1. KitasatoHarvard Symposium October 2001 APower 1 The Future of Genetics in Clinical Medicine Aidan Power Clinical Pharmacogenetics Pfizer Global Research and Development Sandwich, United Kingdom

2. KitasatoHarvard Symposium October 2001 APower 2 Visions of the Future  The humn genome undobtedly offer unprecedented opportunites. All the drugs in the world act on only 479 known molecular targets. If only 10% of the genome represents targets this will produce the possibility of 3000 new molecular entities.  Pharmacogenetics aims at understanding thow genetic variation contributes to variation in response to medicines.  Over 1000 single gene disorders identified affecting 1-4% of the population.  Genetic factors important but environmental, behavioral factors a big influence

3. KitasatoHarvard Symposium October 2001 APower 3 On the other hand… On common diseases ‘It will be difficult, if not impossible, to find the genes involved or develop useful and reliable predictive tests for them.’ And pharmacogenetics ‘Sure, there are few cases where testing patients for certain enzymes involved in drug metabolism may help but it’s ridiculous to suggest that drug senstivity and resistance are wholly determined by inheritied genetic profiles.’ Neil Holtzman, Johns Hopkins University ‘It has been said that the four letters of the genetic code are H, Y, P, and E, and medical providers must realize that the molecular biology business is as adept at promoting its wares as is any other.’ Steve Jones, University College London

4. KitasatoHarvard Symposium October 2001 APower 4 And the reality will depend on...  Understanding of the molecular basis of disease  The discovery and development of drugs  The delivery of medicines to patients THE PROGRESS OF SCIENCE AND TECHNOLOGY AND THEIR ABILITY TO IMPACT ON:

5. KitasatoHarvard Symposium October 2001 APower 5 Pharmacogenetics Genetic causes of interpatient variability in phenotype Relationship between patient phenotype and genotype Phenotype:Clinical symptoms Pharmacokinetic variability Response to drug -efficacy -side effects Genotype:A genetic marker(s) distinguishing specific variations within a DNA sequence Non-respondersResponders

6. KitasatoHarvard Symposium October 2001 APower 6 Pharmaceutical Needs: Discovering New Medicines Discover clinically-relevant “drugable” targets Enhance decision making in R & D Increase candidate survival in Phases I - III Identify and use relevant “markers” in R & D that:  parallel or predict disease progression  parallel or predict disease severity  relate to accepted outcomes measures  accelerate drug development and approval  increase drug survivability in Phase IV/post-marketing

7. KitasatoHarvard Symposium October 2001 APower 7 Identification of novel discovery targets ä Expedite Identification of ~5,000 “clinically relevant” targets Adding human relevance to targets ä Improve potential of unprecedented targets Improved clinical trial design and interpretation ä Genetic stratification of patients Pharmacogenomics

8. KitasatoHarvard Symposium October 2001 APower 8 Pharmacogenomics SNPs - markers of genetic variation. Relationships - phenotype-genotype. Phenotype: Disease state Pharmacokinetic variability response to R x Genotype: A specific variation in a DNA sequence from a “consensus” sequence Understand - impact of genetics on R x response outcome. Targets - clinically important “drugable” targets  drug candidates

9. KitasatoHarvard Symposium October 2001 APower 9 Tools for Pharmacogenomics  Access to variation in genes - SNPs  Genotyping tools  Access to phenotypic data  Analysis methodologies

10. KitasatoHarvard Symposium October 2001 APower 10 Candidate Gene Approach Genome-Wide Map Approach Candidate Gene Association Studies: 5 SNP markers/gene (~500,000 markers) Genome-Wide Association Studies Using Linkage Disequilibrium: ~60,000 SNP markers at ~50 kb, or ~300,000 markers at ~10 kb intervals SNP Identification/Mapping/Use

11. KitasatoHarvard Symposium October 2001 APower 11 Candidate Gene vs. Whole Genome  Candidate Gene Approach  Hypothesis dependent  Drug target or genes in the target pathway  Drug metabolizing enzyme genes  Genes that play a role in the disease  Limited by our understanding of disease  Whole Genome SNP Map  Hypothesis-independent  New statistical methods needed to mine data

12. KitasatoHarvard Symposium October 2001 APower 12 Strategy in Pharmacogenomics 1. Collect Patient DNA from Clinical Trials 2. Identify Genetic Variation 3. Correlate Genetic Variation with Clinical Response 4. Predict Patient Response to R x Based on Genetic Variation

13. KitasatoHarvard Symposium October 2001 APower 13 Collecting DNA: General Approaches Aims:  collect samples from relevant clinical trials  obtain widest possible remit for use  avoid retrospective collection - incomplete, inefficient  primary purpose PG (2° purpose disease analysis) Principles:  obtain IEC/regulatory approval  obtain specific informed consent  participation in clinical trial not dependent upon donation of sample for hypothesis generation

14. KitasatoHarvard Symposium October 2001 APower 14 Informed Consent Utilizes a separate consent for donation of a blood sample which will be anonymized prior to analysis. Participation is optional. Consent to “use a small sample of my blood to study the chemicals which make up all of my genes and contain my genetic information.” Purpose for collecting sample is defined. Clearly states that information identifying the subject will not be included with the blood sample. NO INFORMATION WILL BE MADE AVAILABLE TO SUBJECT OR ANY OTHER PARTICIPANT OR MY PHYSICIANS.

15. KitasatoHarvard Symposium October 2001 APower 15 Categories for Genetic Research Samples and Data*  Identified Samples/Data are those labeled with personal identifiers such as Name or Social Security Number. Use of a clinical trial subject number does not make the sample/data identified.  Coded Samples/Data are those labeled with a clinical trial subject number that can be traced or linked back to the subject only by the investigator. Samples do not carry any personal identifiers.  De-Identified Samples/Data are double coded and labeled with the unique second number. The link between the clinical study subject number and the unique second number is maintained, but unknown to investigators and patients. Samples do not carry any personal identifiers.  Anonymized Samples/Data are double coded and labeled with the unique second number. The link between the clinical study subject number and the unique second number is deleted. Samples do not carry any personal identifiers.  Anonymous Samples/Data are those that do not have any personal identifiers and identification of the subject is unknown. Anonymous samples may have population information (e.g., the samples may come from patients with diabetes, but no additional individual clinical data).  ____________________________________________________________________________ *From the Pharmacogenetics Working Group Working Paper 1

16. KitasatoHarvard Symposium October 2001 APower 16 Anonymization Process Central Lab Study Data De-Identified Study Data 1 2a2a 2b2b 3 Phenotype Processing Sample Processing

17. KitasatoHarvard Symposium October 2001 APower 17 Clinical Trial Ends Genotype Get DNA and Drug Response Phenotypes Establish Genotyping Assays Statistical Analysis Scientific Approaches Develop Hypotheses: Candidate Gene vs. SNP Map Prospective Clinical Trial Time

18. KitasatoHarvard Symposium October 2001 APower 18 Table 1. Examples of Poor/Non Responders Following Therapy* DiseaseDrug ClassPoor/Non Responders(%) Cancer (breast, lung, brain)Various70 – 100 DiabetesSulfonylureas25 – 50 AsthmaBeta-2 agonist40 – 75 OA/RANSAID, COX-220 – 50 Duodenal UlcerProton pump20 – 90 HypertensionThiazides50 – 75 Beta-blockers20 – 30 ACE inhibitors10 – 30 Angiotensin IIs10 – 30 HyperlipidemiaHMGCoA reductase inhibitors30 – 75 DepressionSRRIs20 – 40 Tricyclics25 – 50 MigraineSerotonin25 – 50 BPHSteroid 5  -reductase40 – 100 *from BM Silber, Pharmacogenomics, Biomarkers, and the Promise of Personalized Medicine, in Pharmacogenomics, W. Kalow and U. Meyer, editors, Marcel Dekker publishers, New York, 2000, in press. DiseaseDrug ClassPoor/Non Responders(%) Cancer (breast, lung, brain)Various70 – 100 DiabetesSulfonylureas25 – 50 AsthmaBeta-2 agonist40 – 75 OA/RANSAID, COX-220 – 50 Duodenal UlcerProton pump20 – 90 HypertensionThiazides50 – 75 Beta-blockers20 – 30 ACE inhibitors10 – 30 Angiotensin IIs10 – 30 HyperlipidemiaHMGCoA reductase inhibitors30 – 75 DepressionSRRIs20 – 40 Tricyclics25 – 50 MigraineSerotonin25 – 50 BPHSteroid 5  -reductase40 – 100 *from BM Silber, Pharmacogenomics, Biomarkers, and the Promise of Personalized Medicine, in Pharmacogenomics, W. Kalow and U. Meyer, editors, Marcel Dekker publishers, New York, 2000, in press.

19. KitasatoHarvard Symposium October 2001 APower 19 Generating Hypotheses: DMEs or Drug Transporter Mechanisms Are there genetic differences in key drug metabolism pathways? Do transporter protein genotypes influence bioavailability? Are levels of active metabolites influenced by genetic variation? Do allele frequencies vary among ethnic groups?

20. KitasatoHarvard Symposium October 2001 APower 20 Generating Hypotheses: Disease Genes Are there known genetically-defined patient subpopulations with more uniform disease characteristics? Are there known genetic markers for populations at-risk for the disease? Are there known genetic predictors of clinical outcomes? Are there known genetic differences among ethnic groups?

21. KitasatoHarvard Symposium October 2001 APower 21 Generating Hypotheses: Drug Target or Related Pathways Which genotypes used in Discovery’s screens/assays? Are they found in the disease population? What are the functional consequences of different genotypes? Does drug binding/activity differ among variants? Any genetic differences in related pathways influencing drug activity (e.g., ligand turnover; upstream/ downstream signaling)? Do any inherited diseases result from mutations in drug’s target? Do allele frequencies vary among ethnic groups?

22. KitasatoHarvard Symposium October 2001 APower 22 Pharmacogenetics: Getting the right drug to the right patient  Sources of variability in drug response:  diagnosis of disease  disease severity  compliance with pharmacotherapy  genetic profile: disease, drug metabolism, drug target

23. KitasatoHarvard Symposium October 2001 APower 23 Sources of genetic variation and drug response  Disease pathways  Drug metabolism  Drug target

24. KitasatoHarvard Symposium October 2001 APower 24 Disease pathway genes and treatment

25. KitasatoHarvard Symposium October 2001 APower 25 Disease pathway genes and pharmacogenetics: adducin  Gly460Trp variant of  -adducin associated with increased renal tubular absorption of sodium  also associated with  renin activity  Positive and negative association studies in hypertensives  Frequency in hypertensives:  ~ 20% in Europeans, ~ 65% in Japanese  In response to diuretics, the average BP drop is twice as great in Trp heterozygotes Cusi et al (1997); Manunta et al (1998)

26. KitasatoHarvard Symposium October 2001 APower 26 Drug response and DME variation Adapted from Evans and Relling (1999)

27. KitasatoHarvard Symposium October 2001 APower 27 Target genes and drug reponse Adapted from Evans and Relling (1999)

28. KitasatoHarvard Symposium October 2001 APower 28 Pharmacogenetics and ethnicity Adapted from Evans and Relling (1999)

29. KitasatoHarvard Symposium October 2001 APower 29 Ethnicity and genetic variation drug response  Individual drug response vs ethnicity  For DMEs the key difference is a genetic one  Similarly for other genes  Clinical trials can take account of key genetic variation  Where correlation between genetic variation and drug response is close this can give greater understanding of ethnic differences

30. KitasatoHarvard Symposium October 2001 APower 30 Potential Benefits for Pharmacogenomic Data  Portfolio Management in Early Development  confirm molecular mechanism of action  increase clinical confidence in rationale  evidence of pharmacodynamic response  rational dose selection  path to proof of concept  cost saving by identifying non-viability early  requirement to show effect on disease progression  identification of novel indications  Feedback to Discovery  target validation  ID new target pathways

31. KitasatoHarvard Symposium October 2001 APower 31 Hurdles/Challenges to the Implementation of Pharmacogenetics  Predictive power of genetic testing in relation to drug response  Cost, availability, utility of diagnostics  Societal responses  public attitudes  regulatory/legal frameworks

32. KitasatoHarvard Symposium October 2001 APower 32 How Genomics and Proteomics May Change Medicine and Therapeutics in the Next 20 Years 200020102020 Approval of 1 st NCE linked to genetically-based Point-of-Care (POC) Diagnostic (D x ) Robust and Economical HT Genotyping Platforms (1 MM/day) Robust HT Haplotyping Tools Sequencing of Human Genome Complete 10% of NCEs have genetic “POC” D x 40,000 Gene Structures/Proteins Known; all SNPs in Genes Identified Genes/Proteins Involved in Top 20 Common Diseases Defined HT Gene Function Technology Widespread Medical Screening with SNP Chips High-Risk SNPs IDed; Prophylactic R x Approved Pharmas Working on 3,000 Targets 30% NCEs have Genetic “POC” D x -

33. KitasatoHarvard Symposium October 2001 APower 33 New Millennium: Personalized Medicines Disease Susceptibility Genes/Targets Right R x and Dose at the Right Time Genomic, Genetic, Haplotype, Links Gene Links to Efficacy/SAEs Biomarkers Linked to Disease

34. KitasatoHarvard Symposium October 2001 APower 34 Back up slides

35. KitasatoHarvard Symposium October 2001 APower 35 ‘As genome technology moves from the laboratory to the health care setting, new methods will make it possible to read the instructions contained in an individual person's DNA. Such knowledge may foretell future disease and alert patients and their health care providers to undertake better preventive strategies.’ Francis Collins, NIH Visions of the Future... ‘Preventive medicine is an economic necessity, and genomic medicine represents the best route we have to preventive medicine…pharmacogenomics will become part of routine therapeutics in some fields within 3-5 years.’ Gordon Duff, University of Sheffield ‘We are on the verge of being able to identify inherited differences between individuals which can predict each patient’s response to a medicine. This ability will have far- reaching benefits in the discovery, development and delivery of new medicines.’ Allen Roses, GlaxoSmithKline

36. KitasatoHarvard Symposium October 2001 APower 36 Genetics and Identification of Novel Genes Families with disease Genetic research to identify region in the genome that contains disease causing gene Chromosomes Normal Affected Disease Gene Normal Gene DiseaseControls Populations of disease sufferers and healthy controls New gene leads to: Novel Drug Target Genetic marker of drug response variation Increase in the understanding of disease biology ATT-GCG-ACG ATT-GGG-ACG

37. KitasatoHarvard Symposium October 2001 APower 37 The Research Environment Gene Function Screening/ Chemistry Drug Candidate Protein Target Efficacy Approved Drug Discovery:Development: Identify clinically relevant targetsConfirm drug candidate’s mechanism of action Confirm target relevance in chronic disease Confirm drug response Discovery and Development of Medicines

38. KitasatoHarvard Symposium October 2001 APower 38 Human Genetics SNPs Haplotypes Sequencing Expression Profiling Specific transcript levels Total RNA profiling Proteomics Specific biochemical markers Protein profiling Phenotype Drug response Disease Prediction Pharmacogenomics