Boston Biotech Clinical Research (BBCR) Presentations at the BIO International Convention.

1. Rare Diseases Experience as a Model to Critically Affect Innovation in Biomarker Strategy and Precision Medicine Moderator Candida Fratazzi MD Speakers Claudio Carini, PhD, FRCPath GioraFeuerstein MD, MSc. F.A.H.A. Mark TrusheimPhD Colin Williams PhD

2. Precision Medicine The Time is now Claudio Carini, MD, PhD, FRCPathPfizer Inc.

3. Drug Development is a lengthy, high- attrition process More Spending – Less Apparent Productivity and Innovation?

5. What is missing? Biomarkers

6. Biomarker Definition A molecule that indicates an alteration of the physiological state of an individual in relationship to health or disease state, drug treatment, toxins etc Biomarkers are by virtue of their short term availability predictors of long term events

7. Why Biomarkers are Important in Medicine? Staging or Severityof Disease Patient/Subject Selection Safety/Prediction of AE Prognosis of TX intervention Patient/Subject Selection Discriminate Health from Disease Stage Monitoring ClinicalResponse to Therapy

8. The Elephant in the Room Putting it all together Understanding A multi - ” omics ” Qualified Biology Strategy Biomarkers Genechip Target n It ’ s It ’ s UC UC Efficacy n RT - PCR PK/PD n It ’ s It ’ s IHC RA RA Safety/ /Tox It ’ s It ’ s n It ’ s It ’ s SLE SLE AS AS Flow Mechanism n cytometry Pharmacology n Molecular imaging Disease progression n It ’ s It ’ s It ’ s It ’ s Classification Protein n JIA JIA analysis CD CD Precision Medicine n Mass Understanding Spectrometry Drug PK/PD Proteomics profiling

9. Biomarkers: Potential Guides to Effectiveness and Safety The -omics Clinical New study paradigms* Experimental human biology Imaging An Integrated Approach Proteomics Pharmacogenetics Metabonomics

10. Building Bridges Between Research and Clinical Development Exchange of Information Biomarkers - PK - PG - Experimental CP

12. What Patients Expect Today and More So in The Future? BIOMARKERS Drugs that work Drugs that are safe Doses that are right for me 12

13. A fit-for-Purpose Biomarker Qualified Biomarker Clinical endpoints Biology A qualified biomarker must link a biomarker with biology and clinical end-points 13

14. Why Do we Need Biomarkers? To treat diseases more effectively: Disease Biomarkers Disease BM will enable the: 1. Differentiation/stratification of otherwise similar disease states 2. Better identifies which disease states are more responsible to the study drug 3. Evaluation of disease susceptibility 4. Treat high risk pts before the onset of symptoms 5. Tracking disease progression To predict clinical efficacy: Patient Selection BM Patient BM will provide: 1. Explain why Pts are responding differently to different drugs 2. Basis for differentiating “high responders” from “low responders” 3. To target “ high responders” who stand better chances of success

15. What is Personalized Medicine? 15

16. Major Drugs Ineffective for Many… Beneficial to Some 16

17. Harmful to Others 17

19. Why do individuals require different doses?

20. Why do some experience AE after taking the drug?

21. Genetic variation of the drug target gene

22. Genetic variation in the biochemical pathways affected by the drug

23. Genetic and genomic factors related to the etiology of the disorder

24. Other factors (age, sex, diet, environment ...)Pharmacogeneticslooks at inherited factors that may influence these differences.

25. Responders Non-Responders Adverse Drug Events Current Treatments Take Little Account of Human Variation

26. Personalized Medicine Foresees Greater Use of Diagnostics in Therapeutic Decision Making Responders Non-Responders Adverse Drug Events Choose the RIGHT DRUGat the RIGHT DOSEfor the RIGHT PERSON A B Dx Test C

28. 2.2 million people are hospitalized and 100,000 deaths occur each year due to adverse effects of prescription drugs

29. To better treat disease:

30. Development of predictive markers would allow for earlier treatment

31. To identify novel drug targets:

33. Six Blinded Scientists Examining an Elephant Translational Medicine: the lacking piece of the puzzle 23

34. 24 Thank You Questions?

35. Gene discovery and genomic prediction of Atherosclerosis disease states and susceptibility

36. 26

37. Biomarkers In Drug Discovery and Development: Orphan Diseases and Orphan Therapeutics Giora Feuerstein MD FARMACON LLC Washington DC June 28, 2011

38. …and its getting worse Translational Medicine in Pharmaceutical Industry: from “nice to have” to “do or die”

40. From “Bench to Bed and Bed to Bench” (BB2)

41. Identification, validation and implementation of Biomarkersin lieu of clinical end-points

43. Biomarkers that define the direct interaction of the compound with its discrete target

44. Biomarkers that define consequencesof compound interaction with the target relative to PK

47. Rare genetic disorder provides biomarkers that ‘pave the way’ for target validation

48. The case of anti-IL-1b development for RA

49. PK/PD- Biomarkers

50. Bayesian methodology

51. Target validation in M-W syndrome

53. Diabetes drugs are available but unmet medical need high

54. Insulin injection is the ultimate treatment in chronic diabetes

55. Insulin sensitivity drugs (TZDs) of limited efficacy and carry safety issues

56. 11beta-HSD-1 inhibitors have the potential to improve insulin sensitivity

59. Tissue cortisol production is significant

60. In non-diabetics:

61. Liver: Major source (~90%) of splanchniccortisol release into the circulation

62. SubQ fat: Account for ~10% of cortisol release

64. What We Mean by Stratified Medicine Matching therapies to patient sub-populations with clinical biomarkers Objective: Do more good (efficacy) or avoid ill (adverse reactions) Clinical Biomarkers -- beyond genotyping Molecular (gene expression, proteomic, biochemical) Imaging Clinical observation Patient self-reporting Clinical Biomarkers: Any information which shows a reliable, predictive correlation to differential patient responses

65. The Patient Therapeutic Continuum: Stratified Medicines are not “Personalized” Nature Reviews Drug Discovery: April 2007

66. Orphan Drugs Demonstrate Economic Potential Stratified Medicines Increasingly Approaching Orphan sizes (thousands of patients, average yearly price in $thousands)

67. Comparing Orphan and Stratified Medicines Orphan Stratified Known mechanism & marker Small population Strong patient and provider networks Modest payer impact Known mechanism & marker May be small or large population Perhaps unrecognized strata and no networks Modest payer impact for one, but large if entire field (like oncology) stratifies

68. When are Orphan Drugs Good Models for Stratified Economics? Stratification creates a small population Strong patient advocacy exists Clinical trial and regulatory models for small populationsBUT Market exclusivity and lower competition may not apply Payer concern that a large stratified condition is not ‘rare’10% of all Alzheimer’s patients is a lot of patients, and cost.

69. Orphans Modeling Stratified Medicines: Expect Price and Profitability Premiums? Supporting Arguments Stratified medicines will perform substantially better for their target populations than alternative treatments (assumption) Recently introduced therapies have commanded price premiums: biologics, stratified medicines, adjuvant therapies Payers have formal or informal policies to “pay for performance” Counter Arguments Limited payer ability to afford increased costs Diagnostics will siphon profitability Multiple entrants in new “stratified” drug classes will lower prices Analytical Task Develop a Performance Differential/Price Premium curve by examining price premiums obtained in the market today by “classic” therapies LIKE ORPHAN DRUGS Price Premium Performance Differential

70. Orphans Modeling Stratified Medicines: Development Processes Opportunities Strong patient and provider networks to enable clinical trials Novel clinical trial designs to accommodate few patients available can speed development and lower costs Potentially more rapid entry into man based on strong mechanism understanding and high need Challenges Need to develop and validate biomarker lower since embedded in diagnosis Regulatory skepticism that stratification is tactic to avoid ‘gold standard’ clinical trials Clinical Trial Size Biomarker Driven Performance Differential Potential: Lower Cost and Higher Success Probability of Regulatory Approval

71. Orphans Modeling Stratified Medicines: Public Policy and Incentives? Federal Research and Development Support NIH grants for research, and even development (Bench to Bedside Awards) Expedited regulatory pathways Federal Financial Incentives Market exclusivity grants to INDICATION High value reimbursement R&D support above Registries to identify, monitor and involve patients and samples Role for Disease Foundations Awareness, network creation and dissemination Direct research support Expert science panels validates early, small company science

72. Orphan Learnings in Stratified Medicine Examples Tysabri re-introduction for Multiple Sclerosis enabled by patient advocacy, patient registries and now, a biomarker Rare oncology sub-populations receiving Orphan level reimbursement Provenge autologous stem cell therapy: $93,000 for 3 course regimen Erbitux and Vectibis: Up to $80,000 for 18 week regimen Revlimid: Up to $10,000 per month for multiple myeloma Gleevec: Up to $54,000 per year for CML High market shares (>80%) are possible

73. Conclusions Rare diseases and orphan drugs have blazed the trail for stratified medicine economic models From clinical development through regulatory to reimbursement and public policy, the lessons of rare diseases are being translated to stratified medicine However, the aggregate size of some stratified medicine markets may strain payers and induce skepticism by regulators that special treatment is appropriate An ‘integrated stakeholder chain” from research foundations to companies, regulators, payers and advocates is critical

74. Information in Biomarker discovery How effective use of information resources can support innovation Dr. Colin Williams Thomson Reuters

77. Decreases risk

78. Enhances project level decision making

79. Focuses resource allocation

80. Empowers scientists to drive innovation

82. Wide range of sources - Literature, Patents, Conferences and medical meetings, Press releases, Clinical trial reports

83. Google 425k hits – Her2 and biomarker

84. Highly variable terminology in Biology

85. Lack of standard or Universal criteria on Biomarker development

86. Variation in protocols, statistical analysis

87. So many parameters not enough subjects

89. the inner surface of the chest wall where it is known as the pleura

90. the abdomen, where it is known as the peritoneum.

91. Organs within those cavities eg lung, heart

94. Compared to 1104 drugs under development for Obesity

96. 3,400 hits in Google search

99. Cisplatin

100. Gemcitabine hydrochloride

101. Pemetrexed disodium

102. Troglitazone

103. PPAT agonist inhibitors

104. LenvatinibMesylate

105. BEZ-235 (Novartis Phase I/II breast cancer, mTor inhibitor)

106. Celecoxib

107. AZD-1152-HQPA

110. The Core cell cycle, Source MetaCore

112. Analyzing gene expression data from mesothelioma can give a glance in to the mechanism of the disease

113. Analyse publically available dataset from Geo (GDS2604)

117. Improve efficiency in project design through researching the area

118. Biomarker information is:

119. published in a non standard way

120. is fragmented way

121. is expanding rapidly

122. Disease segmentation based on molecular characteristics is going to create many new orphan diseases.

123. Will the ophan disease ‘model’ become the life blood of pharmaceutical research

124. There are many biomarkers available which can prove efficacy of a compound.

125. Using Mesothlioma (or other orphan diseases) as a model can prove efficacy against a target quickly.

126. Understanding the biological function of that target can open new indications for a therapeutic