Lon Cardon Quantitative Sciences GlaxoSmithKline Capitalizing on the human genome Applications and interface with academia for medicine discovery and use

Complex disease gene discovery Perception & Promise 1990sEarly 2000s2006+ Technology: Human Genome Project RFLP, microsatellite, SNPs Studies: Candidate genes ‘Genome-wide’ linkage 1000s ‘discoveries’ – unreplicated GWAS GWAS: large(-ish) samples + specific traits + subset of genome

Disease Gene Discovery: 2007 

Human genetics points of impact New targets ADME Efficacy/Pers Med Safety Drug repositioning Drug Discovery & Development Market

Efficacy. Few examples of responder v non-responder (cf oncology) Why? Theory: multigenic with small effects Practice: well-designed studies not yet conducted …a future opportunity for collaboration Less than expected success New Targets. Clear point of interface, but limited success Genetic data often one small piece of puzzle Safety Efficacy Unmet need Plausible mechanism … Genetically validated …

Continued optimism 1996: Microsatellites & linkage 2007: SNPs & genome-wide association 2010: Rare variants & sequencing

Present areas of translational genetics success Oncology (becoming mainstream) Mechanism of action (ad hoc but highly informative) Rare Diseases (sequencing technology enabling wave of progress) Adverse Events (numerous and increasing, but implementation lags behind discovery)

Adverse Events Genetics

Antiretroviral drug abacavir commonly used in treatment of HIV-1 Abacavir hypersensitivity reaction (ABC HSR) observed –Multiorgan clinical syndrome –Rechallenge is permanently contraindicated and can be fatal –Affects ~8% of clinical trial patients Abacavir hypersensitivity 1 Control Arm Data Only OR (immunulogically confirmed, white):0.03 (0.00 – 0.19) Mallal et al, NEJM 2008 PosNeg Immunologically Confirmed HSR 1 HLA-B* Pos PV 48% Neg PV 100% Sens 100% Spec 97% HSR No HSR

Consequences of a predictive genetic marker: Abacavir Goldman & Faruki Genetics in Medicine 10: Graph courtesy LabCorp Treatment guideline changes DHHS (USA) BHIVA (British HIV Association, UK) UK guidelines EACS (European AIDS Clinical Society) pan- European guidelines Regulatory Recommendations GSK Core Safety Information, Aug 2007 ‘EU Summary of Product Characteristics update, Jan 2008 US Prescribing Information update, July 2008 HSR reduction based on screening W. Australia, UK, France, US Increase in HLA*B5701 tests

Genetic Influences on ADR Risk Large effects, predictive utility Drug Adverse Drug ReactionGenetic Risk Factor ReactionPrevalenceRisk AlleleFreq. 1 Effect 2 Clopidogrel Cardiovascular events0.13CYP2C19*2/3/4/ Gefitinib Diarrhea0.28ABCG2 Q141K0.075 Isoniazid Hepatotoxicity0.15CYP2E1*1 & NAT Co-amoxiclav Hepatotoxicity<0.001HLA-DRB1* Irinotecan Neutropenia0.20UGT1A1* Ticlopidine Hepatotoxicity (cholestatic)<0.001HLA-A* Tranilast Hyperbilirubinemia0.12UGT1A1* Flucloxacillin Hepatotoxicity<0.001HLA-B* Allopurinol Severe cutaneous reaction<0.001HLA-B* Abacavir Hypersensitivity reaction0.08HLA-B* >1000 Carbamazepine Stevens-Johnson<0.001HLA-B* >1000 Following from Nelson et al, Pharmacogenomics J

But AE predictions not always ‘perfect’ Large effects do not always mean ‘perfect’ prediction (cf abacavir) Imbalanced prediction (NPV/PPV)  people at risk excluded with some certainty  some people not at risk denied otherwise effective treatment Variables affecting utility: Indication Risk/benefit Access Genetics expectation was to differentiate on individual-level efficacy, but reality today is potential to differentiate on individual-level risk

Summary (1): Applications today Genetics has under-delivered on translation promise Genetic findings of practical utility now exist They are not widely used Why not? Physician education Market and culture Engagement of regulators …. Widespread availability of tests If the genotyping data were readily and simply available at the time of prescribing, should it be used? Stated this way, the answer would almost certainly be “yes”. Roden and Shuldiner, Circulation, June 2010

Summary (2): Towards populations Collaboration to capitalize Genetic factors have translational utility today To broadly exploit the genome, greatest challenges are not –Technology –Computation / how to analyze data –Know-how Greatest need involves clinically well-characterized collections Genetics discovery with populations Genetics translation with samples Genetics applications applied to individuals

Genetics, Biobanks & Electronic Medical Records All pieces in place –Large numbers of individuals –Rich, broad clinical information –High throughput, complete genome, low-cost technology Convergence opens up the genome –Sub-group identification –Natural settings to see consequences of up/down protein levels –Treatment comparisons: both safety and efficacy –New indications for existing treatments –..more Meslin, EM & Goodman, KW (2010) Science Progress

Individual  Population  Individual Single gene disorders Complex disorders Biobanks to Electronic Medical Records Start here

Acknowledgements GSK Vincent Mooser Matt Nelson John Whittaker Colin Spraggs Stephanie Chissoe Frank Hoke Philippe Sanseau WellcomeTrust NIH/NHGRI