ASENT.6 March 2009

Genomic /Genetic Considerations in CNS Drug Development: Current Status and Approaches

1. Basic Science Standpoint

Orest Hurko, MDAVP, Wyeth ResearchProfessor (Hon.), University of DundeeDirector: Translational Medicine Research Collaboration

American Society for Experimental NeuroTherapeutics11th Annual Meeting

Arlington Virginia6 March 2009

ASENT.6 March 2009

Genomic /Genetic Considerations for CNS drugs

Genes & the pharmaceutical enterprise

Methods for studying genes

Genome-wide association studies

Opportunities

ASENT.6 March 2009

Decoding the human genome delivered lots of promise … but the downstream challenges were underestimated

This enabled the shift from 500 pharmacologically proven targets to 10,000’s of unproven targets, changing the entire drug discovery process

Easy wins in rare single-gene disorders promptedunrealistic optimism for common diseases with complex genetics

Focus was on technology, not on analysis

Hundreds of millions were spent with minimal return

Millenium has completely exited genetics to becomea conventional drug company

Many companies have abandoned efforts in geneticsIn favor of genomics

ASENT.6 March 2009

But now, ten years on, genetics has finally become a practical translational tool for industry

• Understanding of statistical issues• High-fidelity high-throughput genotyping• Large repositories of population-based samples• Consortia with standardized procedures• Growing appreciation of the heuristic value of outliers

• Ever-growing number of robust validations

Target ValidationTarget Validation

Target/CompoundTarget/CompoundInteractionInteraction

Pharmacodynamic Pharmacodynamic ActivityActivity

Disease BiomarkerDisease Biomarker& Disease & Disease

ModificationModification

Patient StratificationPatient Stratification

o

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Rare diseases have simple genetics.

Common diseases have complex genetics.

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Why should a big drug company be interestedin a rare disease?

Goldstein JL, Brown MS (1973) Familial hypercholesterolemia: identification of a defect in the regulation of

3-hydroxy-3-methylglutaryl coenzyme A reductase activity associated with overproduction of cholesterol.

Proc Natl Acad Sci U S A. 70: 2804-8.

“The homozygous form of the autosomal dominant disorder, familial hypercholesterolemia, is characterized by the presence

in children of profound hypercholesterolemia, cutaneous planar xanthomas, and rapidly progressive coronary

vascular disease that usually results in death before age 30 years….. “

.

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Demonstrable value of studying extremephenotypes

Goldstein JL, Brown MS (1973) Familial hypercholesterolemia: identification of a defect in the regulation of

3-hydroxy-3-methylglutaryl coenzyme A reductase activity associated with overproduction of cholesterol.

Proc Natl Acad Sci U S A. 70: 2804-8. “The homozygous form of the autosomal dominant disorder, familial

hypercholesterolemia, is characterized by the presence in children of profound hypercholesterolemia, cutaneous planar

xanthomas, and rapidly progressive coronary vascular disease that usually results in death before age 30 years. ….”

.

Abbott stands to gain as cholesterol-fighters cut risk in other heart

issueBy Bruce Japsen | Tribune reporter November 13, 2008

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But now, genome-wide association studies allow efficient study of common

diseases as well.

ASENT.6 March 2009

Genomic /Genetic Considerations for CNS drugs

Genes & the pharmaceutical enterprise

Methods for studying genes

Genome-wide association studies

Opportunities

ASENT.6 March 2009

Genetics The study of variation and its inheritanceIntrinsically probabilisticHeritability, segregation analysis, linkage, association

Genomics The study of expression of all genes of an organismDeterministicTranscriptional profiling , in situ hybdrization, difference libraries

Molecular Biology The study of molecules underlying genetics & genomicsDeterministicCloning; sequence and structural analyses; cross hybridization; site-directed mutagenesis; si RNA knockdowns; transgenics &b knockout abimal models

Three distinct methodologies for three different questions

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Heritability estimates are always relative to the genetic and environmental factors in the population

Heritability describes the population, not individuals within that population

Heritability can be estimated in controlled experiments & in population studiesPhenotype (P) = Genotype (G) + Environment (E).

Var(P) = Var(G) + Var(E) + 2 Cov(G,E). If Cov(G,E) = 0. then H2 = Var(G)/ Var(P)

Genetic analyses should only be undertakenif there is significant heritability

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Unusual high density families -- dominant -- point mutations (or microdeletions / duplications) in genes of major effect

Linkage

Common, adult disorders without pronounced familial grouping-- multiple genes of additive effect-- often major environmental interactions

Association

The pattern of inheritance dictates the optimal genetic approach

Rare sporadic childhood disorders-- Chromosomal rearrangements (or recessives)-- Responsible genes in breakpoints or duplications/deletions

Sequencing

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Figure 1. Pedigree structure of the two Chinese families with tooth agenesis.

High-density families ideal for linkage analysis

ASENT.6 March 2009

Genomic /Genetic Considerations for CNS drugs

Genes & the pharmaceutical enterprise

Methods for studying genes

Genome-wide association studies

Opportunities

ASENT.6 March 2009

Linkage & Association – same general principle, different time scales

Crossovers increase with distance

Linkage

Association

20 generations

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1 Score genomic DNA from a very large sample of cases & controls for a very large number of single-nucleotide polymorphisms (SNPs)

Compare the frequencies among cases & controls

Sites that differ significantly between cases and controls are then validated in independent samples

Genome-wide

Genome–wide association studies are based on a very simple idea

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Lessons learned from Genome-Wide Association Studies (GWAS)

1. Genome-Wide Association Studies (GWAS) work 2. Effect sizes are usually small, so big samples needed 3. Rigorous quality control is paramount 4. GWAS may fail to detect certain susceptibility genes5. Important to look well beyond the top few ‘hits’ 6. Collaboration is important 7. Phenotype/selection is important 8. Validation is critical9. Every SNP counts10. “Low-hanging fruit” lead to more variants

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Genome-Wide Association Studies (GWAS) work

Reliable reproduction in follow-up studies:PPAR & transcription factor TCF7L2 - Diabetes mellitus IL23R, CARD15, NOD2 -- Crohn’s disease chromosome region 8q24 - prostate cancerGSTM1 null -- bladder cancer & acute leukemia NAT2 slow acetylator -- bladder cancerMTHFR C677T -- gastric cancer

Proof of Principle Complement factor H gene - -age-related macular degeneration

Confident associations in other common diseasescoronary artery disease atrial fibrillation asthma rheumatoid arthritis obesity breast cancer coeliac disease

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Effect sizes are usually small, so big samples are needed

Theory predicts that alleles of small effect are more frequent than alleles oflarge effect

Wellcome Trust Case Control Consortium (WTCCC) GWASs of seven common diseases found per-allele odds ratios of 1.2–1.5

Reasonable power to detect such loci requires 2000 cases and 2000 controls

Failures to replicate findings in modestly sized samples do not constitute refutation

Confidence attributable to a ‘significance level’ is influenced by sample size

Rate of true positives increases with sample size because power to detect true effects increases

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Rigorous quality control is paramount

Enormous data sets (samples & SNPs) in GWASs provide large opportunities for spurious ‘associations’

Data must be cleaned thoroughly to remove low-quality DNA samples, genotype calls & individual samples

Within WTCCC the best predictor of an SNP with poor QC was a highly significant difference in genotype distributions between cases and controls

-- validation is critical

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GWAS fail to detect some susceptibility genes

Underpowered studies were a leading cause of failure Current technology surveys only a limited subset of potentially relevant sequence variation

Poor coverage of large genes Some mutations – such as copy number variations (CNVs) from microduplications or microdeletions –are not detectable in many SNP-based platforms used for GWAS

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Phenotype & case-selection is important

Example: association of FTO gene with Type 2 Diabetes Mellitus

WTCCC (not matched for body mass)-- 2000 cases & 3000 common controls

-- significant association @ P = 1.3 x 10-12

DGI (matched for body mass)-- 14,000 cases and controls

-- no association whatsoever

Subsequent work has shown that fat mass & obesity-associated (FTO) influences risk of T2D

through a primary effect on body mass

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Collaboration is important

Benefits from collaborations that increase total sample sizes , test consistency & generalizability of findings

Aggressive, very early, proactive data sharing key to identification of several susceptibility loci not evident in any single study alone

Standard phenotyping, threshholds for genome calls, raw data sharing

Diabetes Mellitus (Types 1 & 2), coronary artery disease, ankylosing spondylitis benefited from collaborations

ASENT.6 March 2009

Genomic /Genetic Considerations for CNS drugs

Genes & the pharmaceuticals enterprise

Methods for studying genes

Association studies

Opportunities

ASENT.6 March 2009

Lessons for Experimental Neurotherapeutics

Learn from rare Mendelian variants of genes encoding potential drug targets (OMIM)

Schizophrenia, autism, restless legs syndrome, early onset depression, bipolar disease, multiple sclerosis, Alzheimer disease, ADHD, & dyslexia are heritable common diseases tractable for GWAS

Do not succumb to the temptation of relaxing diagnostic criteria to boost sample size

ASENT.6 March 2009

Special Thanks

Douglas BlackwoodNeil Craddock Dan CrowtherCharles ffrench-Constant Fred ImmermanMaha KarnoubRobin FearsGino Miele

Ralph McGinnisVictor McKusick

E.A.MurphyColin Palmer

David PorteousNigel Spurr

David St. ClairKeith Vass