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Leveraging functional genomics analytics for target discovery

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Leveraging functional genomics analytics for target discovery

  1. 1. Leveraging functional genomics analytics for target discovery Enrico Ferrero, PhD Computational Biology @ GSK Data Science for Pharma 27.01.2016
  2. 2. The drug discovery pipeline New medicine: $2.5+ bn, 20+ years Leveraging functional genomics analytics for target discovery Enrico Ferrero – Computational Biology @ GSK 2
  3. 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. 4. Late failure costs more How to reduce late phase attrition? 4Leveraging functional genomics analytics for target discovery Enrico Ferrero – Computational Biology @ GSK 0 200 400 600 800 1000 1200 0 10 20 30 40 50 60 70 80 90 100 Lead discovery Lead optimization Pre-clinical FTIH Phase 2 Phase 3 Relativecost(permolecule) Nmolecules Manhattan Institute, 2012
  5. 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. 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. 7. Functional genomics and high-throughput sequencing Transcriptomics Epigenomics Regulomics RNA-seq ChIP-seq DNase-seq BS-seq
  8. 8. Disease understanding
  9. 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. 10. Differential expression analysis 10 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. 11. Methylation data generation and processing optimization BS-seq 11  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. 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. 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. 14. Target discovery
  15. 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. 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. 17. Drug MOA
  18. 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. 19. Neurogenesis-inducing compounds MOA RNA-seq Leveraging functional genomics analytics for target discovery Enrico Ferrero – Computational Biology @ GSK
  20. 20. What else?
  21. 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. 22. Centre for Therapeutic Target Validation (CTTV) https://www.targetvalidation.org/ Leveraging functional genomics analytics for target discovery Enrico Ferrero – Computational Biology @ GSK
  23. 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. 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. 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

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