Genomics and drug discovery. John Whittaker

Transcription

1 Genomics and drug discovery John Whittaker

2 Outline Background Motivation: success and failure in drug discovery Direction of travel Examples Genetics Genome scale experimentation 2

3 Background Supporting heading

4 Motivation Eroom s Law Probability of success at target selection 3% 4

5 AbbVie Takeda Johnson Roche Pfizer Eli Lilly Amgen Gilead Novartis Teva GlaxoSm Merck & AstraZe Sanofi Bristol- Genetic support increases probability of success Nelson et al, 2015 Nature Genetics 100% 80% 60% 40% 20% 0% genetic_association other 5

6 Path to a medicine Genetic associations yield > 2x increase in POS >1 rare disease gene a week GWAS revolution Informatics allows integration Genetics drives target choice Cellular /tissue model Appropriate IPS or primary cells Molecular fingerprint via omics Understand genetic mode of action Gene editing to confirm hypothesis Biological understanding drives population and endpoint choice May be genetically defined Experimental medicine study chemistry Largely pre-competitive biology 6

7 COPD target engine NB: similar approaches for other diseases/mechanisms Genetics Transcriptomics Tool / Tractability Target hypothesis Individual targets Cellular models Review Tool compounds Gene editing Network modules 7

8 Genetics Focus on UK Biobank

9 UK Biobank A resource of 500k people A trillion new genetic association data points in 2018 Target identification Target validation Safety assessment Indication expansion Patient stratification 9

10 Example: GLP1R-agonists See Scott et al, 2016, Sci.Trans.Med; Nelson et al, Science 2012 Effective and becoming widely used for T2D Cardiovascular risk? We found a (rare) variant that mimics drug mechanism of action 10

11 UK Biobank transforms genetic research Can study 2000 phenotypes v 1m SNPS in 500k individuals.. >18 months and >1000 s 1 day Disease outcome N cases N controls N cases N controls Type 2 diabetes 25, ,393 16, ,915 CHD 61, ,728 12, ,483 Pancreatic cancer ,490 Ovarian cancer ,071 Breast cancer , ,895 Prostate cancer ,468 Parkinson's disease ,537 Alzheimer's disease , OR per minor-allele OR per minor-allele 11

12 Phenome-wide analysis of GLP1R in UKBB For modest effects, we remain underpowered in some instances 12

13 GWAS for target ID Example: Asthma GWAS in UK Biobank (39k cases) IL33 13

14 PheWAS Example: IL33 Large-scale sequencing efforts have identified informative LOF variants $415 M partnership between Amgen and decode Do we see a similar association with eosinophils in UKB? What else can we do? YES! (p=9.6x10-65 ) Smith D, Helgason H, Sulem P, Bjornsdottir US, Lim AC, et al. (2017) A rare IL33 loss-of-function mutation reduces blood eosinophil counts and protects from asthma. PLOS Genetics 13(3): e

15 LOF and IL33 genomewide expression predictor Reproduces known results and find new signals 15

16 Is it actionable? Eg, tractability Most genetically validated targets not druggable Often need activators Other modalities? Pathway approach? 16

17 Pathway based approaches: eg Type 1 IFN What s a pathway? Core STING network 22 genes - Literature mining -Pathway repositories -Neighbour expansion DNA and RNA-sensing trigger pathways 381 genes Network building Algorithm DNA and RNA-sensing trigger pathways linked by glue genes added to network 1261 genes Cluster diseases based on more phenotypes in common than expected by chance 896 Diseases 17

18 Genome scale experiments Example: synthetic lethality

19 Platform experiments Synthetic Lethal Screens What is synthetic lethality? When knockout of either gene A or gene B isolation is tolerated but loss of both is lethal Genomic Lethality Why is this useful in oncology? Cancers often lose tumor suppressor genes through deletion or mutation, which can be exploited to target their synthetic lethal pair A clinical example is the approved use of PARP inhibitors (olaparib, rucaparib, niraparib) in BRCA mutant ovarian cancers PARP-BRCA Synthetic Lethality 2017 Nat. Rev. Drug Discov. 19

20 Synthetic lethality Increasing biotech interest. 20

21 Post Genome wide screens for synthetic lethality An unbiased approach to target discovery Genome wide lentiviral grna library ~1 grna per cell 500 cell lines enrichment grna library grna lethality 500 cell lines grna library grna lethality Cas9 expressing cancer cell line days Depleted grna = lethal interactions Genome wide screens of this scale require large research institutions for execution depleted Pre 500 human cancer cell lines Iden fy molecular features correlated with lethality Muta ons Gene expression Methyla on Drug sensi vity Iden fy molecular features correlated with lethality Muta ons Gene expression Methyla on Drug sensi vity 21

22 Data is a platform for research NB: inverts usual dry lab -> wet lab ordering Hypothesis Mining Visualization Canned analysis tools Random Forest Target Validation Single gene KOs (confirmation) Rescue experiments (functional validation) Invalidation of literature hypotheses Chemistry Tractability? Modality comparison? 22

23 Summary Starting with the genetics Lots of associations but are traits relevant? Mechanism often uncertain EHR + genetics will be key eg UKB Model systems Many cell / tissue models but how do we validate? Sources of variability? What can animal models tell us? Gene editing will be transformative Genome scale experiments will be transformative Tractability Most targets are not tractable Pathways? New modalities? 23

24 Acknowledgements Chris Larminie Jo Betts Ben Schwartz Robert Scott Laura Yerges Armstrong Matt Nelson Josh Hoffman Kijoung Song Ashutosh Pandey Ioanna Tachnazidou Giovanni Dall Ollo Mathias Chiano Toby Johnson Meg Ehm 24

25 Back up

26 Genome hype v3? 26

27 Pathway based approaches: eg Type 1 IFN Current project Type I interferon pathway Genetics analysis of targets with autoimmune and/or IFN connection Chemical tractability Informatics targets (10-50) Gene editing to validate Tool compounds (~200 targets) Focused compound screen RNP or lentivirus IFNa/b, TNFa protein IFN mrnas THP-1 (monocytes) detection Plate and bulk analysis Increased pathway activity associates a wide spectrum of autoimmune diseases +/- +/+ -/- -/- +/- +/+ +/- +/+ -/- +/- ligand or Single cell analysis 27

28 PheWAS of IL33 LoF allele in UKBB recapitulates previous associations and identifies new ones 28

29 Phenome-wide predixcan analyses recapitulate results from LoF variant 29

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