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Pharmacogenomics Data Management and Application In Drug Development Chuanbo Xu Senior Director, Bioinformatics San Antonio, TX. 13 January 2003 HL7/CDISC.

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Presentation on theme: "Pharmacogenomics Data Management and Application In Drug Development Chuanbo Xu Senior Director, Bioinformatics San Antonio, TX. 13 January 2003 HL7/CDISC."— Presentation transcript:

1 Pharmacogenomics Data Management and Application In Drug Development Chuanbo Xu Senior Director, Bioinformatics San Antonio, TX. 13 January 2003 HL7/CDISC Work Group Conference - 2003

2 Drug Development Future Targeted Discovery, Predictive Medicine

3 Beyond Pharmacodynamics and Pharmacokinetics Regulatory Target Metabolism Secondary Interaction Tertiary Interaction T M X Y

4 Introducing Pharmacogenetic/Pharmacogenomics Regulatory T M X Y T M X Y Target Metabolism Secondary Interaction Tertiary Interaction

5 Drivers for Personalized Medicine … We believe that the central issue is not whether PGt- or PGx-guided Drug prescriptions will happen, but when and how.

6 What Is PGt/PGx? Pharmacogenetics (PGt) studies the genetics basis of therapeutics and the individual reactions resulted from genotypes; originally, it studies the effect exerted on drug ADMET (absorption, distribution, metabolism, excretion, & toxicity) process by the human cytochrome family proteins. Pharmacogenomics (PGx) is the extension and enhancement of the PGt studies in the molecular sequence context of the individual genetic structures of the whole genome.

7 What Constitutes PGx Data? Key Components: 1. Gene, genomic structure (primary sequence and higher level organization) of the genes, subject DNA, protein, variation (SNP, INDELs, Haplotpyes, etc.), genotypes, gene expression profiling 2. Therapeutics (compound, vaccine, antibody, siRNA, etc.), PK/PD profiling 3. Subject demographics (age, gender, ethnicity, etc.), clinical measurements, phenotype, outcomes, statistical association analysis

8 Conservation vs. Variation 99.9% similar between individuals.1% differences has functional consequences

9 Exons Promoters SNPs Chromosome locus of gene Gene SNPs 0101 0101 0101 0101 0101 Haplotypes 0 1 0 0 1 1 0 1 1 0 Causative Site Haplotypes are a code for defining and tracking the isoforms of a gene Gene Haplotypes

10 96-well microtiter plate 6 Caucasians (4 grandparents) 5 African-Americans (2 parents) 11 related 21 Caucasians 20 African-Americans 20 Asians 18 Hispanics-Latinos 3 Native Americans 3 Native Americans 82 unrelated 1 Negative control 1 Chimpanzee 1 Gorilla 3 controls Population Sample Constituted Using the Definitions of the U.S. Census Bureau

11 Polyphred analysis Sequencing data confirmed in both directions Electronic trace analysis Phred Score >30 High-Throughput Quality Control of SNPs: I. Electronic

12 Hardy-Weinberg Equilibrium Distribution frequency of heterozygotes: must conform to frequency of individual alleles in ethnic group Example of frequencies: if 5% for an allele, then 10% heterozygotes and no homozygotes Mendelian Inheritance Polymorphisms are confirmed in the reference families Problems Picked Up: Fixed heterozygosity /co-amplification Allele drop-out /primer sits on SNP p 2 +2pq+q 2 =1 High-Throughput Quality Control of SNPs: II. Genetic Reference Families

13 Design: Genaissance Bioinformatics Computing Infrastructure (I)

14 Design: Genaissance Bioinformatics Computing Infrastructure (II)

15 Genaissance Secure Database Infrastructure Change tracking Audit Change tracking Audit Client Mirrors CLIA Compliant HAPTyping DB Production System Clinical System Genaissance LAN Client Users Firewall / Domain Control Access Control Change tracking Audit

16 Genes By Functional Group Binding Proteins Cell Cycle Channel Cytokine Cytoskeletal/Cell Adhesion Effector/Modulator Hydrolase Isomerase Ligase Lyase Kinase Oxidoreductase Phosphatase Transferase Growth Factor Hormone Immunology-related Intracellular transport Lipoprotein Oncogene Gene Expression Cytokine Receptor GPCR Receptor Kinase Ligand Gated Ion Channel R. Nuclear Hormone Transporter Tumor Suppressor Nuclear Hormone Receptor Enzymes 656 Receptors Miscellaneous 600 500 100 200 300 400

17 Distribution of SNPs/kb by gene region (724 genes)

18 Population Distribution of HAP Markers U.S. Census Populations Caucasian African American Asian Hispanic 1 Pop. 2 Pops. 3 Pops. All 4 Pops

19 Mednostics TM Pharmacogenomic Trial Steps Define Hypothesis Define protocol (prospective vs. retrospective) Select candidate genes or SNPs Recruit patients (families vs. unrelated) Collect phenotypic data ($$$) Collect blood samples (affects no. of genes & protocol) Genotyping ($$$) Statistical analysis (depends on all above) Validation

20 STRENGTH (Statin Response Examined by Genetic HAP Markers) Prospective, multicenter, open-label Age 18 to 75 Type IIa or IIb hypercholesterolemia Patients failed 6-week AHA Step I/II diet 4 week washout prior anti-hyperlipidemic medications ~150 patients per each drug specific arm pravastatinsimvastatinatorvastatin

21 STRENGTH Genes and Clinical Endpoints 175 candidate genes Lipid metabolism (CETP, LDLR, APOE) Drug Metabolism (CYP2C9, CYP2D6, CYP3A4) Inflammation (VCAM1, PPARG) – LDL-C percent change (primary endpoint) –HDL-C –LDL/HDL ratios –Total C Clinical Endpoints –triglycerides – C-reactive protein –Apolipoproteins –Adverse events

22 STRENGTH I Baseline Lipids TC257.8 mg/dl LDL-C173.5 mg/dl HDL-C48.9 mg/dl TG177.1 mg/dl

23 Finding Pharmacogenetic Associations Gene associated with drug response will have one or more of its haplotypes clinically segregated according to outcome Average Response per Individual # of Copies of HAP Marker No Association 0 10 20 30 40 50 012 Association 0 10 20 30 40 50 012 # of Copies of HAP Marker

24 Finding Pharmacogenetic Associations Gene associated with drug response will have one or more of its haplotypes clinically segregated according to outcome Best Responders Haplotypes Frequency Haplotypes Partial Responders

25 STRENGTH Analysis Parameters Statistical analysis –ANCOVA with adjustment for multiple comparisons Raw p value significant markers screening Trial design to capture the marker of high market share –Consider appropriate models Dominant Recessive Additive

26 STRENGTH Clinical-Genetic Association Data Flow 1.Define Subsets (individual statin + pool) + Endpoints + Genes 2.Candidate Associations 3.Apply first pass comparison filter: significance and marker distribution 4.Visual inspection 5.Biological/Medical/Literature Analysis 6.Further statistical tests second pass multiple comparison filter Subset analysis (age, sex, ethnicity, alcohol…) DecoGen ® High throughput pipeline

27 Conclusions From STRENGTH Successful, first-ever comparative study using pharmacogenetics to: Define populations with different response Differentiate between drugs in the same class Most associations were statin-specific Results may lead to new insights into differential mechanisms of action for the statins

28 ADME – Drug Metabolism by CYP2D6 Central to the oxidative metabolism of >30 therapeutic drugs. ( http://www.ncbi.nlm.nih.gov:80/entrez/dispomim.cgi?id=124030 ) Examples: haloperidol, codeine, dextromethorphan, lidocaine, tamoxifen Greater than 100-fold variability in CYP2D6 activity has been observed that can be attributed to genetic polymorphism Poor metabolizer (PM) vs. ultrarapid metabolizer (UM)

29 CYP2D6 Family Tree

30 Pharmacogenomics Data Standard Defining New Standard For Drug Development & Submission Data Genomics Data (Anonymized) Clinical Data (Anonymized) Association Data

31 Acknowledgements Medical affairs Genomics Sequencing and HAPTyping Bioinformatics and Database Management Software Development Quality Control & Assurance Business Development and Intellectual Property c.xu@genaissance.com


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