Pharmacogenomics Data Management and Application In Drug Development Chuanbo Xu Senior Director,...

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

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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