pharmacogenomics data management and application in drug development chuanbo xu senior director,...
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Pharmacogenomics Data Pharmacogenomics Data Management and Application Management and Application In Drug DevelopmentIn Drug Development
Chuanbo Xu
Senior Director, Bioinformatics
San Antonio, TX. 13 January 2003
HL7/CDISC Work Group Conference - 2003
Drug DevelopmentDrug Development
FutureTargeted Discovery, Predictive Medicine
Beyond Pharmacodynamics and Beyond Pharmacodynamics and PharmacokineticsPharmacokinetics
Regulatory
Target Metabolism Secondary Interaction
Tertiary Interaction
TTMM
XXYY
Introducing Introducing Pharmacogenetic/PharmacogenomicsPharmacogenetic/Pharmacogenomics
Regulatory
TTMM
XXYYTT
MM
XXYY
Target Metabolism Secondary Interaction
Tertiary Interaction
Drivers for Personalized MedicineDrivers for Personalized Medicine
“… We believe that the central issue is not whether PGt- or PGx-guidedDrug prescriptions will happen, but when and how.”
What Is PGt/PGx?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.
What Constitutes PGx Data?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
Conservation vs. VariationConservation vs. Variation
99.9% similar between individuals
.1% differences has functional consequences
Exons
Promoters
SNPs
Chromosomelocus of gene
Gene SNPs01
01
01
01
01
Haplotypes0 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 HaplotypesGene Haplotypes
96-well microtiter plate
6 6 Caucasians (4 grandparents)Caucasians (4 grandparents)5 African-Americans (2 parents)5 African-Americans (2 parents)
11 related
21 21 CaucasiansCaucasians20 African-Americans20 African-Americans20 Asians20 Asians18 Hispanics-Latinos18 Hispanics-Latinos 3 Native Americans3 Native Americans
82 unrelated
1 Negative control1 Negative control1 Chimpanzee1 Chimpanzee1 1 GorillaGorilla
3 controls
Population Sample Constituted Using the Population Sample Constituted Using the Definitions of the U.S. Census BureauDefinitions of the U.S. Census Bureau
•Polyphred analysis
Sequencing data confirmed in both directions
Electronic trace analysisPhred Score >30
High-Throughput Quality Control of SNPs: High-Throughput Quality Control of SNPs: I. ElectronicI. Electronic
High-Throughput Quality Control of SNPs: High-Throughput Quality Control of SNPs: I. ElectronicI. Electronic
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
p2 +2pq+q2=1
High-Throughput Quality Control of SNPs: High-Throughput Quality Control of SNPs: II. GeneticII. Genetic
Reference Families
DesignDesign: Genaissance : Genaissance Bioinformatics Computing Infrastructure (I)Bioinformatics Computing Infrastructure (I)
DesignDesign: Genaissance : Genaissance Bioinformatics Computing Infrastructure (II)Bioinformatics Computing Infrastructure (II)
Genaissance Secure Database InfrastructureGenaissance 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 ControlAccess Control
Change tracking
Audit
0
50
100
150
200
250
300
Genes By Functional GroupGenes By Functional Group
Binding ProteinsCell CycleChannelCytokineCytoskeletal/Cell AdhesionEffector/ModulatorHydrolase
IsomeraseLigaseLyase
KinaseOxidoreductasePhosphataseTransferase
Growth FactorHormoneImmunology-relatedIntracellular transportLipoproteinOncogeneGene Expression
Cytokine ReceptorGPCRReceptor KinaseLigand Gated Ion Channel R.TransporterTumor Suppressor
Nuclear Hormone Receptor
Enzymes
656
Receptors
Miscellaneous
600
500
100
200
300
400
Distribution of SNPs/kb by gene region Distribution of SNPs/kb by gene region (724 genes)(724 genes)
Population Distribution of Population Distribution of HAPHAP™™ MarkersMarkers
U.S. CensusPopulationsCaucasianAfrican AmericanAsianHispanic
1 Pop.2 Pops.3 Pops.All 4 Pops
MednosticsMednosticsTMTM
Pharmacogenomic Trial StepsPharmacogenomic Trial StepsMednosticsMednosticsTMTM
Pharmacogenomic Trial StepsPharmacogenomic 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
STRENGTHSTRENGTH((StStatin atin RResponse esponse EExamixaminned by ed by GGeneenettic ic HHAPAP™ Markers)™ 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
pravastatin simvastatin atorvastatin
STRENGTH Genes and Clinical EndpointsSTRENGTH 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 EndpointsClinical Endpoints– triglycerides
– C-reactive protein
– Apolipoproteins
– Adverse events
STRENGTH I Baseline LipidsSTRENGTH I Baseline Lipids
TC 257.8 mg/dl
LDL-C 173.5 mg/dl
HDL-C 48.9 mg/dl
TG 177.1 mg/dl
Finding Pharmacogenetic AssociationsFinding Pharmacogenetic Associations
Gene associated with drug response will have one or more of its haplotypes clinically segregated according to outcome
Av
era
ge
Re
spo
nse
pe
r In
div
idu
al
# of Copies of HAP™ Marker
No Association
0
10
20
30
40
50
0 1 2
Association
0
10
20
30
40
50
0 1 2
# of Copies of HAP™ Marker
Finding Pharmacogenetic AssociationsFinding Pharmacogenetic Associations
Gene associated with drug response will have one or more of its haplotypes clinically segregated according to outcome
Best Responders
Haplotypes
Fre
qu
en
cy
Haplotypes
Partial Responders
0
10
20
30
40
50
1 2 3 4 5 60
10
20
30
40
50
1 2 3 4 5 6
STRENGTH Analysis ParametersSTRENGTH 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
STRENGTHSTRENGTHClinical-Genetic Association Data FlowClinical-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
Conclusions From STRENGTHConclusions 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
ADME – Drug Metabolism by CYP2D6ADME – 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)
CYP2D6 Family Tree
Pharmacogenomics Data StandardPharmacogenomics Data Standard
Defining New Standard For Drug Development & Submission Data
Genomics Data(Anonymized)
Clinical Data(Anonymized)
Association Data
AcknowledgementsAcknowledgements
•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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