>> ALL RIGHT, GOOD MORNING,
EVERYONE AND WELCOME TO TODAY'S
GVIRF WEB CZAR ON ARTIFICIAL
VACCINE RESEARCH AND
DEVELOPMENT.
WE HOPE YOU FIND THIS
INFORMATIVE.
DURING THIS MEETING, YOU WILL
EXPLORE THE CROSS CUTTING EDGE
OF INTERSECTION AND ONE OF THE
MOST CRITICAL AREAS OF GLOBAL
HEALTH, VACCINES.
I'M KIMBERLY TAYLOR AND I'M
FROM NIAID ON BEHALF OF THE
SECRETARY AND ORGANIZING
COMMITTEE WE ARE VERY XHIETED
TO BRING YOU THIS WEBINAR.
ALL RIGHT, SO AS I WAS
PREPARING FOR THIS MEETING, I
THOUGHT I WOULD ASK CHAT GBT TO
ASK ME.
THE AI ACTUALLY SHOWED A LOST
PROMISE BUT I WAS LACKING SOME
GOOD INPUT AND ALTHOUGH I
REALLY LIKE THE AI LANGUAGE,
FOR EXAMPLE, AI IS LIKE THE
ULTIMATE SIDEKICK FOR BETTER
VACCINES AND VACCINE
DEVELOPMENT CAN SOMETIMES FEEL
LIKE A RACE AGAINST TIME.
YES WE FELT DURING THAT
PANDEMIC TO ACCELERATE
TECHNOLOGIES AND DEVELOPMENT OF
VACCINES.
SO SIT TIGHT, I FIRST WANT TO
TELL YOU A LITTLE BIT ABOUT THE
GVIRF.
THE GVIRF BRINGS THE VACCINE
COMMUNITY TO IMMUNOLOGY TO
IMPLEMENTATION RESEARCH AND
FROM LOW AND HIGH INCOME
COUNTRIES INNOVATION SXZ
OPPORTUNITIES AND ALSO TO
FACILITATE AND MAX SIZE THE
BENEFIT OF IMMUNIZATION.
BOTH THE GVIRF AND WEB NAZER
ARE COORGANIZED BY THE BILL AND
MELINDA GATES FOUNDATION AND
WORTH HEALTH ORGANIZATION WITH
THE OBJECT TO FACILITATE
PROGRESS TOWARDS GOALS OF
IMMUNIZATION AGENDA 2030.
SO FOR MORE INFORMATION, CHECK
OUT THESE COOL WEBSITES THERE
IS ONE ON THE GVIRF FORUM,
YOU'LL BE ABLE TO SEE VIDEOS AS
WELL AS CHECK OUT THE GREAT
WORK BEING DONE BY NIAID
RESEARCH.
OKAY, LET'S ONE INTO THE
AGENDA, AND A BIG THANK YOU
GOES OUT TO THESE AMAZING
PANELIST AND ALSO TO THE NIAID
TEAM, THERE IS GOING TO BE
THREE STEXES AND WE'LL EXPLORE
AI, AI DISCOVERY AND DESIGN AND
ALSO VACCINE DEVELOPMENT.
AND FOR ALL ON ZOOM, MAKE SURE
THAT YOU ENTER ALL QUESTIONS
AND THE PANELIST WILL TRY TO
ANSWER THE QUESTIONS FOLLOWING
THE SESSIONS.
WITHOUT FURTHER ADO, LET'S
INTRODUCE OUR FIRST SPEAKER FOR
SESSION ONE ON INTRODUCTION OF
AI, DR. PURVESH, THANK YOU FOR
BEING HERE TODAY.
>> THANK YOU, IF I CAN CHAIR MY
SLIDE.
WELCOME EVERYONE AND THANK YOU
FOR HAVING ME THIS FIRST
SESSION.
THE ITEM IS TO ISN'T TO WHAT IS
AI MACHINE WHAT IS MACHINE
LEARNING, GIVE A VERY HIGH
LEVEL OVERVIEW AND THEN IN
GETTING TO MORE SPECIFICS.
SO I'M A PROFESSOR AT INSTITUTE
FOR IMMUNITY AT STANFORD
UNIVERSITY.
I'M--COFOUNDER AND CHIEF
SCIENTIST INFLA MATIX.
NOW I DON'T THINK I NEED TO
TELL ANYBODY HERE HOW MUCH IN
LEARNING AND AI HAVE BEEN
TRANSFORM IFING SCIENTIFIC
DISCOVERY VIRTUALLY ANY FIELD
THAT YOU CAN THINK OF AI HAS
BEEN MAKING SIGNIFICANT
CONTRIBUTIONS.
AND IF THERE WAS ANY DOUBT,
THIS SHARED PRICES WERE IN AI
RELATED WORK.
SO THE QUESTION REALLY BECOMES
WHAT REALLY IS MACHINE LEARNING?
AND PLEASE FORGIVE ME TODAY IN
ORDER TO KEEP THE, THIS
PRESENTATION AT A VERY HIGH
LEVEL, I'VE TAKEN THE LIBERTY
OF OVER SIMPLIFYING CERTAIN
THINGS THAT DOES NOT MEAN THAT
THE COMPLEXITY DOES NOT EXIST
SO. WHAT IS MACHINE LEARNING?
I TEND TO THINK OF AI AS THE
FALLING UNDER THE UMBRELLA OF
MACHINE LEARNING BECAUSE BOTH
MACHINE LEARNING AND AI
ULTIMATELY DO ADDED CODE ONE
THING AND THAT'S IDENTIFYING A
MAPPING FUNCTION.
GIVEN A BUNCH OF INPUT FACTORS,
THIS COULD BE ANY NUMBER OF
DIMENSIONS BUT FOR INPUT THIS
MAPPING FUNCTIONS JOB IS TO
FIGURE OUT WHAT THE OUTPUT
SHOULD BE.
AND THAT AT ITS CORE IS MACHINE
LEARNING.
NOW THE WAY, THE QUESTION
BECOMES HOW DO YOU FIND THIS
MAPPING FUNCTION?
THAT ARE BROADLY SPEAKING WHO
APPROACHES TO WAYS TO DO THIS.
ONE IS A SUPERVISED MACHINE
LEARNING, THE OTHER IS
UNSUPERVISED MACHINE LEARNING
AND THE DIFFERENCE BASICALLY IS
IN SUPERVISED MACHINE LEARNING,
WHEN WE GET THE INPUT WE ALSO
KNOW WHAT THE CORRESPONDING
OUTPUT LABEL SHOULD BE AND
THAT'S WHAT IS INPUT AND LABEL
OUTPUT DATA IS WHAT IS USED TO
FIND THE MAPPING FUNCTION
BECAUSE YOU ALREADY KNOW WHAT
THE OUTPUT LABEL SHOULD BE,
UNSUPERVISED MACHINE LEARNING,
WE DON'T NECESSARILY HAVE THE
OUTPUT LABEL AND WE ARE SIMPLY
ASKING THROUGH THE COMPUTING
PROGRAMMING TO FIGURE OUT
MAPPING FUNCTION ANOTHER WAY IS
TO WHAT IS THE PATTERNS OF DATA
AND DISTRIBUTIONS OF DATA.
SXL THEY'VE BEEN GOING THROUGH
A LITTLE MORE DETAIL FOR THESE
APPROACHES.
NOW BEFORE WE GO INTO THIS, YOU
WILL ALSO HEAR THAT THIS
MAPPING FUNCTIONS ARE
INTERPRETABLE, SOMETHING
INTERPRETABLE, SOMETHING AI,
INTERPRETABLE, WHAT WE'RE
USUALLY TALKING ABOUT IS
SOMETHING THAT WE KNOW HOW THE
INPUT SET OF VALUABLES ARE
ASSOCIATED WITH THE OUTPUT
VARIABLE, WE ARE ABLE TO
INTERPRET THOSE.
WE ARE ABLE TO SEE THAT WHEN A
INPUT CHANGES, WE CAN START
THINKING ABOUT HOW THE OUTPUT
MIGHT CHANGE.
WHERE AS THE BLACK BOX MODEL
AND WHEN YOU THINK ABOUT THIS
INTERPRETABLE MODELS, THE USUAL
EXAMPLES ARE REGRESSION MODELS
THAT WE USING OUR, THERE IS
USUALLY ZOOM, LINEAR
RELATIONSHIP BETWEEN THE
OUTPUTS, BUT THAT'S HOW YOU CAN
THINK OF IT IN SIMPLIFIED TERMS.
USUALLY THE THING THAT ALLOW
WHEN YOU LOOK AT THE MODEL,
PREDICTION MONTHED PEL,
WHATEVER, YOU KNOW HOW TO
INTERPRET THE IMPACT ON
DIFFERENT VARIABLES ON OUTPUT.
ON THE OTHER HAND, THE BLACK
BOX MODEL, WHICH IS THE RESENT
FRENZY IS ABOUT, ARE THESE
COMPLEX MODEL WHERE WE DON'T
NECESSARILY KNOW WHAT THE
RELATIONSHIP BETWEEN THE
DIFFERENT VARIABLES ARE IN THIS
MAPPING FUNCTION.
AND INTERPRETABLE MODELS WE CAN
SEE THE RELATIONSHIP BETWEEN
DIFFERENT VARIABLES ARE BUT NOT
IN THE BLACK BOX MODEL.
THE ADVANTAGE WHILE WE DON'T
UNDERSTAND HOW THESE HOW THESE
VALUABLES ASSOCIATE WITH EACH
OTHER, HOW THEY'RE RELATED TO
EACH OTHER, THEY CAN MODEL VERY
COMPLEX NON LINIER FUNCTIONS.
THIS IS HOW THE LAST 7ISH YEARS
OF EXCITEMENT HAS BEEN FOCUSED
ON THIS.
A NEURONAL NETWORK WAYS,
TRANSFORMERS, DIFFUSION MODELS,
THESE ARE DIFFERENT TYPES OF
NETWORKS.
LET'S START WITH AN EXAMPLE PF
SUPERVISED LEARNING.
THIS WOULD BE A USUAL WHAT FOR
THE SAKE OF THIS TALK, I WOULD
SAY CLASSICAL MACHINE LEARNING
OR SOME MIGHT SAY STATISTICS,
BUT OVERALL IT FALLS UNDER THE
UMBRELLA OF SUPERVISED MACHINE
LEARNING WHERE YOU WOULD
USUALLY HAVE THINK OF IN
BIOMEDICINE, CONTROL STUDIES.
PATIENT HAS A DISEASE OR
HEALTHY, PATIENT RESPONDS TO
TREATMENT OR DOES NOT RESPOND
TO TREATMENT.
SOMEWHERE IN RESPONSE TO
VACCINE DOES NOT RESPOND TO
VACCINE, AND IT DOES NOT HAVE
TO BE BINARY VARIABLES, IT
COULD BE CONTINUOUS VARIABLE AS
AS WELL AS YOUR OUTPUT.
BUT WE KNOW WHAT THE OUTPUT
SHOULD BE AND AND WE START WITH
TRADITIONALLY ONE OR MORE
STUDIES, THE EXAMPLE I'M
SHOWING YOU HERE IS TRYING TO
UNDERSTAND WHETHER THE PATIENT
HAS ACCEPT SEPSIS OR NOT AND
THE SEVERITY OF SEPSIS.
WE STARTED WITH SAMPLES ACROSS
MANY DIFFERENT COUNTRIES ON
EVERY CONTINENT COMING FROM
DIFFERENT HOSPITAL, THAT REALLY
REPRESENTED THE POPULATION,
WHERE SOME PATIENTS HAD VIRAL
INFECTION, SOME CERT NON
INFEBRUARYINGTION INFECTION,
DIFFERENT GENDER, DIFFER
DISEASE COMOORE BILT AND USING
THIS DATA, WE ASKED VERY SIMPLE
QUESTIONS.
CAN WE GIVEN VIFN THE VARIABLE,
CAN WE COME UP WITH A MAPPING
FUNCTION THAT GIVEN A SET OF
INPUT GENES WOULD TELL US
WHETHER THE PATIENT HAS
INFECTION OR NOT SIRS VERSUS
SEPSIS.
AND NOW THAT SOMEBODY HAS
INFECTION CAN WE USE ANOTHER
MAPPING SECTION USING ANOTHER
SET OF GENES AND TELL US
WHETHER IT'S BACTERIAL VERSUS
VIRAL AND USING ANOTHER DATA
SET, WE FOUND A NEW MAPPING
FUNCTION TO IDENTIFY OR PREDICT
SOMEBODY'S RISK OF MORTALITY OR
THE NEXT 30 DAYS.
WE USE PULLING THAT OUTPUT INTO
SINGLE OUTPUT TO SAY WHETHER
SOMEBODY HAS PRESENCE, WHEN
SOMEBODY PRESENTS IN WITHIN
ILLNESS, CAN YOU DIAGNOSE
PRESENCE TYPE AND SEVERITY OF
INFECTION IN THAT PERSON?
THIS WAS DONE ALTHOUGH WE DID
APPLY UNSUPERVISED MACHINE
LEARNING, ONCE WE KNEW WHAT
WERE THE VARIABLE THAT'S
CONTAINED THE INFORMATION FOR
MAKING THIS DECISION, FOR
MAKING THAT CLASSIFICATION AND
PREDICTION.
THE ULTIMATE MODEL THAT
PERFORMED REALLY WELL WAS A
LINEAR MODEL TO SHOW IT
PERFORMED WELL TO CENTER 1200
PATIENT PER SPECTIVE TRIAL AND
THIS IS TWO MONTHS FROM GETTING
FDA APPROVAL.
SO THIS IS WHAT I WOULD SAY IS
A CLASSICAL SUPERVISED MACHINE
LEARNING USING DATA TO FIND
DIFFERENT SET OF VARIABLES FOR
DIFFERENT OUTPUT THAT WE ARE
LOOKING MORE AND ULTIMATELY
BRINGING IT TO FDA APPROVAL.
NOW I THINK YOU PROBABLY
NOTICED THAT ALL THAT ARE FEW
LIMITATION.
POST PAPER CAME OUT IN 2015 AND
APPROVAL EXPECTED IN 2025, NEXT
YEAR.
SO IT TOOK ABOUT 10 YEARS,
ALTHOUGH IT WAS THE SAME SET OF
DATA THAT WE WERE USING
REPEATEDLY AND THAT IS ONE OF
THE ISSUES WITH THE SUPERVISED
MACHINE LEARNING WHILE IT'S
AVAILABLE, WHEN WE ARE, LOOKING
AT THE DIFFERENT INFLAMMATORY
CONDITIONS AND DIFFERENT
CONDITIONS, DESPITE BEING THE
SAME DATA, YOU STILL HAVE TO
LOOK AT EACH INDEPENDENTLY,
EACH DISEASE WE STUDY
INDEPENDENTLY.
AND THIS CAN BE REPETITIVE,
TIME CONSUMER AND EXPENSIVE AND
WE WANT TO MAKE SURE THAT IN
WHAT WE WOULD REALLY LIKE IS
THERE A WAY TO LEARN THE
UNDERLYING MANGE FUNCTION ONCE
AND THEN, TUNE IT OR FINE TUNE
IT FOR DIFFERENT CONDITIONS
THAT WE WANT TO EITHER
DIAGNOSE, PREDICT OR ANY OF THE
THINGS FOR EXAMPLE,--I'LL GIVE
AN EXAMPLE.
>> ONE MINUTE REMAINING.
>> OH, I'LL GO A LITTLE FASTER
ABOUT 6-7 YEARS AGO, THIS PAPER
FROM GOOGLE REALLY REVOLUTIONED
DATA SCIENCE, UNSUPERVISED
LEARNING WHERE THE IDEA WAS
REALLY SIMPLE.
GIVEN A LOT OF TEXT, YOU CAN
MASK A BUNCH OF WORDS RANDOMLY,
PASS IT THROUGH SOMETHING
CALLED A NOODLE TRANSFORMER AND
ALLOW IT TO LEARN WHAT THAT
MISSING WORD IS.
SO YOU ASK US TO PREDICT AND
YOU SHOW THE ANSWER AND THEN
TRANSFORM WHAT THE MISSING WORD
SHOULD BE AND ULTIMATELY
PREDICT WHAT THE MISSING WORD
IS.
WHAT CAN YOU DO WITH THIS?
TRANSFORMING A OF ALL OF THESE
MAPPING FUNCTIONS FOR THE
MISSING TEXT IS WHAT LEADS TO
MODELS LIKE GBT.
A LOT OF THAT WE'VE SEEN, GIVEN
THE INPUT NEW TEXT AS KIMBERLY
WAS SHOWING EARLIER TO COME UP
WITH SOME HUMOR.
BUT A LOT OF ADVANTAGES TO
USING THIS MEDICAL
REPRESENTATION, THE MAPPING
THAT THE MODEL HAS LEARNED
REFERRED TO AS EMBEDING TO
FIGURE OUT MANY NEW
APPLICATIONS.
VERY FIRST APPLICATION, ALPHA
FORD, STARTED USING TRANSFORM
AND THE LATEST TRANSFORMER AND
TRANSFUSION TOGETHER.
WANT LET ME GIVE YOU ONE FINAL
EXAMPLE OF WHAT WE CAN DO THIS
IS AN EXAMPLE ABOUT EVOLVING
ANTI BODIES BUT YOU CAN SEE
THAT IT APPLIES ANTIGENS FOR
VACCINE.
SO GIVEN A LARGE NUMBER OF
CODING SEQUENCE, IF PIFS TO
SHOW YOU THE SEQUENCE ON THE
LEFT AND RIGHT SIDE, AND SAY
WHAT IS THE RELATIONSHIP US
HUMANS WOULD NOT UNDERSTAND THE
LANGUAGE.
BUT PROTEIN MODELS MUST
RANDOMLY AND WE ASK THE
TRANSFER MODEL TO LEARN BASED
ON THE SURROUNDING SEQUENCE IT
CAN BE, WHAT THE MISSING SHOULD
BE WE CAN CREATE A PROTEIN
MODEL.
AND WHAT THEY CAN SHOW IS THEY
CAN TAKE A HIGH LY --HIGHLY
MATURE SANT BODY AND THEY MASS
ONE AND ASKED THE PROTEIN MODEL
TO PREDICT WHAT IT SHOULD BE
GIVEN THAT THEY HAD LOOKED AT
ALL THE OTHER MILLIONS OF OTHER
SEQUENCES.
THEY CREATED THAT ANTIBODY AND
TESTED OUT HOW WELL IT BINDS TO
THE ANTIGEN COMPARED TO THE
DATA.
>> THAT'S TIME.
THANK YOU.
>> 30 SECONDS.
AND WHAT THEY WERE ABLE TO SHOW
IS PROTEIN LANGUAGE MODEL CAN
FIND NEW ANTIBODIES.
THEY USE THESE NEW ANTIBODIES,
KEPT THE PREDICTOR AND NOW
CHANGED MASTER, ANOTHER SET OF
AMINO ACIDS AND FOUND THEY
COULD FIND ANOTHER ANTIBODIES
THAT BOND TO THE ANTIGEN BETTER.
AND IN THE LAST TWO TEN
SECONDS, TWO EXAMPLES REALLY
EXCITING BUT WE CAN DO A LOT
MORE, INTEGRATING DATA SETS AND
PERTUR BING AND SEEING HOW THE
SYSTEM WOULD CHANGE.
BUT WHAT IS REALLY REQUIRED IN
THE NEXT GOING TO IT IS
ADDRESSING HALLUCINATIONS.
AND WITH THAT I'LL STOP JUST AS
IN LANGUAGE, THE AI HAS GONE
FROM 2018 TO 2023, WE ARE HERE
IN BIOMEDICINE AND A LOT OF
EXCITING STARTING TO COME DOWN.
I'LL START HERE AND THANK YOU.
NOW, THE NEXT SPEAKER IS
ADVISOR TALKING ABOUT AI
COMPUTING AND FUTURE OF VACCINE.
TED?
>> IT'S GREAT I'M TED,
MANAGING PARTNER PREVIOUSLY WAS
STRATEGY OFFICER AT HUMAN
IMMUNO PROJECT AND EARLY ON WAS
AT IABI.
SO I WHAT I WANT TO DO HERE IS
GROUND SOME OF THESE AI TOOLS
WHERE IT CAN BE INSERTED IN
DISCOVERY AND WE'RE REALLY
EARLY ON SO I'M GOING TO TAKE
SOME EXAMPLES ABOUT HOW IT
ALREADY IS OR COULD IN THE
FUTURE CHANGE HOW WE DO VACCINE.
BUT I'VE GOT TO EMPHASIZE THAT
IT HAS TO KEY THE KEY OUTPUT OF
WHAT VACCINES ARE SUPPOSE TO DO.
SO HISTORICALLY, IF WE LOOK AT
VACCINES GOING ALL THE WAY BACK
TO THE 18th CENTURY.
TECHNOLOGY HAS BEEN RAPIDLY
INTEGRATED INTO THE VACCINE
DESIGN PROCESS AND IT HAS HAD A
BIG IMPACT ON THE DISEASE
THAT'S WE CAN TARGET AND HOW WE
DEVELOP VACCINES AND MOST
RECENTLY STRUCTURAL BIOLOGY IN
THE LATER HALF OF THE 240th
CENTURY AND GENOMcS HAS
REALLY--WHAT I MEAN BY
TRANSFORM ISING MAKING A HEALTH
IMPACT AND THAT'S WHAT I WANT
TO KEEP THE FOCUS ON TODAY.
LET'S GO BACK TO THE GREATER
PARADIGM, IT'S STILL LARGELY
EMPIRICAL, IT'S DRIVEN BY TRIAL
AND ERROR WHEN WE HAVE A
VACCINE, WE DON'T KNOW HOW IT'S
GOING TO PERFORM IN THE CONTEXT
OF POPULATION.
AND THAT PERIOD BETWEEN THE
ACTUAL CONCEPT AND THE INITIAL
DESIGN OF THE VACCINE TO
LICENSEURE CAN BE A LONG TIME,
IT COSTS A LOT AND THE TIME
FRAME CAN BE REALLY LONG.
WE'LL TALK ABOUT HOW SOME OF
THE SHORTENING STRATEGIES IN
THE PANDEMIC.
BUT RESENT PAPERS CAME OUT AND
SAID THAT ABOUT 10 PERCENT OF
THE VACCINES MOVED TO
LICENSEURE IN TEN YEARS, COST
IS AROUND A BILLION DOLLARS SO.
WHAT IS THE IMPACT ON HEALTH?
WE STILL HAVE NOT MADE THE
ADVANCES--WE HAVE TO REMAKE
INFLUENZA AND WE DON'T REALLY
HAVE THAT GREAT VACCINES FOR
NON COMMUNICABLE DISEASES.
OVER POPULATION OVER ADULTS AND
NEWBORNS AND THOSE IN THE
DEVELOPING WORLD WHERE YOU HAVE
THE HIGHEST BURDEN OF DISEASE,
HAVE DIFFERENT RESPONSE TO SEE
VACCINES.
WE'RE NOT MAKING THEM FAST
ENOUGH AND COST AFFECTIVENESS
IS STILL TLGTS A REALLY CAPITAL
INTENSIVE ENDEAVOR WE DON'T GET
THE VACCINES OUT TO THE PEOPLE
WE NEED.
HERE IS THE VACCINE PARADIGM
GOING THROUGH ALL THE WAY
THROUGH LICENSEURE.
DURING COVID WE FIGURED OUT
REALLY COMPRESSING THIS BUT FOR
A LOT OF TARGETS GOING FORWARD
WE'RE NOT GOING TO HAVE THE
MONEY OR PRESSURE OR RESOURCES
SO DO THAT.
THE BAR FOR SUCCESS FOR AI IS
REALLY HOW DO WE IMPACT HEALTH
AT THE END OF THIS PROCESS?
ARE WE HITTING THE ETHICACY
METRICS ARE WE DESIGNING THE
VACCINES BETTER ARE WE WORKING
IN THE POPULATIONS BETTER?
WE CAN GET IT FASTER WE CAN GET
MORE VACCINE WE CAN PROTECT
MORE PEOPLE AND CAN WE ROLL
THIS OUT ACROSS HEALTH SYSTEMS.
IT'S GOT TO BE SOMETHING NA IS
USEFUL AND APPLICABLE IN A WIDE
RANGE OF AREAS.
SO WHAT I'M GOING TO DO HERE IS
LOOK AT SPECIFIC PARTS OF THIS
VACCINE DEVELOPMENT PARADIGM
AND TALK ABOUT AREAS IN WHICH
AI AND THESE COMPUTATION AL
TOOLS ARE MAKING AN IMPACT AND
CAN DO SO IN THE FUTURE.
IN TERMS OF ALL PRODUCT
DEVELOPMENTS, SMALL MOLECULE
AND VACCINES ONLY TWO ARE THAT
HAVE BEEN DESIGNED BY AI, SO
WE'RE EARLY EARLY ON OF THE
PROCESS.
LET'S LOOK AT THE FIRST STEP
AND WE'RE GOING TO TAKE THIS
FOR EMERGING THREATS.
WE'RE NOT GOING TO GO THROUGH
IT HERE, YOU'VE READ THE
ARTICLE AND NEIL AND OTHERS
WILL TALK ABOUT THIS.
BUT THE NOBEL PRIZE WENT TO
BAKER AND FOLKS AT DEEP MIND
AND WE SAW IN SCIENCE
PREDICTING FROM A SEQUENCE WHAT
IT MAY LOOK LIKE.
WE LOOKED OUT OF PROTEINS AND
ANTIBODIES AND NOW THAT CAN BE
SPED UP INTO A MATTER OF DAYS
AND HOURS WITH A PRETTY GOOD
STRUCTURE COMING OUT.
HOW CAN WE APPLY THAT?
THIS IS AN ARTICLE THAT
RECENTLY CAME OUT WHICH
BASICALLY ARE STARTING TO THINK
ABOUT RAPID APPLICATIONS OF
THESE TYPES OF TOOLS.
SOL THEY HAVE PUT OUT THIS
100-DAY MISSION, FROM EMERGING
PATHOGEN CAN WE HAVE A VACCINE
READY IN 100 DAYS?
100 DAYS IS FAST BUT STILL A
LONG TIME IN TERMS OF PANDEMIC.
THEY SUGGEST THAT USING FOLD,
WHICH IS WAS ALPHA FOLD AND
ROSE TTA FOLLOWED WERE BEHIND
THE NOBEL PRIZE.
SO AS SOON AS WE HAVE THAT
SEQUENCE CAN WE FICK OUT ON
A--WE FIGURE OUT ON A
STRUCTURAL BASIS.
BUT IT'S NOT JUST DESIGNING
IT'S MAKING SOMETHING THAT IS
USEFUL IN A VACCINE.
AND ONE OF THE MOST IMPORTANT
ADVANCES THAT ALLOWED THE
COVID TO MOVE FORWARD IS HAVING
A VACCINE THAT WAS STABLE,
STABLE ANTIGENS.
BUT WE CAN START TO USE
GENERATIVE AI TO MODEL THAT
STABILITY.
SO TO HAVE THE SEQUENCE THAT IS
A MATCHING AN ANTIBODY SEQUENCE
TO HAVE IT STABLE, AND READY TO
BE ENCODED INTO A VARIETY OF
VACCINES PLATFORM WOULD GO A
VERY LONG WAY INTO HELPING US
ACHIEVE THIS 100-DAY MISSION.
SO ANOTHER AREA THAT AI CAN BE
USED TO SORT OF RESPOND RAPIDLY
AND MAYBE EVEN HEAD OFF SOME OF
THESE ISSUES IN TERMS OF VIRAL
THREATS IS ISSUE OF VIVE AL
EVOLUTION.
NOW WE SAW DURING THE COVID
PANDEMIC THAT OUR VACCINE
TECHNOLOGIES REALLY LAG BEHIND
THE RESPONSE THE VIRUS AND WE
HAD TO DEVELOP NEXT GENERATION
VACCINES, BOOSTERS, NEW VACCINE
BECAUSE THEY WERE WAITING AS
THE VIRUS WAS WOULD MUTATE AND
ESCAPE.
THIS IS IN MICE AND EARLY OP,
SO WE HAVE TO TAKE THESE THINGS
WITH A LITTLE BIT OF GRAIN OF
ASSAULT BUT IT'S INTERESTING
CONCEPT WHICH USES THREE
PARAMETERS TO PREDICT THE VIRAL
SEQUENCES WITH ANTIGENS.
SO THAT'S THE MAINTENANCE OF
FITNESS THAT'S THE MUTATIONS
THAT ALLOW OR DON'T ALLOW THE
ANTIBODIES TO ACCESS THE REGION
AND IT'S DISRUPTION.
SO UNDER THIS MODEL WHICH
WAS--PRIOR TO 24020, WE SAW A
VERY GOOD PREDICT I HAVE OUT
WELL AS WELL AS APPLICABLE TO
INFLUENZA, HIV, SOME OF WHICH
HIGHLY VARIANT AND EVOLUTIONARY
DRIVEN VIRUSES.
SO IF WE CAN HEAD OFF IN THE
DESIGN OF ANTIGEN THAT MAY
ANTICIPATE WE MAY HAVE VACCINES
THAT PROTECT BETTER EXAMINE
SAVING LIVES AND INFLUENCING
HEALTH FOR A LONGER PERIOD OF
TIME.
THIS WOULD BE A REALLY REALLY
BIG RESPONSE IN TERMS OF
RESPONDING TO PANDEMICS AND
RESPOND TO GO VIRAL CHALLENGES
WHICH IS DRIVEN BY A LOT OF
EVOLUTION.
NOW THERE IS OTHER PARTS OF,
AREAS WHERE AI CAN HELP AND
ACCELERATE AND IMPROVE VACCINE
DEVELOPMENT.
SOME OF THESE AREN'T, THAT SEXY
AND ON THE SERVICE IS
INTERESTING BUT CAN HAVE A
REALLY REALLY IMPACT.
WE'RE GOING TO LOOK AT VACCINE
STABILITY WHICH IN THE CONTEXT
OF PREVIOUS PANDEMIC WAS A LIG
ISSUE.
>> TWO MINUTES, REMAINING.
>> OKAY, GREAT.
STABILITY, IS IMPACTS THE
ABILITY OF VACCINE TO BE USED
AND DISTRIBUTED AROUND THE
WORLD AND IT'S A MAJOR HEALTH
ISSUE BECAUSE WE CANNOT
DISTRIBUTE TO AREAS THAT LACK
ELECTRICAL INFRASTRUCTURE AND
LACK TRANSPORT AND WE LOSE A
LOT OF VACCINE IN THE PROCESS
WHICH MEANS THAT WE ARE NOT
VACCINATED THE PEOPLE THAT WE
NEED TO DO.
THIS IS A SUPER INTERESTING
EXAMPLE BECAUSE THEY USE
SOMETHING TECHNIQUE CALLED
LATTICE PARSING, THIS LET'S
COMPUTERS MAKE TEXT.
IT TURNS OUT THAT THE CODE
ISING JUST A SEQUENCE OF
LETTERS, BUT THE AMOUNT AND THE
COMBINATIONS OF THE CODE ONS
WOULD TAKE YEARS.
THIS LINEAR DESIGN THAT
SIGNIFICANTLY IMPROVED THE
OPTIMIZATION.
AGAIN THAT WOULD HAVE A REALLY
REALLY BIG IMPACT.
ANOTHER AREA IS THIS CONCEPT OF
DIGITAL TWIN, A VIRTUAL
REPRESENTATION OF A PHYSICAL
SYSTEM DRIVEN BY REALTIME
MONITORING WHERE YOU CAN
PREDICT THE OUTCOMES.
AND THIS WAS BEING USED, DURING
THE PANDEMIC BY AI, ATOS TO
IMPROVE THEIR MANUFACTURING
PROCESS AND IN REALTIME
STIMULATE CHANGES TO MATERIALS
OR OUTPUTS.
WHAT THIS CAN DO IS REDUCE THE
COSTS OF VACCINE AND REDUCE THE
COSTS OF MATERIALS BEFORE YOU
HAVE IT AND IMPROVE YIELD.
ALL OF THINGS MAKE THE COSTS
LOWER AND ALLOW US TO GET MORE
VACCINES OUT THERE.
WE'LL HEAR MORE IN PRESENTATION.
BUT THE QUESTION IS REQUEST WE
RUN BETTER TRIALS WITHOUT DATA
USING BIOLOGY KEY MARKERS OR
PREDICT I HAVE SIGNATURES,
RETHINK THAT LAST LINE OF THE
VACCINE DEVELOPMENT PROCESS
WHICH CAN COST $100 MILLION AND
LAST WELL OVER FIVE YEARS TO
GET SAFETY MEASURES TO GET
ETHICACY MEASURES BUT ALSO TO
UNDERSTAND SOME OF THE DYNAMIC
UNDERLYING HOW WE VALUATE
VACCINES AND SOME DAY IF WE GET
PREDICT I HAVE MODELS CAN WE
START TO RUN MORE AND MORE OF
THIS WITHIN THE CONTEXT OF
COMPUTER TO ACCELERATOR PREDICT
THE PROCESS.
SO I WANT TO END WITH A FEW
SUMMARY AND NOTES HERE.
AI WILL NEED TO OPERATE IN THE
CURRENT PARADIGM, WE STILL HAVE
TO GO THROUGH REGULATORY
APPROVAL IT STILL HAS TO BE
SAFE AND SAVE ON HUMANS BUT
THERE IS A WAY TO DO THAT.
WE CAN DESIGN THEM FASTER MORE
STABLE, WE CAN REDUCE THE COSTS
AND WE CAN DO THAT ALLOW US TO
BE ROLLED OUT AND APPLY TO THE
CONTEXT OF GLOBAL HEALTH WHICH
IS REALLY WHERE THE RUBBER HIT
THE ROAD.
IT CAN BE WORK IF YOU THINK
ABOUT GENOMICS IN 24,000 AND
IT'S INTEGRATED AND EVERYONE
USES.
WE'RE NOT TO THE POINT WHERE
IT'S PREDICT I HAVE WHERE WE
HAVE A SEQUENCE AND ANTIGEN AND
HOW IT'S GOING TO PERFORM THAT
WILL BE YEARS AND JAZZER IN THE
FUTURE BUT THERE ARE WAYS THAT
AI CAN HELP IN SMALLER TWEAKS
AND THEIR IMPACT ON PUBLIC
HEALTH.
THANK YOU.
>> THANK YOU, TED.
AND OUR NEXT SPEAKER IS
YSABEL--ISABELLE.
>> THANK YOU EVERYBODY, THANK
YOU FOR INVITING I'M THE CHAIR
OF MICROBIOLOGY GERMAN
UNIVERSITY IN A SMALL CITY NEXT
TO FRANKFORT.
AND IF YOU NOTICE I HAVE SOME
REGULATORY TOP COMPARING, I
HAVE ALSO SERVED IN THE
REGULATORY AGENCY IN GERMANY
CALLED THE POLISH INSTITUTE FOR
NINE YEARS.
SO HERE WE GO, I THINK WE'RE
ALL AWARE THAT COVID-19 CHANGED
THE ADOPTION AND DEVELOPMENT.
WE SAW THAT CAN INFLUENCE DRUG
DESIGN AND ALSO THE FORMATIONS
AND THERE WAS A LOT OF QUALITY
AND TIME GAIN IN CLINICAL
TRIALS.
CONSISTENCY IS A BIG TOPIC AND
VACCINE DEVELOPMENT BECAUSE
WHEN YOU GO INTO YOUR CLINICAL
PROGRAMS, YOU NEED TO BE SURE
THAT ALL LOTS ARE COMPARABLE
AND THAT YOU HAVE SAME SAFETY
AND ETHICACY THROUGHOUT YOUR
CLINICAL TRIALS AND THAT ALSO
LEADS TO BEING AWARE THAT
MANUFACTURING IS ALSO VERY VERY
IMPORTANT.
I'LL COME TO THIS IN A MINUTE.
BUT I WOULD LIKE TO ALSO MAKE A
NOTE NOTATION OF THE COVID-19
VACCINES HAS ALSO BEEN
INVOLVING AI.
NOW WE LEARNED THAT CAN IMPROVE
STANDARDIZATION OF THE PROCESS
OF MANUFACTURING SO YOU GET NEW
INSIDES INTO THE QUALITY
CONTROL, CENSOR DATA ALL KINDS
OF DATA THAT YOU CAN CAPTURE
AND BETTER UNDERSTAND HOW
ROBUST YOUR PROCESS SXZ YOU
HAVE NUMBERS TO EVALUATE THE
PROCESS TO HELP DEFINE THE
QUALITY ATTRIBUTIONS.
OVERALL, WE LEARNED THAT THIS
IS NOT ONLY INCREASE THE
QUALITY OF PRODUCT BUT
CONSISTENCY OF THE DEVELOPMENT
LATER ON.
HOW WHAT ARE OUR EXPECTATIONS
TO THE TO AI IN VACCINE EARLY
VACCINE DISCOVERY?
BASICALLY, AS ALREADY ALLUDED
TO, WE HOPE THAT AU CAN IMPROVE
EFFICIENCY AND ACCURACY
ETHICACY.
WE MIGHT NOT HAVE ALL OF THIS
DATABASES BUT THIS IS SOMETHING
THAT IS BEING STARTING TO BE
BUILT.
WE CAN USE A I TO PREDICT
IMMUNENICITY THAT WOULD ALSO BE
VALUABLE.
AND THE POINT IS ALWAYS TO BE
TAKEN THAT THAT PREDICT I HAVE
VALUE WILL HAVE PROVEN AND WE
NEED TO FIND WAY TO EVALUATE
THAT.
NOW IN THE AREA OF CLINICAL
TRIALS, AS MENTIONED, THERE IS
INCREASED FLEXIBILITY OF DESIGN.
THIS IS MOVING ON FAST AND WE
CAN OF COURSE USING SEEMLESS
TRIALS WHERE YOU CAN SO
SOMETIMES IT CAN BE DECISIONS
ON WHETHER OR NOT HOW TO DESIGN
THE CONTROL GROUPS.
HOW TO GENETIC DEVELOPMENT.
AND OF COURSE SO THIS GOES VERY
MUCH INTO THE AREA OF BIOINFO
MATICS BUT TO BETTER UNDERSTAND
OUR ANALYSIS.
WE SHOULD REALLY ALLUDE TO CORE
LATIVE PROTECTION THAT NEEDS TO
BE DESIGNED BEFORE YOU START
DEVELOPMENT.
SO WHEN IT'S ARBITRARY BUT NOW
A DAYS, WE KNOW THAT TO FIND A
CORERATIVE DATA, FROM CLINICAL
TRIAL DATA WITH VACCINES AND BY
AGGREGATING ALL OF THIS DATA WE
CAN UNDERSTAND THE DIFFERENCE
BETWEEN NATURAL AND UNDERSTAND
THE WHAT THE VACCINE IMMUNITY
CAN LOOK LIKE.
WE HAVE POSSIBILITIES OF COURSE
TO IDENTIFY THE CORELEX THAT
COULD BE PROTECTIVE WHEN
DEVELOPING VACCINE, SO BUY LOW
THIS IMMUNE PROTECTION WHEN WE
DEVELOP A VACCINE OR EVEN
GENERATE A NEW VACCINE AND
THERE IS A LOT OF HOPE THAT AI
WILL BE AIDING IN DOING SO WHEN
SUPPORTING OUR DECISIONS.
WHEN FOR EXAMPLE, TO DO THE
BOOSTER VACCINATION AND HOW TO
BRIDGE NEW VACCINE PRODUCTS
BASED ON ALREADY DEVELOPMENTS
ALREADY AVAILABLE IN THE
ABSENCE OF THE POSSIBILITY FOR
EXAMPLE TO RUN AN ETHICACY
STUDY ON SOME OF THE DISEASES
THAT ONLY SHOW UP IN VERY RARE
EPIDEMICS.
SO NOW TO REALLY WANT TO DRIVE
VACCINE DEVELOPMENT WE NEED TO
UNDERSTAND THE IMMUNE RESPONSE
AND VACCINE RESPONSE.
SO A LOT OF THIS IS SERAEPIDEM
PIOLOGY AND PLATFORM SO THEY'RE
NOT JUST THERE TO HAVE A
STANDARD PROCEDURE FOR
PRODUCING VACCINES, WE ALSO
NEED TO UNDERSTAND WHAT IS THE
IMMUNE RESPONSE ELICITED BY A
PLATFORM TECHNOLOGY, HOW DOES
IT CHANGE WHEN WE INSERT AN
ANTIGEN AND WHAT ARE THE
LEARNINGS OUT OF THIS AND CAN
WE ACTUALLY DEVELOP ALGORITHMS
TO LATER ON SELECT THE BEST
VACCINE PLATFORM OR TECHNOLOGY
PLATFORM FOR PRODUCTION AND
DEVELOPMENT OF CERTAIN VACCINE
AGAINST DESIRED PATHOGEN.
WE KNOW IN VERY MUCH DETAIL HOW
THEY WORK.
AND I THINK THAT THIS IS ALSO
BIG OPPORTUNITY TO USE AI TO
ACTUALLY UNDERSTAND WHAT THE
BEST FORMATIONS AND COMBINATION
OF ANTIGEN TO ACTUALLY BE
FASTER IN THE DEVELOPMENT OR
ACHIEVEMENT OF A VERY ETHICACY
AND SAFE PRODUCT.
AND OF COURSE AS ALREADY
MENTIONED, IT IS IMPORTANT TO
UNDERSTAND THE BOOSTER
INTERVALS AND CORRELATE DATA
AND TO INTERPRET THIS DATA AND
I THINK AI CAN ALSO MAKE THIS
MUCH EASIER AND ENHANCE THE
SPEED IN THIS.
NOW THIS IS JUST TO REMIND YOU
THAT IN PANDEMIC PREPAREDNESS,
WE SPEAK ABOUT THE VACCINE PLAT
FORMS THAT WE HAVE AVAILABLE TO
DEVELOP NEW VACCINES AND YOU
SEE THEY RANGE FROM WHOLESALES
TO VECTOR VACCINES AND NIRA,
SOME ARE T-CELL PRONE AND SOME
ARE MORE ANTIBODY PRONE, AND WE
HAVE SO MUCH INFORMATION BUT WE
NEED TO INTEGRATE THIS
KNOWLEDGE TOGETHER FOR EXAMPLE
THE INFORMATION THAT WE HAVE
ABOUT THE ANTIGENS HOW THEY
FUNCTION WHAT TYPE OF IMMUNITY
THEY CAN PRODUCE, ARE THEY
DRIVING IMMUNE RESPONSE INTO
AFFECTIVE FUNCTION.
AND THIS OF COURSE WE CAN
EXPLOIT FOR VACCINE DEVELOPMENT
AND I THINK THAT AI WOULD BE
ABLE TO HELP US WITH THIS IF WE
FIND THE RIGHT ALGORITHMS.
NOW THE CLINICAL TRIAL
LANDSCAPE HAS STRONGLY CHANGED
OVER THE LAST TEN YEARS.
OF COURSE WE'VE COME FROM
TRADITIONAL ONSITE TRIALS WITH
SUBJECT RECRUITMENT IN CERTAIN
TRIAL SITES AND NOW WE'RE OF
COURSE LOOKING AT HYBRID TRIALS
AND DECENTRALIZED TRIALS THAT
CAN FURTHER THE POSSIBILITY TO
DO MORE LONG TERM STUDIES ON
DISEASES, LONG TERM SAFETY AND
ETHICACY DATA ANALYSIS AND
COLLECTION OF DATA AND THAT CAN
HELP IMPROVE ALSO COMPLYING AND
CARE OUTCOMES.
SO WITH THIS CHANGE THAT WE
HAVE GOING THROUGH IN THE LAST
FEW YEARS, WE HAVE ALSO
ESTABLISHED THAT THERE IS MORE
AND MORE DIGITAL, LET'S CALL IT
DIGITAL SUPPORT AND DIDGIZATION
--DIGITIZATION AND WE HAVE USED
AI ALGORITHMS FOR
INTERPRETATION FOR THE RESULTS
AND ANALYSIS THAT WE WERE ABLE
TO HAVE OR ACCESS BEFORE.
>> TWO MINUTES.
>> THE LAST POINT, I THINK WE
WILL, THANK YOU, WHERE WE WILL
SEE THAT AI AND MACHINE
LEARNING WILL BE HELPFUL IS WE
NEED TO DECIDE ABOUT
IMMUNIZATION CAMPAIGNS AND
TRIAL DESIGNS ESPECIALLY IN
PANDEMICS OR EPIDEMICS ON HOW
TO GO FORWARD.
THIS IS SOMETHING WHERE WE WILL
LEARN HOW TO INTEGRATE THE DATA
THAT WE NEED AND HOW TO ALSO
USE REAL WORLD EVIDENCE LATER
ON TO INTERPRET THE AVAILABLE
DATA ON ETHICACY AND SAFETY.
SO HERE'S SOME LAST POINTS ON
WHAT IS BEING DONE IN THE
REGULATORY THE MOST IMPORTANT
POINT IS THAT THE INPUT INTO AN
AI ALGORITHM HAS TO BE CLEAR,
IT HAS TO BE TRANSPARENT AND
UNBIASED.
THIS IS NOT REALLY TO DO.
THIS IS AN ICH GUIDELINES THAT
HIGHLIGHTS THAT YOU NEED TO
DISTINGUISH WHETHER THEY'RE
HIGH IMPACT OR LOW OR MEDIUM
IMPACT MODELS AND DEPENDING ON
THAT YOU CAN DEFAOUN A RISK AND
DEPENDING ON THAT RISK, YOU
WILLING YOU --YOU WILL HAVE TO
CONTROL YOUR MODEL AND THE
EUROPEAN COMMISSION HAS STATED
THAT THE AI SYSTEMS IF THEY'RE,
THEY NEED TO BE DECIDE AND
DEVELOPED IN SUCH A WAY THAT
NATURAL PERSONS CAN SEE THE
FUNCTIONING AND THEY NEED THE
NECESSARY TRAINING AND
AUTHORITY TO CARRY OUT THIS
ROLE AND WHAT WE'RE LEARNING IS
THAT WE'LL NEED TO LEARN, WE'LL
NEED TO LEARN WITH SYSTEMS AND
WE NEED TO TRAIN THESE TO
UNDERSTAND WHAT OUR BENCH MARKS
ARE.
AND OF COURSE, WE MIGHT WANT TO
LOCK A SYSTEM JUST TO MAKE SURE
THAT IT DOESN'T CONTINUOUSLY
CHANGE AND THAT WE CAN PROVIDE
CONSISTENCY OF VACCINE
DEVELOPMENT AND LATER ON HAVE
VACCINES.
THIS IS TO SUMMARIZE THE
POTENTIAL OF AI ACCORDING TO MY
OPINION IS THAT WE CAN SEE THE
DATA COULD BE INTEGRATED FOR
COMBINED PATHOGEN AND VARIANT
PATHOGEN ADAPTED AND IMMUNE
DEVELOPMENT.
I THINK IT IS VERY USEFUL FOR
DEFINE ING UNKNOWN IMMUNE FOR
PREDICTING XWINTIONZ OF VACCINE
AND ANTIGEN AND FORMULATIONS.
THEY AI MAY BE HELPFUL THIS
SEEMLESS EXECUTION.
IMMUNIZATION CAMPAIGNS AND
REALTIME AND STUDIES IN
REALTIME.
THERE IS OF COURSE NEEDS, WE
NEED TO BE CAREFUL, WE NEED TO
UNDERSTAND THE CRITERIA FOR
RISK EVALUATION ON THESE AI
ALGORITHMS AND THERE IS
BENCHMARKED NEEDED ON THIS.
WE NEED TO VALIDATE THE SYSTEMS
AND WHAT KIND OF REFERENCES WE
CAN USE AND OF COURSE FINALLY,
WE'RE HOPING FOR IS
PREDICTABILITY FOR RELIABLE
OUTCOMES IN TERMS OF ETHICACY
BUT OF COURSE THIS NEEDS TO BE
SOMETHING THAT WE FIND
MECHANISMS ON HOW TO PROVE THIS
AND MEASURE THIS ON A LONG TERM
SCALE.
AND WITH THIS, I LIKE TO THANK
YOU FOR YOUR ATTENTION.
>> THANK YOU, ISABELLE.
>> THANK YOU AND OUR NEXT
SPEAKER IS FROM UNIVERSITY OF
CAPETOWN AND THEN WE'LL MOVE
INTO PANEL DISCUSSION.
WE CAN'T HEAR YOU.
NO.
CAN ANYONE ELSE HERE HER?
>> NO I CAN'T HEAR HER, MAYBE
WE CAN MOVE TO A QUESTION AND
IN THE MEANTIME WE'LL FIX HER
AUDIO.
>> YEAH.
THIS IS ONE OF THE QUESTION
FROM ANA THAT WAS ALSO GOING TO
BE A QUESTION FOR THE PANEL, SO
I WILLLLINOIS MODIFY A LITTLE
BIT.
AND I WAS HOPING CHANGI, WOULD
BE TOUCHING ON THIS.
ONE OF THE BIG PROBLEM WITH AI
AND THIS GOES TO ALL OF MY
PANEL MEMBERS, TED, ISABELLE
YOU NEED A LOT OF DATA WHEN
CREATING THESE AI MODELS.
WHAT ARE THE ISSUES THAT WE
NEED TO BE AWARE OF FOR USING
AI IN VACCINE DEVELOPMENT?
WHAT KIND OF DATA WE NEED?
WHAT IS THE GAP WHAT SHOULD WE
FOCUS ON?
>> I CAN TAKE THE FIRST SHOT AT
THAT.
THIS IS SOMETHING THAT ACTUALLY
THINK THIS IS GOING TO REALLY
INHIBIT POTENTIALLY INHIBIT THE
IMPACT OF AI.
THERE IS A LOT OF DATA IN THE
VACCINE WORLD AND IMMUNOLOGY
WORLD, BUT IT'S NOT SEQUESTERED
AND NOT ACCESSIBLE.
AND IF YOU SEE CHAT GBT,
THEY'RE SUCKING UP ALL THE TEXT
THAT IS HAS BEEN BEEN WRITTEN
AND PRODUCED.
THAT WAS THE DRIVER TO ALLOW
THESE THINGS TO GET BETTER AND
BETTER AND FOUNDATIONAL MODELS
NOT TRAINED ON WHERE YOU CAN
SEE REALLY BIG ADVANCES.
AND I THINK IN THE CASE OF THE
NOBEL PRIZE, IT SEEMS LIKE IT
JUST HAPPENED BUT THERE WAS AN
ACCESSIBLE DATABASE THAT WAS
THERE FOR YEARS.
DO WE HAVE THOSE SAME THINGS IN
OTHER AREAS AND I'M NOT SO SURE
THAT WE DO.
>> I WOULD LIKE TO PICK UP THE
QUESTION VERY BRIEFLY BECAUSE I
WANT TO POINT OUT THAT WE
SOMETIMES DON'T KNOW WHAT THE
QUALITY OF THE DATA WERE
AVAILABLE AND WE HAVE TO BE
VERY SURE THAT THEY'RE UNBIASED.
SO WE WILL HAVE TO FIND
CRITERIA ON HOW TO JUDGE ON THE
QUALITY AND DECIDE WHETHER THE
QUALITY THRESHOLD IS BASICALLY
REACHED TO INCLUDE THEM IN OUR
DATABASES THAT WE CAN USE AND
MAKE AVAILABLE FOR AI
ALGORITHMS AND I THINK THIS IS
ONE THING OF LAST CHALLENGE FOR
THE NEAR FUTURE.
>> SO LET'S JUMP OFF OF THAT.
THERE ARE TWO THINGS, ONE IS
THIS NEAR FUTURE, RIGHT, SO
WHAT DO WE NEED TO DO NEAR
FUTURE AND WHAT DO WE NEED TO
DO IN THE LONG TERM TO MAKE
SURE THAT TEN YEARS FROM NOW,
EVEN IF DATA DOES NOT EXIST
TODAY, WHAT SHOULD WE BE DOING
SO THAT TEN YEARS FROM NOW, WE
HAVE ANOTHER PROTEIN DATA BANK
SO THERE IS ANOTHER NOBEL PRIZE
IN VACCINE THROUGH AI?
>> A LOT OF THIS IS MUNDANE
STUFF LIKE LIEU AGREEMENTS AND
SHARING OF CLINICAL PROTOCOLS,
HAVING THE SHARING OF, YOU
KNOW, APPROPRIATE SHARING OF
PATIENT PROTECTED DATA BUT NOT
JUST FROM A SINGLE SITE BUT ALL
SITES.
THERE IS A LOST DATA T NOT TO
BILLABLE PHARMA BUT A LOT OF
DATA, PROBABLY THE MOST DATA
THAT IS THERE THAT IS
UNDERLYING IP, IS THERE A WAY
TO USE THAT SO THAT THE WHOLE
FIELD IN A BETTER SPOT FIVE
YEARS FROM NOW THAN WE ARE NOW.
IF IT'S NOT ACCESSIBLE WE'RE
GOING TO HAVE SOME PROBLEMS.
>> ALL RIGHT, AND ISABELLE YOU
HAD SOMETHING ON ABILITY ON THE
LAST SLIDE.
I ALSO SKIPPED A SLIDE ABOUT
RELIABILITY.
WHAT DID YOU MEAN BY THAT?
MAYBE YOU AND I HAVE DIFFERENT
POINT OF VIEW ON THAT BUT IT'S
SOMETHING THAT I ALSO THU ABOUT
A LOT.
WHAT IS THE GAP THAT NEEDS TO
BE ADDRESSED?
>> WE NEED TO LEARN AND TEST
OUR ALGORITHMS FOR PERFORMANCE,
MEANING THAT WE CAN USE THEM
FOR CIRCUMSTANCES AND ALWAYS
PREDICT THE ROBUSTNESS OF AN
OUTCOME AND WE NEED TO
UNDERSTAND HOW TO USE THEM.
LET'S SAY WHEN YOU USE A
PRODUCT, YOU HAVE A TARGET
PROFILE YOU HAVE AN INDICATION
AND WE NEED AN INDICATION
TOERER OUR ALGORITHM TO
UNDERSTAND TO USE FROM ANOTHER
PATHOGEN AND IN THE SETTING
THAT WHEN WE LET'S SAY, DEVELOP
VACCINE FOR A NEW VARIANT OF
THE SAME VIRUS OR IS THERE
SOMETHING VERY GENERAL?
AND HOW DO WE TEST FOR THIS
WHAT ARE THE PERFORMANCE
INDICATORS AND THE MAJOR
THOUGHT IS ALWAYS THAT WE WOULD
NOT ONLY WANT OUR PRODUCT TO BE
CONSISTENT IN TERMS OF ETHICACY
AND SAFETY BUT ALSO IN A WAY
THESE ALGORITHMS THAT WE'RE
USING, THEY NEED TO BE
SOMETHING THAT THEY KNOW VERY
WELL AND WE CAN CONTROL IN THE
SENSE THAT THEY ALWAYS DELIVER
THE SAME SORT OF OUTPUT.
>> UH-HUH, SO THAT'S A GREAT
ANSWER I THINK WE ARE IN LINE
THERE.
ONE OF THE ISSUE FROM THE WORK
THAT WE DO, HAS ALWAYS BEEN
THAT EVEN FOR THE SAME SET OF
INPUT SO SAY EBMC, EVEN IF YOU
DO SOMETHING AS MUNDANE AS SAME
TISSUE OF ACCURACY FROM 240
PERCENT TO 90 PERCENT WHAT THAT
MEANS IS WE CAN TAKE THE DATA
OR THE OUTPUT OF THIS AI MOLD
AS OF YET AS FACE VALUE AND PUT
THEM INTO PIPELINE.
WE STILL NEED TO GO BACK AND
SPOT CHECK THE RESULTS AND
THAT'S ONE OF THE ISSUES THAT
IS LIMITING THE ABILITY OF THIS
AI MODELS OVERALL IN
BIOMEDICINE.
AND WHEN YOU SEE WHAT THEY DID,
THEY HAD A SET OF BENCHMARKS
WHAT THEIR MODELS WERE
OUTPUTTING.
WE DON'T HAVE THAT AND THAT'S
ONE OF THE THINGS THAT WE NEED
TO START THINKING ABOUT,
DEVELOPING THIS DATA SET SO WE
CAN TEST MODELS AS THEY COME A
LONG.
ALL RIGHT, DO WE HAVE CHANG LEE
ON THE CALL AGAIN?
OTHERWISE WE CAN GO TO THE NEXT
QUESTION.
>> YEAH, I HOPE YOU CAN HEAR ME
NOW.
>> YES.
CHANG LEE, GO AHEAD.
>> THANKS, SO THANKS FOR HAVING
ME ON THE PANEL.
FIRST I THINK WHEN THE
GENERALLY TALK ABOUT ISSUES, WE
SPEND TO THINK THE CHALLENGES.
BUT I REALLY LIKE TO START BY
SAYING THAT WE NEED TO FIRST OF
ALL MAKE EFFORTS TO TALK ABOUT
THE BENEFITS OF DATA SCIENCE
FOR RESEARCH AND DEVELOPMENT.
BEING IN TERMS OF DATA SHARING
OR THE DEVELOPMENT OF NEW TOOLS
OR INTERVENTIONS SUCH AS
VACCINES.
AND BUT IN THIS CAUSING
BENEFITS, WE NEED TO MAKE SURE
THAT VARIOUS PEOPLE,
COMMUNITIES, OR RESEARCHERS ARE
AWARE OF ETHICAL ISSUES THAT
MAY ARISE IN THE USE OF DATA
SCIENCE FOR THAT DEVELOPMENT.
AND THIS IS IMPORTANT JUST SO
THAT NO ONE SHOULD PERCEIVE
ETHICAL AND SOCIAL
CONSIDERATION AS CHALLENGES OR
DIFFICULT OPPORTUNITIES TO
THINK ABOUT HOW INNOVATIONS AND
SCIENCE CAN CONTRIBUTE TO THE
VALUES AND COMMUNITIES.
BUT ALSO HOW THEY CAN EMBED
EVERYTHING AND ALL THAT IS
DEVELOPED.
I WOULD LIKE TO TALK ABOUT
ETHICAL ISSUES AND I'LL TALK
ABOUT THEM IN TWO AREAS.
THE FIRST, IS AROUND DATA
SHARING AND THE SECOND IS AI
USE AND DEVELOPMENT.
IN TERMS OF SHARING, WE CAN ALL
AGREE DID ETHICAL, AND ALL
OTHER SPEAKERS TALKED ABOUT THE
HOW THE COVID PANDEMIC HAS MADE
US RETHINK HOW DATA CAN BE
SHARED.
BUT WE WILL ALSO AGREE THAT
WHILE EVERYONE SEES THE
BENEFITS OF DATA SHARING,
RESEARCHERS AND COUNTRIES
SOMETIMES ARE VERY SELECTIVE ON
WHAT DATA TO SHARE AND HOW TO
SHARE AND WHEN TO SHARE.
AND THIS IS FOR VARIOUS BUT
TIMES JUST--REASONS.
SO FOR EXAMPLE, WE THINK OF
ABOUT THE COVID PANDEMIC, HOW
COUNTRIES LIKE AFRICA, WITH
TRAVEL BANS AND.
WE COULD ALSO JUST THINK ABOUT
HOW TO SHARING THE HIV BIOGENIC
DATA CAN TO THE DISCRIMINATION
STIGMATIZED GROUPS OF
POPULATIONS AND IN SOME CASES
ACTIVITIES THAT MAY BE
CRIMINALIZED.
AND BESIDES ISSUE OF
DISCRIMINATION, WE NOW HAVE
DATA LAWS THAT WILL IMPACT HOW
WE COUNTRIES OR RESEARCHERS
SHARE DATA.
SO FOR EXAMPLE, MANY DATA
PROTECTION LAWS ACROSS SHARING
OF DATA AND THAT IS IN LINE
WITH THE SUBJECTS.
SO THAT'S A GOOD THING BUT ALSO
IN TERMS OF PRINCIPALS AROUND
DATA AND LOCALIZATION.
SO WE NEED TO ALSO THINK ABOUT
ETHICAL WAY OF SHARING DATA
THAT WILL BE COMPLIANT WITH
DATA LAWS.
FOR EXAMPLE, WE CAN START WITH
DATA BASICALLY, HOW DO WE SHARE
CONSENT THAT IS IN LINE WITH
THE RIGHTS OF DATA PATIENTS,
HOW DO WE DO IT FROM CONSENT IN
WAYS THAT IN LINE WITH THE DATA
PROTECTION LAWS.
THINK OF WAY TECHNICAL
SOLUTIONS LIKE DATA OR DATA
INTEGRATION, WE SHOULD ALSO BE
THINKING ABOUT LOW INCOME
COUNTRIES PREPARE FOR THIS TYPE
OF DATA MATTERS AND HOW DO WE
SUPPORT THEM.
I THINK WHEN LOW EXAMINE MIDDLE
INCOME COUNTRIES, WE NEED TO BE
VERY INTENTIONAL ABOUT PRO
MOTELING EQUITABLE DATA SHARING
PRACTICES.
NOT SUCH FOR RESEARCHERS BUT
ALSO COMMUNITIES.
AND WE THINK TO THINK ABOUT
THIS IN TERMS OF BENEFICIARIES
AND GOOD EXAMPLE IS HOW IN
2007, INFLUENZA AND HOW DODA AT
THAT ORGANIZATION NO PLANS FOR
SHARING AK ISS TO VACCINE IN
LOW AND MIDDLE INCOME COUNTRIES.
SO BESIDES DATA SHARING, ALSO
IMPORTANT TO REFLECT ON THE
ETHICAL AND SOCIAL ISSUES THAT
MAY ARISE AND THE NEWS OF AI
TECHNIQUES IN VACCINE RESEARCH
AND DEVELOPING.
FIRST OF ALL, WE HAVE TO
REMEMBER THAT MOST DATA
PROTECTION LAWS CONSIDER HEALTH
DATA SENSITIVE DATA AND
THEREFORE THE SPECIFIC
PROVISIONS FOR THAT.
I THINK WE NEED A ROBUST
DISCUSSION ON THE POLITICAL,
THE REGULATION, THE COMMUNITY,
AND THE ENVIRONMENTAL CONCERNS.
I THINK WE DON'T TALK MORE
ABOUT ENVIRONMENTAL CONCERNS
AND ALSO REPRESENTATION AND
DIVERSITY, ESPECIALLY WHERE
HISTORICAL DATA IS USED TO
TRAIN AI.
I THINK PEOPLE HAVE TALKED
ABOUT DATA PRIVACY, WE NEED TO
THINK ABOUT MITIGATION OF BIAS
AND DATA MACHINE AND
STRUCTURES, WE TALK ABOUT
STORAGE OF CLOUD AND ALL THAT,
ENVIRONMENTAL IMPACT OF THAT.
AND INFORM CONSENT.
AND I WOULD LIKE TO
SPECIFICALLY HIGHLIGHT INFORMED
CONSENT.
BECAUSE THE LACK OF INFORMATION
SECOND--DATA BUT VACCINE
RESEARCH DEVELOPMENT CAN REALLY
IMPACT THE ETHICAL IMPACT OF AI
AND SCIENCE APPLICATIONS.
AND I THINK THAT WHAT THIS
DIFFERENT ETHICAL ISSUES RAISE
IS THAT IT IS JUST A SUSTAINED
COMMUNITY ENGAGEMENT AND THEY
NEED TO BUILD TRUST SO. YOU
HAVE TWO SOLUTIONS ONE IS THAT
RIGHT NOW.
IT'S THE NEED TO INVEST IN
TRACING.
SO COMMUNITIES OR VUJZ CAN ONLY
GIVE INFORMED CONSENT IF THEY
ARE WELL-INFORMED.
SO COMMUNITIES THAT ARE NOT
AWARE OF WHAT DATA IS THE VALUE
OF DATA AND HOW THE DATA IS
BEING USED, THEY ARE NOT ABLE
TO GIVE INFORMED CONSENT AND
DIFFERENT STAKEHOLDERS CANNOT
SEE THE VALUE OF DATA SCIENCE
FOR VACCINE DEVELOPMENT IF THEY
ARE DATA AND AWARE OF ETHICAL
ISSUES IN DATA SCIENCE IN
RESEARCH AND DEVELOPMENT.
AND LASTLY, A SECOND SOLUTION
IS I THINK WE NOW NEED TO START
RETHINKING CONSENT FOR DATA
RETRIEVE RESEARCH FOR VACCINE
DEVELOPMENT AND DRUG
DEVELOPMENT.
AND I THINK IT'S TO BEGIN TO
THINK ABOUT HOW TO CONTINUES
COMMUNITY OF PROPER ENGAGEMENT
ON SECOND DATA USED ON INFORMED
CON ISN'T FOR DATA TREATMENT
RESEARCH AND INNOVATION.
THAT'S SIMPLY MEANS NOW TO
REMAIN COMMUNITIES MORE
PEACEFUL WHEN WE USE DATA
DRIVEN TECHNIQUES AS SOLUTIONS
TO SOME HUMANITIES ISSUES.
THOSE ARE SOME OF THE IRKZ THAT
I THOUGHT WE REALLY NEED TO
THINK ABOUT.
IT'S NOT EX --EXHAUSTIVE, BUT
THOSE THOSE ARE IMPORTANT.
>> THANK YOU VERY MUCH, INSIDES
TO CONSIDERATION THAT WE ALL
WANTED THINK ABOUT.
I WANT TO THANK MY PANEL ISSUE,
PURVESH, TED, ISABELLE.
WE'RE GOING TO MOVE ON NOW TO
SESSION TWO.
I'M GOING TO INTRODUCE TONG.
THANK YOU VERY MUCH, TAKE IT
AWAY.
WE'RE GOING TO MOVE FORWARD TO
THE NEXT SPEAKER WHO IS NEIL
KING PROFESSOR AT UNIVERSITY OF
WASHINGTON.
STILL WORK WITHING HIM SO HE
ESTABLISHED THE FIRST
GENERATION COMPUTATION THAT
DERIVE PARTICLES AND NOW
INCLUDE CUTTING EDGE AI DESIGN.
SO LET'S HEAR WHAT HIS PROGRESS
IS.
>> THANK YOU, EXAMINE THANK YOU
TO THE ORGANIZERS WHO INVITED
ME.
I WANT TO FOLLOW-UP ON THE
BEAUTIFUL POINTS THAT BALI DID
ABOUT WHAT AI CAN AND CANNOT DO.
AND THERE ARE INSTANCES.
AS TED MENTIONED EARLIER, YOU
KNOW, 2024, IS THE YEAR OF THE
AI NOBELS.
AND JUST TO REMIND EVERYBODY,
PROTEINS ARE STRINGS OF AMINO
ACIDS THAT ARE VERY DEFINED AT
THE ATOMIC LEVEL AND GIVES US
CLUES ABOUT WHAT THE PROTEINS
DO AND THESE SORTS OF
ALGORITHMS CAN BE USED TO
DESIGN.
SO AS MENTIONED EARLIER, YOU
KNOW, AI WORKS REALLY WELL IN
THOSE SORTS OF PROBLEM FOR
SEVERAL REASONS BUT MOST
IMPORTANTLY DATA SETS HERE ARE
VERY VERY STANDARDIZED.
IT'S VERY STANDARDIZED.
AND THE SECOND POINT IS HERE
THAT THESE DATA ARE PUBLICLY
AVAILABLE.
PROTEIN SEQUENCE AND STRUCTURE
INFORMATION IS VAS AMOUNTS ARE
AVAILABLE IN PUBLIC DATABASES.
SO BECAUSE OF THAT PEOPLE HAVE
BEEN ABLE TO DESIGN AND THEY
ACTUALLY WORK VERY VERY WELL.
THIS IS THE CURRENT PIPELINE,
DEVELOPED BY MY COLLEAGUE DAVID
BAKER AND HIS GROUP TO CREATE
ENTIRELY NEW PROTEINS.
I'LL SHOW YOU HOW WE'RE USING
THESE APPROACHES AND STRUCTURE
BASE DESIGN.
THE CURRENT PIPELINE AS IT
EXIST TODAY AND THE TOOLS THAT
WE USE, FIRST IF YOU WANT TO
MAKE A NEW PROTEIN, YOU START
BY MAKING UP THE BACKBONE.
RIGHT, SO YOU MAKE A ENTIRE
BACKBONE AND SHAPE THAT YOU
WANT TO BUILD.
AND THE TOOL THAT WE'RE USING
IS RF DIFFUSION AND I'LL SHOW
YOU ON THE NEXT SLIDE WHAT THAT
LOOKS LIKE.
THAT SHOULD BE A SEQUENCE THAT
ENCODES THAT BACKBONE STRUCTURE
THAT DRIVES AND THE TOOL THAT
WE'RE USING FOR THIS RIGHT NOW
IS PROTEIN PUBLISHED IN 2021
AND THEN IN THE THIRD STEP, WE
USE STRUCK ROSETTA FOR
STRUCTURE.
SO WE'LL DEFINE MILLIONS OF
SEQUENCES ON THE COMPUTER AND
PREDICT THEIR STRUCTURES TO
FILTER THEM DOWN TO THE
SEQUENCE THAT'S WE THINK ARE
GOING TO WORK BEST IN THE WEB
LAB.
SO THIS IS HOW WE'RE DESIGNING
NEW PROTEINS.
THIS IS WHAT THE FIRST STEP
LOOK LIKE DF DIFFUSION, IT'S A
DENOISING MODEL THAT GENERATES
NEW STRUCTURES VIA PROGRESSIVE
DENOISING.
SO THIS IS LIKE THE GENERATOR
THAT YOU FIND ONLINE THAT SHOWS
A CAT INTO THE INTERNET INTO
THE MODEL AND GRADUALLY ADDING
NOISE UNTIL THEY BECOME PURE
NOISE.
ONCE THEY SEE THAT, YOU CAN ASK
IT TO DENOID AND GENERATE A NEW
IMAGE OF A CAT.
THIS WORKS REALLY WELL FOR DUE
DIMENSIONAL UNITS AND WORKS
REALLY WELL FOR THREE
DIMENSIONAL PROTEINS.
THIS IS TO TRAIN THE MODEL.
SO REALLY A STEP CHANGE IN OUR
ABILITY TO DESIGN NEW PROTEINS,
IT'S A HUGE DIFFERENCE FROM
PREVIOUS METHODS.
OKAY, HOW ARE WE USING THIS FOR
VACCINE?
COME OF EXAMPLES.
MANY PEOPLE ACROSS THE WORLD
HAVE BEEN WORKING OVER THE LAST
20 YEARS TO STABILIZE THIS ANT
AGAIN AND ITS PREFUSION
CONFIRMATION.
THIS IS NECESSARY AS A DESIGN
TASK BECAUSE THE PROTEIN AS A
WHOLE IS META STABLE, THE SUB
ENVELOPE IS THE FUSION
MACHINERY THAT DRIVES INJURY OF
THE VIRUS INTO THE CELLS AND
THAT PROTEIN IS UNSTABLE AND
WANTS TO FLIP TO A MORE STABLE
FUSION CONFIRMATION.
SO IN THE WORDS OF MY
COLLABORATOR, HE FEELS LIKE
HE'S BEEN PUTTING BAND-AIDS ON
THIS PROBLEM FOR THE LAST 20
YEARS BY MAKING MUTATIONS IN
GP41 AND TRY TO STABILIZE IT.
AND ALTHOUGH THAT'S BEEN
SUCCESSFUL, WE ASK THE
QUESTION, IS THERE A BETTER WAY
TO DO THIS?
CAN WE INSTEAD GET RID OF GP41
AND REPLACE IT?
SO YOU WANT EXTREMELY TALENTED
GROUP CAME TO STAY IN THE LAB
FOR NINE MONTHS AND DID EXACTLY
THIS.
SO HE TOOK THE GP120 AND THREW
AWAY GP41 AND HAD TO MAKE
EXTENSIVE MODIFICATIONS TO
ALLOW US TO CONNECT TO TO THIS
BUNDLE THAT WE MADE UP USING
THIS AI METHOD.
SO WE STARTED FROM NANO
COMPONENT AND GENERATED WE
MODIFIED GP120 AND ATTACHED ALL
OF THESE THINGS DESIGNED THEM
AND WELDED THE SCENE SO THAT
EVERYBODY WOULD BE NICE AND
STABLE.
AND WE ENDED UP WITH THIS
NATIVE-LIKE GP1420 TRIEMER
ATTACHED TO ONE OF OUR NANO SUB
UNITS.
I WANT TO SHOW YOU ALL OF THESE
DATA BUT THEY'RE SUPER STABLE
AND WHEN ANDREW, GOT STRUCTURE
OF THIS PROTEIN, WE FOUND THAT
IT MATCHED THE KNOWN STRUCK
FEWER A --ACCURACY AND ADOPTING
THE RIGHT STRUCTURE.
SO THIS IS AN EXAMPLE OF
DIFFICULT PROBLEMS THAT CAN BE
INVOLVED BY THIS AI METHODS.
THIS IS SOMETHING THAT HERE AT
THE DESIGN EVEN WITH THE BEST
METHODS THREE YEARS AGO WOULD
HAVE BEEN IMPOSSIBLE AND NOW
YOU JUST DO IT, THANKS TO AI.
SO THE SECOND EXAMPLE I WANT TO
GIVE IS ON THESE NANO PARTICLES.
IT'S BEEN KNOWN FOREVER, SO
PARTICLES SUPERIOR AND IMMUNE
RESPONSES THROUGH SEVERAL
MECHANISMS.
ONE IS THAT, OUR A ANATOMY HAS
EVOLVED INTO LIMB PAT I CAN AND
LYMPH NODES SO THE SYSTEM CAN
GET A GOOD LOOK AT THEM.
WHERE SMALLER TRAFFICKED INTO
THE BLOODED AND EXCRETED BY THE
BODY.
SO YOU IMPROVE TRAFFICKING AND
THE OTHER MECHANISM BY WHICH
THE IMMUNE SYSTEM DETECTS
PARTICULAR IMMUNE GEN SYSTEM
IS REPETITION.
THIS IS HAPPENS WHERE THE
B-SELL REACCEPT OR BINDS TO
THAT ANTIGEN IN SUCH A WAY THAT
AND CAUSING THAT PIECE TO
MULTIPLY AND EXPAND AND GENERAL
RAUT A BETTER.
THE PROBLEM IS IN SOME UNI
VACCINES WHERE YOU'RE JUST
TAKING A FRAGMENT, YOU LOSE
THAT STRONG ACTIVATION AND YOU
GET A LESS POTENT IMMUNE
RESPONSE.
SO ONE OF THE THINGS THAT WE'RE
DOING IS USING THIS AI TO
GENERATE SO WE CAN RESTORE THAT
REPETITION IN THREE MENTION AL
SPACE.
THE OLD WAY THAT WE DID THIS,
WAS BY TAKING NANO PARTICLES
THAT WE HAD PREVIOUSLY DESIGNED
LIKE THIS ORANGE AND GRAY TWO
COMPONENT PARTICLE.
SO WE HAD PREVIOUSLY DESIGNED
STRUCTURE AND STRUCTURE FIXED
AND TOOK AN ANTIGEN AND WE KIND
OF DOCKED THE ANTIGEN AND
FIGURED OUT HOW TO CONNECT IT.
THIS APPROACH WORKS AND SEVERAL
OF VACCINES HAVE ENTERED
CLINICAL TRIALS AND ONE HAS
BEEN LICENSED SO THIS IS A REAL
VACCINE METHOD.
BUT, WHAT WE'RE DOING NOW IS
WE'RE TRYING TO IDENTIFY THE
IDEAL GEOMETRY FOR INFORMATION
USING DENO VOLUNTARY DESIGN.
SO HERE INSTEAD OF STARTING
FROM A NOME PARTICLE YOU START
FROM AN IDEAL ANTIGEN AND MAKE
UP A NEW PARTICLE TO PRESENT
THE ANTIGEN IN EXACTLY THAT
GEOMETRIC ARRAY.
SO HELEN IS A GRAD GRADUATE
WHO IS LEADING THIS.
>> TWO MINUTES.
>> THANK YOU.
SHE TAKES THE TERMINAL AND USE
IT IN AS MOTIF AND BUILDS A
FRAGMENT.
SO THE ANTIGEN IS EMBEDDED IN
THE ARTICLE AND THIS ALLOWS US
OR ORIENT AND IMPROVES ANTIGEN
STABILITY BY BURYING IT IN
CONCRETE.
SHE DESIGNED THESE PARTICLES
AND WE SCREENED THEM USING SITE
AND OTHER METHODS, YOU CAN SEE
THE PEAKS HERE AND POTENTIAL
HITS.
AND THEN SHE TOOK THESE HITS TO
NEGATIVE STAINING AND GOT TWO
DIMENSIONAL SHOWING THAT
SEVERAL PARTICLES WERE FORM
ING THE INTENDED STRUCTURES.
YOU CAN SEE THAT EACH STRUCTURE
IS DIFFERENT FROM THE OTHER.
BUT THEY ALL PRESENT A C
TERMINAL FRAGMENT EMBEDDED IN
THE PARTICLE.
SO THAT WAS LOW RESOLUTION AND
THEN TOOK HIGH RESOLUTION
CRUISING CCYMN.
SO BECAUSE OF THIS WE HAVE THE
ABILITY TO DESIGN PARTICLES
THAT ARE TAYLORED TO ANTIGEN.
JUST TO SUMMARIZE AI IS A
REVOLUTIONSED TODAY THIS IS
ALREADY WORKING.
REALLY EXCITING TIME TO GO
APPLY THE TOOLS AND SOLVE
PROBLEMS IN REALTIME WORLD.
AND I DIDN'T TALK ABOUT IT
TODAY BUT WE'RE WORK WITHING
BALI AND OTHERS TO APPLY THIS
IN THE SYSTEM.
THANKS.
>> THANK YOU NEIL FOR THIS TALK.
ALWAYS WE ALWAYS GET GOOD
THINGS FROM YOU.
AND LET'S CHANGE GEAR A LITTLE
BIT.
HOW DO WE USE AI TO READ OUT
THE MEMORY RESPONSE?
SO HAS BEEN THE LEADER IN THIS
FOR MANY YEARS.
AND NOW, PROFESSOR THE MEDICINE
AT HARVARD UNIVERSITY SO SHE
REALLY LEAD NEW SYSTEM THAT
SHE, DEVELOPED SEVERAL YEARS
AGO.
AND NOW UTILIZING AI TO TRY TO
IMITATE THE HUMAN RESPONSE AND
HOPEFULLY GET SOME SIGNALS THAT
CAN PREDICT ALL VAC SEEP DESIGN.
>> HELLO EVERYBODY.
I'LL TAKE A LITTLE BIT OF A
DIFFERENT PERSPECTIVE ON THIS
SESSION AND TALK ABOUT
OPPORTUNITIES TO BEGIN TO TRULY
UNDERSTAND IMMUNE CORRELATE AND
TO USE THAT INFORMATION
POTENTIALLY PERSPECTIVE LEON
VACCINE DESIGN.
I LOVE TO START WITH THIS OVER
SIMPLIFIED MOLD OF TRADITIONAL
VACCINE DEVELOPMENT WHICH HAS
BEEN PATHOGEN CENTRIC.
WE DESIGN THEM AND THEN WE PUT
THEM INTO A THIS BLOCK BOX THAT
WE CALL IMMUNE RESPONSE AND
PHASE 3 TO EVALUATE WHETHER OR
NOT THAT DESIGN WAS ACTUALLY
CORRECT.
AND I THINK WHAT WE HEARD FROM
BALI AND WHAT WE HEARD EARLIER
FROM ISABELLE AND THE EARLIER
THAT WE CAN UNDERSTAND WHAT IS
HAPPENING IN THAT BLACK BOX
WHAT IS ASSOCIATED WITH THE
PROTECTED IMMUNE RESPONSE IS TO
OUR PATHOGEN, THE MORE
DELIBERATE THAT WE CAN BE TO
THINK ABOUT HOW TO RATIONALLY
DESIGN THAT IMMUNE RESPONSE TO
ELICIT THAT CONTROL MECHANISM
THAT WE THINK IS GOING TO BE
ESSENTIAL.
SO FOR THIS PRESENTATION I'M
GOING TO FOCUS ON ANTIBODIES
NOT TO DISCREDIT OTHERS, BUT TO
FOCUS ON ANTIBODY FOR ANY
VACCINES ANTIBODIES, REPRESENT
THE PRIMARY CORRELATE OF
IMMUNITY.
BUT HOW THESE ANTIBODIES WORK
TO DRIVE PROTECTED IMMUNITY IS
NOT INTRODUCED AND REALLY THUG
ABOUT BUILDING AND APPLYING NEW
TOOLS THAT ALLOW US TO
DECONVOLUTED THIS.
AND DESIGNING NEW CLASSES OF
ANTIGENS FOR THE FUTURE.
THE STUDIES HAVE BEEN DONE OVER
2400 YEARS AND I LOVE TO REVIEW
THIS WITH THE AUDIENCE THINKING
ABOUT THE FACT THAT IN THE
PREANTIBODY ERA, IMMUNE SERA
WHETHER FROM INDIVIDUALS OR
ANIMALS SERVED AS A PRIMARY
MECHANISM.
THESE WERE UNBELIEVABLY
COMPLICATED PATH GENERALS
INCLUDING TUBERCULOSIS AND MANY
DIFFERENT COMPLICATED BACTERIA
TARGETS.
SHOWING US THAT OFTEN ANYBODY'S
ALONE CAN BE SUFFICIENT TO
PROVIDE PROTECTION AGAINST
THESE PATHOLOGICAL AGENTS.
EVEN AGAINST EVOLVING PATHOGENS
REALLY WELL ORGANIZED, LARGE
CON VOLUNTARY LESS ENTER PLASMA
TRIALS, TENDED TO HAVE LOWER
RISK OF ESSENTIAL BREAKTHROUGH
COVID -19 COMPARED TO
POPULATIONS.
THE PROBABLY THE BEST EVIDENCE
THAT POLY ANTIBODIES ARE
CRITICAL AGAINST PATHOGENS
COMES FROM THE BROAD USE OF
COOBULIN WHICH IS USED IN
POPULATIONS INCLUDING
INDIVIDUALS WHO HAVE HEMA--AND
THE ABSENCE OF OTHER IMMUNE
RESPONSES REALLY DOES CONFER
OVER 490-FOLD PROTECTION
AGAINST BACTERIAL INFECTIONS.
SO WITH THAT, THE FIELD HAS
COME TO THIS APPRECIATION AND
LOOMING DOGMA THAT ANY BODIES
THAT CAN BLOCK ANTIGENS ARE
REALLY THE CRITICAL INGREDIENT.
SO MANY VACCINES ARE DESIGNED
TO ELICIT THOSE ANTIBODY
RESPONSES.
BUT THE IMPORTANT REALIZATION
HAVE HAD MANY OF OUR VACCINES
DO NOT FUNCTION THROUGH THE
SOLE BLOCKADE OF INFECTION.
BUT MOST THAT WE IN OUR TOOL
KIT TODAY FUNCTIONS BY A
TENLATING DISEASE, SO INFECTION
AND THEN IMMUNO RESPONSE KICKS
IN.
AND REALLY BECAUSE OF
PIONEERING WORK OF MANY, AND
MANY OTHERS, YOU COME TO
APPRECIATE THAT ANTIBODIES DO A
LOT MORE THAN SIMPLY BLOCK
INFECTION AND REALLY THIS
RESPONSE IS REALLY POSITIONED
TO ESSENTIALLY RECOGNIZE PATH
AGAIN OR PATHOGEN INFECTED
CELLS AND RAPIDLY, TO DRIVE
RAPID.
AND THIS NEUTRALIZING BIOLOGY
IS NOT EXCLUSIVE .
WE BECAME OBSESSED HOW WE CAN
BUILD LARGE SYSTEMS THAT CAN
INAT THING RATE YOU WILL OF
THESE FUNCTION SOZ WE CAN
ACCELERATE THE ABILITY TO
UNDERSTAND WHAT THE CORRELATES
PROTECTIONS MAY BE AGAINST OUR
GETED PATH OGEN AND TO HELP
WITH THE VACCINE.
SO IF YOU THINK ABOUT
ANTIBODIES ALONE JUST TO GIVE
YOU A PROCESS, THAT GO BEYOND
THE SEQUENCE BUT LOOKING AT THE
DIVERSITY THAT IS BUILT INTO
OUR POLY CLONE AL RESPONSES, IF
YOU TAKE A SINGLE ANTIBODY YOU
CAN BREAK IT DOWN INTO TWO
FUNCTIONAL DOMAIN, WE HAVE THE
ANTIBODY OME AT THE TOP,
ALLOWING US TO EXPLORE ALL
PATHOGENS AND THEN WE HAVE A
CONSTANT DOMAIN THAT CAN MODIFY
ITSELF, AND SUB CLASSES BUT
ALSO THROUGH POST TRANSLATIONAL
CHANGES THAT GIVES IT
ADDITIONAL TWO THOUSAND
ANTIGENS FPT I LIKE TO JOKE
THAT REALLY THINKING ABOUT BOTH
DOMAINS REALLY ALLOWS US TO
EXPLORE THE TRUE BIOLOGY.
AND TECHNOLOGIES THAT HAVE
EXPLODED ALLOW US SEQUENCING
THE ANTIBODY AND EXPLORE
PATHOGENS EITHER THROUGH
DISPLAY OR PROTEIN ARRAYS AS
WELL AS ADDITIONAL ANTIGEN
TARGETS INCLUDING TAR BO
HYDRATES AND SO MANY OTHERS.
CONCURRENTLY THERE IS BEEN A
FUNCTION OF TOOLS THAT HAVE
EXPLODED IN THE MARKET, THAT
ALLOW THE REPRESENT TOUR AND
ABILITY TO INTERACT WITH
DIFFERENT IMMUNE CELLS WITHIN
TISSUE SO REALLY TAKING INTO
CONSIDERATION, THE RESPONSE
WHAT IS REALLY CLEAR IS WE NEED
TOOLS TO UNDERSTAND HELP US
UNDERSTAND THE SIGNATURES OR
FUNCTIONS THAT REALLY CAN BE
DISTILLED OUT FROM PROTECTED
POPULATIONS TO HELP ADVISE
VACCINE DEVELOPMENT.
SO I WANT TO GIVE TWO LITTLE
EXAMPLE WHERE WE BEGAN WITH
THIS TYPE OF IMMUNE CORRELATE
PURSUIT AND IT REALLY BEGAN
ABOUT 145 YEARS--15 YEARS AGO.
WE BEGAN TO THINK WELL IF WE
CAN TAKE POPULATIONS OF
INDIVIDUALS WHO RECEIVE A
PROTECTIVE VACCINE ON THE LEFT.
THIS WAS VACCINE CALLED RB1444
COMPARED TO VACCINE THAT DID
NOT SHOW ANY PROTECTION IN THE
POPULATION.
WE BEGAN TO THINK ABOUT HOW WE
COLLECT THIS DATA AND WHAT IS
DIFFERENT ABOUT THE IMMUNE
RESPONSE.
AND HERE I'M SHOWING YOU
CORRELATION STRUCTURES
CAPTURING SPESITIES AND
FUNCTIONS AND ISO TYPE
DISTRIBUTION AND HOPEFULLY WHAT
YOU CAN SEE THROUGH THESE
CORRELATIONS DICTATED BY LINES
LINKING DOTS IS ARCHITECTURE IS
FUNDAMENTALLY DIFFERENT FROM
THE PROTECTIVE VACCINE AND NON
PROTECTIVE VACCINE.
REALLY ONLY BECAME POSSIBLE
WHEN WE BEGAN TO APPLY THAT
DISCRIMINATE ANALYSIS TO TRY TO
UNDERSTAND WHAT IS SIGNIFICANT
ACROSS THESE GROUPS.
SO SHOWN ON THE TOP RIGHT HERE,
YOU CAN SEE THAT THERE ARE VERY
DIFFERENT RESPONSE SXZ
PROTECTED GROUP IN THE READ
COMPARED TO THE NON PROTECTIVE
GROUP IN THE BLUE AND IF YOU
LOOK AT THE VARIABLES, THIS WAS
THE FIRST IDENTIFICATION OF IG
3* OF BEING A PROTECT PLAYER
AGAINST HIV.
JUST TO GIVE THE IDEA THAT
THESE TOOLS CAN BE APPLIED TO
DIFFERENT TYPES OF POPULATIONS
TO UNDERSTAND SAME LOCATION HOW
DIFFERENT THE MECHANICNIS TIC
IN COLLABORATION WITH JOHNSON
AND JOHNSON AND POEM WERE
CHALLENGES WITH RSV VIRUS,
EVERY IS DIFFERENT TYPE AND
THERE IS INCREDIBLE IN DATA AND
THIS IS WHERE WE HAVE TO APPLY
THE TOOLS TO TRY TO
DISCRIMINATE.
SHOWING THIS CLUSTER, YOU CAN
SEE THAT PROFILES ARE
FUNDAMENTALLY DIFFERENT IN
PROTECTED AND NON PROTECTED AND
ONLY THREE FEATURES WERE
ENRICHED IN THE POPULATION AND
THESE THREE IN THE PINK LINE
WERE MORE PREDICT I HAVE OF
OUTCOME THAN JUST BINDING AND
NEUTRALIZING ANTIBODIES ALONE.
AND WITH THIS, MONOSITE AGAINST
THE ANTIGEN WERE THE MOST
CRITICAL PROTECTION IN THIS
SETTING.
WE DID THE SAME WITH FLU IN
PARTNERSHIP WITH THEY SOOKE A
VACCINE AND FOLLOWED TO SEE WHO
WOULD HAVE A BREAKTHROUGH
INFECTION.
AGAIN, EVERY LINE IS A
DIFFERENT INDIVIDUAL BUT NOW
WHEN YOU APPLY THIS TECHNOLOGY
YOU START TO PULA PART THE
PROFILE AND BEGIN TO UNDERSTAND
WHAT IS FUNDAMENTALLY DIFFERENT
WITHIN THAT HEMO IMMUNE SO
RESPONSE AND WE FOUND THOSE
PROTECT INSIDE YELLOW, THEY HAD
A UNIQUE ENRICHMENT OF ANTIBODY
THAT CAN RECRUIT TO FIGHT
AGAINST THE VIRUS.
FINALLY FOR COVID WE DID A
SIMILAR EXPERIMENT, A THIRD
VIRUS BUT IT TURNED OUT THE
MECHANISM WAS DPIF.
AND EVERY LINE IS A DIFFERENT
INDIVIDUAL, DIFFERENT FEATURE,
AGAIN INCREDIBLE, BUT WHEN YOU
CLUSTER THE DA TAXER YOU SEE
IMMUNO RESPONSES WHERE THE
PEOPLE WHO WERE PROTECTED IN
THE PURPLE WERE REALLY JUST TWO
FEATURES THAT IS ONLY PRESENCE,
TELLING US THAT FOR THIS THIRD
RESPIRATORY VIRUS NEUTRAL
FILLS, SO THREE RESPIRATORY
VIRUSES HERE'S HOW WE CAN USE
THIS INFORMATION.
>> ONE-MINUTE.
>> THANKS.
SO THE IDEA REALLY IS TO
IDENTIFY TO UNLOCK THAT BLACK
BOX AND HOW TO USE HELP ADVANCE
ANTIGEN DESIGN AND ANTIGEN
EVALUATION AND MECHANISM OF
ACTION AS WELL AS HELP TO
DEVELOP EXPLORATORY ASK.
AND WE HAVE A NUMBER OF TOOLS
THAT NEIL DISCUSSED WHERE WE
CAN TWEAK TO HELP DELIVER THE
RESPONSES.
THE LAST THING I WANT TO LEAVE
YOU THIS IDEA WHILE OLD SCHOOL
IDENTIFIES ON THE--VACCINE, AI
CAN NOW EMPOWER US NOT ONLY
UNDERSTAND THE PATHOGEN DID YOU
HELP WITH THE ANTIGEN DESIGN
WHICH IS TO CRITICAL AND
FOCUSING ON THE ANTIGEN.
BUT THE QUESTION IS HOW DO WE
BRING IN THE HOST?
BY ADDING THIS ABILITY TO
REALLY UNDERSTAND THE
PROTECTION, THE IDEA HOER IS
THAT WE CAN BRIT PATHOGEN AND
BRING IN THE HOST WITH ANALYSIS
THAT WE HAVE AND ULTIMATELY
DEVELOP THE VACCINES THAT HAVE
THE GREATEST IMPACT ON HUMAN
HEALTH.
AND I'LL END WITH THAT.
>> THANK YOU, GAIL, FOR SHOWING
US FANTASTIC EXAMPLES ON HOW TO
USE IMMUNE CORRELATES AND
INFORM THE NEXT GENERATION OF
DESIGN.
SO I'LL INTRODUCE OUR NEXT
SPEAKER FROM THE INDUSTRY AND
WHO IS EXPERT OF INNOVATION IN
WORLD TECH AND VE. PLATFORM
THAT CALLED AI IMMUNOLOGY ON
THIS COMPANY AND HERE AND CAN
TELL US ON INDUSTRY SIDE.
>> THANK YOU FOR THAT.
CAN YOU HEAR ME?
>> YES.
>> THANK YOU.
GREAT.
YES, SO I PROMISED KIM THAT I
WILL KEEP IT SHORT SO YOU'LL
GET A QUICK OVERVIEW OF WHAT
WE'RE USING AI WITH TARGET
DISCOVERY FOR INFECTIOUS
DISEASE VACCINES I'LL BE
SHOWING, THIS IS A SLIDE FOR
OUR LAWYERS.
SO JUST TO GIVE YOU A
BACKGROUND ON, WE WERE FOUNDED
IN 2008 WITH THE IDEA OF USING
MACHINE LEARNING TO DESIGN
VACCINES AGAINST INFECTIOUS
DISEASES.
SO AS TONG MENTIONED, OUR
PLATFORM IS CALLED A I
IMMUNOLOGY.
WE HAVE VARIOUS WAY TO USE OUR
PLATFORM BUT WITH INFECTION
DISEASES WE HAVE TWO MOTED ELZ
THAT WE HAVE WITHIN A PLATFORM
BEINGER THE FIRST IS CALLED
EDEN WHICH WE USE SO WE
BASICALLY INPUT TO THIS MOLD IS
SEQUENCE OF A GIVEN PROTEIN AND
THEN WE CAN USE THE MODEL TO
IDENTIFY WHETHER THE GIVEN
PROTEIN IS LIKELY TO BE A
PROTECTIVE AND IMMUNO GENIC
WHEN DELIVERED AS A VACCINE AND
ON THE OTHER SIDE WE HAVE A
RAVEN MODEL WHICH LIKE EDEN
TAKES AND USING THAT IDENTIFIES
T CELL HOTSPOTS THAT WE CAN
THEN ALSO USE IN A VACCINE
POTENTIALLY IN COMBINATION WITH
THE B SELL ANTIGENS IDENTIFIED
WITH THE MODEL.
REPLACES THE INITIAL PHASE OF
VACCINE PROCESS, YOU CAN SAY SO
BASICALLY WE USE IT FOR TARGET
DISCOVERY.
SO YOU KNOW, WE START WITH THE
PATHOGEN AND FIND, YOU KNOW
PPRODIOS THAT WE USE TO
IDENTIFY INTERESTING ANTIGEN OR
ANTIGEN FRAGMENTS AND USING
THESE LIST, WE CAN SORT OF
DESIGN VACCINE CANDIDATES AND
FROM THAT POINT ON, WE MOVE
INTO SORT OF A MORE TRADITIONAL
PRECLINICAL DEVELOPMENT CYCLE
WHERE THE ANTIGENS ARE PRODUCED
AND TESTED IN ANIMAL MODELS AND
YEAH, FOLLOWING THAT CAN THEN
ENTER, YOU CAN SAY THE REAL
CLINICAL TESTING.
YEAH SO WE'VE USED OUR AI
PLATFORM TO BUILD UP OUR
PIPELINE SO WITH INFECTIOUS
DISEASES WE HAVE FOUR CANDIDATE
THREE AGAINST BACTERIA,
GONORRHEA AND UNDISCLOSED
PATHOGEN AND ALSO FINALLY A
VIRAL PROGRAM AGAINST SO THE
GONORRHEA AND UNDISCLOSED WE'RE
DEVELOPING THAT IN
COLLABORATION WITH MERCK AND
ADDITIONAL FOR OUR GONORRHEA
VACCINE, IN LOW AND MIDDLE
INCOME COUNTRIES WHERE WE'LL
HAVE TECHNOLOGY WHERE THEY HAVE
GOTTEN ACCESS TO TO TEST A
GONORRHEA VACCINE.
AND THAT'S IT WE'LL GO TO PANEL
DISCUSSION.
>> THANK YOU, NOW IS OUR PANEL
DISCUSSION.
SO I SEE, YEAH.
SO EVERYBODY COMES UP AND THEN,
I HAVE SOME QUESTIONS, ALSO
THERE IS QUESTIONS FROM THE
AUDIENCE.
THERE IS ONE QUESTION, I THINK
MAYBE TWO MOSTLY TO BALI AND
GALIT.
WE KNOW THAT THERE IS A
RESPONSE, HOW DRIVEN MODELS TO
HANDLE THE RESPONSE, H LA AND
MANY OTHER IMMUNE.
HOW DO WE VIEW--[INAUDIBLE]
>> THANK YOU FOR THAT QUESTION,
I THINK AS WE DISCUSSED IN OUR
TALKS, AI MODELS SEEM REALLY
VERY GOOD AT DEALING WITH DATA
SETS THAT IN VARIANT LIKE AMINO
ACID SEQUENCES WHERE IF THE
EXPERIMENT IS DONE WELL, THERE
IS NO QUESTION WHAT IT IS.
WITH THAT KIND OF DATA THERE
SEEMS TO BE REMARKABLE SUCCESS.
BUT WHEN IT COMES TO RESPONSES,
SO VARIABLE, THE NUTRITIONAL
STATUS OF PEOPLE COULD AFFECT
HOW THEY RESPOND, THE GENETIC
CAN AFFECT THE BIOMEDICS.
I THINK IT'S ORDER IS MAGNITUDE
IS MORE COMPLEX FOR US TO
EXPECT AI TO BE AS SUCCESSFUL
IN PREDICTING.
BUT THAT'S NOT TO SAY THAT
THAT'S NOT GOING TO HAPPEN.
IT MAY BE THAT SOMETIME IN THE
FUTURE WE'LL HAVE AI MODELS
WITH LOTS OF DATA.
AND AGAIN THE POINT IS GARBAGE
IN GARBAGE OUT, THERE IS A LOT
OF DATA THAT AI ACCESS THAT MAY
NOT BE RELIABLE.
THAT'S ANOTHER IMPORTANT
FACTORS THAT WEEDS INTO THIS.
>> CAN I ADD TO THAT AND TO
BALI'S POINT BUT ALSO THE
POINTS EARLIER, THE QUESTION IS
HOW MANY DATA DO WE NEED TO
HELP US TRULY UNDERSTAND THE
RELATIONSHIP BETWEEN GENO TYPE
AND FINA TYPE, WHERE YOU BEGIN
TO UNDERSTAND THIS BIOLOGY IN
CASE BY CASE DISEASE BY DISEASE
TYPE OF APPROACH, WE WE KNOW
WHERE THERE ARE CERTAIN TO LEAD
TO RESPONSES OR DEFICIENCIES
AND NOT QUALITY AND SO ON AND
SO FORTH.
SO THE QUESTION REALLY IS WE
WILL NOT KNOW UNTIL WE START TO
DO THIS EXERCISE HOW TO BUILD
THIS FOUNDATIONAL DATA SETS
THAT ALLOW US TO MAKE THESE
PREDICTIONS.
WHAT WE'RE ASKING NOW IS GO
BEYOND WHAT COMES NEXT AND
LANGUAGE MODEL BUT ASKING
DIVERSITY AND HOW IS THAT
INFLUENCED BY THESE OTHER
FEATURES.
I THINK TO YOUR POINT, AND I
WOULD LIKE TO HEAR YOUR
THOUGHTS, GENO TYPE WOULD BE
EASIER THAN PUTTING ALL TYPES
OF DIFFERENT DEMOGRAPHIC
ENVIRONMENT ALT EXPOSURE THAT
ARE MORE GRADE ATED AS OPPOSED
TO SNIP OR NO SNIP.
BUT THIS IS WHERE WE HAVE TO
MAKE A CONCERTED DECISION AS A
COMMUNITY TO SAY THIS IS WHAT
WE WANT TO PUT IN AND THIS IS
WHERE WE WANT TO START TO BUILD
AND THINK ABOUT THEN START TO
ADD THESE OTHER VARIABLES TO
THINK CAN WE PREDICT THAT
DIVERSITY?
>> YEAH, THANKS.
THIS IS REALLY GREAT.
SO AS WE ALREADY HEARD THAT AI
CAN BE STILL, NOT PREDICT IVE
BUT STILL DATA DEPENDENT
PRACTICE.
BUT IN THE CONTEXT EVERYBODY
MAYBE INTERESTED IN PANDEMIC
PREPAREDNESS, HOW CAN WE
QUICKLY IDENTIFY IMMUNE
CORRELATES UNTIL EARLY STAGE OF
OUTBREAK IS THAT POSSIBLE AND
CAN WE DO IT?
>> CAN I GO FIRST.
AND THEN YOU CAN ADD.
THE IDEA THAT WE'RE GOING AFTER
PROTOTYPE PATH AGAINS HOW TO
DESIGN ANTIGENS, THE SAME
APPROACH CAN BE TAKEN FOR
IMMUNO TYPE CORELENTS.
THREE INFECT SIF CELL TYPES
HAVE DIFFERENT REPLICATION
CYCLES TO OVERCOME IMMUNITY BUT
IF WE START TO BEHIND THESE TO
PROTOTYPE INFECTION MODELS ONE
WITH COME UP WITH CORELENTS FOR
CORONA VIRUS FOR ORTHO POX
VIRUS THIS IS THE WAY THAT WE
CAN EMPOWER OURSELVES WITH THAT
INFORMATION SO ONCE THE ENTER
--ANTIGEN IS KNOWN WE CAN
MAXIMIZE THE PROBABILITY OF
PROTECTION.
THAT'S ONE IDEA I'M SURE THERE
IS OTHERS.
>> YEAH, SO MAYBE, ANYONE?
>> SURE, VERY QUICKLY, I AGREE
BUT I THINK WE SHOULDN'T BE
ASKING, I MEAN THE QUESTION OF
HOW CAN WE INTEGRATE AI INTO
DEFINING CORE LATIVE DURING
PANDEMIC IS NOT THE RIGHT
QUESTION THE QUESTION IS, HOW
CAN WE HAVE IT AT ALL TIME.
THE MOST IMPORTANT TIME IS
PREPANDEMIC, WE SHOULD NOT HAVE
TO WAIT FOR PANDEMIC TO SAY
HEY, LET'S APPLY AI, ALL
VACCINE SHOULD INTEGRATE THE O
MIX APPROACH SO WE'RE PREPARED
WHEN THE PANDEMIC STRIKES.
>> YEAH, SO THAT'S GREAT.
SO NEXT, MAYBE I'LL DIRECT TO
NEIL AS WE ARE EVERYBODY
EXPERIENCED, THAT CAPE CHANGING
VIRUSES.
SO FOR THOSE VIRUSES THAT HAS
ANTIGEN, YOU KNOW, SHIFT DURING
THE PANDEMIC, HOW DO WE
CONSIDER THOSE?
>> WE SAW THE PAPER EARLIER
TODAY FROM JESSE AND OTHERS ARE
DOING SIMILAR WORK, REALLY
EXPLORING ALL POSSIBLE
MUTATIONS FOR EXAMPLE, WHAT THE
VIRUS MAY MAKE AND ITS AFFECTS.
SO THAT GIVES YOU A MAP OF ALL
MUTATIONS AND SOME OF THEIR
FUNCTIONAL AFFECTS.
SO I THINK THERE ARE SEVERAL
COMPANIES AND ACADEMIC GROUPS
NA ARE TAKE THATING DATA AND
ALSO DATA FROM REFLICKATION
VIRUSES THAT YOU PUT UNDER
SELECTIVE PRESSURE AND USING
MACHINE LEARNING METHODS TO TRY
TO PREDICT EVOLUTIONARY BOTTLE
NECKS AND TO PROVIDE ANTIGEN
THROUGH THOSE BOTTLE NECKS.
IT'S STILL EARLY DAYS TO KNOW
HOW AFFECTIVE.
BUT WE'RE GOING TO GET A LOT OF
DATA ON THAT PARTICULAR
QUESTION IN THE NEXT 12 TO 18
MONTHS AND I THINK THAT'S FWG
TO BE VERY INFORMATIVE
>> THAT'S GREAT.
MAYBE ONE MORE QUESTION MAYBE
TO EVERYBODY.
THIS IS THE WORD WITH DIVERSITY
SO MANY AREAS AND ALSO SO MANY
DIFFERENT PEOPLE FROM, YOU
KNOW, POPULATED DIFFERENT.
HOW USING AI TO DESIGN KIND OF
UNIVERSAL VACCINE THAT WITH
CONSIDERATION ACROSS THE
DIVERSE DEMOGRAPHIC AND GENETIC
BACKGROUND SO WE CAN MAYBE ONE
SOLUTION TO EVERYTHING, THERE
IS A COCKTAIL IS TO ANYBODY?
>> YEAH, I CAN GO ON THAT ONE.
SO DEFINITELY, YOU KNOW, ONE OF
THE KEY DIFFERENCES IN HOW WE
RESPOND TO VACCINE IS THE HLA
TYPES THAT WE HAVE AND WE KNOW
THAT THESE CAN BE VERY DIVERSE
IN DIFFERENT POPULATIONS AND I
WILL SAY THERE IS ACTUALLY A
LOT OF GREAT DATA OUT THERE ON
HOW DIFFERENT POPULATIONS, WHAT
KIND OF HALI TYPES THEY HAVE
AND WE HAVE GREAT' GREAT AI
MODELS THAT WITH ABLE TO
PREDICT WHAT WILL BE PRESENTED
ON EACH OF THESE AT DIFFERENT H
LAs, SO THAT'S A WAY THAT WE
CAN USE HLA TO ENSURE THAT THE
VACCINE THAT WE END UP
DESIGNING HAS PREDICTED EP TOTE
THAT CAN COVER ALL POPULATION
THAT'S WE WANT TO COVER WITH
THE VACCINE AND HAVE THIS
PROTECTIVE VACCINE IN A
DIFFERENT.
>> CAN I BE CONTROVERSIAL FOR
FUN.
I THINK THE POWER OF AI IS NA
MAYBE WE DON'T HAVE TO BE
COMPLETELY UNIVERSAL.
MAYBE, THE POWER OF AI IS THAT
WE'LL BE ABLE TO START TO
DESIGN PURPOSE VACCINE FOR
AGING POPULATIONS, POPULATIONS
OF A DIFFERENT IMMUNO
COMPROMISE.
AND POWER OF AI THAT ALLOW US
TO HAVE MORE FLEXIBILITY.
MAYBE THAT IS THE POWER OF AI
THAT WE SHOULD BE THINKING
ABOUT RIGHT?
IS TRADITIONALLY IT'S BEEN A
ONE SIZE FITS ALL MODEL BUT NOW
BY PUTTING IN ALL OF THIS
INFORMATION, WE CAN ACCESS THE
TRUE DIVERSITY OF HUMAN
GENERALITY I CAN AND BIOLOGY.
SO JUST FOR FUN.
>> I THINK YOU ARE, I THINK
VALID.
AND FROM THE AUDIENCE TO BE SO
THERE IS ONE INTERESTING
QUESTION, WE ARE NOW USING
ANIMALS.
CAN WE USING, USING AI PREDICT
VACCINE RESPONSE WITHOUT USING
MONKEY OR ANY MODEL ANIMALS SO
YOU KNOW,
>> GO AHEAD NEIL.
>> I WAS GOING TO VOLUNTEER YOU
AS WELL.
>> WELL, I THINK THAT THAT'S A
GREAT ASPIRATION GOAL ONE CAN
IMAGINE IN THE FAR FUTURE,
FORGET ANIMALS MODELS MAYBE WE
DON'T NEED THE HUMAN MODELS,
MAYBE YOU CAN DO YOUR TRIAL.
THAT'S LIKE 20-30, 40 YEARS.
BUT RIGHT NOW, I THINK BY
COMPARING I MEAN THE FIRST STEP
TOWARDS THAT GOAL OF PREDICTING
USING A I TO PREDICT ANIMAL
MODELS WOULD BE TO DATA IN
MICE, MONKEYS AND HUMANS AND
THEN TO ASK, IF YOU IMMUNIZE IT
WITH EXACTLY THE SAME VACCINE,
THE SAME REGIMEN TO WHAT EXTENT
PARALLELS AND DIFFERENCES
BETWEEN MICE, MONKEYS AND
HUMANS AND USE THAT AS A
FOUNDATION TO BUILD THE AI
MODELS THAT CAN NOW CITY COMMON
ELEMENTS AND DIFFERENCES.
AND THAT'S THE FIRST STARTING
POINT.
>> CAN I ADD ONE MORE, I DON'T
THINK WORE GOING TO BE
DISCUSSING HERE BUT BUILDING
THE A I MODELS THAT CAN
ANTICIPATE SAFETY EVENTS.
YOU CAN DERISK RIGHT NOW WE
MOVE FORWARD IN VACCINOLOGY
SPACE.
IT WOULD BE HIGH VALUED TO
ESSENTIALLY ANTICIPATE THAT
BIOLOGY AS WELL SO WE CAN MOVE
MORE RAPIDLY TOWARDS THAT END
GOAL.
>> THAT'S GREAT.
YEAH, SO REALLY THANK YOU
EVERYBODY FOR FAN TASTIC
PRESENTATION AND ALSO REALLY
GOOD DISCUSSION.
TOUCHING A LITTLE BIT ON ALL
ASPECTS.
WITH THAT, I'LL HAND THIS BACK
TO KIMBERLY SO FOR THE NEXT
SESSION.
THANK YOU FOR KEEPING THE TIME.
>> ALL RIGHT, GREAT, THANK YOU
SO MUCH THAT WAS AN AMAZING
SESSION AND SHOWS A LOT OF
PROMISE RIGHT NOW EXAMINE ALSO
FOR THE FUTURE.
SO I APPRECIATE ALL OF YOUR
THOUGHTS AND COMMENTS.
ALL RIGHT LET'S MOVE ON TO
SESSION THREE FOR VACCINE
DEVELOPMENT.
I WOULD LIKE TO INTRODUCE OUR
MODERATOR AND SPEAKER DR.
PHILIP FROM THE GATES
FOUNDATION.
TAKE IT AWAY.
>> HELLO GOOD MORNING, I'M
GOING SHARE MY PRESENTATION.
AND PLEASE LET ME KNOW WHEN YOU
SEE IT.
>> GOOD TO GO.
SORRY.
>> WE CAN SEE YOUR SCREEN.
NOW YOU NEED TO SWITCH TO
PRESENTER MODE.
>> OH.
>> PERFECT, THANK YOU.
>> PERFECT, I WOULD LIKE TO
INTRODUCE A TOPIC SO WE'RE
GOING TO SHIFT GEAR TOWARDS THE
VACCINE MANUFACTURING SPACE AND
OBVIOUSLY, THAT'S AN IMPORTANT
TOPIC TO COVER.
AND I WOULD LIKE THE CHALLENGE
OF SPEAKER IN TERMS OF GOING
TOWARDS VISION CENTRIC APPROACH.
AND THAT WILL CREATE PROBABLY A
NEW POWER IN TERMS OF PRODUCING
SMALLER BATCHES, ET CETERA.
BUT WE'RE READY FOR THAT.
MY NAME IS PHILIPE I'M PART OF
THE GATES FOUNDATION I'M SEEN
VACCINE DEVELOPMENT FIELD.
ONE OF MY COLLEAGUE COULD NOT
COVER TODAY AND I'LL TRY TO
COVER CAN AFTER THAT I'LL
INTRODUCE MY OTHER PARTICIPANTS.
BEFORE I START I WOULD LIKE TO
INTRODUCE TO THE FOUNDATION
GATES FOUNDATION GRAND
CHALLENGE WE WERE ABLE TO GET A
LOT OF INTERESTING PROPOSAL.
I WILL NOT COVER THIS TODAY I
WOULD ENCOURAGE YOU TO GO TO
THE SITE AND SEE THE INNOVATIVE
APPROACHES THAT ARE TAKING
PLACE USING AI IN GENERAL.
AND BEFORE WE START, I'LL START
I WOULD LIKE TO MENTION OPEN AI
AND ENTERPRISE VERSION THAT HAS
BEEN EXTREMELY TRANSFORMATIVE
INTO WORKING DEVELOPMENT.
SO MA NDERMA, ESSENTIALLY
PEOPLE WERE USING 3.5 AND NOW
WE SWITCHED TO 4.0 BUT LATELY
REF --REVOLUTIONARY MODELS THAT
BRINGS ABOUT Ph.d THINKING TO
USING AI.
I'LL COVER VERY QUICKLY ON
BEHALF OF MY MISSING COLLEAGUE,
STEP HERE IN TERMS OF THE AI
ROLE IN TRANS FOERPING VACCINE
DEVELOPMENT CHALLENGES AND
INTEGRATING AI INTO PROCESSES
AND THEN OUTCOME AND EFFICIENCY
AND SCALE ABILITY.
MY COLLEAGUES COVERED CLINICAL
EFFICIENCY AND WE'LL COVER THAT
A LITTLE BIT BETTER DURING THE
SESSION IN TERMS OF HOW WE
IMPLEMENT THIS IN THE VACCINE
MANUFACTURING SPACE.
BUT I THINK IT'S REALLY
IMPORTANT TO GO BACK TO THE
VACCINE DEVELOPMENT PIPELINE,
THE VACCINE MANUFACTURING
COSYSTEM.
SXL HOW IT APPLIES TO DIFFERENT
PLACES.
SO I'M GOING TO BE COVERING THE
CMC PART OF IT AND JUST WANT TO
MENTION THAT AI CAN BE A
EMPLOYED TO ANY OF THESE STEPS
AND IT BECOMES, OBVIOUSLY, A
VERY VERBAL PARTNER IN THE
SMALL SCALE PROCESS DEVELOPMENT
FROM THE MANUFACTURING, SPACE
DEEMPLOYMENT, SUPPLY ET CETERA
SO THERE IS MANY POSSIBILITIES
AND THEN RIGHT NOW THERE IS A
RAPID DEDUCTION, THE VACCINE
PROCESS ITSELF OR PROCESSES
RELL TOUGHLY COMPLICATED, MOST
OF THEM HAVE 10,000 STEPS IF
YOU START COUNTING THEM.
MULTIPLE OPERATION UNIT AND
MULTIPLE PLATFORM THE VACCINE
PLATFORM, NOT ONLY TALKING
ABOUT PLATFORM BY BIOPLATFORM
BUT OTHER PLATFORM.
EVERY ONE REQUIRE A OUT STREAM
PROCESS AND THAT CREATES LIKE A
MASSIVE AMOUNT OF INFORMATION.
BUT OVERALL, WHAT IS NEEDED IS
THAT EVERY SINGLE STEP OF THESE
UNIT.
EVERY STEP NEEDS TO PROVIDE THE
RIGHT QUALITY.
AND AI IS BRINGING A NEW
TRANSFORMATION AL EVENT BECAUSE
NOW WHILE WE WERE USING THE
DESIGN FOR THE PAST 15 YEARS IN
TERMS OF HAVING MORE
TRADITIONAL, LET'S SAY APPROACH
USING DESIGN OF EXPERIMENTS
THAT ANALYSIS TO TRY TO BE IN
CONTROL OF THOSE PROCESSES, NOW
WE CAN INJECT A LOT MORE OF
THAT THINKING AND THEN WE CAN
REALLY LEVERAGE AI AND BRING
NEW TYPE OF TECHNIQUES SUCH AS
REAL TIM ANALYSIS, PREDICT I
HAVE MODELING ADAPTIVE CONTROL
ET CETERA.
HOW DO WE DO THAT?
I THINK THOSE CAN BE A MAJOR
SHIFT IN THE INDUSTRY IN TERMS
OF INDUSTRY INTERNET OF THINGS.
INTERNET OF THINGS HAS BEEN
USED FOR MANY YEARS.
INDUSTRIAL SPACE, THAT BECOMES
INTERESTING IN TERMS OF BEING
ABLE TO DEVELOP THE RIGHT
ARCHITECTURE.
SO WITH THE INTRODUCTION OF IT
NOW, YOU WOULD SAY THAT IT WAS
HUMANLY IMPOSSIBLE TO POSSES
THE AMOUNT OF DATA WITH THOSE
PROCESSES.
AND NOW WE HAVE TO WRITE
ENVIRONMENT AND TOOL AND
SUPPORT TO PROCESS THE DATA,
AGGREGATE DIFFERENT TOIP OF
DATA LAKE, PERFORM THE DATA
AGGREGATION AND YOU NEED TO
UNDERSTAND THAT IT HAS THE
OPPORTUNITY TO CODE ITS UPPER
LAYER.
TO GENERATE THE APPLICATION
LAYER OR THE INTERFACE NEEDED
FOR THE USER INTERFACE.
AND THIS BEING DEVELOPED RIGHT
NOW, WILL ENABLE I THINK THE
RAPID DEVELOPMENT OF THE EARLY
PHASE NEEDED FOR THIS INTERNET
OF THINGS AK TECH TOUR
--ARCHITECTURE WHICH IS A
SENSOR AND THESE SMART DWIESZ
WILL BE CONNECTED TO ALL OF
THAT MACHINERY AND START
COLLECTING MORE EXAMINE MORE
DATA I THINK THAT'S WHERE THE
FUTURE IS GOING.
ACTUALLY, THIS IS CONSISTENT
WITH A LOT OF THINKING WITH
GENERATIONAL PLATFORM AND WHILE
TECHNIQUE SUCH AS SPECTRUM WILL
REALLY RISE AS AS BEING
TECHNIQUE, THERE IS BEEN A LOT
OF NEW DEVELOPMENT IN THE SPACE
RELATED TO OTHER TYPE AND HERE
IN THE CENTRAL BUB, I WILL NOT
NAME IT, I WILL NOT NAME ALL OF
THEM BUT OBVIOUSLY THERE IS A
LOT OF OPPORTUNITY TO
MINIATURIZE THE TECHNOLOGY AND
MAKE THEM PORTABLE AND
ACCESSIBLE AND CONNECT THEM TO
DIFFERENT PLACES IN THE PROCESS
THAT WILL GENERATE THE LEVEL OF
DATA NEEDED TO BRING AI TO THE
QUESTION.
AND BASICALLY, THIS IS IN A
LIEP AND CONSISTENT WITH WHAT
WE'RE CALLING INDUSTRY 4.0 IN
THE ENGINEERING JARGON.
AND IT WILL TRANSFORM INTO A
CERTAIN LEVEL OF AUTOMATION IN
TERMS OF HAVING MORE CONTROL
PROSES, BUT WITH THE INPUT OF
QUALITY AND PREDICTION AND
QUALITY.
SO IN TERMS OF THE GLOBAL
IMPACT OF THOSE TECHNIQUES THAT
ARE START TO GO RISE RIGHT NOW,
SO OBVIOUSLY COST REDUCTION
BECAUSE WORE GOING TO BE ABLE
TO OPTIMIZE PROCESSES AND
LOCALIZE MANUFACTURING WILL BE
SOMETHING THAT WILL BE EASIER
TO IMPLEMENT AND OBVIOUSLY
SUPPORT LIKE DIFFERENT TYPE OF
TECHNOLOGY IN THE MOTHER OF
SPACE PRODUCTION, WE MENTION
ZEN TRICK APPROACHES REQUIRE
SETTING.
AND THEN OBVIOUSLY SUPPLY CHAIN
RESILIENCE.
SO THERE IS MANY KEY CHALLENGE
I WILL NOT GO THROUGH ALL OF
THEM AND MY COLLEAGUE MENTIONED
MANY OF THEM.
WORE GOING TO BE COVERING WITH
GUEST FROM FDA, THAT NEEDS TO
BE ADDRESSED.
AND OBVIOUSLY FROM A
ENGINEERING PERSPECTIVE WE NEED
TO WORKFORCE THAT ISSUE.
BUT THERE ARE SOLUTIONS FOR
THAT AND THIS IS BEING SQUUSED.
--DISCUSSED.
THERE ARE MORE AND MORE THEM
AND ACTUALLY, THERE ARE ACTUAL
RATING AND BECOMING LIKE
CONCRETE APPLICATIONS OF AI IN
THE MANUFACTURING SPACE.
ALLOW MY COLLEAGUE MENTIONED
THE PANDEMIC AND RESPONSE TO IT.
I THINK FROM ENGINEERING POINT
OF VIEW, THIS IS BECOMING
EXTREMELY APPEALING BECAUSE
FROM A RAPID RESPONSE SYSTEM
NOW THERE IS A CHANCE TO AUTO
MATE THE PROCESS, WHEN THERE IS
A PANDEMIC THAT REQUIRES A
CERTAIN, LET'S SAY A SCREENING
PROCESSES.
JUST MEANT A LOT OF WORK DONE
FOR EXAMPLE ON THE BROAD
PROTECTION SEQUENCES OPTIMIZE
WITH MACHINE LEARNING AND YOU
CAN SEE THE POWER OF BEING ABLE
TO SCREEN FOR THE SEQUENCES,
SELECT THEM AND REPERFORM ALL
OF THOSE EFFORTS AND PROVIDE
THE STRAIN OF INTEREST.
I THINK WE CAN DO BETTER
POSSIBLE THAT THIS COULD BE IN
DAYS IF YOU HAVE ACCESS TO ALL
THE DATA NECESSARY AND THAT
WOULD REALLY PREPARATION AND
PANDEMIC PREPARATION AVAILABLE.
BASICALLY THOSE IN THE SYSTEM
CAN RECEIVE DIGITALLY THE
SEQUENCE SXZ START TO PRODUCE
ON DEMAND AT DIFFERENT SCALE
AND MULTIPLE EFFORTS RIGHT NOW
SUCH AS FOR EXAMPLE AS MIT, FDA
AND RESI PHARM.
SO OBVIOUSLY THAT WILL REQUIRE
A LOT OF INVEST AND
COLLABORATIVE INITIATIVES TO
FOCUS ON EQUITY.
SO WE'RE GOING TO BE ABLE TO
DEVELOP THE NEXT GENERATION OF
AI INFRASTRUCTURE AND THIS IS
ALREADY ON GOING.
SXL ALL THE VACCINE ECOSYSTEM
SYSTEMS BECAUSE THERE IS AN
OPPORTUNITY FOR EVERY PIECES
AND OBVIOUSLY TO MAKE THOSE
TOOLS AVAILABLE FOR THE UNDER
SERVED REGIONS.
I WANT TO END WITH ONE THOUGHT
IN THE CHAT THAT WE WERE
QUESTION, WE CAN HAVE ACCESS TO
VACCINE KNOWLEDGE OR IMMUNOLOGY
IN GENERAL.
WE'RE THINKING ABOUT THE POWER
OF AI AND THOSE LLM WILL START
MORE FUNCTION AND INTER ABILITY.
PRETTY SOON IT WILL INTEGRATE
THE WORK FLOW, THE EXPERTISE OF
AN EXPERT AND ENTRY THAT WILL
ALSO INTEGRATE MEMORY AND YOU
CAN START CREATING VACCINE
AGENCY, SO ENTITIES PROVIDING
MIOCULAR AND THEY CAN HAVE
ENTITIES AND REALLY HAVE ACCESS
TO STATE-OF-THE-ART INFORMATION
BUT ALSO SUBMIT RECOMMENDATION
DISCUSS, OF A CERTAIN
RECOMMENDATION SO THERE IS A
VERY USUAL DURING CREATING THE
ASSISTANCE AND THE FOUNDATION
IS INVESTIGATING HOW WE CAN
DEPLOY THESE IN THE LOW MEDIUM
COUNTRY.
>> ONE-MINUTE PLEASE.
>> I'M GOING TO SAVE YOU ONE
MINUTE BECAUSE I'M DONE WITH
THE PRESENTATION.
>> WONDERFUL, THAT WAS
EXCELLENT.
>> THANK YOU, I'M GOING TO STOP
SHARING AND I WOULD LIKE TO
INTRODUCE MY NEXT SPEAKER,
FIRST OF ALL LET ME STOP
SHARING.
AND I WOULD LIKE MY NEXT
SPEAKER TO TAKE OVER THE
PRESENTATION.
I WOULD LIKE TO INTRODUCE YOU
TO DR. SAMBASS FROM PFIZER AND
DRIVING INNOVATION LIKE MERCK
AND PFIZER, AND YOU'LL BE
SPEAKING ON TRANSFORMING
CLINICAL DEVELOPMENT THROUGH AI.
ALL YOURS.
>> THANK YOU PHILIPPE.
GOOD EVENING, EVERYONE THANK
YOU TO THE ORGANIZERS AND
ESPECIALLY TO KIMBERLY FOR THE
INVITATION TO SHARE WHAT WE'RE
DOING AND HOPEFULLY HELP
ADVANCE THE DEVELOPMENT ACROSS
OUR ENTIRE INDUSTRY.
FIRST, THE OBLIGATORY
DISCLOSURE SLIDE.
THERE IS A LARGE ARRAY OF TOOLS
HERE WITHIN THE CLINICAL
OPERATION SPACE TO REALLY
OPTIMIZE HOW IT WORKS.
IF I SKIP SOME OF THE RIGHT
HERE, SOME OF THE PREVIOUS
PRESENTERS FOCUSED ON STARTING
JUST THE FUNDAMENTAL TRIAL
DESIGN AT THE LEVEL OF DOUBLE
BLIND CONTROL.
BUT MORE SPECIFICS OF THE STUDY
DESIGN THAT WOULD REFLECT SITE
BURDEN, PATIENT BURDEN TO
ENSURE THAT WE CAN ASSURE
THROUGH STUDIES AND NOT HAVE
INDIVIDUALS DROP OUT.
DURING THE START UP PROCESSES
OPPORTUNITIES TO SELECT SITES
AND WHEN WE SAY SELECT SITES,
IT IS BEYOND JUST,
PRODUCTABILITY BUT HISTORICAL
QUALITY PERFORMANCE AT THE
SITE, THE ABILITY OF DIFFERENT
CENTERS TO RECUTE CERTAIN
POPULATIONS AND DURING THAT
PROCESS BEING THAT WE WORK IN
AN UNREGULATED INDUSTRY BEING
ABLE TO GENERATE THOUSANDS OF
DOCUMENTS GENERATED DURING THE
CONDUCT OF THE START UP AND THE
CONDUCT OF CLINICAL TRIAL.
AND BEING ABLE TO PHOTO
GENERATE THOSE CONTEXTS REALLY
ELIMINATES BURDEN ON OUR STAND
AND SITE STAND TO SIMPLIFY
PROCESS AND HAVE A MORE
CONSISTENT QUALITY THROUGHOUT.
DURING THE RECRUITMENT PROCESS
IDENTIFYING THROUGH AN AUTO
MIZED HEADS THAT--BUT EVEN
SPECIFIC INSTITUTES THAT THEY
MAY HAVE, THAT MAY HAVE IN
THOSE CASES, THEY MAY NOT BE
CENTERS OF EXPERIENCE TRIALS,
WHICH WE CAN OFF TRAINING WITH
THAT RESPECT.
DURING THE CONDUCT STUDY WHERE
WE'VE HAD EFFORTS SOME
SIGNIFICANT TOOLS TO PROVE HOW
WE MONITOR THE STUDY AND HOW WE
MONITOR THE DATA FROM THE STUDY
TO ASSURE THAT ANY SIGNALS AND
ANOMALIES THAT WE SEE WE CAN
REACT TO QUICKLY AND NOT DEPEND
ON PERIODIC DATA REVIEWS BUT
REALLY IN REAL TOMB ABLE TO SEE
TRENDS AND REACT TO THEM.
IN MOST CASES THEY LEAD TO
OTHER ASPECTS OF IMPROVISING
OUR CONDUCT BUT IT IS IMPACTFUL.
EXAMINE THEN AT THE CONCLUSION
OF THE STUDY, BEING ABLE TO
GENERATE THE OPTIMAL SET OF
FIGURES TO STUDY REPORTS,
NARRATIVES OR AT LEAST FOR
STRESS TO HAND TO A KIND OF
PROFESSIONAL TO FINAL.
ONE OF THE BIG EXAMPLE IS
CALLED SMART DATA QUALITY, THIS
IS THE TECHNOLOGY THAT WE
DEVELOPED ROUGHLY DATA POINTS
WORK ON TRIALS AND ROUGHLY
200,000 QUERIES THAT WERE
ISSUED BY STUDY PERSONNEL TO
CLARIFY SOME MATTER IN DATA SET.
LEAD TO IT 50 ACCURATE RE.
THE ACCURACY WAS HUMAN IN THE
LOOP METHOD.
AFTER SIX MONTHS, MY TEAM
REVIEWING AND APPROVING THUMBS
UP OR THUMBS DOWN OR EVEN A
SIDE WAYS THUMB IF THEY AGREED
BUT DISAPPROVED WITH THE
LANGUAGE, WE WERE ABLE TO GET
THE ACCURACY AT ABOUT 90
PERCENT AND AFTER THAT WE USED
IT IN CLINICAL TRIALS.
COVERS SOME OF THE MOST
CRITICAL DOMAINS TO LAB RESULTS.
ROUGHLY AROUND THE SAME TIME,
WE COULD DEVELOP CODING FOR
THOSE NOT FAMILIAR WITH THE
CLINICAL CONDUCT PROCESS, WE
RECEIVED VERBATIMS FOR ADVERSE
OF THAT.
THOSE VER BAYED UM HAVE TO BE
CODED TO COMMON TERMS THEY CAN
BE ROLLED UP TO A CORRECT
TOTALITY FOR A SPECIFIC EFFORT
OF THAT.
THE SAME IS TRUE FOR
MEDICATIONS AND AS YOU CAN
IMAGINE THE VERBATIM AND VARY
ACROSS THE WORLD NOT JUST THE
BRAND NAMES.
SO IT'S SOMETHING WAS IF ANY
VERBATIM WAS RECEIVED IN THE
PAST IT STORES IT AND IF IT
REOCCURS THE MACHINE WILL CODE
IT.
BUT WHAT IS INTERESTING HERE IS
IF IT HAS NOT BEEN OBSERVED IN
THE PAST, THE MACHINE WILL
ACTUALLY PRODUCE THE THREE MOST
PROBABLE CODINGS FOR THAT TERM.
WE USED TO PRESENT FIVE BUT IT
TURNED OUT THAT THE MACHINE WAS
RIGHT FOR AT LEAST JUST THREE
AND WE REDUCED TO TWO.
THESE WERE BROUGHT TO CLINICAL
PROFESSIONAL ATTENTION TO
PRESENT WHAT IS THE CORRECT
CODING AND IF IT'S NOT
INCLUDED, IT CAN BE OVER RULED
AND THE INDIVIDUAL CAN IDENTIFY
A DIFFERENT CODING THAN WHAT
THE TOOL HAS PRODUCED.
PIVOTING A LITTLE BIT FROM
THOSE KIND OF TRANSACTIONS
UTILITIES, I THINK IT'S
IMPORTANT TO NOTE THAT HOW WE
SCORE OUR DATA HAS A HUGE
IMPACT ON HOW WE CAN LEVERAGE
IT IN THESE METHODS AND
SOMEBODY MENTIONED EARLIER
ABOUT HOW MUCH DATA WE HAD IN
OUR INDUSTRY THAT ACCESS CAN
HAVE INSIDE AND I DO WANT TO
SHARE WITH ALL THE LISTENERS
THAT IF ANYONE, IF ANYONE
WISHES TO GET ACCESS TO OUR
DATA FOR RESEARCH PURPOSES,
WITH COMMITTED WITH AN
ORGANIZATION, TO MAKE OUR DATA
AVAILABLE, THERE IS AN
APPLICATION PROCESS THAT THEY
HAVE IN THEIR WEBSITE THAT THEY
CAN LOOK INTO AND THEY DECIDE
IF THE APPLICATIONS IS WORTHY.
SO, ONE INTERESTING APPROACH
HAS BEEN TAKEN IS TO STORE DATA
IN THE FORM OF KNOWLEDGE RATHER
THAN TRADITIONAL RELATIONAL
DATABASES.
WITH KNOWLEDGE GRAPHS IS YOU
START TO STORE THE RELATIONSHIP
BETWEEN DIFFERENT ELEMENTS.
THOSE ELEMENTS ARE CALLED--AND
IF YOU HAVE DATA YOU START TO
GRAB INSIDES AND REALLY REALLY
CAN START TO AUTOMATE A
SIGNIFICANT AMOUNT.
THE CONCEPT IS IF TRULY
PROTOCOL YOU CAN AUTO MATE SO
MUCH MORE DOWNSTREAM AND WHEN
WE SAY DIGITIZED, WE JUST DON'T
MEAN HAVING A WORD DOCUMENT BUT
RATHER MEANING TO THE
COMPREHEND.
AND SOME EXAMPLES THAT WE'VE
BEEN ABLE TO LEVERAGE THESE
KNOWLEDGE GRAPHS IN SOME CASES
WITH JUST THE DATA AND SOME
CASES COMBINING THE DATA WITH
DATA FROM PUBLIC SOURCES.
THESE EXAMPLES ARE SPECIFIC
ONLY TO VACCINE THEY REALLY
STAND ALL OF OUR DIFFERENT
THERAPEUTIC AREAS WHERE, AS
TEAMS ARE DELIBERATING DESIGN
ELEMENTS OF THE STUDY, WHAT MAY
HAVE BEEN OBSERVED IN PRIOR
STUDIES, THEY CAN REALLY
THROUGH PROMPT INTERACT THESE
AGENTS AND THESE AGENTS RETURN
ANSWERS TO IT MIGHT BE ANYTHING
FROM OBSERVE EFFORTS EVENTS TO
WHAT WERE THE INCLUSION
EXCLUSION CRITERIA THAT CAUSED
THIS TO LOSE POTENTIAL SUBJECTS
IN DIFFERENT STUDIES?
AND MAYBE REALTIME ANSWERS BUT
WITH THAT CHANGE OF THOUGHT
THAT WAS REFERRED TO EARLIER,
SO THAT AS THEY RECEIVED THEIR
ANSWERS THEY SEE WHAT THESE
AGENCIES GO BACK WHAT IS NORMAL
PROTOCOLS THE ORGANIZATIONS OR
ARE THERE ORGANIZATIONS WHAT
WERE THE DIFFERENTIATED ITEMS
IN THOSE PROTOCOL AND WHAT IT
IS RECOMMENDED IN RESPONSE.
>> ABOUT ONE MINUTE REMAINING.
>> SO HAVING THESE AGENTS HAVE
HAD AN SIGNIFICANT IMPACT FROM
THE SPEED.
IF THE DOCUMENT EXAMPLES
REALLY, IT'S A SIMPLE CONCEPT.
THE TOOLS CREATE DRAFT
DOCUMENTS.
THE DRAFT DOCUMENTS SHARE A
LOST INFORMATION AND REFER
WHOEVER IS DOCUMENT TO
SPECIFICS THAT ARE NEEDED
FINALIZE IT AND CIRCULATE IT
AND THE BENEFITS ARE PRETTY
SUBSTANTIAL.
I'M GOING TO SPEAK TO THE FIRST
ONE, THESE METRICS ARE FROM THE
COVID VACCINE STUDY WE RAN AND
TO THE MENTION, THERE WERE
STILL TWO YEARS LEFT IN THE
STUDY.
THE TOOLS HAD HELPED US DATA
POINTS, TO LOOK THROUGH
MILLIONS OF COMMENTS AND
IDENTIFY POTENTIAL AEs THAT
WERE NOT REPORTED TO US SO WE
CAN GAK TO THE CLINIC AND ASK
IF THEY WERE ALLUDED THAT
SHOULD BE PROPERLY DISCLOSED IN
THE DATA.
AND I'LL CONCLUDE WITH WE'RE
NOT LOOKING AT AGENTS TO
MONITOR THE SYSTEM AND BRING
MAERTZ OF CONCERN TO ATTENTION
OF DATA MANAGER AND
EPIDEMIOLOGIST, IT'S AVAILABLE
TO US NOW WE CAN MONITOR IN
REALTIME AND BRING THE
POTENTIAL SIGNAL TO SOMEBODY TO
GO INVESTIGATE ON THEIR OWN AND
MAKE A DETERMINATION.
THANK YOU.
>> THANK YOU DEMITRIS.
LOOKING THE QUESTIONS WE'LL
HAVE QUESTIONS FOR YOU.
NOW WE GO TO TOPIC OF I WOULD
LIKE TO PRESENT TO YOU THE NEXT
PRESENTERS, SO FIRST WE HAVE
DR. HUSSEIN ESELDENE SENIOR
DIGITAL FOCUS SCIENCE AND
DIGITAL HEALTH TOOLS, LEADS THE
BEST INNOVATIVE INITIATIVE AND
SUPPORT CUTTING EDGE TECHNOLOGY.
WE'LL ALSO HAVE DR. MANUAL
ASORI SENIOR SCIENTIST,
ENSURING INNOVATION TO MEET
EVOLVING PUBLIC HEALTH
CHALLENGE.
ALL YOURS.
>> THANK YOU EVERYONE.
I'M HUSSEIN AND I'M JOINED WITH
MY ESTEEMED COLLEAGUE DR.
MANUEL.
CAN YOU SEE MY SLIDES FOR
PRESENTATION?
>> YES.
>> FANTASTIC.
THANK YOU VERY MUCH FOR THE
OPPORTUNITY TO SPEAK TO YOU
ABOUT THE REGULATORY LANDSCAPE
OF AI AND SHOW YOU ARTIFICIAL
INTELLIGENCE AND MACHINE
LEARNING.
LOCAL IN THE CENTER FOR DRUGS
AT THE US FDA.
SO TWO SO TO GIVE YOU A HIGH
LINE.
I'LL TALK ABOUT THE APPROACH
AND SHARE WITH YOU SOME OF THE
WORK THAT WE HAVE SEEN WITHIN
THE CENTER FOR BIOLOGIC RELATED
TO ARTIFICIAL INTELLIGENCE.
THIS IS GOING TO BE A BUSY
SLIDE BUT THE TAKE AWAY IS THE
AMOUNT AND AWARD THAT THE
AGENCY IS PUT ING.
SO PRIOR TO 2019, WE INITIALED
THE INFORMED INITIATIVE WHICH
IS VERY CLOSE TO AI AND MACHINE
LEARNING BECAUSE IT'S A
SIMULATION BASE PROGRAM, WE
STARTED STEAK COMMUNITY OF
PRACTICES, WE STARTED HAVING
TRAININGS AND TRACKING AI AND
DEVELOPING FRAME FOR ACTIVITIES.
INTERESTINGLY LATE 2021, THE
CENTER FOR DEVICES AT THE FDA
IN COLLABORATION AT MHRA
DEVELOPED WHAT IS CALLED A GOOD
MACHINE PRACTICES.
IT'S RELEVANT FOR AI LEARNING.
IN 2022 WE COULD NOT --CONVENE
THE EXPERT WORKSHOP.
IN 2023CRH CENTER FOR DEVICES
DEVELOP RELEVANT AND VERY
IMPORTANT FOR LOOKING AT THE
USE OF AI LEARNING AND VERY
SPECIFIC AND VERY RELEVANT TO
MANUFACTURING AS WELL.
THEN IN LATE IN 2023, WE ISSUED
A COUPLE OF DISCUSSION PAPERS
LOOKING AT THE USE OF AI IN
MEDICAL PRODUCTS AND MY ESTEEM
COLLEAGUE IS GOING TO SPEAK
ABOUT THIS. ALSO SHARED
ANALYSIS ABOUT WHAT OTHER TYPES
OF AI SUBMISSION AND THEN OF
COURSE EVERYONE KNOWS THAT LATE
IN 2023, THE EXECUTIVE ORDER
CAME OUT FROM THE WHITE HOUSE
ABOUT THE USE OF AI AND WITHIN
FEDERAL GOVERNMENT IN GENERAL.
FOLLOWING THE DISCUSSION
PAPERS, WE, WE RECEIVED A LOT
OF COMMENTS ABOUT MORE THAN 600
COMMENT FROM PUBLIC AND THEN WE
FOLLOWED WITH PAPER THAT
SUMMARIZED THE AREAS OF FOCUS
SPACE ON THESE COMMENTS FROM
THE PUBLIC.
AND THEN WE HAD THE COUPLE OF
WORKSHOPS THAT SPOKE ABOUT AI
AND CLINICAL TRIALS AND AS WELL
EVIDENCE AS WELL.
AND CRH RELEASED A GLOSSARY
THAT IS SPECIFIC AND THIS IS
REALLY IMPORTANT TO MAKE SURE
THAT OUR INTERACTIONS WITH THE
PUBLIC AND INDUSTRY IS BASED ON
A COMMON UNDERSTANDING OF WHAT
THE TERMS AND TERMINOLOGY THAT
WE'RE USING AND OF COURSE I
CANNOT REMISS AND MISS SAYING
THAT WE HAVE A DRAFT GUIDANCE
ON THE USE OF AI ON MEDICAL
PRODUCTS THAT IS COMING OUT
SOON.
I MENTIONED THAT WE ISSUED A
GOOD MACHINE LEARNING PRACTICES
PRINCIPLES.
AND I WANTED TO A FEW OF THESE
TO MANUFACTURING AND VACCINE
DEVELOPMENT WHICH IS THE NEED
TO HAVE MULT TIE DISCIPLINARY
EXPERTISE THROUGHOUT YOUR
LIFECYCLE, YOUR PRODUCT
LIFECYCLE.
YOU ALSO WANT TO MAKE SURE THAT
YOUR STUD EYES AND DATA SETS
THAT YOU ARE USING IN YOUR
MODELS IS REALLY REPRESENTATIVE
AND IS FOR THE INTENDED
POPULATION THAT YOU ARE
TARGETING WITH YOUR AI OR
MACHINE LEARNING MODELS.
I WANT TO MAKE SURE THAT YOU'RE
MODELS ACTUALLY TAYLOR TO THE
DATA AND VERY IMPORTANT IT'S,
REFLECTING THE INTENDED USE OF
YOUR MODEL THAT IT CAME OUT FOR
THE VICE CONTEXT BUT IF YOU'RE
THINKING ABOUT MACHINE LEARNING
OR AI FOR MEDICAL PRODUCTS IT
HAS TO BE ALSO REFLECTIVE THIS
INTENDED USE AND OF COURSE YOU
NEED TO FOCUS ON HOW THE HUMAN
IN THE LOOP IS GOING ABLE TO
ASSESS AND EVALUATE THE
PERFORMANCE OF YOUR MACHINE
LEARNING AND AI MODEL.
AND THEN OF COURSE YOU WANT TO
MAKE SURE THAT YOU'RE MODEL IS
CLINICALLY RELEVANT AND YOU
HAVE CONTINUOUS MONITORING OF
THE PERFORMANCE AND MAYBE A
RETRAINING IF ANYTHING IS
OBSERVED TO MINIMIZE AND
MITIGATE.
AND FOLLOWING THE TEN GUIDING
PRINCIPALS, THE SAME GROUP,
CANADA EXPANDED WITH THE THAT
SPEAKING TRANSPARENCY.
YOU WANT TO LOOK AT WHO IS
GOING TO BE LOOKING AT YOUR
MOLD IS GOING TO BE IMPACTED
AND MAKE SURE YOU HAVE THE
RELEVANT INFORMATION, WHERE
YOU'RE GOING TO INCLUDE THIS
INFORMATION ON YOUR INTERACTING
WITH REGULATORY BODIES AND THEN
WHEN CAN YOU COME AND SPEAK
WITH REGULATORY BODIES AS WELL.
AND THEN, OF COURSE HOW YOU'RE
GOING TO MAINTAIN THIS
TRANSPARENCY SO WHAT ARE THE
METHODS THAT YOU HAVE FOR THAT.
SO JUST VERY QUICKLY SPEAKING
ABOUT ARTIFICIAL INTELLIGENCE
DISCUSSION PAPER AS I MENTIONED
IT CAME OUT IN MAY OF 2023, IT
WAS A COLLABORATION BETWEEN THE
CENTERS.
AND WE RECEIVED ABOUT 800
COMMENTS FROM MORE THAN 60
ENTITIES AS I MENTIONED, WE
FOLLOWED UP WITH A SUMMARY OF
THESE.
BUT WHAT WE GOT FROM THE
COMMENT IS THAT WE REALLY HAVE
TO FOCUS ON HUMAN GOVERNANCE
AND TRANSPARENCY IS REALLY KEY
AND ACCOUNTABILITY AND YOU SAW
THE TRANSPARENCY GUIDING
PRINCIPALS THAT CAME OUT, FOCUS
ON THE QUALITY RELIABILITY
REPRESENTATIVES OF DATA IS VERY
IMPORTANT THAT CAME OUT FROM
THE FEEDBACK AND THEN
CONTINUOUS MONITORING OF
PERFORMANCE AND MONITORING AND
VALIDATION IS ALSO IMPORTANT.
FOR KEY AREAS THAT CAME OUT
FROM THE SUMMARY OF THESE
FEEDBACK AND COMMENTS FROM THE
PUBLIC AND INDUSTRY.
AND WE WANT TO WORK TOGETHER
WITHIN THE AGENCY ALSO WITH
OTHER REGULATORS TO HARMONIZE
OUR STANDARDS AND THEN WE NEED
TO THINK ABOUT HOW WE'RE GOING
TO BE EVALUATING THE
PERFORMANCE OF AI.
VERY BRIEFLY I'LL SPEAK ABOUT
THE RISK BASE APPROACH AS
EVERYONE MENTIONED IN THE PRIOR
PRESENTATIONS, AI CAN UTILIZED
AND APPLIED IN MULTIPLE STAGES
THROUGHOUT OUR DEVELOPMENT FROM
DISCOVERY ALL THE WAY TO
MANUFACTURING AND POST
MARKETING MONITORING.
SO THERE ARE MANY THINGS THAT
ARE AI CAN BE USED FOR.
AND THE REASON TO SAY THIS IS
TO WHEN WE ARE INTERACTING WITH
INDUSTRY, WE REALLY HAVE TO
THINK ABOUT HOW AI OR MACHINE
LEARNING IS GOING TO BE POSING
RISK TO THE PUBLIC AND THE
PATIENCE AND PARTICIPANTS OF
THE CLINICAL TRIALS OR USERS OF
THESE MEDICAL PRODUCTS.
AND WE ASSESS THESE RISK BASED
ON TWO DIMENSIONS.
WE LOOK AT THE MOLD INFLUENCE,
WE LOOK AT HOW THE POSITION
PROVIDED BY IN MACHINE LEARNING
OR ATTRIBUTER EFFICIENTLY IS
GOING TO CONTRIBUTE TO THE
TOTALITY OF EVIDENCE SUPPORTING
THIS PROGRAM.
AND THE OTHER MENTION IS THAT
WHAT IF THE MODEL GETS IT WRONG?
WHAT IS THE CONSEQUENCE?
WHAT IS THE SNAG WAS BASED ON
THE DECISION MODEL SEEM TO POSE
MAYBE ISSUES TO THE PARTICIPANT?
OR MAYBE EVEN POSE AFFECTENCE
OF ETHICACY AND THE PROGRAM
ITSELF AND DOES NOT ALLOW THE
INDUSTRY TO ACHIEVE THEIR GOALS.
SO I THINK THESE ARE THE TWO
DIMENSIONED THAT WE USE AT HOW
THESE MODELS ARE GOING TO BE
EVALUATE AND SUBMISSIONS AND
REGULATORY SUBMISSIONS AND
BASED ON HOW THEY FAIR ON THE
INFLUENCES AND CONSEQUENCE,
THEN, THIS IS GOING TO REALLY
DETERMINE WHAT TYPE OF
INFORMATION WE'RE GOING TO BE
ASKING FROM INDUSTRY TO PROVIDE
TO US.
>> ONE MINUTE.
>> PLEASE WRAP UP.
THANK YOU.
>> YEP, THANK YOU.
SO GIVING YOU A VERY HIGH LEVEL
HERE, OF COURSE NOT
SURPRISINGLY, WE'VE SEEN AN
INCREATION SUB OF SUBMISSIONS
OF BIOLOGICS AND YOU CAN
IMAGINE THAT WE COVER A
MULTIPLE PRODUCT OF DOMAIN AND
VACCINE AND THERAPY, WE SEE A
LOT OF THINGS.
AND WHEN WE LOOKED AT THE
SUBMISSIONS YOU CAN SEE HERE
THAT, MOST OF THEM WERE IN THE
ONCOLOGY SPACE BUT ALSO
VACCINES FOLLOWED BY MAYBE I
IMMUNE TOLOGY AND AS WELL AS
SUPPORTING MULTIPLE OBJECTIVES
AND I'LL JUST LEAVE WITH YOU
THIS BEFORE I HAND IT OVER TO
MY COLLEAGUE IS AS I MENTIONED
TIMING IS VERY IMPORTANT AND
THERE ARE MULTIPLE PROGRAMS
WITHIN THE AGENCY THAT YOU CAN
LEVERAGE TO COME AND SPEAK TO
US VERY EARLY SO WE CAN HAVE
THIS DIALOGUE AND HELP YOU
UTILIZE AI AND MISSION LEARNING
IN YOUR CLINICAL PROGRAMS.
SO AS I MENTION APPROXIMATED,
THERE IS A LOT OF WORK THAT
WE'RE DOING.
OUR STANDARDS ARE THE SAME,
IT'S NOT GOING TO CHANGE.
WE LEVERAGE THIS RISK BASE
APPROACH AND WE KNOW THAT AI
HAS GREAT POTENTIAL, BUT WE
ALSO ENCOURAGE YOU TO COME
EARLY AND ENGAGE WITH US.
THANK YOU SO MUCH.
AND OF COURSE WOULD LIKE TO
THANK ALL OF MY COLLEAGUES.
ALL RIGHT AND I'LL HAND IT OVER
TO YOU.
>> THANK YOU, HUSSEIN AND THANK
YOU TO THE ORGANIZERS FOR THE
OPPORTUNITY TO SPEAK HERE.
I'M NOT ABLE TO SHOW MY SLIDES.
I DON'T KNOW IF THAT CAN BE
DONE FROM YOUR SIDE?
>> TRY AGAIN.
I THINK --
~>> NEVER MIND I'LL JUST SPEAK.
SO I'M IN THE OFFICE OF
DIRECTOR BIOLOGICS AND I'M THE
LEAD FOR THE TECHNOLOGY
PROGRAMS.
IT'S A PROGRAM THAT HAS SEVERAL
INITIATIVE WITH THE PURPOSE OF
REALLY ENCOURAGING THE ADOPTION
AS WELL AS IMPLEMENTATION OF
TECHNOLOGIES.
CAN WE HAVE DIFFERENT
INITIATIVES LIKE I MENTIONED
FOR EXAMPLE, WE FUND RESEARCH
AND I WAS HAPPY TO SEE THAT.
PHILIPE HAD A SLIDE THAT HE
SHOWED THIS COLLABORATION WITH
MIT AND FDA IN DEVELOPING AN
CONTINUED MANUFACTURING PROCESS
FOR VACCINES AND INITIATIVE TO
TRY TO DEVELOP THE SCIENTIFIC
AND REGULATORY EXPERTISE THAT
IS GOING TO BE NECESSARY TO
EVALUATE THESE TECHNOLOGY SUCH
AS MACHINE LEARNING.
AND HUSSEIN ALSO MENTIONED THE
CAT.
CAT PROVIDES AN OPPORTUNITY FOR
TECHNOLOGY DEVELOPERS.
TO HE REALLY DISCUSS WITH THE
CENTER ISSUES THAT AND
REGULATORY ISSUES THAT MAY BE
ASSOCIATED WITH EMERGING
TECHNOLOGIES SUCH AS AI AND
THESE MEETINGS OCCUR AT THE
EARLY STAGE OF DEVELOPMENT EVEN
BEFORE YOU HAVE A IDENTIFIED
SPECIFIC PRODUCT FOR
DEVELOPMENT, UM COME INTO US
AND GET SOME GENERAL ADVISE.
YOU KNOW, IT REALLY PRO*E
VIESED BENEFIT TO DEVELOPERS AS
WELL AS THE AGENCY BECAUSE YOU
WILL GET SOME REGULATORY ADVISE
THAT THAT MAY MINIMIZE THE RISK
OF INVESTING IN SPECIFIC
TECHNOLOGY BUT ALSO FOR US TO
LEARN ABOUT NEW HE MEMBERERING
TECHNOLOGIES THAT MAY BE COMING
DOWN THE PIPELINE.
SO THROUGH CAT DISCUSSIONS
WE'VE HAD SOME DISCUSSIONS THAT
COVER TOPICS RELATED TO AI AND
THESE WOULD INCLUDE,
APPLICATIONS OF AI SUCH AS
INITIAL INSPECTION FOR
MANUFACTURES, SELECTION OF
CLONES FOR SELF THERAPY
MANUFACTURING, IF YOU IMAGINE
IF YOU'RE DIFFERENTIATING CELLS
FROM SYSTEM CELLS AND PHYSIO
CULTURE, WE SAW AN APPLICATION
WHERE AI WAS USED TO EVALUATE,
AND ATTRIBUTES COLONIES USING A
LASER BASE SYSTEM TO GET RID OF
THE ONES THAT DID NOT MEET THE
SPECIFICATIONS DESIRED.
SO A LOT OF IMPLEMENTATION OF
AI THAT WE HAVE SEEN HAVE BEEN
IN THE AREA OF GENE AND CELL
THERAPY MANUFACTURING.
REALLY HAVE NOT SEEN MUCH AI IN
MANUFACTURING VACCINES.
WHAT WE HAVE SEEN, WELL THE ONE
AREA THAT WE HAVE SEEN SOME
APPLICATION OF MACHINE LEARNING
MODELS HAS BEEN ANTIGEN
SELECTION, IDENTIFICATION
SELECTION FOR PERSONALIZED
CANCER VACCINES.
AND HUSSEIN, THE SLIDE WHERE WE
HAVE SEEN THE IMPLEMENTATION OF
MODELS, THESE KINDS OF MODELS.
FOR VACCINE LIKE I SAID WE HAVE
NOT SEEN MUCH.
OUR EXPERIENCE IS VERY LIMITED.
AS WE HEARD FROM THE SPEAKER IN
AN EARLIER PRESENTATION, WE
KNOW THAT THERE IS INTEREST IN
INDUSTRY AND MANUFACTURING TO
IMPLEMENTATION AI FOR
MANUFACTURING VACCINES EXAMPLES
GIVEN BY TED ANDISABELLE BUT
THEY'RE STILL IN EARLY
DEVELOPMENT WE HAVE NOT SEEN
THEM THROUGH THE GAP
INTERACTIONS.
NOW IN THE MEANTIME, WE KNOW
THAT THESE ARE COMING, WE'LL
SEE THEM SOON.
SO WHAT WE'RE DOING RIGHT NOW
IS THAT THE AGENCY IS TRYING TO
DEVELOP THE REGULATORY
FRAMEWORK THAT WILL BE
NECESSARY TO EVALUATE THE
TECHNOLOGY.
HUSSEIN MENTIONED THE FRAME
INITIATIVE FOR REGULATORY
EVALUATION OF FRAME.
AND ONE OF THE TECHNOLOGIES
THAT IS BEING SORT OF EVALUATED
FOR GOID ANS DEVELOPMENT IS
APPLICATION OF AI MANUFACTURING.
AND SO WE'VE AS HUSSEIN
MENTIONED, WE PUBLISHED
DISCUSSION PAPER ON THE GAPS
AND PAIN POINTS IN THE
REGULATORY FRAMEWORK, WE HAD
WORKSHOPS WITH THE PURPOSE OF
OBTAINING FEEDBACK.
>> ONE MINUTE, PLEASE.
>> UPON DEVELOPING THIS
GUIDANCE.
AND AS HUSSEIN ALSO MENTIONED,
THERE HAVE BEEN A GUIDANCE THAT
SHOULD BE COMING OUT SOON.
THAT REALLY PROVIDES SORT OF A
GUIDE ON ESTABLISHING THE
CREDIBILITY OF AI MODELS FOR
REGULATORY DECISION-MAKING.
THAT SHOULD BE COMING OUT SOON.
AND BASED ON THE FEEDBACK THAT
WE GOT FROM THAT DOCUMENT,
WE'LL THINK ABOUT MAYBE
DEVELOPING MORE SPECIFIC
GUIDANCE DOCUMENTS FOR THE
IMPLEMENTATION OF AI AND
MANUFACTURING.
SO I'LL STOP AT THAT.
>> THANK YOU VERY MUCH, MANUEL.
SO I WOULD LIKE TO INVITE THE
PANEL TO COME TO THE SCREEN.
PERFECT.
AND SO WHAT I PROPOSE IS I'LL
BE PROVIDING A COUPLE OF
QUESTIONS AND THEN WE CAN GO
THROUGH THE Q AND A TO SEE IF
WE CAN ADDRESS THAT AS A PANEL.
I WOULD LIKE TO BEGIN WITH
DEMETRIS WHAT HAVE THE BENEFIT
AND RISK WITH THIS CAPABILITY?
>> WELL I THINK THE MOST
IMPACTFUL ASPECT IS THE
REALTIME COMPONENT, BECAUSE
MOST ORGANIZATIONS, WHEN WE
STARTED STUDY WE HAVE A PLAN
REVIEW PLAN AND HAS SIMILAR
THING, FIELD MONITORING SOME OF
THE THING.
BUT, BECAUSE THESE ARE COMPLEX
REPORT THEY HAVE TIME TO CREATE
THE REPORTS, IT'S TIME TO
CONSUME AND REVIEW IN CERTAIN
FREQUENCIES THAT IS PRACTICAL.
BUT THE BIGGEST DIFFERENCE IS
THAT THE SMART DATA INTEGRITY
ITEM I MENTIONED, THAT REVIEWS
THE DATA EVERY SINGLE NIGHT SO.
THAT INDIVIDUALS CAN ASSESS THE
OUTPUT EVERY SINGLE MORNING
WITHOUT HAVING TO RUN REPORTS
AND DECIDE OR VISUAL DECIDE
WHICH ITEM THAT IS FAR AWAY
FROM THE SCATTER PLOT AND WHAT
IT GOES INTO.
AND I LIKE HE SAID MOST IS NOT
CHANGING HE IS ESTHETICS
DESIGN, MOST IS THIS EXCLUDES
CRITERIA, WE REALLY HAVE TO
RETHINK, OR SITE 5 OR 6 SEEMS
TO BE UNDERMINING, LET'S SEE IF
IT'S A GPT TRAINING
UNDERSTANDING OF THE PROTOCOL
AND DETAILS THAT WE CAN ADJUST
AND OF COURSE CORRECT IN
REALTIME.
WHEN IT'S EARLY ON CAN THERE IS
A LITTLE BIT OF DATA LEFT.
IF YOU USE A MORE SPACE
MONITORING APPROACH YOU NEED
SIGNIFICANT AMOUNT OF DATA TO
COME IN BEFORE THE SIGNAL CAN
BE OBSERVED THAT IT MAY BE TOO
LATE TO DO SOMETHING ABOUT IT.
WHEN YOU THINK ABOUT THE
VACCINES THAT CAN BE ONE IN
2000 PEOPLE A DAY.
THERE IS NO TIME TO REACT IF
YOU WERE NOT VOLUNTEER.
>> THANK YOU, AND NOW WE HAVE
IN TERMS OF THE WORK THAT WAS
PRESENTED WHAT FRAMEWORK DOES
IT RECOMMEND FOR THE AI
ALGORITHM ITSELF?
>> SO I DON'T THINK, WE MANDATE
OR REQUIRE ANY SPECIFIC
FRAMEWORK.
WE KNOW THAT THE STANDARDS
ORGANIZATIONS HAVE BEEN WORKING
ON MULTIPLE FRAME WORKS, THERE
IS THE NEXT FRAMEWORK AS WELL.
THERE IS THE ISO FRAMEWORK.
ANY FRAMEWORK THAT HA IS GOING
TO BE LEVERAGE HAS TO BE LOOK
AT THE INTENT USE OF AI OR
MACHINE LEARNING MODEL.
WHAT ARE THE OTHER DATA
INFORMATION SUPPORTING YOUR
CLINICAL PROGRAM?
AND BASED ON THIS YOU CAN LOOK
AT HOW YOU'RE GOING TO BE
EVALUATE, WHETHER YOUR MODEL IS
GOING TO BE USED MAYBE FOR
EXAMPLE IN POST APPROVAL IN A
PHASE 4 STUDY OR YOU'RE
THINKING THIS IS SOMETHING THAT
IS GOING TO BE USED.
SO I THINK THAT THE DEPENDING
ON THE CONTEXT OF USE, THEN,
YOU CAN FIND OUT OR MAYBE GET A
RECOMMENDATION ON WHAT TYPE OF
FRAMEWORK YOU CAN USE FOR
EVALUATION PERFORMANCE.
>> RIGHT, THANK YOU.
MANUEL, IN THE DISCUSSION YOU
WERE CLOSE TO THE PHARM
PROJECT, I WAS TRYING TO
UNDERSTAND CAN YOU ELABORATE A
LITTLE BIT MORE ON THIS.
ARE YOU PLANNING TO USE THAT
EFFORT TO TRAIN THE FDA OFFICER?
>> THAT'S CORRECT.
SO WE AS YOU'RE RIGHT, WE'RE
NOT A FUNDING AGENCY BUT, WE DO
FELLOW LIKE WE'RE IN A UNIQUE
POSITION TO SEE WHERE SOME GAPS
THAT THERE ARE SOME GAPS IT
ADVANCE THIS TECHNOLOGY.
AND THE PURPOSE OF THAT IS TO
FUND RESEARCH THAT IS GOING TO
ADVANCE REGULATORY SCIENCE.
SO IT'S GOING TO PROVIDE THE
EXPERIENCE AS WELL AS THE
KNOWLEDGE THAT IS NECESSARY TO
ESTABLISH A REGULAR TORY
FRAMEWORK THAT WILL BE
NECESSARY TO EVALUATE THE
PROJECT.
NOW THE PROJECT WITH MIT AND
WHAT USED TO BE WHAT USED TO BE
RESTA PHARM IT IS TO PROVIDE
THAT KNOWLEDGE.
SO WE PLAN TO TAKE OUR EXPERTS
TO THE FACILITY TO LEARN HOW
THE PROCESS ESTABLISHED CONTROL
SO THAT WILL BE A GREAT SOURCE
OF, OF INFORMATION FOR US AS
WELL AS TRAINING OPPORTUNITIES.
AND SO FAR AND IN THE LAST FOUR
OR FIVE YEARS WE HAVE SUPPORTED
ABOUT 23 PROJECTS, NOT AS
SIGNIFICANT AS THE ONE WITH MIT
BUT YEAH, BUT THAT'S THE IDEA
BEHIND IT.
>> THANK YOU.
SO NOW I THINK I'M GOING TO ASK
THE SAME QUESTION TO THE THREE
US.
AND I EXPECT THREE DIFFERENT
ANSWERS I SEE.
SO I'LL ASK YOU TO DEMETRIS THE
INCREASE OF AU AND BIG DATA ARE
THERE PRIVACY CONCERNS INTEREST?
>> YEAH THESE PROJECTS THAT I
MENTIONED, EACH ONE OF THEM
REQUIRED SIGNIFICANT
DISCUSSIONS WITH OUR PRIVACY
OFFICE, OUR LEGAL OFFICE, THE
REGULATORS AND TO START WITH
THE DATA WE HAVE ACCESS TO IS
ALWAYS IDENTIFIED, THE SPONSORS
IDENTIFYING INFORMATION ABOUT.
BUT EVEN SO WE STILL HOLD
OURSELVES VERY RIGIDLY TO WHAT
WAS CONSENTED FOR THE USE OF
THAT DATA.
FIRST IT WAS CONSENSUS HAS TO
BE APPROVED BY ETHICAL
COMMITTEES AND THEN THEY HAVE
BEEN PRESENTED TO THE PATIENT
EXAMINE THE PATIENT HAS TO
AGREE.
WE HOLD OURSELVES VERY HIGH,
EVEN IF SOMEBODY HAS TO
DEVELOPING A MODEL IT HAS TO GO
THROUGH CHECKS AND BALANCES.
>> THANK YOU.
SO HUMANE AND MANUEL THE SAME
THING, BUT I'LL EXTEND LIKE
INDUSTRY DATA IN GENERAL?
HUMANE?
>> THANK --HUSSEIN.
>> THANK YOU FOR THIS VERY
IMPORTANT QUESTION.
I THINK PATIENT PRIVACY IS ONE
OF THE FACTORS THAT WE THINK
ABOUT WHEN WE ARE LOOKING AT
THE USE OF THESE MODELS AND OF
COURSE, GEN AI, BROUGHT A LOT
OF CHALLENGES WHEN IT COMES TO
PATIENT PROIF SEE.
ONE OF THE MAJOR THINGS THAT
WE'RE INTERESTED EVEN THAT WHAT
DOES IT MEAN?
CAN YOU REALLY MASK ALL THE
PRIVATE INFORMATION AND PUBLIC
INFORMATION FROM THE DATA SET
TO BE ABLE TO USE IT?
AS YOU CALL, FOSTERING
COLLABORATION IS ONE OF THE
MAJOR THINGS THAT WE RECEIVED
FROM THE PUBLIC AS COMMENT.
BUT WE KNOW THAT COLLABORATION
HERE MEANS SHARING DATA BUT WE
ALSO WANT TO BE CAREFUL WHEN
WE'RE DOING SOMETHING LIKE THAT
WHEN WE DO SUPPORT SHARING OF
DATA IT IS DONE IN THE PROPER
WAY AND DOES NOT POSE ANY RISK
TO THE PATIENT PRIVACY.
>> THANK YOU.
MANUEL YOU HAVE THE LAST WORD
IN THIS.
>> MORE GENERAL BUT THROUGH
MANUFACTURING, THROUGH THE
WORKSHOP, WE HEARD THAT THERE
WERE CONCERNS ABOUT THE
AVAILABLE ABILITY OF THE DATA,
THAT'S A REAL CONCERN.
CONCERNS ABOUT THE SECURITY OF
THE DATA, SECURITY CONCERNS
RIGHT.
THE FACT THAT I THINK DEMETRIS
REALLY GOT TO THIS ABOUT THERE
IS A LOT OF DATA OR MAYBE OTHER
SPEAKERS TOO, BUDDHA AT THAT
EXIST IN SILOS SO THERE IS A
LACK OF E WILLINGNESS TO SHARE
DATA.
AND THERE IS NOT A LOT THAT THE
AGENCY CAN DO ABOUT BUT THAT
QUESTION CAME OUT.
AND I DON'T KNOW THAT WE CAN,
BUT WE'RE STILL TRYING TO SEE
IF THERE IS ANYTHING THAT WE
CAN DO BUT, YEAH, SO THOSE ARE
SOME OF THE MAJOR ISSUES THAT
CAME UP DURING THAT WORKSHOP.
>> THANK YOU, MANUEL.
SO NOW I'M GOING TO JUMP TO THE
Q & A THE ONE IS ADDRESSED TO
ME.
IS THERE ANY EXPERTS TO EXPLORE
VACCINE USE IN LOW RESOURCE
SETTING.
SO THIS IS AN IMPORTANT
QUESTION BUT I THINK I WOULD
LIKE TO REFER TO WHAT IS DONE
WITH SEPI IN THE 1 HYUNDAI
INITIATIVE AND THESE INITIATIVE
NEED TO INTEGRATE AI TO GO
FASTER TO PHASE ONE.
OBVIOUSLY THERE IS A FOCUS ON
EXTENDING ANY VACCINE
DEVELOPMENT BECAUSE LET'S SAY
IT'S A PLATFORM A LITTLE BIT
MORE FLEXIBLE DURING THE
CHANGES.
SO I THINK THAT REALLY COULD
POTENTIALLY BRING AFFORDABLE
RESOURCE SETTING IN GENERAL.
SO I'LL GO TO ANOTHER QUESTIONS
IN THE Q & A.
AGAIN ANOTHER QUESTION THIS ONE
WAS IN RELATION SHIP TO VACCINE
SUPPLY CHAIN.
I WILL NOT READ THROUGH ALL OF
IT BUT VERY EXTENSIVE QUESTION
IN LEAP TIME AND COSTS
MANUFACTURING DISCOVERY RATES,
ALL IMPORTANT TOPICS IN TERM OF
LOW MEDIUM INCOME SETTING.
AND I THINK I WANT TO REFER TO
PHILIPE THERE WAS AN
INTERESTING EFFORT CALLED THE
MARKETPLACE DURING THE COVIX
TIME, TO ADDRESS COVID AND
WEBSITE WITH VENDORS
OPTIMALIZING AND REDUCING ENTRY
IN GENERAL, LOWERING
MANUFACTURING AND IT'S VERY
INTERESTING BECAUSE THE MORE I
WAS THINKING ABOUT IT, THE MORE
IT LOOKS LIKE GABI EFFORT WHEN
YOU THINK ABOUT IT.
GAVI FOR VACCINES SO THE
QUESTION IS SHOULD VERY WITH
HAVE A GAVI FOR RAW MATERIALS
AND THEN WE APPLY A PRINCIPAL
LE--PRINCIPLE AND THE
IMPLEMENTATION OF AI.
I'LL ASK THE ORGANIZER DO WE
HAVE TIME FOR QUESTIONS FOR THE
PANEL.
>> IF THERE IS ANY OTHER URGENT
OR INTERESTING QUESTIONS, I
WOULD SAY GO FOR IT.
MAYBE ONE MORE SHORT QUESTION
AND THEN WE'LL GO FOR A WRAP UP.
>> PERFECT SO ONE MORE QUESTION
SO I GUESS I'M GOING TO HAVE TO
ASK A GENERAL QUESTION.
I THINK ONE CONCERNS THAT COMES
IS, WHAT STRATEGIES ARE BEING
APPLIED TO PREPARE AND TRAIN
THE CURRENT WORKFORCE FOR
INTEGRATION OF AI IN GENERAL?
AND SO IT WOULD BE NICE TO HAVE
YOUR THOUGHTS, ANY VOLUNTEER.
>> I WOULD BE HAPPY TO START.
SOME COLLEAGUES AND I IN THE
INDUSTRY JUST WROTE A PAPER
ABOUT THIS AND SO I HAD A
MANAGEMENT EXTERNAL.
I MEAN IT'S PART STROKE
EVERYTHING FROM EVERYTHING FROM
UP TRAINING AND UP SCALING IN
CERTAIN SPECIFIC TECHNOLOGY TO
REALLY FUNDAMENTAL CRITICAL
THINKING.
AND THE LATTER ONE IS AN
INTERESTING ONE HOW DO YOU
TEACH CRITICAL THINKING, IT'S
NOT SO SIMPLE.
IN OUR SCENARIO USED TO USED
CASE SCENARIO WE WHERE TRAIN ON
SCENARIOS THAT MAY ARISE.
BUT IT IS A UNDERTAKING EXAMINE
WE HAVE A MULTI ROAD MAP ON
THIS TRAINING IN MY
ORGANIZATION.
WE CALL IT TRANSITIONING FROM
CLINICAL MANAGEMENT TO
SCIENTIST SUCH AS A GENERAL
THEME BUT, IT IS IT IS
SOMETHING WHERE WE SEE A
SIGNIFICANT GAP BECAUSE WE'RE
TAKING ORGANIZATIONS FROM
FOLLOWING A CHECKLIST TO MORE
SIGNIFICANT CHECKS AND BALANCES
IN THE APPROACHES WHERE WE CAN
GET, WE CAN STILL GIVE THEM A
CHECKLIST BUT IT'S NOT GOING TO
COVER, IT'S NOT GOING TO COVER
ALL THE BASIS.
>> THANK YOU.
HUSSEIN MANUEL ANY COMMENTS?
>> GO AHEAD HUSSEIN.
>> PLEASE.
>> I WAS GOING TO SAY WE HAVE
HEARD FROM STAKEHOLDERS THAT'S
A BIG PROBLEM IN
PHARMACEUTICAL, YOU NEED
TRAINED PERSONNEL TO IMPLEMENT
AI BUT IN THAT SPACE BUT ALSO
THAT CAN THAT ARE ABLE TO
INTEGRATE THE APPLICATION WITH
AI WHICH EXISTING SYSTEM WHICH
IS ANOTHER CONCERN.
SO THE WORKFORCE IS A BIG
CONCERN.
>> THANK YOU.
HUSSEIN?
>> YES, I'LL JUST SAY WHEN
WORKSHOPS THAT I MENTIONED IN
THE TIMELINE, THAT WE FOR
EXAMPLE THE ONE FOR THE CITY
THE CLINICAL TRIAL INITIATIVE.
DOCTOR FROM STANFORD BROKE TO
OP MIEZ THE USE OF AI MACHINE
LEARNING DEVELOPMENT.
YOU HAVE TO HAVE A MULTI FLUENT
INDIVIDUALS THAT NOT ONLY VERY
EXPERIENCED FOR EXAMPLE IN
DRAWING DEVELOPMENT IMMUNOLOGY
OR COMPUTER SCIENCE, YOU HAVE
TO HAVE INDIVIDUALS THAT CAN
TRANSCEND AND CAN SPEAK TO EACH
OTHER.
YOU REALLY NEED THE CULTURE
CHANGE AND WE'RE DOING THIS ON
OUR SIDE, AS YOU MENTIONED THIS
IS A HUGE AMOUNT OF TRAIN
THATING ARE EVOLVING TO ALLOW
OUR STAFF TO SPEAK TO EACH
OTHER AND BRIDGE THESE GAPS.
WE'RE ALSO NOT ONLY TALKING THE
TALK BUT ALSO WALKING.
WE ARE BRINGING THESE
TECHNOLOGIES WITHIN OUR
ENVIRONMENTAL WITHIN OUR FIRE
WALLS AND TRYING THESE
ENVIRONMENTS SEEING HOW WRECK
USE IT IN DIFFERENT
APPLICATIONS SO WE CAN GIVE
THEM INFORMED ADVISE AND HELP
THEM LEVERAGE THIS TECHNOLOGY
IN A SAFE AND EFFECTIVE WAY.
>> THANK YOU VERY MUCH, THANK
YOU VERY MUCH FOR THE THE
PANELS.
>> GREAT THANK YOU SO MUCH.
EVERYONE ON PANEL 3, REALLY
APPRECIATE IT.
I KNOW, I'M GLAD TO SEE HOW
MUCH WORK IN THE CLINICAL IN
BOTH THINKING ABOUT THE
REGULATORY ASPECTS OF IT AS
WELL.
THANK YOU ONCE AGAIN FOR A
FASCINATING SESSION.
I NOW WANT TO HAND IT OVER TO
MY COLLEAGUE AND COORGANIZER
FOR SOME FINAL THOUGHTS AND
COMMENTS.
TAKE IT AWAY ANNIE.
>> THANK YOU.
I'M OFFICER FROM NATIONAL
INSTITUTE OF INFECTIOUS DISEASE
ON BEHALF OF THE COMMITTEE WE
WOULD LIKE TO SINCERELY THANK
EVERYONE OUR SPEAKERS AND A
PANE LISTS AND ATTENDEE FOR
INFORMATION AND CALLING FOR OUR
NEXT GENERATION PROFESSIONALS
TO BE MORE TECH SAVVY AND THIS
INSPIRATION AI VACCINE WHICH
COULD BE AVAILABLE TO THE
PUBLIC.
AGAIN REALLY TRULY THANK YOU
FOR THE GREAT DISCUSSION.
NOW AI MACHINE LEARNING,
APPARENT TRANSFORMING EVERY
STAGE FROM EARLY DISCOVERY TO
CLINICAL DO RECORD PROCESS AND
ORDER ECOSYSTEM.
SO BY HARNISHING THIS AND
ENHANCE OUTCOME, WE CAN
ACCELERATE VACCINE DEVELOPMENT
AND BETTER ADDRESS OUR GLOBAL
HEALTH CHALLENGES.
SO WE HOPE TODAY' WEBINAR
SPARKS SOME MEANINGFUL
CONVERSATION AND IN THIS AREA.
FOR FUTURE WEBINAR, WE WILL
HAVE A FORUM IN-PERSON FOR IN
BRAZIL IN MARCH NEXT YEAR.
SO OUR NEXT WEBINAR WE'LL
RESUME IN SUMMERTIME AROUND
JUNE OR JULY NEXT YEAR.
YOU CAN CHECK BACK TO OUR
WEBSITE WHEN IT'S GETTING CLOSE.
AND TODAY'S VIDEO RECORDING
WILL BE AVAILABLE ON THIS
WEBSITE.
SO PLEASE CHECK BACK IF YOU
NEED TO GO BACK AND LOOK AT IT.
AGAIN THANK YOU FOR
PARTICIPATING AND HOPE WE CAN
SEE YOU IN OUR NEXT WEBINAR.
AND THAT CONCLUDES TODAY'S
WEBINAR.
THANK