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Pharmacogenomics: Changing The Paradigm

Pharmacogenomics: Changing The Paradigm . Aidan Power MD Clinical Pharmacogenomics Pfizer Global Research and Development. Presentation. Why do genetics/pharmacogenomics? Types of studies Uses in drug development Drug Discovery Drug Development Applications of gene expression

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Pharmacogenomics: Changing The Paradigm

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  1. Pharmacogenomics: Changing The Paradigm Aidan Power MD Clinical Pharmacogenomics Pfizer Global Research and Development

  2. Presentation • Why do genetics/pharmacogenomics? • Types of studies • Uses in drug development • Drug Discovery • Drug Development • Applications of gene expression • The future?

  3. The route to a new medicine… Registration Full Development Exploratory Development Discovery

  4. …is a long one Exploratory Development Full Development Discovery Phase IV Phase I Phase II Phase III 0 15 10 5 Years 11-15 Years Marketed Drug Idea Patent life 20 years

  5. …and an expensive one! It costs >$800 million to get a drug to market $ Millions spent in 9 months in 2001 3,332 2,660 2,487 2,281 1,916 1,955 1,740 1,645 1,499 1,402 1,116 934  SGP ABT AHP BMY LLY MRK PHA AZN AVE JNJ GSK

  6. Pharmacogenomics can help! • Creating opportunities to increase the value of the drugs we develop using genetics • Obtain greater understanding of disease • Predict disease severity, onset, progression • Identify genetic subtypes of disease • Aid in discovery of new drug targets • Distinguish subgroups of patients who respond differently to drug treatment • Aid interpretation of clinical study results

  7. We Are Studying Genetic Diseases… Heritability: The proportion of the disease that is due to genetic factors

  8. Complex Phenotypes – What Can We Expect? Gene 1 Gene 1 Gene 2 Gene 5 Gene 2 Environment Gene 3 Gene 4 Environment Few genes and environmental factors each contributing a large risk. Many genes and environmental factors each contributing a small risk.

  9. The study of genome-derived data, including human genetic variation, RNA and protein expression differences, to predict drug response in individual patients or groups of patients. Pharmacogenomics at Pfizer Pharmacogenomics includes Pharmacogenetics

  10. Markers of Genetic Variation • Polymorphism:A genetic variation that is observed at a frequency of >1% in a population • Types of Polymorphisms • Single Nucleotide Polymorphism (SNP): GAATTTAAG GAATTCAAG • Simple Sequence Length Polymorphism (SSLP): NCACACACAN NCACACACACACACAN NCACACACACACAN • Insertion/Deletion: GAAATTCCAAG GAAA[ ]CCAAG

  11. Human Genetic Association Study Design Control Non-responder Disease Responder Allele 1 Allele 2 Marker A: Allele 1 = Allele 2 = Marker A is associated with Phenotype

  12. Whole Genome Associations Disease Population N=500 Matched Control Population N=500 • ~3,000,000 common SNPs across genome • Representing every gene 22 1 Regions of association P value 1 22 Chromosomal Location Informatics to ID gene(s) mapped to associated SNP

  13. Applying Pharmacogenomics Discovery Development DISEASE TARGET SELECTING PHARMACO- GENETICS RESPONDERS GENETICS VARIABILITY Improving Early Decision Making Choosing the Best Targets Better Understanding of Our Targets Predicting Efficacy and Safety .

  14. Target Prioritisation • HDL modulation • A significant market • So many targets • Which is the best? • Locus specific genetic association study • Candidate genes screened for polymorphism • Correlate genotypes with HDL levels • Increase CIR in the target

  15. Cholesteryl Ester Transfer Protein • Spans 22 kb on human chromosome 16 • Several polymorphisms identified • Implicated in modulation of HDL levels • SNPs genotyped in 110 healthy subjects

  16. CETP Association Study (1) -629/promoter VNTR

  17. Clinical Study Population ACCESS data set samples available • 54-week Phase IIIb open label assessment of the safety and efficacy of Atorvastatin • 3916 patients randomised into 5 treatment groups • Subjects with coronary heart disease (CHD) and/or CHD risk factors • 4 pretreatment visits, data on blood pressure, lipids etc including HDL level

  18. CETP Association Study (2) • Genetic variation in CETP • Associated with protective HDL levels • Increasing CIR for target • Additional information obtained • Linkage disequilbruim • Ethinic diversity • Studies in larger populations required

  19. Challenges of Studying Depression • Complex multi-factorial polygenic trait • Genetic heterogeniety • Phenotype is variable & subjective • 30-50% non responders to drug • Placebo response rates are high (50%) • Many trials “fail”

  20. SSRIs • Selective Serotonin Reuptake Inhibitors • Impacted on treatment of depression • Improved tolerability and efficacy BUT • Not all patients benefit • The challenge for new compounds • Increased efficacy • Reduction in adverse events • Differentiation

  21. Target Variation – 5HTT Short/Short Long/Long • Variation in promoter sequence • 44bp insertion/deletion (L and S alleles) SLC6A4 expression Long (528bp) SLC6A4 expression Short (484 bp)

  22. Association With Drug Response?

  23. 5HTT and Sertraline Response • Does genotype influence time to response • Study R-0552 • 8 week, double-blind, placebo-controlled study of sertraline in elderly depressed outpatients with DSM-IV major depression • 66 sites within the US • Anonymized DNA samples collected to test for genotype effect on time-to-response to sertraline • 4-14 day washout period prior to randomization • Age >60 • HAM-D 18 • HAM-D and CGI-I measures of response • Predominantly Caucasian (95% )

  24. Case control evaluation • Responders defined as: • HAM-D •  50% reduction in HAM-D from baseline • CGI-I • Individual with a score of 1 or 2 • Response defined at each time point post-baseline and evaluated for a significant difference in response between the LL and SL/SS groups. • Direct association testing a functional polymorphism for effect on response.

  25. CGI Response by Genotype P=.01 P=.01 • L/L genotypes respond more rapidly to Sertraline

  26. CGI Response by Genotype • Response time to placebo not significant

  27. Clinical Impact of PG Effect • Enhancing study population to increase the probability of earlier response • Enrich LL in POC study to provide maximum probability of successful phase II trial. • POC study exclusively in LL group to make Go/No Go decision on test drug • Smaller trials? • Differentiation over comparator based on response time • Design study with equal representation of alleles across each test arm • Population Stratification • Do S-allele carriers have a distinct disease?

  28. Pharmacogenomics • Human Genetics • SNPs • Haplotypes • Sequencing • Expression Profiling • Specific transcript levels • Total RNA profiling • Proteomics • Specific biochemical markers • Protein profiling • Phenotype • Drug response • Disease Prediction

  29. Cancer: a Model for PG Approaches • Genetics of Cancer • Accumulation of molecular events • LOH • Oncogene activation • Tumor suppressor inactivation • cytogenetic alterations • Phenotype of Cancer • Stages of phenotype • dysplasia/premalignant • differentiation • invasive • metastases • Outcomes • Response Accumulation of molecular events Tumor Phenotype

  30. Genomic Technologies: Somatic Isolate DNA Isolate RNA Fluorescent label Amplify region of interest Oligonucleotide Hybridization Can these approaches provide clues into the state and future of tumor pathogenesis?

  31. Somatic Expression Signals Expression-based signature Genomic profile vs IPI Ash et al. Distinct types of diffuse B-cell lymphoma identified by gene expression profiles. Nature 2000, 403:503-11

  32. Breast Cancer Profiling for Prognosis Working with Agilent to develop microarray based diagnostic A Gene-Expression Signature as a Predictor of Survival in Breast Cancer. van de Vijver etalNEJM 2002 347:1999-2009

  33. Towards Precision Prescribing • Identifying molecular subtypes of disease • Understanding genetic basis of response to treatment • Integrating genetics with other technologies • Transcriptomics, Proteomics, Metabonomics, Imaging, Pop. PK/PD modelling • A combined approach to diagnosis & prescription

  34. 1990s 2000sBeyond Linkage studies Regulatory scrutiny Sequencing Candidate gene association studies ‘omics’ integration Large scale SNP detection Whole genome association studies Pharmacogenetics Personalized sequencing Precision therapies Pharmacogenomic diagnostics What the future holds…

  35. Acknowledgements John Thompson Patrice Milos Maruja Lira Suzin McElroy Albert Seymour Katey Durham Hakan Sakul

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