Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Pharmacogenomics: Changing The Paradigm Aidan Power MD Clinical Pharmacogenomics Pfizer Global Research and Development 1 Presentation Why do genetics/pharmacogenomics? Types of studies Uses in drug development Drug Discovery Drug Development Applications The of gene expression future? Kitasato-Harvard Symposium Oct2003 2 The route to a new medicine… Registration Full Development Exploratory Development Discovery Kitasato-Harvard Symposium Oct2003 3 …is a long one Discovery Exploratory Development Phase I 0 Full Development Phase II Phase III 10 5 Phase IV 15 Years 11-15 Years Marketed Drug Idea Patent life 20 years Kitasato-Harvard Symposium Oct2003 4 …and an expensive one! It costs >$800 million to get a drug to market 3,332 2,487 $ Millions spent in 9 months in 2001 2,660 2,281 1,916 1,955 1,402 934 SGP 1,740 1,499 1,645 1,116 ABT AHP BMY LLY MRK PHA AZN AVE Kitasato-Harvard Symposium Oct2003 JNJ GSK 5 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 Kitasato-Harvard Symposium Oct2003 6 We Are Studying Genetic Diseases… Heritability: The proportion of the disease that is due to genetic factors Huntington's Disease Schizophrenia Genes Environment Rheumatoid Arthritis HDL level 0% 50% 100% Kitasato-Harvard Symposium Oct2003 7 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. Kitasato-Harvard Symposium Oct2003 8 Pharmacogenomics at Pfizer 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 includes Pharmacogenetics Kitasato-Harvard Symposium Oct2003 9 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): Simple Sequence Length Polymorphism (SSLP): Insertion/Deletion: GAATTTAAG GAATTCAAG NCACACACAN NCACACACACACACAN NCACACACACACAN GAAATTCCAAG GAAA[ ]CCAAG Kitasato-Harvard Symposium Oct2003 10 Human Genetic Association Study Design Disease Responder Allele 1 Control Non-responder Allele 2 Marker A: Allele 1 = Allele 2 = Marker A is associated with Phenotype Kitasato-Harvard Symposium Oct2003 11 Whole Genome Associations Disease Population N=500 1 Matched Control Population N=500 ~3,000,000 common SNPs across genome • Representing every gene 22 P value Regions of association 1 Chromosomal Location 22 Informatics to ID gene(s) mapped to associated SNP Kitasato-Harvard Symposium Oct2003 12 Applying Pharmacogenomics Discovery DISEASE GENETICS Choosing the Best Targets . Development TARGET VARIABILITY Better Understanding of Our Targets SELECTING PHARMACORESPONDERS GENETICS Improving Early Decision Making Kitasato-Harvard Symposium Oct2003 Predicting Efficacy and Safety 13 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 Kitasato-Harvard Symposium Oct2003 14 Cholesteryl Ester Transfer Protein VNTR-1946 -629/Prom +279/In1 1 2 Taq1B +16/Ex14 +9/3' +199/In12 +82/Ex15 +383/In8 345 67 8 MspI 9 10 11 12 13 14 15 16 I405V R451Q • Spans 22 kb on human chromosome 16 • Several polymorphisms identified • Implicated in modulation of HDL levels • SNPs genotyped in 110 healthy subjects Kitasato-Harvard Symposium Oct2003 15 CETP Association Study (1) Association of CETP markers and baseline phenotype 0.20 -629/promoter CETP mass HDL 0.15 0.10 0.05 0.0 R-square from ANOVA VNTR 0 5000 10000 15000 20000 Distance in bases from transcription start Kitasato-Harvard Symposium Oct2003 16 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 Kitasato-Harvard Symposium Oct2003 17 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 Kitasato-Harvard Symposium Oct2003 18 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” Kitasato-Harvard Symposium Oct2003 19 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 Kitasato-Harvard Symposium Oct2003 20 Target Variation – 5HTT Variation in promoter sequence 44bp insertion/deletion (L and S alleles) Long SLC6A4 expression (528bp) Short SLC6A4 expression (484 bp) Long/Long Kitasato-Harvard Symposium Oct2003 Short/Short 21 Association With Drug Response? Kitasato-Harvard Symposium Oct2003 22 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% ) Kitasato-Harvard Symposium Oct2003 23 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. Kitasato-Harvard Symposium Oct2003 24 CGI Response by Genotype 100 Sertraline group: Percentage of CGI responders by w eek and 5HTTLPR genotype 60 21 31 24 40 percent 80 SS or SL genotype LL genotype 67 60 26 20 63 30 66 0 64 P=.01 1 P=.01 2 4 6 8 study week • L/L genotypes respond more rapidly to Sertraline Kitasato-Harvard Symposium Oct2003 25 CGI Response by Genotype 100 Placebo group: Percentage of CGI responders by w eek and 5HTTLPR genotype 60 40 percent 80 SS or SL genotype LL genotype 74 81 23 20 78 22 21 81 83 0 23 1 22 2 4 6 8 study week • Response time to placebo not significant Kitasato-Harvard Symposium Oct2003 26 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? Kitasato-Harvard Symposium Oct2003 27 Pharmacogenomics Human Genetics • SNPs • Haplotypes • Sequencing Expression Profiling • Specific transcript levels • Total RNA profiling Phenotype • Drug response Proteomics • Specific biochemical markers • Protein profiling Kitasato-Harvard Symposium Oct2003 Prediction • Disease 28 Cancer: a Model for PG Approaches Genetics of Cancer Accumulation of molecular events Phenotype of Cancer Stages of phenotype – LOH – Oncogene activation – Tumor suppressor inactivation – cytogenetic alterations – – – – – – dysplasia/premalignant differentiation invasive metastases Outcomes Response Accumulation of molecular events Tumor Phenotype Kitasato-Harvard Symposium Oct2003 29 Genomic Technologies: Somatic Isolate RNA Isolate DNA Fluorescent label Amplify region of interest Oligonucleotide Hybridization Can these approaches provide clues into the state and future of tumor pathogenesis? Kitasato-Harvard Symposium Oct2003 30 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 Kitasato-Harvard Symposium Oct2003 31 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 etal NEJM 2002 347:1999-2009 Kitasato-Harvard Symposium Oct2003 32 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 Kitasato-Harvard Symposium Oct2003 33 What the future holds… 1990s 2000s Linkage studies Beyond Regulatory scrutiny Sequencing Candidate gene association studies ‘omics’ integration Large scale SNP detection Whole genome association studies Pharmacogenetics Personalized sequencing Precision therapies Pharmacogenomic diagnostics Kitasato-Harvard Symposium Oct2003 34 Acknowledgements John Thompson Patrice Milos Maruja Lira Suzin McElroy Albert Seymour Katey Durham Hakan Sakul Kitasato-Harvard Symposium Oct2003 35