Presented at LT (Laboratory Technology) CON 21. A brief overview of the possibilities and obstacles for ML in drug development

1. ML & AI in Drug
development: an
introduction &
overview
Paul Agapow
Statistics & Data Science
Innovation Hub, GSK

2. Disclosure
– No conflicts of interest
– Own views and does not reflect official company
thought or projects
– Based on experience in current & previous
positions
– Data Science / Statistics @GSK
– ML&AI / Health Informatics @AZ
– Data Science Institute @ICL
– Bioinformatics @Health Protection Agency (UK) …
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3. What is drug development, how does it work?
Agenda
3
Why ML & AI is difficult in pharma
Where ML & AI can be powerful in pharma and what
we need to do
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2
3

4. How we make drugs
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5. Clinical trials
Identifying and
understanding
disease, unravelling
the molecular
machinery,
pinpointing targets
Drug development is a long & complex process
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Pathophysiology
Developing
molecules that can
be synthesized and
delivered safely to
the target
Drug candidates
Testing via trials,
dissecting failures
and successes,
tracking adverse
events, seeking
regulatory approval
Who gets the drug,
how is it re-
imbursed, tracking
long-term adverse
events
Post-approval

6. 6
• ~ $2B and 10 years to
develop & launch a drug
• The “valley of death”: most
candidate drugs will fail
• Can be difficult to predict
what will work
The tough maths of drug development
ePharmacology.hubpages.com

7. Why ML & AI is
difficult in pharma
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8. 10 June 2021 8
“AI will not replace
drug hunters, but drug
hunters who don’t use
AI will be replaced by
those who do.”
-Andrew Hopkins, CEO Exscientia

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10. Why?
– Biology is outrageously complex
– Data is frequently biased, irregular, incomplete,
in different formats
– Biomedicine is a label desert
– As a consequence:
– Advances are throttled by domain knowledge
– How to represent & analyse complex domain
– Suitable data is often scarce
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11. 12 July 2021 11
The complexity of biomedicine:
About 50 trillion cells of 200 types
Each cell has 23 pairs of chromosomes
In total 6.4 billion basepairs (positions)
Organised into about 18,000 genes
(Or maybe more like 40,000 genes)
Genetic material elsewhere in the cell
Epigenetic modification
1 million different types of molecules
Lifestyle & history
Exposure & environment
Immune system repertoire & priming
…
Of which we know only a fraction

12. The classic
analytical
tension
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What we need to solve
What we tend to solve
Easy things
Available, ideal data
Ground truth
Simplify
“Interesting”
“Table-land”
Useful things
Incomplete messy data
Unclear biological reality
Uncertain findings
Needful
“Network-land”

13. Where can ML & AI be
powerful in pharma?
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14. 14
Radiology & imaging widely used in healthcare
• Capture important & difficult to
abstract data
– E.g. presence, size, shape of
tumor
• Radiologists
– Never enough of them
– Rushed
– Frequently wrong
• But AI is good at interpreting
images …
SubtleMedical.com

15. 15
Not just X-rays & MRI but microscopes
• Cancers are associated with
certain proteins
• Traditionally have to be stained
& examined visually
• Deep learning can automatically
do this for us
• Faster, more consistent
Li et al. 2021

16. 16
Precision medicine: subtypes of diseases & patients
• Because many conditions have
similar clinical presentations
but vastly different underlying
molecular machinery
• Precision medicine
• The right drug for the right patient at
the right time
• Clustering
• But as simple as seems
• E.g. asthma
Kermani et al. 2018

17. 10 June 2021 17
• A lot of biomedical
knowledge is associative
or relational & multimodal
• Knowledge graphs /
GCNs help us to capture
and analysis
• Have been used to
propose new drugs and
patient subtypes

18. Good (engineering) practices & production quality is
vital
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Wynants et al. 2021

19. 19
We need more data
• Many possible types of
useful data
• For many purposes
• From where?
• How to manage &
interoperate?
• Issues of representation &
diversity

20. Interpretability (etc.) is vital
– May feedback to inspire mechanistic research, but …
– But what actually is interpretability?
– Essential for:
– a smoke test, validation
– check for bias
– communication
– Likewise calibration
– Important to understand how (un)sure we are
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21. Takeaways
Drug
development
is a
enormously
complex
process
Although
attractive,
ML & AI are
often
hindered by
the nature of
the data
Areas of
definite
value
include
subtyping,
imaging &
knowledge
graphs
More and
wider data
& better
engineering
is key to
further
progress
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22. Some light
reading
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Academic Press (2021)

23. Looking for
work?
– If you are driven by science and passioned
about improving lives, why not look at a job in
pharma?
– Principal Statistician, Internship, Software
Engineer, Data Analyst, Apprentice, Future
Leaders Programme …
– Visit our careers website for much, much
more: https://www.gsk.com/en-gb/careers/
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