lon cardon quantitative sciences glaxosmithkline
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Lon Cardon
Quantitative Sciences
GlaxoSmithKline
Capitalizing on the human genomeApplications and interface with academia for medicine discovery and use
Complex disease gene discovery
Perception &Promise
1990s Early 2000s 2006+
Tech
nolo
gy:
Hum
an G
enom
e Pr
ojec
t
RFL
P, m
icro
sate
llite,
SN
Ps
Stud
ies:
Can
dida
te g
enes
‘Gen
ome-
wid
e’ li
nkag
e
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
MarketTarget ID Validate target Candidate First time
humanProof of concept
Phase II to III
• 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)
SampleMale CADMale MI
Cases28901134
Controls31283128
OR0.770.72
0.5 0.6 0.7 0.8 0.9 1 1.1 1.2
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
Pos Neg
Immunologically Confirmed HSR1
HLA-B*5701
23 0
25 794
Pos PV48%
Neg PV100%
Sens 100%
Spec 97%
HSR
No HSR
Consequences of a predictive genetic marker: Abacavir
Goldman & Faruki 2008. Genetics in Medicine 10: 874-78. Graph courtesy LabCorp
• Treatment guideline changes
DHHS (USA) BHIVA (British HIV Association, UK) UK guidelinesEACS (European AIDS Clinical Society) pan-
European guidelines
• Regulatory Recommendations
GSK Core Safety Information, Aug 2007‘EU Summary of Product Characteristics update,
Jan 2008US Prescribing Information update, July 2008
• HSR reduction based on screening
W. Australia, UK, France, US
• Increase in HLA*B5701 tests
Genetic Influences on ADR RiskLarge effects, predictive utility
DrugAdverse Drug Reaction Genetic Risk Factor
Reaction Prevalence
Risk Allele Freq.1 Effect2
Clopidogrel Cardiovascular events 0.13 CYP2C19*2/3/4/5 0.03 3
Gefitinib Diarrhea 0.28 ABCG2 Q141K 0.07 5
Isoniazid Hepatotoxicity 0.15 CYP2E1*1 & NAT2 0.133 7
Co-amoxiclav Hepatotoxicity <0.001 HLA-DRB1*1501 0.20 10
Irinotecan Neutropenia 0.20 UGT1A1*28 0.32 28
Ticlopidine Hepatotoxicity (cholestatic)
<0.001 HLA-A*3303 0.14 36
Tranilast Hyperbilirubinemia 0.12 UGT1A1*28 0.30 48
Flucloxacillin Hepatotoxicity <0.001 HLA-B*5701 0.04 81
Allopurinol Severe cutaneous reaction
<0.001 HLA-B*5801 0.15 678
Abacavir Hypersensitivity reaction 0.08 HLA-B*5701 0.04 >1000
Carbamazepine
Stevens-Johnson <0.001 HLA-B*1502 0.04 >1000Following from Nelson et al, 2009. 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 populationsCollaboration 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 populationsGenetics translation with samplesGenetics 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 toElectronic Medical Records
Start here
Acknowledgements
GSKVincent MooserMatt NelsonJohn WhittakerColin SpraggsStephanie ChissoeFrank HokePhilippe Sanseau
WellcomeTrustNIH/NHGRI