asent.6 march 2009 genomic /genetic considerations in cns drug development: current status and...
TRANSCRIPT
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.
ASENT.6 March 2009
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….. “
.
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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
ASENT.6 March 2009
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