The document discusses using systems biology and genetics approaches to improve drug discovery for diabetes. It argues that current approaches have high failure rates due to a lack of understanding disease biology. It proposes using multi-disciplinary experiments and computational modeling to map signaling networks, identify modulation points, and predict drug targets. This would require new academic-industry partnerships and integrating various omics data to better understand disease mechanisms and improve drug development outcomes.

1. Systems Biology and Genetics in Drug Discovery for Diabetes Preston Hensley Senior Director Pfizer Biotherapeutics and Bioinnovation Center

2. SCIENCE VOL 322 3 OCTOBER 2008 Despite the tools and technologies of modern medical science, we are still in the Dark Ages of understanding our own biology and discovering agents that can provide cures

3. Human Genetics and Drug Discovery Molecular Targets Clinical Outcome Human Genetics Traditional Discovery David Cox Historically Difficult Now more Feasible

4. Biology in the 21rst Century

5. Link Genetics and Systems Biology

6. Utilize New Academic-Industrial Partnerships Drug development Knowledge and tool implementation IP Knowledge and tool development Public domain Academia Industry

7. Fundamental Industry Problem: High Cost, Low Productivity 56 NME/16 B$ 19 NME/54 B$

8. Source of High Cost

9. Despite Industry Issues - Unmet Need For New Medicine Is Great

10. Estimated Market for New Medicines Is Significant - 2012 Source: Pfizer Annual Review 2007 ~600 B$

11. Source of Low Productivity

12. Reasons for Phase II Attrition Animal Toxicity (Non-hepatic) Human Hepatic Toxicity Human Toxicity (Non-hepatic) Safety Pharmacology Clearance Selectivity Distribution/Exposure Absorption Efficacy/Differentiation Confidence in Mechanism Drug Product Displacement Market Potential 80% Failure For Industry

13. Lesson 1 We don’t understand the fundamental pathobiology Pharma is not set up to do this sort of research Academia is We need to work together

14. Current Safety Optimization And ADME Approaches Bruce Car, BMS

15. Yet Toxicity Remains a Problem Bruce Car, BMS

16. Lesson 2 We don’t understand the fundamental toxicobiology Pharma is not set up to do this sort of research Academia is We need to work together

17. Diabetes – Historic Drug Discovery Failure 50 years of research have produced three new classes of diabetes medicines - > 60% of patients have unmet need Pierre de Meyts

18. Advice from Wall Street Successful companies will invest far more in creating a more holistic understanding of disease pathophysiology and epidemiology before embarking on development programs. PriceWaterhouseCooper – Pharma 2020: The vision - Which path will you take?*

19. The O/D Disease State Continuum

20. Understanding Biology and Safety

21. IRP Discovery Cycle Topology Prediction Validation Chemical Genetics (Shokat) siRNA, Bioinformatics Biological Data Collection Target Predict- ion Validation siRNA, drugs Build ODE Model, Sensitivity Analysis

22. Systems Biology Using Global Unbiased Quantitative Discovery Technologies Other Omics Metabolomics Lipidomics Etc. Other Proteomics Acetyl- Ubiquitinyl- SUMOyl- NOyl-

23. Global Unbiased Phosphoproteomics Forest White, MIT

24. Insulin Signaling Recapitulated Forest White, et al., DIABETES 2006, 55 , 2171-2179 20% of pY phosphoproteomic data recapitulates current pY signaling knowledge What is the remaining 80% reporting?

25. Ernest Fraenkel, MIT ChIP-Seq Genome-Wide Chromatin Immunoprecipitation E2F4 ~1500 Transcription sites

26. Changes in Transcription Profiles Time Courses Bob Garofalo, Pfizer Transcriptional changes

27. Primary Data - Unlinked Many 100’s Many 1000’s

28. Pre-existing Knowledge Time course data phosphorylation tf binding expression profiling Pathway knowledge Other knowledge interactome, etc. metabolome 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0 10 20 30 40 50 Time [min] Amount normalized with 5 min . 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0 10 20 30 40 50 Time [min] Amount normalized with 5 min .

29. Spectrum of Computational Tools To Link Observations

30. Topology Predictions Lauffenburger, MIT Validate with cell-based assays using: RNAi Drugs Chemical genetics Bioinformatics

31. Confirm and Extend Topologies with Chemical Genetics Chemical genetics: Substrate ID Kevan Shokat, UCSF Norman Kennedy, UMass … Kinase 1 Kinase 2 Many substrates

32. Transform Topologies to Mathematical Models – incorporate time course data SEDAGHAT, et al ., Am J Physiol Endocrinol Metab 283: E1084–E1101, 2002 . Rate Constants Model based in ordinary differential equations, ODEs Frank Doyle - UCSB

33. One Approach: Sensitivity Analyses Identify Targets

34. PTEN Non-obvious modulation points - targets Frank Doyle - UCSB Computational Ranking * *

35. Alternative Approach: Screen Against Phenotype (aspirational) Phenotype Input

36. Use Multiplex Assay Tools to Fingerprint Network Dynamics Cell Communication Network Assays could be Phosphoproteomics, etc. Expression profiling Metabolomic markers Etc. Epitome Biosystems

37. Phenotypic Screens Facilitate Network Pharmacology Screen for desired biology Deconvolute hits using pathway tools Develop hit to lead understanding mode of action Avoid target based toxicity

38. Use Assays to Deconvolute Phenotype Network Screening Tools

39. Goal: More Successful Progression To and Through Development Robust targets, potential biomarkers and defined patho- and toxico-biology should speed progression and increase success at later stages

40. Messages As currently configured, small molecule drug discovery has a high failure rate >99% for the industry Failure is due to safety in preclinical and Phase I, as well as efficacy in Phase II Failure in both cases is largely due to a lack of knowledge of fundamental biological processes

41. Messages, cont Elucidating these processes will require new multidisciplinary experimental and computational approaches Success will require new research partnerships These approaches should work resolve both safety and efficacy issues and increase the success in bringing new medicines to the market

42. Thanks to many WEST: P Hensley S Kennedy E Kuja S Hansen S Faraci CVMED: B Garofalo W Soeller J Hadcock D Flynn RTC & Comp Bio: C Cho D Nathan J Miret R Herrera A Seymour W Loging MIT: D Lauffenburger F White E Frankel Entelos: J Trimmer UMASS: R Davis N Kennedy Drug Safety: A Ryan R Chapin Clinical Research: R Calle A Taylor UCSB: F Doyle L Petzold Cal Tech: John Doyle PFE, Other: O Basavapathruni J McNeish J Treadway

43. Backup Slides

44. Integrated Target Generation-Validation Effort

45. Chemical genetics: Upstream Kinase ID Norman Kennedy, UMass … Kinase 1 Kinase 2 Upstream Kinases Kevan Shokat, UCSF

46. Whole body time course modeling

47. Current diabetes medicines Sales Scripts TM Nature Reviews | drug discovery, 2007, 6, 777-778 TM TM TM TM TM TM TM TM TM TM

48. An Industry Wide Problem 1 in 5 80% Failure

49. Diabetes - Medical Need Major epidemic in obesity and diabetes 21 MM people in the US (7% population) 180 MM people WW. Number will double by 2030 Epidemic cost 174 B$ in the US in 2007 95% with diabetes have Type II Diabetes 60% of patients are unresponsive to current therapies

50. Project deliverables Relation- ships Partners Know- ledge Sharing Clinical Correlates Efficacy Bio- markers Safety Tools Comp- utational Tools Data Sharing Exper- imental Tools This Method Robust Targets Disc- overy Cycle

51. Messages Biological processes are complex and robust Insight into the origins of pathology (dysfunctional processes) comes from human genetics To learn how to modulate processes, one must interrogate them as a whole Modulation points may produce drug targets (our hypothesis) Interrogating these processes will require new multidisciplinary experimental and computational approaches

52. Future If validated, this experimental approach should have broad application to discovery efforts involving phosphorylation-mediated signal transduction

53. Next Steps Connect basic biology to target identification in humans Correlate predicted targets with genome-wide association studies Understand disease progression and subtypes Develop diagnostic tools Test predicted targets in patients Use new methods to predict/understand safety issues

54. Reverse Pathway Engineering

55. Multiple phenotyping – range finding

56. Understand pathobiology The IRP Project strategy is to define targets from a sensitivity analysis of insulin binding-promoted signal transduction pathways defined in a global and unbiased way. How will we do this?

57. IRP Consortium Systems Biology Proposal We will use state of the art methods Global time-resolved phosphoproteomics Expression profiling and ChIP-Seq analysis Network development using a spectrum of c omputational modeling methods Validation using chemical genetics, RNAi, drugs Global sensitivity analysis Whole body disease modeling

58. Data Collection in Three Stages

59. Targeted CSR stimuli*time points*measurements*inhibitors*[I]*cell types (n=replicates)

60. Chemical genetics: Substrate tagging Kevan Shokat, et al., NATURE METHODS , 4 2007, 511-16

61. Project Overview White Fraenkel Garofalo Kennedy Responses Comprehensive effort to document changes in both proximal and distal signaling In the same system(s) Using a broad, unbiased analysis To provide a network view of cellular insulin resistance Lauffenburger F Doyle, J Doyle Petzold Entelos Pfizer Systems Biology (Cho) Distal TF Binding Gene Expression Proximal (Phosphoproteome)

62. Developing Diabetes Medicines

63. Reasons for Efficacy Failure Confidence in mechanism Differentiation from internal pipeline Differentiation from external pipeline Distribution and exposure

64. Genome-wide association studies - GWAS

65. Sequence rare variants Rare variant very obese – no diabetes is there a loss of function, LOF, in a gene that results in prevention of diabetes? such genes function as human knockouts how are such genes chosen to sequence? Rare variant very thin – but diabetic is there a loss of function in a gene that results in increased risk of diabetes? how are such genes chosen to sequence?

66. Framework to Build CIS

67. Diabetes is a Multi-organ Disease Initial focus on adipocytes Kahn NATURE | VOL 414 | 13 DECEMBER 2001 |

68. Visceral Adipose Tissue as an Endocrine Organ SHOELSON, et al., GASTROENTEROLOGY 2007;132:2169–2180 From Kershaw and Flier, J Clin Endocrinol Metab, June 2004, 89(6):2548–2556

69. Improved National Productivity - 2023

70. Attrition Contributes to the High Cost of Pharmaceutical Discovery

71. Phenotypic Assays Modified from Sheng Ding and Peter G Schultz, Nature Biotechnology 22, 833 - 840 (2004) Phosphoproteomics