Biomarker Based PGx Strategies

1. Biomarker Based PGx Strategies Rick Hockett, MD Chief Medical Officer Affymetrix

2. Why Are Biomarkers So Important? “Providing meaningful improved health outcomes for patients by delivering the right drug at the right dose at the right time.” Goal:Improve individual patient outcomes and health outcome predictability through tailoring drug, dose, timing of treatment, and relevant information Targeted Therapy One size fits all Tailoring (e.g. oncology productscomprising drug and companion diagnostic) assess spectrum of patient response to therapy; stratify patient populations; optimize benefit/risk. Measure something in a patient to learn how to prescribe medicine Tailoring is Broader Than Pharmacogenomics

3. Increased Benefit:Risk Scenarios “Providing meaningful improved health outcomes for patients by improving diagnosis, prognosis, or therapy choice.” Diagnosing patients with particular traits • Identifying “Patient” • Diagnosis • Prognosis • Therapy Identifying responders for targeted therapies (essentially highly tailored therapies) Identifying who have an alternate prognosis (perhaps needing additional therapy) Optimize dosing regimen for patient subpopulation(s) to achieve optimal benefit/risk • Tailoring “Dose” Identify time to intervene during disease progression, time to complete therapy, or time to alter treatment regimen • Tailoring “Time” Accommodate info for patient diversity, questions specific to payors or providers, or provide tools to meet needs of customers • Tailoring “Information/Tools” Can apply one or more scenarios to each Lilly compound. Scenarios can often be interdependent.

4. Why do we think genetics will play?

5. DNA ----ACGTGGGCAGTAGACTCAT---- ----TGCACCCGTCATCTGAGTA---- RNA Protein ----ACGUGGGCAGUAGACUCAU---- ----Thr Trp Ala Val Asp Ser ---- Pinpoint the ‘right’ biomarker Large Scale Fishing DNA – 100K to 2x106 SNPs Chip Based Electrophoresis RNA – 30K+ Chip Based -oligos Slide Based - cDNAs Protein – 1K upward Mass Spec Med. Scale Confirm. DNA–2K to 30K Chip Based Electrophoresis PCR Based RNA– 30 to 1K Chip Based -oligos Slide Based – cDNAs RT-PCR Based Protein– 50 to 500 Mass Spec Luminex Type Small Scale Valid. DNA– 1 to 25 PCR Based Electrophoresis RNA – 1 to 25 RT-PCR Based Protein – 1 to 30 ImmunoAssay Large Scale ‘Fishing’ Whole Genome Scan Medium Scale ‘Confirmation’ Many Different Groups Small Scale ‘Validated’ Clinical Trial Support

6. Interesting Genetic Associations Genetic Polymorph With Good Sens. & Spec Genetic Polymorph With Rel. Risk Y Response Y Response N N Genetic Variant Freq N Y N Y Variation Variation Prospective Clinical Proof Shrinking Clinical PGx Funnel Needs Examples Disease vs. Response Predictive ≥ 3 Apo E, CETP 5 - LO vs. Variants in Growth Genes Situation Specific Onc vs. Neuroscience DMET OncotypeDx Clinically Utilized PGx Tests UGT1A1 Hercept Test CYP2C9/VKORC1 c-kit Tissue of Origin HLA EGFr TPMT Philly Chromosome

7. X ?? Hurdles to applying -omics to medicine • Strategic • Gearing the infrastructure • Obtaining the talent • Technologic • Information overload • Lack of biologic understanding • Platform challenges • Regulatory • Understand how to apply technology • Implementation • Clinician education, understanding, and acceptance

8. DMET PlusDrugMetabolismEnzymes&TransportersAn Example of Applying New Technologies to the Clinical Marketplace

9. The Genesis of DMET: • March of 2004: Collaboration initiated between Lilly, ParAllele, and Affymetrix • The goal for Eli Lilly was to develop a clinical solution for better understanding the genetic components behind metabolism and transport: • Better ability to understand PK outliers in early phase trials • Build a database for selective recruitment of healthy volunteers with a defined genotype • Work with the FDA in an attempt to decrease the number of biopharm (DDI) trials needed for future NDAs • June 2006 was the inception of a working assay for clinical trials • Dec 2007 first NDA was submitted the FDA

10. Pinpoint the ‘right’ biomarker Using existing Affymetrix technology Large Scale Fishing DNA – 100K to 2x106 SNPs Chip Based Electrophoresis RNA – 30K+ Chip Based -oligos Slide Based - cDNAs Protein – 1K upward Mass Spec Med. Scale Confirm. DNA–2K to 30K MIP Based True Materials RNA – 30 to 1K Panomics Expression Protein – 50 to 500 Mass Spec Luminex Type Small Scale Valid. DNA– 1 to 25 MIP Based True Materials RNA – 1 to 25 Panomics Expression Protein – 1 to 30 ImmunoAssay Large Scale ‘Fishing’ Whole Genome Scan Medium Scale ‘Confirmation’ Many Different Groups Small Scale ‘Validated’ Clinical Trial Support

11. Setting the stage for adoption of genetic analysis tools for use in personalized medicine Risks associated with taking popular heart disease medication Plavix (Clopidogrel) • Paper published in New England Journal of Medicine • Conclusion: Patients taking Clopidogrel and who were carriers of a certain gene variation had higher rates of heart attack, death and other cardiac-related events • Two additional independent studies recently published in NEJM and Lancet show similar PGx associations.

12. No Relationship between Genetics and PK/PD for Prasugrel, Significant Effect for Clopidogrel Pharmacokinetics Pharmacodynamics Integrated Genetic Analyses in Healthy Subjects

13. PGx associated clinical outcomes of 1459 acute coronary syndrome patients treated with clopidogrel were significant • 1477 Patients were randomly assigned Plavix treatment with 98.8% being genotyped • CYP2C19 variant allele (1) frequency in treated population was 27.1%. • Primary efficacy outcome: composite of death from cardiovascular causes, MI and stroke. • 395 variant carrier patients had a 1.5 fold higher risk of death vs non-carriers. • 1389 Rx patients had stents implanted with a secondary endpoint of stent thrombosis. • 375 2C19 variant patients had a 3 fold increase in risk of thrombosis. • Two additional independent studies recently published in NEJM and Lancet show similar PGx associations.

14. Literature Cell Lines Tissues DNA marker RNA expression level Protein Pharmacogenomics in drug development Development of a biomarker Biomarker: A physiological response or laboratory test that occurs in association with a pathological process and that has putative diagnostic and/or prognostic utility DNA or RNA Samples A. B. Identification of potential biomarker or drug target C. D. Retrospective confirmation on clinical samples Use of marker in prospective clinical trials Patient Stratification Plasma or Serum Samples Patient Samples are the Key

15. What we Must Do to Enable -omics Impact • Align focus on what can be done and where genetics is likely to work • Analyze, Integrate and Learn from data • Enable the field of Molecular Epidemiology • Enhance our biologic understanding of genetic influence of complex traits and produce more examples • Develop and validate technologies for clinical use • IT Infrastructure • Standards & Controls • Educate the medical infrastructure • Engage patients and third party payers

16. Academics Labs FDA Industry Diagnostic Companies How Do We Enable -omics Uptake? • We cannot maintain silos • We must enable certain, common functions • Sample banking • Clinical trials • We must look to the regulators for direction • Standards • Controls • Critical Path Initiative

17. The Biotech/Genomics Revolution: Increase the Benefit:Risk Ratio Develop clinical aids for: Diagnosis Prognosis Dosing Therapy Decisions