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

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

15. Yet Toxicity Remains a Problem Bruce Car, BMS

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

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

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

29. Spectrum of Computational Tools To Link Observations

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

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

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

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

54. Reverse Pathway Engineering

55. Multiple phenotyping – range finding

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

62. Developing Diabetes Medicines

64. Genome-wide association studies - GWAS

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