Slides presented at Data Science for Pharma (London, January 2016).

1. Leveraging functional genomics
analytics for target discovery
Enrico Ferrero, PhD
Computational Biology @ GSK
Data Science for Pharma
27.01.2016

2. The drug discovery pipeline
New medicine: $2.5+ bn, 20+ years
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK
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3. Challenges in the pharma industry
Time and costs are increasing but success rate is declining
3Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

4. Late failure costs more
How to reduce late phase attrition?
4Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK
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Lead discovery Lead optimization Pre-clinical FTIH Phase 2 Phase 3
Relativecost(permolecule)
Nmolecules
Manhattan Institute, 2012

5. Rethink the drug discovery pipeline
Spend more time and resources in target validation to reduce attrition in later phases
5Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK
Targetvalidation
Potentialtargets
Pre-clinical FTIH LaunchPhase 2 Phase 3
Lead discovery
Lead optimisation
Launch
PotentialtargetsPotentialtargets
Lead discovery Lead optimisation Pre-clinical FTIH Phase 2 Phase 3
Target
validation

6. 6
Supporting the drug discovery pipeline and drive innovation
Target Preclinical Clinical Launch
Disease
understanding
Target
discovery
Drug
MOA
Indication
mining
Patient
stratification
Efficacy and
safety
Drug
repositioning
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK
Computational Biology @ GSK

7. Functional genomics and high-throughput sequencing
Transcriptomics Epigenomics Regulomics
RNA-seq ChIP-seq DNase-seq BS-seq

8. Disease understanding

9. Disease progression in rheumatoid arthritis
RNA-seq + BS-seq
 Part of the BTCURE research project, in collaboration with the Academisch Medisch Centrum
(Amsterdam, NL).
 Pilot study involving a small number of synovial biopsies from RA patients at different stages and
degrees of severity profiled by RNA-seq and BS-seq.
 Objective: identify gene expression and methylation signatures that could highlight disease
progression mechanisms.

10. Differential expression analysis
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RNA-seq
 Challenges:
 Data-driven identification
of clinical parameters that
are indicative of disease
progression
 Differential expression
analysis with limited
number of samples and
high variability
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

11. Methylation data generation and processing optimization
BS-seq
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 Challenges:
 Set up and optimise protocol(s) in the lab
 Big strain on sequencing facilities and computational environment
 Identification of appropriate analytical methods
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

12. Genomic responses to viral infection
RNA-seq + DNase-seq
 Part of an ongoing collaboration with the University of Washington Department of Genome
Sciences (Seattle, WA, USA).
 Pilot study with primary epithelial cells from healthy volunteers infected with human rhinovirus.
 Samples profiled by RNA-seq and DNase-seq to identify gene expression and regulatory chromatin
responses to viral infection.
 Objective: Identification of biological mechanisms and pathways relevant for respiratory diseases
with a strong infection component.
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

13. Genomic responses to viral infection
DNase-seq
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK
 Challenges:
 Differential analytical
framework for DNase-seq
data
 Interpretation of biological
signal from DNase
hypersensitive sites

14. Target discovery

15. Identifying novel Crohn’s targets with strong genetic evidence
Integration of disease genetics with cell-specific functional genomics data
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

16. Identifying novel Crohn’s targets with strong genetic evidence
Crohn’s-associated SNPs in T cell-specific regulatory elements and putative regulated genes
16
Overlapping gene
Correlated gene
ChIA-PET gene
Nearest gene
TFBS
TF motif
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

17. Drug MOA

18. Neurogenesis-inducing compounds MOA
RNA-seq
 Study to understand the mechanisms of action of two neurogenesis-inducing compounds and
discriminate between the pathways they activate.
 Neural progenitor cells profiled by RNA-seq to identify gene expression responses to the two
compounds.
 Objective: Identification of off-target effects and safety risks.
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

19. Neurogenesis-inducing compounds MOA
RNA-seq
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

20. What else?

21. GSK partnerships with academic institutions
A collaborative and pre-competitive effort to improve the target discovery process
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

22. Centre for Therapeutic Target Validation (CTTV)
https://www.targetvalidation.org/
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

23. Conclusions
Leveraging functional genomics analytics for target discovery
 Making drugs is a very failure-prone business. To increase our chances of success, we need to have
better understanding of the biology of:
– Our diseases;
– Our targets;
– Our drugs.
 High-throughput sequencing assays and functional genomic data are more and more widely used
in GSK to drive and support these activities.
 This type of data poses two main challenges:
– Data plumbing: create an infrastructure that is able to deal with the size of these datasets, in terms of both
storage and processing power.
– Data analytics: develop appropriate analytical pipelines that allow to integrate, visualise, analyse and
interpret the data.
 Partnerships with CTTV and Altius demonstrate our vision of a pre-competitive, collaborative space
for target identification and validation.
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

24. Acknowledgements
 Disease progression in rheumatoid arthritis
(in collaboration with BTCURE and AMC)
– Rab Prinjha (Epinova DPU, GSK)
– Paul-Peter Tak (Immuno-inflammation TA, GSK)
– Danielle Gerlag (Clinical Unit Cambridge, GSK)
– Huw Lewis (Epinova DPU, GSK)
– Erika Cule (Target Sciences, GSK)
– Klio Maratou (Target Sciences, GSK)
– George Royal (Target Sciences, GSK)
 Neurogenesis-inducing compounds MOA
– Hong Lin (Regenerative Medicine DPU, GSK)
– Aaron Chuang (Regenerative Medicine DPU, GSK)
– Julie Holder (Regenerative Medicine DPU, GSK)
– Jing Zhao (Regenerative Medicine DPU, GSK)
– Erika Cule (Target Sciences, GSK)
 Genomic responses to viral infection
(in collaboration with StamLab and UW)
– Edith Hessel (Refractory Respiratory Inflammation DPU,
GSK)
– John Stamatoyannopoulos (StamLab, UW)
– David Michalovich (Refractory Respiratory Inflammation
DPU, GSK)
– Soren Beinke (Refractory Respiratory Inflammation DPU,
GSK)
– Nikolai Belyaev (Refractory Respiratory Inflammation DPU,
GSK)
– Peter Sabo (StamLab, UW)
– Eric Rynes (StamLab, UW)
 Identifying novel Crohn’s targets with strong genetic
evidence
– David Michalovich (Refractory Respiratory Inflammation
DPU, GSK)
– Chris Larminie ( Target Sciences, GSK)
Leveraging functional genomics analytics for target discovery
Enrico Ferrero – Computational Biology @ GSK

25. We’re hiring!
Computational Biology jobs at:
 http://www.gsk.com/en-gb/careers/search-jobs-and-apply
 https://www.linkedin.com/company/glaxosmithkline/careers