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>> Lisa Cordis: Hello to all of
our IBPCR [phonetic sp.]

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participants from
around the world.

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My name is Dr. Lisa Cordis,

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and I'm one of the
directors for this course

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and I'll be serving
as a moderator for this session.

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In this panel interview today,

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we will get to know members
of our research team.

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We've assembled a team
of research professionals

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from the US National
Institutes of Health

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here in Bethesda, Maryland.

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And our team has a lot of
experience in clinical research

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and is hoping to share some of
that insight with you today.

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So, let's begin
with introductions.

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Dr. Apolo, will you
please start us off.

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>> Andrea Apolo: Hi and
thank you for having me.

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This is a great idea.
My name is Andrea Apolo

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and I'm a Senior
Principal Investigator

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at the National
Cancer Institute.

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>> Denise Francis:

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Hello, thank you for having me.
My name is Denise Francis,

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and I'm a Senior Research
Nurse Specialist

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at the National
Cancer Institute.

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>> Elizabeth Ness:
Hi, I'm Elizabeth Ness,

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and I am the director
of our Office of Education

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and Compliance here in the NCI's
intramural program,

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and it's wonderful to be here.

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>> Paul Wakim: I'm Paul Wakim.
I'm a biostatistician.

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I'm actually retired
as of two weeks ago, I --

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but before retirement, I was
chief of the biostatistics

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and clinical
epidemiology service

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at the NIH Clinical Center.

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>> Robbie Kattappuram:

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Hi. Thanks for having me.
My name is Robbie Kattappuram.

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I'm an investigational
drug pharmacist

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at the NIH Clinical Center.

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>> Cody Peer: Hi. Cody Peer.

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I'm a Clinical Pharmacologist at
the National Cancer Institute.

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>> Stephanie Wilmot: Hi, my
name is Stephanie Wilmot,

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and I'm a Clinical
Data Manager Supervisor

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here at the National Cancer
Institute.

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>> Lisa Cordis: Thanks to you
all for introducing yourselves.

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I'd like to further discuss
the roles and responsibilities

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of each of our team members.

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And Dr. Apolo, will you please
provide some insight

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regarding how you conceptualize
a study and also

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the key responsibilities
of a principal investigator

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as you move a trial
towards implementation?

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>> Andrea Apolo: Yeah, so
it's a great question.

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So, how do we come up
with the ideas

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that we are going to then
implement into a clinical trial,

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there are many ways of doing it,

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we take drugs that are active
in certain cancers

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and only apply them
to other cancers,

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we take two active therapies,
and then we can combine them.

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Or we can take an
interesting molecule

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that has shown a lot
of preclinical data

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that is exciting,

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and we do first
in human studies.

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So, that's usually you know,
multiple ways of doing it.

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And that's usually more
for phase one studies;

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for phase two studies we take --
for phase one studies,

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we're really trying to
understand the toxicity

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and the tolerance
of the drugs for the patient

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and what the best dose
is that can be safely given.

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And then for phase twos,

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then we're really
looking at efficacy.

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And whether the drug itself
or the combination works

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in a particular
group of patients.

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And then for phase threes,
then you really compare a drug

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that has shown efficacy
to the standard of care.

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>> Lisa Cordis:
Thank you for that answer.

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Working closely with
the principal investigator

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is our research nurse
or study coordinator.

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Denise, will you
please share with us

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some of your day-to-day
responsibilities?

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>> Denise Francis:

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So, the principal,
the day-to-day activities,

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let's start from
the beginning for

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when the protocol is approved.

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I work with
the primary investigator

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and the research team to build
that protocol within our system

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to make sure all the checks
are done

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to make sure
we get quality data.

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And then once we start
to enroll patients,

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I work with the team
to screen those patients.

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Make sure they're eligible based
on the eligibility criteria

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of that protocol.
And once they're eligible,

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enroll them in the
clinical trial.

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And once they start treatment
I --

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in my job, we give them
their treatment schedule

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and work with the infusion suite

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and any other ancillary teams
that we need to work with

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to make sure they get
the treatment on time

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and monitor them for any adverse
drug reactions,

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or any reactions at all.

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And make sure they're safe while
they're getting treatment, so.

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>> Lisa Cordis:

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Which is obviously
our number one goal here.

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Unfortunately,
the principal investigator

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and the research nurse
are not alone in this process.

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And we have a number
of regulatory experts

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and experts in human subjects'
research protections

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which help our investigators
along the way.

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And Elizabeth Ness is one of
these regulatory experts.

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Ms. Ness, would you please share
how you provide assistance

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to the teams conducting
clinical trials?

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>> Elizabeth Ness: Sure.

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Myself and my office, we provide
general research education

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that's grounded
in the regulations,

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first and foremost.
We also conduct our own audits,

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audits of our standard
operating procedures, et cetera.

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We review monitoring and
auditing findings for trends,

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especially as they relate
to human subjects' protection,

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and good clinical
practice issues.

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We provide consultation,

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really in any aspect of clinical
research implementation.

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Most of our consultations tend
to be around informed consent,

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specifically non-English
speaking participants

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as well as adverse events.

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And you know whether an event
should be reported

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expeditiously to the IRB
or to the sponsor.

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We also assist teams
with their monitoring visits

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or auditing, or even
FDA inspections.

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This can include supporting
their own educational needs,

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or preparing or helping them
prepare for the visit,

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or even being physically
with the team

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such as with an FDA inspection
to provide support

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and additional
information as needed.

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We also, myself
and my office, provide,

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develop our own
research specific policies

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and procedures
for our research program

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that are consistent
with our regulations

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and with our other human
research protection program

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policies as well as
other institutional policies.

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We provide more
granular detail

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on how to stay compliant
with the regulations,

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we can develop corrective
and preventive action plans,

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helping to identify
the root cause

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and how to correct
or prevent in the future.

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And so, overall,
in a nutshell,

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we really are here
to provide support consistent

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with the regulations and
policies for our research teams.

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>> Lisa Cordis:
Elizabeth Ness,

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I don't know what we would do
without you,

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your team and your group
really keeps us

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on the straight and narrow.

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So, thank you
for all of your work

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in compliance and regulations.

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Next up, we have
investigational pharmacists.

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So, investigational pharmacists
have unique training

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and expertise specifically
in investigational products.

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And this is in addition
to the standard training

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that a traditional pharmacist
would receive in FDA

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approved drugs.

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Dr. Kattappuram,
what is the role

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of an investigational pharmacist
in clinical research?

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>> Robbie Kattappuram:
Sure.

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I think that investigational
pharmacists play

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kind of a crucial role
in clinical trials

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by really leveraging
our pharmaceutical

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and clinical expertise

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with kind of the administrative
and regulatory functions

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to kind of help drive
drug discovery.

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So, our primary
responsibilities

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are really to support
the protocol teams

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and then also to do
management of the study drug.

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So, really, this entails
reviewing the study documents

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like the protocol,

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the investigative brochure,
the pharmacy manual,

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and also developing
study specific

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SOPs and drug
information documents

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and dispensing instructions.

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We also provide feedback
to the study teams

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on their protocols.
And even in some cases,

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we can assist in writing
parts of the protocol,

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or some of the sections
of the pharmacy manual as well.

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One of the biggest things
that we do is ensuring

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that the investigational
products are received,

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are stored and dispensed
in compliance with sponsor,

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institutional state,
and federal regulations as well.

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So, this makes --
so we make sure that the drugs

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are stored under proper
temperature conditions,

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we make sure
that sterile preparations

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of hazardous
or nonhazardous drugs

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are made under USP 797
or 800 environments.

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And we maintain steady
blinding as well.

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And through all this,
we maintain the documentation

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for all things drugs, so --
because we also participate

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in FDA and sponsor audits
as well.

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And in some cases,

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the institutions also have
a pharmacist sit in

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on the IRB committee or any
other institutional committee

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to kind of give some insight
as to upcoming protocols.

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So, I mean, you can tell
from this discussion

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that there are a lot of moving
parts to clinical trials.

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And so, as the pharmacists,
we really try to take over

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that drug management
portion of it

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so that we can let
the study teams,

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kind of, do their other tasks,

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so that we can also ensure
Safe Medication Practices.

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>> Lisa Cordis:
Thank you so much,

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you play a vital role
to the team for sure.

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In addition to the
investigational pharmacist,

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there's also a clinical
pharmacologist

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that's involved
with clinical trials,

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but in a very different role.

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So, they're focused
more on pharmacokinetics

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and modeling, for
example, Dr. Peer,

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would you please elaborate
on your involvement

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in drug development trials?

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>> Cody Peer: Sure.
Thank you, Lisa.

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The pharmacologist's job
is really to analyze

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both what the drug
does to the body

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when administered
to a patient,

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and also what the body
does to the drug.

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And so, each of these processes

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combined constitutes
pharmacology.

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So, what the body
does to the drug

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is considered
pharmacokinetics,

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also called PK, what the drug
is doing to the body?

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Some change in, you know,
some signaling pathway over time

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that change is a
dynamic change.

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So, it's pharmacodynamics is
that discipline, abbreviated PD.

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So, a lot of PKPD studies are
conducted in clinical trials,

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and it's a pharmacologist's role
to analyze that PK data,

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that PD data, and ultimately
answer two big questions

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how much of a dose to give,
and how often to get it.

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Now another major reason
we do pharmacology

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other than optimizing the dose

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is to understand patient
to patient differences.

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In other words, inter
individual variability.

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So, if you and your neighbor,
for example,

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both take the same dose
of the same drug,

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there's a good chance
that the two of you

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will have different experiences,
whether that's from the intended

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on-target
pharmacological effects,

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or from unintended off-target
toxicological effects.

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Now, the reason,
in this example,

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you and your neighbor might
have differences in response

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could be some pharmacodynamic
reason like you

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have different expression of
the target receptor of the drug,

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or it could be a
pharmacokinetic reason

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such as one person has
a slower metabolism

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of the drug in clearance,

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which could be further
due to genetic reasons.

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So, with pharmacokinetics,
the word pharmacokinetics

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broken down is drug movement.

248
00:12:36,856 --> 00:12:39,492
It understands the
rate processes

249
00:12:39,558 --> 00:12:41,427
of how a drug moves
throughout the body,

250
00:12:41,494 --> 00:12:43,095
how it's absorbed.

251
00:12:43,662 --> 00:12:45,598
From the formulation,
administered,

252
00:12:45,664 --> 00:12:47,099
how its distributed
throughout the body

253
00:12:47,166 --> 00:12:49,168
and gets to the site of action,

254
00:12:49,235 --> 00:12:52,805
how the body when it recognizes
this foreign substance

255
00:12:52,872 --> 00:12:54,907
tries to metabolize it
and get rid of it.

256
00:12:55,541 --> 00:12:58,043
There's a rate of metabolism
and a rate of elimination

257
00:12:58,110 --> 00:13:00,346
and pharmacokinetically,
you can build them,

258
00:13:01,547 --> 00:13:04,583
you can apply a mathematical
equation or a model

259
00:13:05,117 --> 00:13:07,853
to describe the disposition
of the drug over time.

260
00:13:08,787 --> 00:13:10,956
And in trying to understand

261
00:13:11,023 --> 00:13:13,893
why some people react
differently to drugs,

262
00:13:14,560 --> 00:13:16,028
from the pharmacokinetic
standpoint,

263
00:13:16,095 --> 00:13:21,901
you can also factor
in variables such as age,

264
00:13:21,967 --> 00:13:25,271
sex, race, body size,
body composition,

265
00:13:25,337 --> 00:13:28,908
the genotype for a particular
drug metabolizing enzyme

266
00:13:28,974 --> 00:13:31,243
or transporter
that handles the drug,

267
00:13:31,310 --> 00:13:35,281
if the study drug is given
with another concomitant drug

268
00:13:35,347 --> 00:13:38,784
that might be affecting
its metabolism.

269
00:13:39,919 --> 00:13:41,921
If it's an oral drug,

270
00:13:41,987 --> 00:13:45,090
are you experiencing differences
in absorption without food,

271
00:13:45,157 --> 00:13:46,692
or with a low fat
or a high fat meal,

272
00:13:46,759 --> 00:13:50,029
all of these things can be
studied clinically and analyzed

273
00:13:50,095 --> 00:13:53,966
by a pharmacologist
through building models.

274
00:13:54,767 --> 00:13:59,705
So, ultimately, you know,
the personalization of dosing

275
00:13:59,772 --> 00:14:01,140
is a goal of a
pharmacologist,

276
00:14:01,207 --> 00:14:03,108
that's not always achievable
with a clinical trial,

277
00:14:03,175 --> 00:14:07,279
but it's still the type of data
we analyze and look for

278
00:14:07,913 --> 00:14:09,748
and we want to make sure
that the doses

279
00:14:09,815 --> 00:14:13,018
we give in a clinical trial

280
00:14:13,085 --> 00:14:16,422
are going to maximize efficacy
and minimize toxicity

281
00:14:16,488 --> 00:14:17,856
in a vast majority
of patients

282
00:14:17,923 --> 00:14:19,558
which gives the drug
the best chance

283
00:14:19,625 --> 00:14:21,460
of being approved by the FDA.

284
00:14:22,695 --> 00:14:24,129
Might have been a
long-winded answer,

285
00:14:24,196 --> 00:14:29,068
but I really enjoyed doing this
type of pharmacology work

286
00:14:29,134 --> 00:14:33,038
and with oncology, especially
that I work in the NCI,

287
00:14:33,572 --> 00:14:34,940
oncology drugs are very toxic.

288
00:14:35,007 --> 00:14:37,876
So, really pinpointing
that optimal dose

289
00:14:37,943 --> 00:14:41,313
that minimizes toxicity and
maximizes your benefit really

290
00:14:41,380 --> 00:14:44,049
is a crucial question
to answer. Thank you.

291
00:14:44,116 --> 00:14:46,051
>> Lisa Cordis:
Thank you, Dr. Peer.

292
00:14:46,118 --> 00:14:49,188
It's such an important role
on the Clinical Research Team.

293
00:14:49,255 --> 00:14:52,424
Another key role on the team
is the statistician.

294
00:14:53,692 --> 00:14:56,395
A lot of our investigators have
an understanding of statistics

295
00:14:56,462 --> 00:15:00,499
but really having the expertise
of a biostatistician is key.

296
00:15:01,200 --> 00:15:02,735
Dr. Wakim,
would you please explain

297
00:15:02,801 --> 00:15:05,871
how biostatistician works
with the principal investigator

298
00:15:05,938 --> 00:15:07,840
on the study statistics?

299
00:15:07,906 --> 00:15:09,108
>> Paul Wakim:

300
00:15:09,174 --> 00:15:13,879
Yes, the statistician is
quite involved at the beginning

301
00:15:13,946 --> 00:15:15,281
and at the end
of the study,

302
00:15:15,347 --> 00:15:17,349
I mean, that's the
heaviest involvement.

303
00:15:17,983 --> 00:15:22,655
And so, let me
take those two separately,

304
00:15:22,721 --> 00:15:26,492
at the beginning, there's,
like two parts, connected parts,

305
00:15:27,593 --> 00:15:31,063
the design of the study
and the analysis plan.

306
00:15:32,698 --> 00:15:34,266
So, the design of the study

307
00:15:34,333 --> 00:15:36,769
starts with the primary
research question,

308
00:15:37,503 --> 00:15:38,937
what's the primary
research question?

309
00:15:39,004 --> 00:15:41,807
And what is the measure,
the quantifiable measure

310
00:15:41,874 --> 00:15:44,910
that will be analyzed
to answer that question?

311
00:15:44,977 --> 00:15:46,578
That's extremely important,

312
00:15:46,645 --> 00:15:49,114
the most important part
of a study.

313
00:15:50,416 --> 00:15:53,919
So, sometimes PIs are
pretty clear about it.

314
00:15:54,820 --> 00:15:57,623
And sometimes they're exploring,
they're just thinking,

315
00:15:57,690 --> 00:15:58,924
and they're --

316
00:15:58,991 --> 00:16:01,560
so the statistician
in that latter case,

317
00:16:01,627 --> 00:16:05,964
the statistician can help
organize thoughts, thinking,

318
00:16:06,031 --> 00:16:09,735
how to quantify, how to design,
basically answer that.

319
00:16:11,136 --> 00:16:14,540
Also, part of the design is
the, like, urbanization,

320
00:16:14,606 --> 00:16:17,376
and how many treatment arms
you're going to have.

321
00:16:18,043 --> 00:16:19,678
Do you do an
observational study?

322
00:16:19,745 --> 00:16:21,680
Are you going to do
randomized trial?

323
00:16:21,747 --> 00:16:24,383
And things like that?
The sample sizes?

324
00:16:24,450 --> 00:16:27,219
What sample size
are we going to need?

325
00:16:27,753 --> 00:16:31,290
So, that's the design part
still, at the beginning, also,

326
00:16:31,357 --> 00:16:34,393
at the beginning of the study,
is the analysis plan.

327
00:16:35,060 --> 00:16:37,629
So, the analysis
plan is basically,

328
00:16:38,497 --> 00:16:40,799
well, how is the data
going to be analyzed?

329
00:16:42,167 --> 00:16:44,403
And what's the best approach?

330
00:16:44,470 --> 00:16:48,774
And all the details that are
around the analysis plan?

331
00:16:48,841 --> 00:16:53,746
Like you would have what
if the assumptions are not met?

332
00:16:53,812 --> 00:16:57,449
That, you know, like you thought
the data was going to be normal

333
00:16:57,516 --> 00:16:59,952
and now it's not normal,
what are we going to do?

334
00:17:00,986 --> 00:17:03,856
Or like, how are we going
to handle missing values?

335
00:17:05,924 --> 00:17:08,627
How -- what if we're doing
a longitudinal study

336
00:17:08,694 --> 00:17:12,698
or a cluster study,
and we have correlated data?

337
00:17:12,765 --> 00:17:14,666
How are we going to handle
the correlated data?

338
00:17:14,733 --> 00:17:17,636
Are we going to do
an interim analysis?

339
00:17:17,703 --> 00:17:20,472
All of these are part
of the analysis plan.

340
00:17:22,141 --> 00:17:23,942
So, that's at the beginning.

341
00:17:24,009 --> 00:17:27,646
And of course, this is very
close collaboration with the PI,

342
00:17:27,713 --> 00:17:29,848
and the team, the whole team,

343
00:17:29,915 --> 00:17:32,117
because it's a
back-and-forth thing,

344
00:17:32,184 --> 00:17:34,286
it's not just the
statistician goes away,

345
00:17:34,353 --> 00:17:37,289
disappears, and comes back,
and here it is.

346
00:17:38,056 --> 00:17:39,658
So, that's the beginning.

347
00:17:39,725 --> 00:17:41,527
Then the study is conducted.

348
00:17:41,593 --> 00:17:44,430
And then you've got
the data's collected.

349
00:17:44,496 --> 00:17:49,067
And then the statistician
is, again, quite involved

350
00:17:49,134 --> 00:17:51,303
in the, of course, the
analysis of the data.

351
00:17:52,204 --> 00:17:55,240
And it's -- again, it's
an iterative process,

352
00:17:55,307 --> 00:17:59,778
it's, you know, every time,
you know, I do something,

353
00:17:59,845 --> 00:18:02,080
I show it to the PI's,
does it make sense,

354
00:18:02,147 --> 00:18:03,916
does it makes clinical sense.

355
00:18:03,982 --> 00:18:07,653
You know, it's easy for me
to do the stat modeling,

356
00:18:07,719 --> 00:18:11,023
and poof, here's the result.
But does it make sense?

357
00:18:13,058 --> 00:18:16,428
And sometimes the answer is yes,
sometimes the answer is no.

358
00:18:16,495 --> 00:18:18,096
And if the answer is no,

359
00:18:19,264 --> 00:18:22,167
it doesn't mean that something
was wrong with the analysis.

360
00:18:22,234 --> 00:18:23,435
It could be two things.

361
00:18:23,502 --> 00:18:27,139
One is, well, it's something
is not quite right,

362
00:18:27,206 --> 00:18:28,874
whether the analysis,

363
00:18:28,941 --> 00:18:32,544
or maybe there is an outlier
somewhere that's driving things.

364
00:18:33,212 --> 00:18:35,481
So, if -- so we look into it.

365
00:18:36,381 --> 00:18:39,918
Sometimes it's not
what is expected,

366
00:18:39,985 --> 00:18:43,622
but it is what it is,
the statistician looks into it

367
00:18:43,689 --> 00:18:47,259
doesn't see anything odd.
And that's the way it is.

368
00:18:49,528 --> 00:18:53,298
And it's sometimes you know,

369
00:18:54,433 --> 00:18:57,302
you wonder, I mean,
let's put it the way it is.

370
00:18:57,369 --> 00:19:00,606
And then -- and let's be
transparent about it,

371
00:19:00,672 --> 00:19:02,374
even though
it doesn't make sense,

372
00:19:03,275 --> 00:19:05,944
let's be transparent about it
in the manuscript.

373
00:19:06,879 --> 00:19:10,315
I have to mention one thing
that happens quite often

374
00:19:12,551 --> 00:19:15,988
is there's a tendency
with the PIs to say,

375
00:19:16,054 --> 00:19:18,457
okay, why don't we try this?
Why don't we try that?

376
00:19:18,524 --> 00:19:19,925
What -- why don't
we include this?

377
00:19:19,992 --> 00:19:22,060
This is after the data
has been collected,

378
00:19:22,127 --> 00:19:23,929
the analysis has been in print,

379
00:19:23,996 --> 00:19:27,599
and I don't blame
PIs for doing that.

380
00:19:27,666 --> 00:19:29,368
I wouldn't have done
the same thing

381
00:19:29,434 --> 00:19:31,103
if I were in their shoes.

382
00:19:31,169 --> 00:19:33,372
But there's a --
there's this tendency,

383
00:19:33,438 --> 00:19:35,974
you know, try this, try
that, try this, try that.

384
00:19:36,875 --> 00:19:40,078
And one has to be careful
as long as we're transparent

385
00:19:40,145 --> 00:19:42,381
in the manuscript
about what was the plan

386
00:19:42,447 --> 00:19:45,217
and what was not the
plan, it's fine.

387
00:19:45,284 --> 00:19:51,323
I mean, at the NIH, we're about
exploring and understanding.

388
00:19:51,890 --> 00:19:55,928
So, it's okay to explore
and try things but statisticians

389
00:19:55,994 --> 00:19:59,298
call it the fishing expedition,
you know, meaning, you know,

390
00:19:59,364 --> 00:20:04,002
if you try this, you try that,
you try things enough times,

391
00:20:04,069 --> 00:20:06,171
eventually you're going
to get what you want.

392
00:20:06,238 --> 00:20:08,640
So, one has to be
careful about this.

393
00:20:12,411 --> 00:20:16,882
And that's why a single study
is not definitive,

394
00:20:16,949 --> 00:20:20,519
I always say a single study
is not the definitive answer.

395
00:20:20,586 --> 00:20:24,990
And that's when meta-analyses
are very valuable

396
00:20:25,057 --> 00:20:27,326
because they collect
several studies

397
00:20:27,392 --> 00:20:29,795
and see if there is
consistency in the results.

398
00:20:30,462 --> 00:20:32,097
So, that's the -- at the end.

399
00:20:32,164 --> 00:20:34,766
And finally, at the writing
of the manuscript,

400
00:20:36,034 --> 00:20:37,936
as a statistician,
obviously, is going to write

401
00:20:38,003 --> 00:20:41,340
the statistical methodology
section of the manuscript.

402
00:20:42,674 --> 00:20:48,246
The results, you know, typically
the PI writes the results,

403
00:20:48,313 --> 00:20:49,681
but the statistician --

404
00:20:49,748 --> 00:20:51,450
I'm not talking about
the discussion,

405
00:20:51,516 --> 00:20:53,118
just the results, the fact --

406
00:20:54,753 --> 00:20:58,357
the statistician has to review
very, very closely,

407
00:20:58,423 --> 00:20:59,591
the results section,

408
00:20:59,658 --> 00:21:02,427
all the numbers has to have
to be double checked.

409
00:21:02,494 --> 00:21:07,532
And the wording, how the results
are being presented have to be,

410
00:21:07,599 --> 00:21:09,901
you know, closely reviewed.

411
00:21:09,968 --> 00:21:13,839
And of course, when you know,
the manuscript is submitted,

412
00:21:13,905 --> 00:21:17,142
and reviewers are
sending comments,

413
00:21:17,209 --> 00:21:19,244
and if there are
statistical issues,

414
00:21:20,646 --> 00:21:22,614
the statistician
is also involved

415
00:21:22,681 --> 00:21:25,484
in the addressing of the
reviewers' comments.

416
00:21:25,550 --> 00:21:27,519
>> Lisa Cordis:

417
00:21:27,586 --> 00:21:29,588
Thank you, that's very
valuable insight.

418
00:21:30,422 --> 00:21:32,324
Another member
of our research team

419
00:21:32,391 --> 00:21:36,128
that is key to our success
is our data manager.

420
00:21:36,194 --> 00:21:37,863
So, Ms. Wilmot,
would you please share

421
00:21:37,929 --> 00:21:40,699
how a clinical trial data
gets collected,

422
00:21:40,766 --> 00:21:42,067
and the role in the data --

423
00:21:42,134 --> 00:21:44,403
of the Data Manager
in that process?

424
00:21:44,469 --> 00:21:45,737
>> Stephanie Wilmot:

425
00:21:45,804 --> 00:21:47,572
Hi, everybody,
thanks for having me.

426
00:21:48,507 --> 00:21:51,143
The data managers
are usually assigned

427
00:21:51,209 --> 00:21:54,680
according to the branches
that they're assigned to,

428
00:21:54,746 --> 00:21:58,517
for example,
the molecular imaging branch,

429
00:21:59,084 --> 00:22:02,788
your oncology Branch,
things of that nature.

430
00:22:02,854 --> 00:22:06,224
So, what happens when data
managers when they're assigned,

431
00:22:06,291 --> 00:22:11,096
they're usually contacted
by either Liz Ness's team,

432
00:22:11,163 --> 00:22:16,702
or the sister contract, which is
our central support contract

433
00:22:17,269 --> 00:22:22,507
that consists of builders'
protocol analysis,

434
00:22:22,574 --> 00:22:27,412
these individuals
are responsible

435
00:22:27,479 --> 00:22:30,182
for building the
actual protocol.

436
00:22:30,849 --> 00:22:33,452
We attend, we are also
invited again

437
00:22:33,518 --> 00:22:36,188
from the beginning,
middle and end.

438
00:22:36,254 --> 00:22:39,791
So, we do attend
the site initiation visit

439
00:22:39,858 --> 00:22:44,329
when the protocol is presented
to the scientific community.

440
00:22:44,830 --> 00:22:49,468
And we start by working
with Liz Ness's team

441
00:22:49,534 --> 00:22:52,738
in either a preclinical data
capture meeting,

442
00:22:53,405 --> 00:22:57,409
or we work specifically
with a research nurse,

443
00:22:57,476 --> 00:23:00,479
and the PI in how the
protocol is going

444
00:23:00,545 --> 00:23:04,182
to be billed
according to the protocol.

445
00:23:04,816 --> 00:23:08,754
That usually takes place
in various meetings

446
00:23:08,820 --> 00:23:12,924
with the Central Support Program
protocol builders,

447
00:23:12,991 --> 00:23:17,929
and it's usually referred to
as data specification testing.

448
00:23:17,996 --> 00:23:20,398
This is extremely important.

449
00:23:20,465 --> 00:23:23,401
And Liz Ness does
a really good job of this.

450
00:23:23,468 --> 00:23:26,972
And the research nurse, we all
come together in a meeting,

451
00:23:27,739 --> 00:23:30,108
along with the PI
to see actually

452
00:23:30,175 --> 00:23:33,378
what is going to be
recorded in the case

453
00:23:33,445 --> 00:23:36,915
reporting forms
commonly known as CRFs.

454
00:23:37,816 --> 00:23:41,286
Once this is done
and the protocol is built,

455
00:23:41,820 --> 00:23:44,556
then it's a process
of communicating

456
00:23:44,623 --> 00:23:46,591
with the research nurse,

457
00:23:47,225 --> 00:23:50,295
usually with
the Denise Richard --

458
00:23:50,862 --> 00:23:53,999
Denise Francis,
on specific protocols.

459
00:23:54,065 --> 00:23:56,635
So, Denise will reach out
and say, you know,

460
00:23:56,701 --> 00:23:59,137
we have a patient coming in,

461
00:23:59,204 --> 00:24:01,673
we're looking for
the press enrollment

462
00:24:02,674 --> 00:24:05,043
email with
the patient information.

463
00:24:05,610 --> 00:24:09,147
And usually what we do
is the Data Manager

464
00:24:09,214 --> 00:24:14,052
will wait for the nurse
or we check the client,

465
00:24:14,753 --> 00:24:17,088
the Clinical
Research Information System,

466
00:24:17,756 --> 00:24:19,591
also known as CRIS.

467
00:24:20,158 --> 00:24:23,562
So, we check that for the
first registration note,

468
00:24:24,062 --> 00:24:27,432
we check for the research
note, progress note,

469
00:24:27,499 --> 00:24:32,037
various notes that
incorporate the enrollment,

470
00:24:32,103 --> 00:24:35,373
the history,
the screening of the patient.

471
00:24:36,474 --> 00:24:38,844
And we usually,
once we are notified,

472
00:24:38,910 --> 00:24:41,880
we have about 10 business days
to enter that,

473
00:24:41,947 --> 00:24:45,784
but we usually enter it
a few days after

474
00:24:45,851 --> 00:24:49,654
or right at the same time.
Sometimes the Data Manager,

475
00:24:49,721 --> 00:24:53,258
just to make sure for data
consistency and accuracy,

476
00:24:53,325 --> 00:24:56,361
we meet biweekly
with a research nurse

477
00:24:56,428 --> 00:24:59,397
to see if the data
that is being captured

478
00:24:59,464 --> 00:25:03,335
is reflected in the client
in the case reporting form,

479
00:25:04,436 --> 00:25:06,171
especially adverse events,

480
00:25:06,238 --> 00:25:10,242
because data managers don't
do calculations or attributions.

481
00:25:10,308 --> 00:25:13,445
And so, sometimes when we read
some of the notes,

482
00:25:13,511 --> 00:25:17,382
it's best just for all of us
to meet and have a conversation

483
00:25:17,449 --> 00:25:22,587
on what actually is going to be
recorded in the ongoing CRFs.

484
00:25:23,355 --> 00:25:25,891
So, we follow
the patient treatment cycle

485
00:25:25,957 --> 00:25:27,893
according to the protocol.

486
00:25:27,959 --> 00:25:30,028
We follow that along
with a nurse.

487
00:25:30,095 --> 00:25:34,232
So, it's a back-and-forth email,
teams meeting.

488
00:25:35,166 --> 00:25:39,604
And sometimes even chatting
on teams, just to see,

489
00:25:39,671 --> 00:25:44,075
especially if there is a site
monitoring visit

490
00:25:44,142 --> 00:25:47,746
or an interim monitoring visit
that is coming up.

491
00:25:47,812 --> 00:25:50,982
And we want to ensure
that all the data is in.

492
00:25:51,549 --> 00:25:53,985
Some of the patients
according to the protocol,

493
00:25:54,052 --> 00:25:58,790
according to the reaction
to the investigational drug

494
00:25:58,857 --> 00:26:02,394
have a significant amount
of case reporting forms

495
00:26:02,460 --> 00:26:03,895
that have to be filled out,

496
00:26:03,962 --> 00:26:06,731
including waiting
for attributions.

497
00:26:06,798 --> 00:26:12,203
And again, we communicate
consistently throughout the day

498
00:26:12,270 --> 00:26:15,507
with a research nurse
that is our point of contact.

499
00:26:15,573 --> 00:26:18,009
And if something else comes in,
last minute,

500
00:26:18,076 --> 00:26:21,179
the research nurses that
I've worked with over the years

501
00:26:21,246 --> 00:26:26,151
are really good about either
sending us a teams chat

502
00:26:26,217 --> 00:26:27,919
or sending us an email

503
00:26:27,986 --> 00:26:33,224
encrypted saying this patient
is ready to go, and so forth.

504
00:26:33,291 --> 00:26:39,764
We're also involved in working
with the research teams,

505
00:26:39,831 --> 00:26:41,967
once some of the data

506
00:26:42,033 --> 00:26:45,036
is in periodically
throughout the year,

507
00:26:45,570 --> 00:26:50,308
there are certain reporting
regulatory doc documents

508
00:26:50,375 --> 00:26:52,711
that are needed
continuing review,

509
00:26:54,245 --> 00:27:00,352
FDA reporting, and also
some of the research teams want

510
00:27:00,418 --> 00:27:04,789
what is called J review
standardized template reports.

511
00:27:04,856 --> 00:27:06,658
If they're not available
in the system,

512
00:27:06,725 --> 00:27:09,461
the Data Manager
will work with the team

513
00:27:09,527 --> 00:27:12,597
and get those reports out
as soon as possible.

514
00:27:13,498 --> 00:27:18,636
And we also on our end, of this
clinical data manager contract,

515
00:27:18,703 --> 00:27:22,607
we do have our own review team
that reviews the data

516
00:27:22,674 --> 00:27:26,745
periodically to make sure
that the data is going in,

517
00:27:26,811 --> 00:27:31,049
according to data manager
standard operating procedures.

518
00:27:31,116 --> 00:27:34,853
Another thing that we do as well
is we follow the patient

519
00:27:34,919 --> 00:27:37,288
throughout
the patient treatment cycle,

520
00:27:37,355 --> 00:27:41,760
including offs, off treatment,
off steady, follow up

521
00:27:42,293 --> 00:27:48,199
and participate
in all post IMVSMV* meetings

522
00:27:48,266 --> 00:27:52,837
to ensure that what is monitored
is accurate,

523
00:27:52,904 --> 00:27:57,409
according to the protocol.
And we also have a conversation

524
00:27:57,475 --> 00:27:59,878
on how we're going to
move forward with data

525
00:27:59,944 --> 00:28:02,847
capturing if the study
is either on hold

526
00:28:02,914 --> 00:28:08,486
by the sponsor on hold --
and it's run its course.

527
00:28:08,553 --> 00:28:12,791
And at that point, we do attend
a post closure of the meeting.

528
00:28:12,857 --> 00:28:16,428
So, we do a song and dance what
I call with a research team.

529
00:28:16,494 --> 00:28:20,131
But our main point of contact
is the research nurse,

530
00:28:20,198 --> 00:28:23,468
we really could not do it
without the research nurse.

531
00:28:23,535 --> 00:28:25,136
And then at the beginning,

532
00:28:26,071 --> 00:28:30,608
when Liz Ness does
that data capture meeting

533
00:28:30,675 --> 00:28:36,114
in creating the actual CRF
is extremely important.

534
00:28:36,915 --> 00:28:40,652
Because she does have
that history on what is needed

535
00:28:40,718 --> 00:28:45,056
in those client reporting forms,
and how it's going to pertain

536
00:28:45,123 --> 00:28:48,226
to capturing the data
for that protocol.

537
00:28:48,293 --> 00:28:51,796
So, overall,
we're very involved.

538
00:28:52,363 --> 00:28:54,165
And we're just really happy
to work

539
00:28:54,232 --> 00:28:56,701
with a research team on this.
So, thank you very much.

540
00:28:56,768 --> 00:28:59,137
>> Lisa Cordis:
Thank you, Ms. Wilmot.

541
00:28:59,204 --> 00:29:01,306
So, I'm sure everybody
has seen by now

542
00:29:01,372 --> 00:29:03,975
that these clinical trials
are such a team approach.

543
00:29:04,776 --> 00:29:08,513
And each member of our team
is so vital.

544
00:29:08,580 --> 00:29:09,914
One of the most common questions

545
00:29:09,981 --> 00:29:11,449
we actually get
from our participants

546
00:29:11,516 --> 00:29:14,352
is the background
of our research team members.

547
00:29:15,253 --> 00:29:16,588
So, I would like
for anybody to share

548
00:29:16,654 --> 00:29:17,822
that there are a couple people

549
00:29:17,889 --> 00:29:20,859
I'd like to specifically call
out to share their background

550
00:29:20,925 --> 00:29:22,994
and how they got into
clinical trials.

551
00:29:23,061 --> 00:29:24,896
Dr. Apolo, do you want to share
your background

552
00:29:24,963 --> 00:29:26,097
and your history?

553
00:29:26,164 --> 00:29:30,101
>> Andrea Apolo: Of course, yes.
So, I am a medical oncologist.

554
00:29:31,035 --> 00:29:33,104
So, I went to medical school,

555
00:29:33,171 --> 00:29:39,010
and I did a residency
in Internal Medicine

556
00:29:39,077 --> 00:29:43,181
and then fellowship
in medical oncology.

557
00:29:43,248 --> 00:29:45,383
And from then
I could have gone to practice

558
00:29:45,450 --> 00:29:49,854
but I also have
some lab research

559
00:29:49,921 --> 00:29:54,392
and have always enjoyed
doing clinical trials

560
00:29:54,459 --> 00:29:56,060
when I was doing my fellowship

561
00:29:56,794 --> 00:29:58,396
and participating
in clinical trials.

562
00:29:58,463 --> 00:30:03,735
So, I was, during my fellowship
training in medical oncology.

563
00:30:04,536 --> 00:30:07,005
I wrote several clinical trials

564
00:30:07,071 --> 00:30:12,677
and became interested in running
my own clinical trials

565
00:30:12,744 --> 00:30:15,013
and asking questions
and drug development.

566
00:30:15,079 --> 00:30:17,048
And that's how
I came to the NCI,

567
00:30:17,115 --> 00:30:19,117
and I had the opportunity
to do that.

568
00:30:19,184 --> 00:30:21,186
So, medical oncology background,

569
00:30:21,886 --> 00:30:24,355
and also lab research
background.

570
00:30:24,422 --> 00:30:27,525
And right now, I have a lab
and I have a clinical team.

571
00:30:28,193 --> 00:30:30,395
We do preclinical work,
and we --

572
00:30:31,029 --> 00:30:32,797
and then we also
run clinical trials

573
00:30:32,864 --> 00:30:34,199
for patients
with advanced cancer.

574
00:30:34,265 --> 00:30:37,268
>> Lisa Cordis:
Thank you, Dr. Apolo.

575
00:30:37,335 --> 00:30:39,270
Denise, do you also want
to share your background

576
00:30:39,337 --> 00:30:41,039
and your history and how
you became a research nurse?

577
00:30:41,105 --> 00:30:45,410
>> Denise Francis:
My background is very varied,

578
00:30:45,476 --> 00:30:48,112
I have a mechanical
technology bachelor's,

579
00:30:48,179 --> 00:30:51,716
and then I decided to go
into nursing after 10 years.

580
00:30:51,783 --> 00:30:53,618
So, nursing is
my second career.

581
00:30:54,319 --> 00:30:57,989
I was -- I started
my oncology career at NIH

582
00:30:58,056 --> 00:31:04,028
as a medical oncology
nurse intern in 1998.

583
00:31:04,796 --> 00:31:07,031
And I worked at the bedside,

584
00:31:07,098 --> 00:31:10,235
and I worked outpatient
in the infusion suite,

585
00:31:10,301 --> 00:31:13,972
administering chemotherapy
immunotherapy.

586
00:31:14,038 --> 00:31:16,040
In those days,
it was mainly chemotherapy

587
00:31:16,107 --> 00:31:18,676
and some targeted therapies.

588
00:31:18,743 --> 00:31:22,914
And then after a few years,
I left NIH

589
00:31:22,981 --> 00:31:24,382
and went out
to the community

590
00:31:24,449 --> 00:31:26,517
and worked in
a community hospital

591
00:31:26,584 --> 00:31:28,486
setting for like eight years.

592
00:31:28,553 --> 00:31:31,656
And it was during that time
that I became a research nurse.

593
00:31:32,223 --> 00:31:35,393
And I came back to NIH
as a clinical research nurse

594
00:31:35,460 --> 00:31:39,897
a few years ago.
So, I have a varied background

595
00:31:39,964 --> 00:31:42,667
and I think it works well
for my patients

596
00:31:42,734 --> 00:31:44,335
and my interaction
with my team

597
00:31:44,402 --> 00:31:47,538
in that I worked with
on the outside

598
00:31:47,605 --> 00:31:49,741
with pharmaceutical companies,

599
00:31:49,807 --> 00:31:53,444
and their clinical trials,
and then work in here at NIH

600
00:31:53,511 --> 00:31:56,581
with our own investigators
sponsored clinical trials.

601
00:31:56,648 --> 00:31:59,851
So, it's a plus-plus for me

602
00:31:59,917 --> 00:32:03,221
to have experienced both areas,
or both arenas.

603
00:32:03,288 --> 00:32:05,523
>> Lisa Cordis:
Thank you, Denise.

604
00:32:05,590 --> 00:32:06,858
And a common question we get

605
00:32:06,924 --> 00:32:08,559
is actually
about our data managers

606
00:32:08,626 --> 00:32:10,528
and how you become
a data manager.

607
00:32:10,595 --> 00:32:12,263
Stephanie, do you want
to share with us

608
00:32:12,330 --> 00:32:13,531
how you became
a data manager?

609
00:32:13,598 --> 00:32:15,199
>> Stephanie Wilmot: Okay.

610
00:32:16,234 --> 00:32:19,604
So, my background again
is a little bit different.

611
00:32:19,671 --> 00:32:23,441
I started at NIH,
December 2015.

612
00:32:23,508 --> 00:32:26,110
Previously, I worked
in Fort Lauderdale

613
00:32:26,177 --> 00:32:29,213
at an 85-bed
emergency room at Broward

614
00:32:29,280 --> 00:32:32,183
at another hospital
in Fort Lauderdale,

615
00:32:32,250 --> 00:32:33,418
doing crisis --

616
00:32:33,484 --> 00:32:37,422
that my training is crisis
intervention in addiction

617
00:32:37,488 --> 00:32:40,091
for co-occurring disorder.
So, it worked both Psych

618
00:32:40,658 --> 00:32:45,730
and in the lock lockdown
facility for detoxification.

619
00:32:46,297 --> 00:32:47,699
When I moved back to Maryland,

620
00:32:47,765 --> 00:32:51,336
I transitioned into
clinical data manager.

621
00:32:51,402 --> 00:32:54,205
So, there was a training
process for that,

622
00:32:54,272 --> 00:32:57,675
as well as having
a medical background

623
00:32:57,742 --> 00:32:59,844
or access to interpreting
medical records

624
00:32:59,911 --> 00:33:01,512
and things of that nature.

625
00:33:01,579 --> 00:33:04,816
So, currently,
that is where my field is,

626
00:33:04,882 --> 00:33:08,252
and I've been working
at it since 2015.

627
00:33:08,753 --> 00:33:12,123
And I think working
in the emergency room

628
00:33:12,190 --> 00:33:18,363
has created a lot of learning
to deal with individuals

629
00:33:18,429 --> 00:33:21,966
and learning to problem
solve and troubleshoot.

630
00:33:22,033 --> 00:33:24,202
Right there and then
so that's one of the reasons

631
00:33:24,268 --> 00:33:27,372
why I kind of enjoy
working in clinical data manager

632
00:33:27,438 --> 00:33:30,908
because everything every day
is interesting.

633
00:33:30,975 --> 00:33:33,378
And every day
we're working with a team

634
00:33:33,444 --> 00:33:35,546
and it's just amazing to see
it everything come together.

635
00:33:35,613 --> 00:33:36,781
So, thank you.

636
00:33:36,848 --> 00:33:38,683
>> Lisa Cordis: Thank you.

637
00:33:38,750 --> 00:33:39,951
Is there anyone else
in the group

638
00:33:40,017 --> 00:33:42,086
that wants to share their
background and their history?

639
00:33:42,153 --> 00:33:43,955
>> Cody Peer:

640
00:33:44,021 --> 00:33:48,159
Yeah, Lisa, I can share mine,
clinical pharmacologist

641
00:33:48,226 --> 00:33:50,094
and how I got there,

642
00:33:50,161 --> 00:33:53,364
a chemistry degree
undergraduates,

643
00:33:53,431 --> 00:33:58,302
and then I went to graduate
school and did pharmacology,

644
00:33:58,369 --> 00:34:01,205
but specifically what I did
was drug metabolism.

645
00:34:02,673 --> 00:34:05,843
Got well versed
in drug metabolism,

646
00:34:05,910 --> 00:34:11,549
and then did a postdoctoral
fellowship in a pharmacology lab

647
00:34:11,616 --> 00:34:15,853
and learned more about
the mathematical analyses

648
00:34:15,920 --> 00:34:18,656
that accompany pharmacology.

649
00:34:19,724 --> 00:34:21,426
Realized that's a whole
other discipline.

650
00:34:21,492 --> 00:34:24,962
So, I went and got
a master's after the PhD.

651
00:34:25,863 --> 00:34:27,799
The master's was
in pharmacometrics,

652
00:34:27,865 --> 00:34:32,236
which is more of a statistical
approach to pharmacology data,

653
00:34:32,303 --> 00:34:39,410
and it incorporates
not just the PKPD data

654
00:34:39,477 --> 00:34:44,215
but incorporates more anatomy,
physiology, disease states

655
00:34:44,282 --> 00:34:49,053
and so you can really take

656
00:34:49,754 --> 00:34:52,356
the numerical statistical
approach to,

657
00:34:52,990 --> 00:34:57,395
you know, pharmacology
and pinpoint the dose regimens.

658
00:34:57,462 --> 00:34:59,130
So, that was my track.

659
00:34:59,197 --> 00:35:00,865
>> Lisa Cordis:
Thank you, Dr. Peer.

660
00:35:00,932 --> 00:35:02,433
I think it's really important
to highlight

661
00:35:02,500 --> 00:35:05,636
that a lot of us didn't get here
through a traditional path.

662
00:35:05,703 --> 00:35:07,705
At least I know I didn't.
And many of you didn't either.

663
00:35:07,772 --> 00:35:10,675
And I think that's important
for our audience to remember.

664
00:35:11,375 --> 00:35:14,212
What advice would you
all give to someone

665
00:35:14,278 --> 00:35:15,680
who is interested
in clinical research,

666
00:35:15,746 --> 00:35:17,348
but doesn't know
where to start?

667
00:35:20,218 --> 00:35:21,619
>> Andrea Apolo:
I would say get in,

668
00:35:21,686 --> 00:35:25,423
get involved with
ongoing research and a team,

669
00:35:25,490 --> 00:35:28,025
there's so many
different ways to do that.

670
00:35:28,092 --> 00:35:31,262
We have fellows
that join our team,

671
00:35:31,329 --> 00:35:35,466
we have post backs
that join our team, postdocs.

672
00:35:36,067 --> 00:35:39,904
We have medical students;
we have at all different levels.

673
00:35:40,671 --> 00:35:44,976
Get involved and see
what do you like about it,

674
00:35:45,042 --> 00:35:47,578
which part, you know,
do you enjoy,

675
00:35:47,645 --> 00:35:50,414
and how, you know,
you would envision yourself

676
00:35:51,015 --> 00:35:52,884
participating
in a research team,

677
00:35:52,950 --> 00:35:54,752
there's so much research
to be done.

678
00:35:57,388 --> 00:36:00,958
>> Denise Francis:
I would say as a nurse,

679
00:36:01,025 --> 00:36:02,560
not necessarily
a research nurse,

680
00:36:02,627 --> 00:36:05,496
but as a nurse, there are
a lot of organizations,

681
00:36:05,563 --> 00:36:08,132
if you're interested
in clinical research

682
00:36:08,199 --> 00:36:10,067
that you can take classes.

683
00:36:10,134 --> 00:36:15,940
I am, I belong to the Society of
Clinical Research Professionals,

684
00:36:16,007 --> 00:36:18,342
SOCRA,
S-O-C-R-A,

685
00:36:18,409 --> 00:36:21,045
and they have a lot
of training classes

686
00:36:21,112 --> 00:36:23,781
that healthcare professionals
and you don't necessarily

687
00:36:23,848 --> 00:36:25,383
have to be
a healthcare professional,

688
00:36:25,449 --> 00:36:28,519
there are a lot of classes
that you can take at SOCRA,

689
00:36:28,586 --> 00:36:31,822
as well with more
of a regulatory aspect,

690
00:36:31,889 --> 00:36:33,891
not necessarily
a clinical aspect.

691
00:36:33,958 --> 00:36:39,030
So, you can take
their classes and trade,

692
00:36:39,096 --> 00:36:41,399
you know,
their training sessions,

693
00:36:41,465 --> 00:36:45,069
they have different conferences,
that's one area to start with

694
00:36:46,170 --> 00:36:49,440
if you want to get into
clinical research,

695
00:36:49,507 --> 00:36:51,375
and you're not sure
where to start.

696
00:36:52,376 --> 00:36:53,511
>> Elizabeth Ness:

697
00:36:53,578 --> 00:36:56,514
Well, I think I'm just
going to add to that, if I can,

698
00:36:58,950 --> 00:37:05,423
that there are many master's
programs and bachelor programs

699
00:37:05,489 --> 00:37:10,695
now in various aspects
of clinical research,

700
00:37:10,761 --> 00:37:15,266
they can be regulatory,
they can be pharmacology based,

701
00:37:15,333 --> 00:37:19,537
they can be, you know,
there's just all kinds

702
00:37:19,604 --> 00:37:23,107
of bachelor's
and master's programs out there.

703
00:37:23,174 --> 00:37:27,011
And I think getting a feel
for what that might look like,

704
00:37:27,078 --> 00:37:32,883
and trying to connect with maybe
an academic medical center

705
00:37:32,950 --> 00:37:35,987
to see if they have
any shadowing experiences,

706
00:37:36,053 --> 00:37:39,190
which is what
sort of Dr. Apollo

707
00:37:39,256 --> 00:37:42,460
was referencing
a little bit, I think,

708
00:37:43,027 --> 00:37:46,597
is to sort of see where you
can get your foot in the door.

709
00:37:48,165 --> 00:37:50,768
And sometimes that foot
in the door

710
00:37:50,835 --> 00:37:54,872
can be from a data
management perspective.

711
00:37:55,373 --> 00:38:00,244
And you can work your way up to
a clinical research coordinator.

712
00:38:00,311 --> 00:38:02,013
I've had people from --

713
00:38:03,014 --> 00:38:06,751
researchers from the labs
want to leave the bench

714
00:38:06,817 --> 00:38:10,788
and go into sort of
the clinical aspects of that.

715
00:38:10,855 --> 00:38:15,192
And, you know, I always say,
you know, it is challenging.

716
00:38:15,993 --> 00:38:18,929
But sometimes the data
management activities

717
00:38:18,996 --> 00:38:21,599
for a year,
gets that foot in the door,

718
00:38:21,666 --> 00:38:25,403
you get to know who's who
on the research team,

719
00:38:25,469 --> 00:38:28,539
and then can begin
to carve your path

720
00:38:28,606 --> 00:38:30,941
on the clinical
research side,

721
00:38:31,008 --> 00:38:34,311
after having been on the basic
research side of things.

722
00:38:34,378 --> 00:38:37,415
So, sort of a different
perspective as well.

723
00:38:40,551 --> 00:38:42,753
>> Lisa Cordis:
That's great advice. Thank you.

724
00:38:42,820 --> 00:38:44,955
All right. As we've all seen,
our research team

725
00:38:45,022 --> 00:38:47,124
is so involved throughout
the protocol lifecycle.

726
00:38:47,191 --> 00:38:49,126
Dr. Apolo,
will you explain a little bit

727
00:38:49,193 --> 00:38:50,795
about the protocol lifecycle

728
00:38:50,861 --> 00:38:53,330
in terms of how a protocol
goes from concept

729
00:38:53,397 --> 00:38:55,099
into dosing
the first patient?

730
00:38:55,933 --> 00:38:57,668
>> Andrea Apolo:
Yeah, and it's a --

731
00:38:57,735 --> 00:38:59,837
there's a lot of
different avenues.

732
00:38:59,904 --> 00:39:05,209
And it depends on the protocol.
So, the protocols can be,

733
00:39:06,310 --> 00:39:10,514
they can be sponsored and funded
in a lot of different ways.

734
00:39:11,615 --> 00:39:13,684
So, this can be
an investigator-initiated trial,

735
00:39:13,751 --> 00:39:16,120
which is something
that we commonly do here

736
00:39:16,187 --> 00:39:17,788
at the intramural NCI.

737
00:39:18,456 --> 00:39:21,792
They can be larger trials
through the extramural NCI,

738
00:39:21,859 --> 00:39:24,328
like cooperative groups
or CTEPH studies.

739
00:39:24,395 --> 00:39:27,164
They can be pharma
sponsored studies.

740
00:39:27,698 --> 00:39:30,668
And you know,
these are all different ways,

741
00:39:30,735 --> 00:39:32,503
but we have to communicate
first,

742
00:39:33,304 --> 00:39:35,906
you know, we have to kind of
start the contracts

743
00:39:35,973 --> 00:39:38,242
and see who's going
to support the study.

744
00:39:38,309 --> 00:39:41,679
And then we have to design
the study, like we talked,

745
00:39:42,379 --> 00:39:43,614
design it with the statistician,

746
00:39:43,681 --> 00:39:45,916
meet, kind of, decide how big
the study is going to be,

747
00:39:45,983 --> 00:39:47,551
who's going to be
the population?

748
00:39:47,618 --> 00:39:49,954
What are the questions
that we're going to ask?

749
00:39:50,821 --> 00:39:53,858
What are the additional
secondary questions

750
00:39:53,924 --> 00:39:57,428
that we can ask and what about
any scientific questions

751
00:39:57,495 --> 00:40:00,664
that we can ask
so that takes a lot of time

752
00:40:00,731 --> 00:40:03,501
in terms of the contracts
can take a long time.

753
00:40:03,567 --> 00:40:06,737
And then the building
of the protocol initially,

754
00:40:06,804 --> 00:40:09,440
that kind of what kind of
protocol it's going to be,

755
00:40:09,507 --> 00:40:12,243
can take some time
where I work very closely

756
00:40:12,309 --> 00:40:14,512
with the statistician,
I work with the labs.

757
00:40:15,146 --> 00:40:16,647
I work with the pharmacist,
you know,

758
00:40:16,714 --> 00:40:19,517
all kinds of thinking
about you know, do we do PKs?

759
00:40:19,583 --> 00:40:21,185
How often do
we do blood draws?

760
00:40:21,852 --> 00:40:24,288
All of these questions
that kind of come up initially.

761
00:40:24,355 --> 00:40:28,159
So, once that is kind of
established,

762
00:40:28,225 --> 00:40:31,061
then we write a an LOI.

763
00:40:33,164 --> 00:40:36,167
And that's the concept
to present

764
00:40:36,233 --> 00:40:38,803
to the scientific committee
who then says,

765
00:40:38,869 --> 00:40:41,238
this makes sense
in this population,

766
00:40:41,305 --> 00:40:43,107
this, there's a need,
we should do this,

767
00:40:43,174 --> 00:40:45,009
we should -- they support it,
or they don't support it.

768
00:40:45,075 --> 00:40:47,111
And then they also have
really great suggestions

769
00:40:47,178 --> 00:40:49,880
as to how can we make
this study better,

770
00:40:49,947 --> 00:40:54,952
maybe add another group,
maybe don't treat for as long,

771
00:40:55,019 --> 00:40:57,755
you know, they may have
multiple different suggestions.

772
00:40:58,289 --> 00:41:00,858
So, it goes through
all these several committees,

773
00:41:00,925 --> 00:41:03,727
it first starts off in,
you know, your department,

774
00:41:03,794 --> 00:41:07,565
then it goes off to the larger
scientific community

775
00:41:07,631 --> 00:41:09,633
within your institute.

776
00:41:10,367 --> 00:41:15,439
And then after that, it,
if you have to apply for an IND,

777
00:41:15,506 --> 00:41:20,177
that's something else, which is
really asking for a new way

778
00:41:20,244 --> 00:41:23,547
to give a drug, you know,
you're basically saying

779
00:41:23,614 --> 00:41:25,449
I'm going to give this drug

780
00:41:25,516 --> 00:41:27,451
or this combination of drug
in this population,

781
00:41:27,518 --> 00:41:31,388
and the FDA reviews that,
and then it goes through the IRB

782
00:41:31,455 --> 00:41:33,224
and the IRB's job
is really to make sure

783
00:41:33,290 --> 00:41:37,027
that it's safe
to give to the patients,

784
00:41:37,094 --> 00:41:39,997
and that we're doing
everything possible to ensure

785
00:41:40,064 --> 00:41:43,100
the safety of the patients
within this clinical trial.

786
00:41:43,167 --> 00:41:47,972
So, that process itself can take
anywhere from one year

787
00:41:48,038 --> 00:41:50,274
to three years
just to get started.

788
00:41:51,075 --> 00:41:54,478
And then once the trial opens up
and starts enrolling patients,

789
00:41:54,545 --> 00:41:57,281
then it depends on how large
the study is, you know,

790
00:41:57,348 --> 00:41:59,650
if it's a small phase one
with 40 patients,

791
00:41:59,717 --> 00:42:02,887
or if it's a large phase
three with 800 patients,

792
00:42:02,953 --> 00:42:07,024
you know, they can take anywhere
from a year to, you know,

793
00:42:07,091 --> 00:42:09,393
six, you know,
five, six years,

794
00:42:10,194 --> 00:42:11,495
maybe even longer sometimes

795
00:42:11,562 --> 00:42:14,098
if especially
if there's issues with accrual.

796
00:42:14,164 --> 00:42:15,833
So, the process can take
a long time.

797
00:42:15,900 --> 00:42:17,167
And then once that is done,

798
00:42:17,234 --> 00:42:18,936
then we have
to analyze the data,

799
00:42:19,003 --> 00:42:20,471
we have, you know,
have to put all the data

800
00:42:20,537 --> 00:42:22,406
together and meet again
with the statistician

801
00:42:22,473 --> 00:42:25,576
to talk about the data.
So, it is a long process,

802
00:42:25,643 --> 00:42:28,946
and it depends also on
who's sponsoring the study

803
00:42:29,013 --> 00:42:30,447
and, you know,

804
00:42:30,514 --> 00:42:33,217
how large the trial is,
how complex the study is.

805
00:42:33,284 --> 00:42:34,885
>> Lisa Cordis:

806
00:42:35,452 --> 00:42:38,122
Thank you so much, Dr. Apolo,
for that great overview.

807
00:42:38,188 --> 00:42:40,190
And thanks to all of our panel
members today

808
00:42:40,257 --> 00:42:41,659
and we hope the
audience has enjoyed

809
00:42:41,725 --> 00:42:43,160
getting to know
our research team.

810
00:42:43,227 --> 00:42:44,828
Thank you so much.