05:32,494 --> 08:05:37,800
I was evaluated and got into Dr.
Thein’s program.

10080
08:05:37,800 --> 08:05:40,436
Ever since I started
taking mitapivat,

10081
08:05:40,436 --> 08:05:44,640
I have not had
one sick day in my life,

10082
08:05:44,640 --> 08:05:46,642
one crisis in my life.

10083
08:05:46,642 --> 08:05:48,310
[applause]

10084
08:05:48,310 --> 08:05:52,681
So, I thank God for NIH.
I thank God for the team,

10085
08:05:52,681 --> 08:05:57,152
Dr. Thein’s team, and Wynona,
who was the very first person

10086
08:05:57,152 --> 08:06:01,590
I met when I walked through NIH.
I wish I had more time.

10087
08:06:01,590 --> 08:06:05,427
But she’s driving me off stage.
It’s not my fault. Thank you.

10088
08:06:05,427 --> 08:06:06,762
[applause]

10089
08:06:06,762 --> 08:06:08,197
Morette Wright: Don’t go.

10090
08:06:08,197 --> 08:06:09,798
Patrick Omwuemene: Okay.

10091
08:06:16,505 --> 08:06:17,806
Jennifer Knight-Madden: Okay.

10092
08:06:17,806 --> 08:06:20,676
I’m going to ask the speakers
earlier this session

10093
08:06:20,676 --> 08:06:22,911
to just join us back
on the platform.

10094
08:06:25,347 --> 08:06:31,787
So, Dr. Walters, Dr. Little,
and Dr. Telen.

10095
08:06:33,255 --> 08:06:39,962
And as they come,
we’re going to turn the panel

10096
08:06:39,962 --> 08:06:41,697
discussion around a little bit.

10097
08:06:41,697 --> 08:06:43,799
Because what we’re actually
going to do

10098
08:06:44,700 --> 08:06:47,970
is that we’re going to ask
our patient representatives

10099
08:06:47,970 --> 08:06:50,873
to lead off with the questions,
and they have the freedom

10100
08:06:50,873 --> 08:06:54,643
to ask anybody in the room
or their fellow panelists,

10101
08:06:54,643 --> 08:06:56,578
anything that might have
come to them

10102
08:06:56,578 --> 08:07:00,749
that they might be wondering
and would like answered

10103
08:07:00,749 --> 08:07:05,254
before they go home.
All right. Any questions?

10104
08:07:05,254 --> 08:07:06,522
Morette Wright: Well --

10105
08:07:06,522 --> 08:07:08,590
Jennifer Knight-Madden: There’s
a mic there,

10106
08:07:08,590 --> 08:07:10,192
or you can use this one.

10107
08:07:17,232 --> 08:07:20,269
Morette Wright: Okay. Thanks
for that opportunity, Prof.

10108
08:07:21,436 --> 08:07:28,076
This section of the presentation
on the complications,

10109
08:07:30,612 --> 08:07:35,884
okay, right, the cardiac disease
by Dr. Thomas.

10110
08:07:38,487 --> 08:07:41,690
Renal disease became heavy --

10111
08:07:41,690 --> 08:07:45,460
that section really,
really became heavy for me.

10112
08:07:46,795 --> 08:07:50,432
Having the burden of living
with sickle cell disease

10113
08:07:50,432 --> 08:07:53,902
and then there are
these added complications

10114
08:07:54,469 --> 08:07:57,873
that we might experience

10115
08:07:57,873 --> 08:08:01,443
and know we have to live
with that, it had me thinking.

10116
08:08:02,344 --> 08:08:06,014
Are there any strategies?
Are there any techniques?

10117
08:08:06,014 --> 08:08:11,119
How can you guide people
living with sickle cell disease

10118
08:08:11,119 --> 08:08:17,359
so that we can mitigate some
of these other complications?

10119
08:08:17,359 --> 08:08:21,363
That’s what was on my mind
during that segment.

10120
08:08:22,130 --> 08:08:25,267
So, you know,
it’s more preventative strategy.

10121
08:08:25,267 --> 08:08:28,670
Because here,
you’re teaching or sharing

10122
08:08:28,670 --> 08:08:32,407
with the healthcare workers
how to treat us.

10123
08:08:32,407 --> 08:08:36,078
But even before you get there,
how can you help us?

10124
08:08:36,078 --> 08:08:37,980
That’s where my thought is.

10125
08:08:37,980 --> 08:08:40,349
Jennifer Knight-Madden: Okay.
That sounds like a question

10126
08:08:40,349 --> 08:08:44,319
for a jack-of-all-trades,
adult clinician.

10127
08:08:45,921 --> 08:08:48,390
Or yes, if you want to come,
please go.

10128
08:08:48,390 --> 08:08:49,958
Female Speaker: Nice shorts.

10129
08:08:51,860 --> 08:08:53,595
Thomas d’Humières: Yeah.
I changed my --

10130
08:08:53,595 --> 08:08:57,866
[laughter]

10131
08:08:57,866 --> 08:09:00,435
Sorry about that.
I’m in Jamaica.

10132
08:09:01,803 --> 08:09:03,271
This is actually
a very good question.

10133
08:09:03,271 --> 08:09:08,543
Because I talk about
how we can provide some help

10134
08:09:08,543 --> 08:09:12,681
to diagnose rhythm issues.

10135
08:09:12,681 --> 08:09:17,052
But before this -- and I didn’t
have time to talk about this.

10136
08:09:17,052 --> 08:09:21,490
But for me, this is -- there is
a very, very deep question

10137
08:09:21,490 --> 08:09:24,660
about physical exercise
and metabolic disorders.

10138
08:09:24,660 --> 08:09:26,928
These are the two main things.

10139
08:09:26,928 --> 08:09:32,701
And we are very into
physical exercise again.

10140
08:09:32,701 --> 08:09:34,202
Because for a long time,

10141
08:09:34,202 --> 08:09:37,039
no exercise
for sickle cell patients.

10142
08:09:37,539 --> 08:09:41,710
And then we are seeing now,
more and more data about, yeah,

10143
08:09:41,710 --> 08:09:45,113
it should be -- yeah,
you could do it but be careful.

10144
08:09:45,113 --> 08:09:47,816
But without any real frame.

10145
08:09:47,816 --> 08:09:51,853
So, we are currently doing
an ongoing trial, randomized,

10146
08:09:51,853 --> 08:09:57,192
with, really, how are we
going to implement into life,

10147
08:09:57,192 --> 08:10:04,099
into real life, the start
of a good physical exercise

10148
08:10:04,099 --> 08:10:08,003
that we know is beneficial
for the quality of life,

10149
08:10:08,670 --> 08:10:10,439
the occurring of
a vaso-occlusive crisis,

10150
08:10:10,439 --> 08:10:13,375
and the --
even the quality of the muscle

10151
08:10:13,375 --> 08:10:17,412
when we look at it
in the microscopic way.

10152
08:10:18,180 --> 08:10:20,282
And the second part
is metabolic way.

10153
08:10:21,349 --> 08:10:23,051
For a long time,
my patient asked me,

10154
08:10:23,051 --> 08:10:26,488
"Do I have to pay attention
to what I eat, to what --"

10155
08:10:26,488 --> 08:10:28,490
I was like,
there’s so much for them.

10156
08:10:30,258 --> 08:10:34,629
I won’t bother telling them
to pay attention to the diet

10157
08:10:34,629 --> 08:10:36,431
because there is so much --

10158
08:10:36,431 --> 08:10:39,401
and actually, I’m going to --
coming back to this statement,

10159
08:10:39,401 --> 08:10:42,037
because I think
metabolic disorder

10160
08:10:42,037 --> 08:10:44,439
is amplifying the disease.

10161
08:10:44,439 --> 08:10:48,043
So, I think that
in terms of prevention,

10162
08:10:48,610 --> 08:10:52,681
there is an access to diet
and healthy condition

10163
08:10:52,681 --> 08:10:54,850
also to prevent
the complication.

10164
08:10:54,850 --> 08:10:57,953
This is speculative,
but I truly think --

10165
08:10:58,487 --> 08:11:02,023
[applause]

10166
08:11:02,023 --> 08:11:05,927
Marilyn Telen: Can I --
I would just add, you know,

10167
08:11:05,927 --> 08:11:07,929
when we heard
about heart disease,

10168
08:11:07,929 --> 08:11:09,831
lung disease,
kidney disease,

10169
08:11:09,831 --> 08:11:13,001
one of the points
that kept on being made is,

10170
08:11:13,001 --> 08:11:17,672
right now, we’re picking these
things up for the most part,

10171
08:11:18,206 --> 08:11:22,010
when they’re there already,
not before they’re there.

10172
08:11:22,010 --> 08:11:25,981
And I think the registries,
like Dr. Little talked about,

10173
08:11:27,015 --> 08:11:28,283
my own effort,

10174
08:11:28,283 --> 08:11:33,722
which is looking at what
non-hemoglobin genetic variation

10175
08:11:33,722 --> 08:11:36,258
affects outcomes
like kidney disease --

10176
08:11:36,258 --> 08:11:41,530
and we’ve developed a risk score
that can predict the progression

10177
08:11:41,530 --> 08:11:44,166
of decreasing kidney function.

10178
08:11:44,166 --> 08:11:48,036
That needs to be tested
prospectively still,

10179
08:11:48,036 --> 08:11:51,907
but these are the things
that help us figure out,

10180
08:11:52,407 --> 08:11:55,577
you know,
who needs what treatment,

10181
08:11:55,577 --> 08:11:59,648
so we prevent things
like renal failure

10182
08:11:59,648 --> 08:12:02,751
or bad lung disease
or bad heart disease.

10183
08:12:03,618 --> 08:12:07,889
And we really need to put a lot
more effort into learning that,

10184
08:12:07,889 --> 08:12:10,926
so that when people like you
ask the question,

10185
08:12:10,926 --> 08:12:13,361
how are you going to keep me
from getting kidney disease

10186
08:12:13,361 --> 08:12:16,531
that I don’t have now,
we have an answer for you.

10187
08:12:16,531 --> 08:12:19,434
Either we can test you,
and you’re low risk.

10188
08:12:19,434 --> 08:12:22,671
Or, well, you are high risk
by these standards,

10189
08:12:22,671 --> 08:12:24,272
and this is what we could do.

10190
08:12:24,272 --> 08:12:27,609
Or at least, this is the study
that we’re doing with people

10191
08:12:27,609 --> 08:12:31,046
who are at high risk to find out
what the best thing to do is.

10192
08:12:37,719 --> 08:12:38,954
Jane Little: I was just going
to say,

10193
08:12:38,954 --> 08:12:41,423
I think this is a little bit
like cancer used to be,

10194
08:12:41,423 --> 08:12:44,926
where they would do -- you know,
do anything to save your life.

10195
08:12:44,926 --> 08:12:46,795
And then they realized
there were side effects

10196
08:12:46,795 --> 08:12:48,163
of the treatments

10197
08:12:48,163 --> 08:12:50,932
that they could change
that would make things better.

10198
08:12:50,932 --> 08:12:53,101
So, I think your question
is really going to go back

10199
08:12:53,101 --> 08:12:55,403
to the pediatricians,
who, for a long time,

10200
08:12:55,403 --> 08:12:58,473
have just been trying to
get kids through the keyhole.

10201
08:12:58,473 --> 08:13:01,509
And now, I think they’re trying
to refine what they’re doing,

10202
08:13:02,611 --> 08:13:03,845
so that the kids have better --

10203
08:13:03,845 --> 08:13:06,248
you know, I’m sure that people
are working on exercise,

10204
08:13:06,248 --> 08:13:07,549
working on diet.

10205
08:13:07,549 --> 08:13:09,684
Because the kids are going
to be the answer, right?

10206
08:13:09,684 --> 08:13:11,019
Like, if they’re healthier,

10207
08:13:11,019 --> 08:13:13,889
it’s going to make the stuff
that Mark does easier.

10208
08:13:13,889 --> 08:13:16,358
It’s going to make -- if they
don’t have a transformative

10209
08:13:16,358 --> 08:13:18,960
or curative therapy, easier.
I mean, I think it’s --

10210
08:13:19,661 --> 08:13:22,163
the kids are going to
really change that, I think.

10211
08:13:23,798 --> 08:13:26,301
Patrick Omwuemene: Okay.
For someone such as me

10212
08:13:26,301 --> 08:13:28,370
dealing with a mild form
of sickle cell --

10213
08:13:29,271 --> 08:13:31,873
I mean, I didn’t think it was
mild form with all the pain

10214
08:13:31,873 --> 08:13:33,508
[unintelligible]
even, until I was told,

10215
08:13:33,508 --> 08:13:38,246
well, I’m not sick enough to get
onto the genome generic program.

10216
08:13:38,246 --> 08:13:44,185
But I found out recently
that self-care

10217
08:13:45,120 --> 08:13:50,992
is supposed to be one of the --
foremost things to do,

10218
08:13:52,627 --> 08:13:57,332
not just talking about it,
hearing about it, watching TV,

10219
08:13:58,033 --> 08:14:01,703
sitting down,
and getting the information

10220
08:14:01,703 --> 08:14:04,072
and without doing
anything with it.

10221
08:14:05,340 --> 08:14:09,411
Eating whole foods, vegetables,
blending your own smoothie,

10222
08:14:11,279 --> 08:14:15,951
not eating anything like
what they offer in fast food

10223
08:14:15,951 --> 08:14:17,419
and all that.

10224
08:14:17,419 --> 08:14:23,091
I have practiced that for years,
and it’s kept me healthy.

10225
08:14:23,591 --> 08:14:27,295
And then this summer --
now,

10226
08:14:27,295 --> 08:14:29,864
this is the part you guys
are going to cringe at --

10227
08:14:29,864 --> 08:14:34,602
this summer, I found out,
well, I’m exercising,

10228
08:14:34,602 --> 08:14:38,907
but my body
is not recovering fast enough.

10229
08:14:38,907 --> 08:14:42,944
So, I --
what do athletes do

10230
08:14:42,944 --> 08:14:45,280
to get their body
to recover fast enough?

10231
08:14:45,280 --> 08:14:50,352
They soak themselves in ice.
So, I did that this past winter.

10232
08:14:51,486 --> 08:14:55,023
Yeah, I know. That’s
the reaction I was expecting.

10233
08:14:55,957 --> 08:14:58,960
But [laughs]
yes, I did that all winter,

10234
08:14:58,960 --> 08:15:03,698
and it helped me a great deal.
No, I won’t recommend --

10235
08:15:03,698 --> 08:15:04,899
[laughter]

10236
08:15:04,899 --> 08:15:10,338
I won’t recommend you send your
patients going down this route.

10237
08:15:10,972 --> 08:15:15,143
This is just me.
So, I ate properly. I exercised.

10238
08:15:15,143 --> 08:15:18,079
I watched my exercise.
I didn’t overdo it.

10239
08:15:18,079 --> 08:15:23,451
I took cold --
you know, cold baths.

10240
08:15:23,952 --> 08:15:25,387
And during this winter,

10241
08:15:25,387 --> 08:15:28,289
I began to learn
how to swim in cold water.

10242
08:15:28,289 --> 08:15:31,526
So, again,
this is not something you --

10243
08:15:31,526 --> 08:15:33,595
Mark Walters: Don’t do this
outside the NIH.

10244
08:15:33,595 --> 08:15:35,130
[laughter]

10245
08:15:35,130 --> 08:15:38,199
Patrick Omwuemene: But for
people with mild sickle cell --

10246
08:15:38,199 --> 08:15:41,469
and Dr. Thein did not --
please, do not point at her.

10247
08:15:41,469 --> 08:15:44,506
She did not know anything about
this before I began doing it.

10248
08:15:45,140 --> 08:15:48,309
I did not tell
I was going to do it [laughs].

10249
08:15:48,309 --> 08:15:52,714
But then again, I’m just trying
to say self-care is paramount

10250
08:15:52,714 --> 08:15:54,816
if you’re going to,
you know,

10251
08:15:55,750 --> 08:15:59,587
think of preserving your kidney
and liver or other organs,

10252
08:15:59,587 --> 08:16:02,924
at least for people dealing with
the mild form of sickle cell,

10253
08:16:02,924 --> 08:16:04,592
and drinking a lot of water,

10254
08:16:04,592 --> 08:16:07,062
listening to your doctor too,
unlike me.

10255
08:16:07,062 --> 08:16:08,430
Jennifer Knight-Madden: Patrick,
do you have any questions

10256
08:16:08,430 --> 08:16:09,731
for any of our panelists?

10257
08:16:09,731 --> 08:16:11,533
Patrick Omwuemene: So,
my question

10258
08:16:11,533 --> 08:16:16,738
was for Dr. Allan Doctor,
but he doesn’t seem to be --

10259
08:16:16,738 --> 08:16:18,006
Jennifer Knight-Madden:
[unintelligible]

10260
08:16:18,006 --> 08:16:19,207
Patrick Omwuemene: Okay.

10261
08:16:19,207 --> 08:16:23,678
So, I guess, yeah, that was
my only question really.

10262
08:16:23,678 --> 08:16:25,513
He said something
that sparked my interest,

10263
08:16:25,513 --> 08:16:27,649
when he talked about eNOS

10264
08:16:27,649 --> 08:16:30,952
and the nitric oxide
and how they react.

10265
08:16:30,952 --> 08:16:32,987
And that was my question
was going to be.

10266
08:16:34,422 --> 08:16:37,725
Is it something that --
is eNOS something

10267
08:16:37,725 --> 08:16:40,628
that could be
artificially generated,

10268
08:16:40,628 --> 08:16:42,363
to be introduced to the body,

10269
08:16:43,231 --> 08:16:46,334
so that it helps
the nitric oxide in your body,

10270
08:16:46,334 --> 08:16:49,537
and therefore
the oxygen level increase

10271
08:16:49,537 --> 08:16:52,173
and all that,
for you to function --

10272
08:16:52,173 --> 08:16:54,375
for your cells
to function much better?

10273
08:16:56,111 --> 08:16:58,680
I got a brief answer from him.

10274
08:16:58,680 --> 08:17:01,449
I was bringing up this question
for the benefit of everybody.

10275
08:17:01,449 --> 08:17:03,384
He said, they are
still walking on it.

10276
08:17:03,384 --> 08:17:04,786
It’s still being worked on.

10277
08:17:04,786 --> 08:17:09,324
But the best way to go about it
right now is through exercise.

10278
08:17:09,324 --> 08:17:11,559
Because it does that too.

10279
08:17:16,297 --> 08:17:17,832
Jennifer Knight-Madden: Thank
you very much.

10280
08:17:17,832 --> 08:17:21,369
So, you can see
that our patients have listened

10281
08:17:21,369 --> 08:17:26,508
and have very good questions
that really interrogate

10282
08:17:26,508 --> 08:17:29,544
what we’ve been saying over
the last two years -- two days.

10283
08:17:31,012 --> 08:17:34,549
Okay. I’m going to now
throw the floor open.

10284
08:17:34,549 --> 08:17:38,119
If there are any questions
in the room,

10285
08:17:38,119 --> 08:17:41,189
either to our patients or to
any of our other panelists,

10286
08:17:41,189 --> 08:17:42,790
we could take those now.

10287
08:17:44,926 --> 08:17:47,095
Female Speaker: Comment on
your question

10288
08:17:47,095 --> 08:17:49,831
about what can we do?
So, I’m a pediatrician,

10289
08:17:49,831 --> 08:17:53,935
and I agree with Dr. Little
that we have to do good job

10290
08:17:53,935 --> 08:17:56,171
with our patients
when they are young.

10291
08:17:56,171 --> 08:17:59,140
And I want to tell you we have
a clinic at my institution

10292
08:17:59,140 --> 08:18:02,177
where we are working on
providing whole-person care

10293
08:18:02,177 --> 08:18:03,444
to our patients.

10294
08:18:03,444 --> 08:18:05,313
So, we are focusing
on sickle cell,

10295
08:18:05,313 --> 08:18:07,916
all the disease-modifying
therapies,

10296
08:18:07,916 --> 08:18:09,117
and other things,

10297
08:18:09,117 --> 08:18:12,020
but also looking at
how can we help with the pain?

10298
08:18:12,020 --> 08:18:14,522
So, using non-pharmacologic
measures,

10299
08:18:14,522 --> 08:18:19,427
like acupuncture, mindfulness,
nutrition, very important,

10300
08:18:19,427 --> 08:18:20,828
also physical therapy,

10301
08:18:20,828 --> 08:18:22,063
and what are the things
people --

10302
08:18:22,063 --> 08:18:26,434
kids can do, like at young age,
exercise, stretching,

10303
08:18:26,434 --> 08:18:28,303
and things like that?

10304
08:18:28,303 --> 08:18:30,305
So, those are some of
the things we are doing

10305
08:18:30,305 --> 08:18:33,441
and including psychology
and psychiatry.

10306
08:18:33,441 --> 08:18:35,343
Because many times,
you know, depression,

10307
08:18:35,343 --> 08:18:39,380
anxiety, these are very --
they are risk factors for pain.

10308
08:18:39,380 --> 08:18:41,849
So, we work as
a multidisciplinary clinic,

10309
08:18:41,849 --> 08:18:44,152
and starting at
the young age of eight,

10310
08:18:44,152 --> 08:18:47,655
when patients can understand
why these things are important,

10311
08:18:47,655 --> 08:18:49,691
and hopefully we can
improve the outcome.

10312
08:18:49,691 --> 08:18:52,794
We also have access
to education specialists.

10313
08:18:52,794 --> 08:18:55,363
So, patients, you know,
they don’t miss school.

10314
08:18:55,363 --> 08:18:57,332
Because our goal
is for them to have --

10315
08:18:57,332 --> 08:19:00,068
be successful like you
and be employed

10316
08:19:00,068 --> 08:19:01,636
and financially independent.

10317
08:19:01,636 --> 08:19:03,504
So, we are working at,
you know --

10318
08:19:03,504 --> 08:19:05,840
so, I think pediatrics
is the key.

10319
08:19:05,840 --> 08:19:09,110
We need to do all these things
at a young age. Thank you.

10320
08:19:09,110 --> 08:19:10,411
[applause]

10321
08:19:10,411 --> 08:19:12,013
Jennifer Knight-Madden: One
more question.

10322
08:19:12,947 --> 08:19:15,016
Swee Lay Thein: I should also
add that, you know,

10323
08:19:15,016 --> 08:19:16,618
we are all here together.

10324
08:19:18,219 --> 08:19:21,656
I say that to you repeatedly.
We work together.

10325
08:19:23,191 --> 08:19:27,295
On your part, the patient,
as you say, practice self-care.

10326
08:19:27,295 --> 08:19:29,264
As you know, I keep saying
that all the time

10327
08:19:29,264 --> 08:19:31,199
when I see you in the clinic.

10328
08:19:31,199 --> 08:19:34,168
You know, it’s not
[unintelligible] limitation,

10329
08:19:34,168 --> 08:19:36,170
even for us
without sickle cell disease.

10330
08:19:36,738 --> 08:19:39,107
We exercise.
We make sure we eat properly.

10331
08:19:39,874 --> 08:19:41,242
And then the other thing is,

10332
08:19:41,242 --> 08:19:43,811
if you want to bring
more treatments on the market,

10333
08:19:44,746 --> 08:19:47,148
is for the patients
to be more research aware

10334
08:19:47,815 --> 08:19:51,552
and to participate when you can.
Because if you don’t,

10335
08:19:51,552 --> 08:19:54,622
we will not be able to bring
more medicines into the market.

10336
08:19:56,991 --> 08:20:00,295
Jennifer Knight-Madden: Okay.
Thank you. Any more questions?

10337
08:20:00,295 --> 08:20:02,697
Okay, Dr. Obi.

10338
08:20:02,697 --> 08:20:04,899
Obiageli Nnodu: Yes.
Thank you very much.

10339
08:20:04,899 --> 08:20:07,368
I’ve had an exciting time
at this meeting.

10340
08:20:08,002 --> 08:20:13,741
And I was just wondering,
especially when Jane was talking

10341
08:20:13,741 --> 08:20:17,679
about the import
of the disease registry

10342
08:20:17,679 --> 08:20:22,116
that has been established
in high-budget countries,

10343
08:20:22,116 --> 08:20:25,853
I don’t know if there is
any interest in exploring

10344
08:20:26,621 --> 08:20:28,890
the usefulness
of that registry.

10345
08:20:28,890 --> 08:20:31,659
Because we in --
sickle in Africa,

10346
08:20:31,659 --> 08:20:37,665
we have over 34,000 patients
registered now in that registry.

10347
08:20:37,665 --> 08:20:39,033
Jennifer Knight-Madden: That’s
the one I was telling you.

10348
08:20:39,033 --> 08:20:40,635
Obiageli Nnodu: But I haven’t
seen anyone

10349
08:20:41,369 --> 08:20:45,707
take an interest in it,
apart from me [laughs].

10350
08:20:45,707 --> 08:20:49,310
So, I’m just wondering how we
are going to collectively --

10351
08:20:49,310 --> 08:20:50,678
because [unintelligible]

10352
08:20:50,678 --> 08:20:52,146
was saying that
we should work together.

10353
08:20:52,146 --> 08:20:53,381
Jane Little: Yeah.

10354
08:20:53,381 --> 08:20:55,416
Obiageli Nnodu: And I believe
that working together;

10355
08:20:55,416 --> 08:20:57,552
it’s working together
globally.

10356
08:20:57,552 --> 08:20:58,886
Jane Little: Yes, I agree.

10357
08:20:58,886 --> 08:21:01,255
I absolutely agree with that.
And I think --

10358
08:21:02,123 --> 08:21:05,093
I remember when GRNDaD was born,
it was at a meeting

10359
08:21:05,093 --> 08:21:09,297
where these different people
from different leadership things

10360
08:21:09,297 --> 08:21:11,966
kept saying a registry
is coming.

10361
08:21:12,700 --> 08:21:16,170
I thought, you know, I can’t
wait that long, you know.

10362
08:21:16,170 --> 08:21:20,041
And so, I think there is
some role in --

10363
08:21:20,041 --> 08:21:26,013
as individual investigators
and people with some power,

10364
08:21:26,514 --> 08:21:30,785
to start investigating
registries where you are,

10365
08:21:30,785 --> 08:21:34,689
and we have --
I came to Jamaica day early

10366
08:21:34,689 --> 08:21:36,090
because we’re sort of talking
about

10367
08:21:36,090 --> 08:21:39,427
whether we can try
to develop some sheets

10368
08:21:39,427 --> 08:21:40,928
that are appropriate
for low-

10369
08:21:40,928 --> 08:21:44,332
and middle-income countries
that might be shareable.

10370
08:21:44,332 --> 08:21:46,567
So, we might have a committee
that we’re going to form.

10371
08:21:46,567 --> 08:21:48,736
I have a really
good partner in Malawi,

10372
08:21:48,736 --> 08:21:50,905
and she’s very interested
in doing that.

10373
08:21:50,905 --> 08:21:52,774
So, I don’t know if that’s
going to be the best thing,

10374
08:21:52,774 --> 08:21:54,475
but I think it’s worth a try.

10375
08:21:54,475 --> 08:21:57,845
Because I think, you know,
like WhatsApp, like I didn’t --

10376
08:21:57,845 --> 08:22:00,915
when I was in clinic here
on Tuesday,

10377
08:22:00,915 --> 08:22:04,018
they were using WhatsApp
to reach out to patients.

10378
08:22:04,018 --> 08:22:05,853
And that would never
have occurred to me.

10379
08:22:05,853 --> 08:22:07,688
So, I think there are --

10380
08:22:07,688 --> 08:22:10,458
you can’t base it
on internet in the clinic.

10381
08:22:10,458 --> 08:22:12,927
Because I think it’s unstable
in a lot of places.

10382
08:22:12,927 --> 08:22:14,429
But you can certainly
store it there,

10383
08:22:14,429 --> 08:22:16,330
and you can probably
communicate through phones

10384
08:22:16,330 --> 08:22:18,299
in ways
that I don’t understand.

10385
08:22:18,299 --> 08:22:20,635
So, I think if we do try
to work together --

10386
08:22:20,635 --> 08:22:21,903
and if you’re interested,
let me know.

10387
08:22:21,903 --> 08:22:24,105
Because I don’t know
if our efforts going to work,

10388
08:22:24,105 --> 08:22:27,074
but we’re going to try to make
some common elements

10389
08:22:27,074 --> 08:22:29,710
that are SPARCO-compatible,
GRNDaD-compatible,

10390
08:22:29,710 --> 08:22:32,013
so we can learn together
over the next decade,

10391
08:22:33,281 --> 08:22:34,982
Jennifer Knight-Madden: The two
registries actually are

10392
08:22:34,982 --> 08:22:36,751
fairly close.

10393
08:22:36,751 --> 08:22:39,353
Because we’ve looked
at the ontology, both of them.

10394
08:22:39,353 --> 08:22:42,089
Because, you know, in terms of
us starting a registry here,

10395
08:22:42,089 --> 08:22:45,226
clearly we have links
in different places.

10396
08:22:45,226 --> 08:22:47,728
And they actually
overlap quite a bit.

10397
08:22:47,728 --> 08:22:50,898
And I think it would be
a great PhD project

10398
08:22:50,898 --> 08:22:54,769
for the appropriate person
to actually try to come up

10399
08:22:54,769 --> 08:23:00,141
with a way to really bridge
those properly,

10400
08:23:00,141 --> 08:23:03,411
so that, you know, people
who come on board afterwards

10401
08:23:03,411 --> 08:23:05,813
would actually be speaking
to both registries.

10402
08:23:05,813 --> 08:23:07,315
Jane Little: Yeah. I think
that’s going to be the key.

10403
08:23:07,315 --> 08:23:08,549
Jennifer Knight-Madden: Yeah.

10404
08:23:08,549 --> 08:23:10,384
Jane Little: Because I’m trying
to be too top down about it,

10405
08:23:10,384 --> 08:23:13,321
but make sure that the elements
can speak to each other.

10406
08:23:13,321 --> 08:23:18,593
And what we did in GRNDaD
is that we try to update it

10407
08:23:18,593 --> 08:23:21,028
once a year with international
partners, so no one --

10408
08:23:21,028 --> 08:23:23,598
so, we’re not mutating too far.

10409
08:23:23,598 --> 08:23:26,868
You want them
to change consistently.

10410
08:23:27,435 --> 08:23:28,703
But I think the needs
of the low-

10411
08:23:28,703 --> 08:23:30,571
and middle-income countries
are different,

10412
08:23:30,571 --> 08:23:33,307
and I don’t think GRNDaD
will be purely applicable.

10413
08:23:33,307 --> 08:23:35,376
So, I think there are changes
that we need to make.

10414
08:23:35,376 --> 08:23:36,711
But I think
it’s really important.

10415
08:23:36,711 --> 08:23:37,945
Jennifer Knight-Madden: So,
definitely something

10416
08:23:37,945 --> 08:23:39,146
coming out of this meeting

10417
08:23:39,146 --> 08:23:42,683
is a meeting of the mind
so that -- going forward.

10418
08:23:44,285 --> 08:23:45,920
Patrick Omwuemene: One last
question.

10419
08:23:45,920 --> 08:23:49,123
I want to ask
if any of the researchers here

10420
08:23:49,123 --> 08:23:54,161
or any researcher you know,
is doing something about --

10421
08:23:55,429 --> 08:23:57,665
somebody spoke about
insomnia yesterday,

10422
08:23:57,665 --> 08:24:00,334
and I’m greatly affected
by insomnia.

10423
08:24:00,334 --> 08:24:05,439
And the reason
I’m greatly affected by insomnia

10424
08:24:05,439 --> 08:24:09,410
is because of priapism.
I wake up every two hours,

10425
08:24:10,011 --> 08:24:12,647
and sometimes I can’t
go back to sleep.

10426
08:24:12,647 --> 08:24:15,650
Sometimes I have to drink water,
do some exercise,

10427
08:24:15,650 --> 08:24:20,254
just to get some relief.
So, open question.

10428
08:24:25,159 --> 08:24:35,403
Monika Haack: This is one topic
where [unintelligible].

10429
08:24:35,970 --> 08:24:41,008
So, I think we talked
about diet and exercise.

10430
08:24:41,576 --> 08:24:45,146
And I think sleep
is another big issue

10431
08:24:46,314 --> 08:24:50,184
that I think can be used
as a preventive measure,

10432
08:24:50,184 --> 08:24:51,919
so -- to really strengthen.

10433
08:24:51,919 --> 08:24:56,257
Because sleep is really involved
in so many different diseases.

10434
08:24:56,891 --> 08:25:00,227
And for example,
it can increase inflammation,

10435
08:25:00,227 --> 08:25:03,998
which is a big issue
also in sickle cell.

10436
08:25:03,998 --> 08:25:07,602
It increases pain.
Pain processing can be changed.

10437
08:25:07,602 --> 08:25:10,037
And it can also
not be easily changed.

10438
08:25:10,037 --> 08:25:12,206
So, it’s not like,
oh, I get better in sleep.

10439
08:25:12,206 --> 08:25:16,510
So, I think this preventive
measures of really making sure

10440
08:25:16,510 --> 08:25:20,047
not to get any sleep disorders,
I think is very important.

10441
08:25:20,047 --> 08:25:22,750
And there’s very --
a lot of things out there.

10442
08:25:22,750 --> 08:25:26,087
There’s wearable out there.
There’s diaries, questionnaires,

10443
08:25:26,087 --> 08:25:30,925
where we really can target sleep
early on, and then do measures.

10444
08:25:30,925 --> 08:25:34,128
But then once insomnia
is there, right,

10445
08:25:34,128 --> 08:25:38,399
what you just described of
waking up frequently at night,

10446
08:25:38,399 --> 08:25:43,871
I think that would then really--
needs follow up to really see,

10447
08:25:43,871 --> 08:25:46,273
what are the causes
of that insomnia?

10448
08:25:46,273 --> 08:25:49,310
Is that related --
is there a specific cause?

10449
08:25:49,310 --> 08:25:51,879
Or is that --
what kind of the --

10450
08:25:51,879 --> 08:25:53,814
what are the reasons for that?

10451
08:25:53,814 --> 08:25:58,319
To really then go --
that was what?

10452
08:26:01,656 --> 08:26:03,624
Female Speaker: So, he has
a cause for it.

10453
08:26:03,624 --> 08:26:07,194
Monika Haack: Oh, yes. Yeah.
Exactly.

10454
08:26:07,194 --> 08:26:11,399
So, that would be then important
to really figure out

10455
08:26:11,399 --> 08:26:12,967
what that is
and what can be done.

10456
08:26:12,967 --> 08:26:14,168
And it also can be --

10457
08:26:14,168 --> 08:26:16,404
I mean, there’s also
a lot of medications

10458
08:26:16,404 --> 08:26:18,439
that we are aware of.

10459
08:26:18,439 --> 08:26:20,141
They have sleep
disturbing effects.

10460
08:26:20,141 --> 08:26:22,910
So, I think that’s also
important to take

10461
08:26:22,910 --> 08:26:24,812
into consideration.

10462
08:26:24,812 --> 08:26:26,247
Jennifer Knight-Madden: 
Thank you.

10463
08:26:26,247 --> 08:26:29,150
Okay. We’re going to give
Morette the last question.

10464
08:26:29,150 --> 08:26:31,952
And then I’ll end --
we’ll wrap up the panel.

10465
08:26:31,952 --> 08:26:34,722
Morette Wright: Right.
Just a quick question, you know,

10466
08:26:34,722 --> 08:26:36,891
following up
on Patrick’s lead.

10467
08:26:36,891 --> 08:26:42,463
Is there anyone here who has
been treating avascular necrosis

10468
08:26:44,799 --> 08:26:46,500
outside of hip replacement?

10469
08:26:47,535 --> 08:26:50,771
If you have been, I’d really
love to hear your approach

10470
08:26:51,272 --> 08:26:55,109
to helping your patient
manage AVN.

10471
08:26:58,345 --> 08:27:02,249
Jennifer Knight-Madden: Okay.
Nobody is -- okay.

10472
08:27:02,249 --> 08:27:04,185
So, I’d like you
to join with me

10473
08:27:04,185 --> 08:27:07,221
with thanking the panel
for their --

10474
08:27:07,755 --> 08:27:11,659
[applause]

10475
08:27:11,659 --> 08:27:15,362
Okay. And that brings
the end of this session.

10476
08:27:16,197 --> 08:27:18,365
So, I’m going to invite
Swee Lay back on.

10477
08:27:20,234 --> 08:27:22,369
Swee Lay Thein: I thought you
heard enough from me already.

10478
08:27:30,311 --> 08:27:34,949
Well, the people
listening on Zoom,

10479
08:27:35,683 --> 08:27:38,786
and I hope you
all agree with me

10480
08:27:38,786 --> 08:27:42,623
that this has been
a really fantastic conference.

10481
08:27:42,623 --> 08:27:44,225
It has rejuvenated me.

10482
08:27:44,825 --> 08:27:49,063
And I am so pleased to meet
so many new faces too.

10483
08:27:49,730 --> 08:27:53,234
And I hope we can start
collaborating on many things.

10484
08:27:54,535 --> 08:27:57,938
What this conference brought
is it raised many questions,

10485
08:27:58,839 --> 08:28:01,308
but we’re slowly
trying to answer them.

10486
08:28:02,243 --> 08:28:04,745
And I’m so pleased also to see

10487
08:28:04,745 --> 08:28:07,748
how far curative
therapies have come.

10488
08:28:10,251 --> 08:28:15,623
But we’re going to improve
on the pharmacology approaches,

10489
08:28:15,623 --> 08:28:17,358
so that you won’t have
so many patients

10490
08:28:17,358 --> 08:28:21,462
for your curative
therapies [laughs].

10491
08:28:21,462 --> 08:28:24,799
And I love to see you
all next year at the NIH.

10492
08:28:24,799 --> 08:28:26,100
Thank you, all.

10493
08:28:26,100 --> 08:28:27,334
[applause]

10494
08:28:27,334 --> 08:28:31,872
Oh, yes. Because I forget,
I have to give a great,

10495
08:28:31,872 --> 08:28:36,677
great thanks to Nancy, Tiffany,
Marivi [phonetic sp],

10496
08:28:36,677 --> 08:28:37,878
and [inaudible].

10497
08:28:37,878 --> 08:28:39,079
[applause]

10498
08:28:39,079 --> 08:28:43,417
They have been fabulous.
I couldn’t --

10499
08:28:43,417 --> 08:28:45,653
we couldn’t have
a better team, I tell you.

10500
08:28:46,821 --> 08:28:51,058
This is done in 
their part time, you know.

10501
08:28:51,058 --> 08:28:53,360
It’s not their profession.

10502
08:28:54,862 --> 08:29:01,268
[applause]