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Lon Cardon Quantitative Sciences GlaxoSmithKline

Capitalizing on the human genome Applications and interface with academia for medicine discovery and use. Lon Cardon Quantitative Sciences GlaxoSmithKline. Complex disease gene discovery. 1000s ‘discoveries’ – unreplicated. Technology: Human Genome Project RFLP, microsatellite, SNPs.

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Lon Cardon Quantitative Sciences GlaxoSmithKline

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  1. Capitalizing onthe human genomeApplications and interface with academia for medicine discovery and use Lon Cardon Quantitative Sciences GlaxoSmithKline

  2. Complex disease gene discovery 1000s ‘discoveries’ – unreplicated Technology: Human Genome Project RFLP, microsatellite, SNPs Studies: Candidate genes ‘Genome-wide’ linkage Perception & Promise GWAS 1990s Early 2000s 2006+ GWAS: large(-ish) samples + specific traits + subset of genome

  3. Disease Gene Discovery: 2007

  4. Human genetics points of impact Market ADME New targets • Safety • Efficacy/Pers Med • Drug repositioning

  5. 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 • … • 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

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

  7. 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)

  8. Adverse Events Genetics

  9. Abacavir hypersensitivity • Antiretroviral drug abacavir commonly used in treatment of HIV-1 • Abacavirhypersensitivity reaction (ABC HSR) observed • Multiorgan clinical syndrome • Rechallenge is permanently contraindicated and can be fatal • Affects ~8% of clinical trial patients Immunologically Confirmed HSR1 HLA-B*5701 Pos Neg 23 0 Sens 100% HSR 25 794 Spec 97% No HSR Pos PV Neg PV 48% 100% OR (immunulogically confirmed, white): 0.03 (0.00 – 0.19) Mallal et al, NEJM 2008 1 Control Arm Data Only

  10. Consequences of a predictive genetic marker: Abacavir • HSR reduction based on screening W. Australia, UK, France, US • 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 • Increase in HLA*B5701 tests Goldman & Faruki 2008. Genetics in Medicine 10: 874-78. Graph courtesy LabCorp

  11. Genetic Influences on ADR RiskLarge effects, predictive utility Following from Nelson et al, 2009. Pharmacogenomics J

  12. 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

  13. 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

  14. Summary (2): Towards populationsCollaboration to capitalize • Genetic factors have translational utility today • To broadly exploit the genome, greatestchallenges 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

  15. 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

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

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

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