Presentation I recently gave at the Centre for Genomic Regulation in Barcelona - providing a glimpse into my career in AstraZeneca over 20 years with some advice for younger data scientists getting into the field.

1. My Personal Journey
through Data, Innovation
& AI in AstraZeneca
Nick Brown
Executive Director, Imaging & Data Analytics
Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca
EXTERNAL PRESENTATION
Centre for Genomic Regulation, 13th February 2024

3. Work Ethic
Mix of Influences
Entered & won all sorts of competitions !
Taught to code at 5
My parents worked 2 jobs and up at 5am
Team sports

4. University of York
Genetics
Focused on 4 year course
combining my love of
maths, biology and practical
skills – wanted to do
something innovative.
Final year project
was confocal image
analysis of stomata

5. Forensic Science Service
UK Government
Focused on
a project
around
capturing the
DNA from
touched
objects, as
seen on TV !

6. Bioinformatics Masters in 2000
Studentship with AstraZeneca – Global Biopharmaceutical
Respiratory,
Charnwood,
AstraZeneca –
handling 1000’s of
gene pathways
Perl programming,
hierarchical
clustering &
developing HMM
(early neural nets)

7. Scientist in Safety Assessment 2001
Toxicogenomics & Data Analytics
Affymetrix chip in-vivo
experiments analysing
across 100,000s of genes
Developing algorithms and
applying complex statistics
and clustering models

8. Senior Informatician in 2004
Advanced Science & Technology Lab (DS)
Automated
image
classification,
computational
clusters and
analysing 1
million in-vitro
images a day
Partnered with
companies like
Definiens and
built automated
IT cluster
infrastructure
with 50TB for
imaging.

9. Elaine Sullivan – Ally
Snr VP ASTL, (now CEO Carrick Therapeutics Pharma)
my job
Dr Elaine Sullivan, co-founder and
CEO of Carrick Therapeutics, was
named winner of the 2018 EY
Emerging Entrepreneur of the
Year TM (EOY) Award
“do what’s in
the box, then
push and
innovate in
every direction”

10. Associate Director, Informatics in 2007
New Opportunities, R&D
Leveraging 100
million unstructured
scientific
documents for new
disease franchises
($XXM deals), drug
repositioning & DDI

11. Associate Director in 2011
Joined AZIT to build enterprise search
R&D Search R&D Departments R&D Journals
R&D Intelligence
R&D Mobile R&D ChemSearch
R&D KOLs
R&D Experts
Helping scientists find what the need out of 200 million unstructured
documents and building search based applications

12. Angela Yochem - Coach
CTO, (now Chief Transformation & Digital Officer, Novant Health)
Secondment with CTO in 2012
Innovation & Digital Technology


13. Director, Tech Innovation Lab 2012
CTO Office, IT

14. Shobie Ramakrishnan - Mentor
CTO (now Chief Digital & Technology Officer, GSK)
RECRUIT THE HEART
TRAIN THE BRAIN

15. My dad had throat cancer in 2013, having his voice box
removed the following year

16. Sponsored Oncology Project
Extreme Blue Studentships with IBM
Internship program where students are challenged to solve a business
problem in 10 weeks. The team developed oncology patient website to
initially record their real voice, phonetic algorithms, and a mobile android
application to capture GPS and predict sentences that laryngectomy
patients would use in real life.
The team went on to win the 2013 European Expo within IBM across all
projects and were interviewed by Computer Weekly (press release). IP given
to Cancer Research UK and NHS.

17. Global Head of Enterprise AI Services in 2017
R&D IT
deep learning for automated
image segmentation
augmented drug design optimization algorithms
time-series forecasting
AI Platforms Connected Intelligence
natural language processing Knowledge graph
engineering

18. Early stage Late stage
Right Dose
Clinical
Candidate
Clinical pharmacology,
pharmacometrics,
clinical bioanalysis,
regulatory filings
New indications, global
roll out of submissions,
supplemental NDAs,
product license
maintenance, impurity
assessment
Post marketing
GLP toxicology,
pathology,
bioanalysis, clinical
pharmacology,
pharmacometrics
Right Safety
TSID to LOID
Target safety
assessment,
mechanistic safety,
DMPK, ADME
Lead optimization
to CDID
Chem Tox, ADME,
DMPK, investigative
safety, pathology, CDID
safety package
18
CPSS involved end to end in drug discovery and development
Embedding the right digital solutions (imaging, data and AI) to improve efficiency
Executive Head of Imaging & Data Analytics – 2022
Clinical Pharmacology & Safety Sciences, R&D

19. Building Key Foundations
Imaging & Data Analytics
bioinformatics imaging data AI & ML Innovation
Maria Chiara
Francesca
Javier
Marija

20. What do run today?
• >50 ML models deployed in
production and incorporated
in the DMTA cycle
• Physicochemical properties,
ADME & safety
• Greater than 4 million
molecule calculations per
day
• 141 million molecules
processed in one month
20
Combining individual models
to create virtual organ safety
models (such as DILI for liver
or CV risk for heart) for more
accurate prediction using end
point assays.
Target Organ Assay
Cardiovascular
hERG
NaV1.5
Iks
Kv4.3
L-type calcium channel
Cardiomyocyte
Structural Cardiovascular Tox
Hepatic
Glu/Gal Mitochondrial Assay
High Content Mitotox assay
Cytotoxicity
Hepatic Spheroid
Liver Transporters (BSEP &
MRP2)
Genetic Toxicity
AMES Mutagenicity Test
In vitro Micronucleus
Various
Secondary Pharmacology Panels
Phospholipidosis
AhR (CYP1a1)
physical assays individual predictive models virtual safety models
Predicting Safety Liabilities
Using In-Silico Predictive Models

21. 21
Human assessment
Tumour cell –ve +ve Immune cell -ve +ve
+++
years
20min
10-20%
+++
days
seconds
0.65%
AI-based assessment
Complexity
Training
Time
Error rate
Complexity
Training
Time
Error rate
:
:
:
:
:
:
:
:
In focus Out-of-focus
Intermediate
Out-of-focus
Initial Scan Image QC Results
Providing quantitative data readouts upfront to
support Pathologist decision making
AutoQC applied to >40k images accelerating
delivery for pathology review
Digital Pathology
Using Analytics & Deep Learning

22. 22
• Free scientists from
manual model building
• Speed up popPK model
development
• Identify better, more
stable models
AutoPK
Pharmacometrician AutoPopPK
Automated
structure
discovery tool
Biological
understanding &
insights
High
performance
popPK models
Case study - AutoPopPK: Discovers population pharmacokinetic models automatically
Tested using clinical trial data from 2 phase 1 trials (184 patients)
matched the structural expert model and/or improved the residual error
AI model search took <40hrs total, saving 2-3 weeks
Saves almost 3 years in computational time annually if we had a virtual pharmacometrician
Predicting The Right Patient Dose
Using AI & Machine Learning

23. The Harder You Work, The Luckier You Get…
Go make your own luck !!
do more of what
you love, find
problems to solve
that inspire you
Keep on
innovating –
intersection
between two
disciplines
Knowing what you
want to do in 10
years time will
help you in 6
months time
Always give
120% but don’t
expect
overnight
success
embrace each
other’s
differences &
build your
networks
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