same medicine, different result pharmacogenetics: where are we now?
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Same Medicine, Different Result Pharmacogenetics: Where Are We Now?. Dr Richard FitzGerald Molecular & Clinical Pharmacology Institute of Translational Medicine University of Liverpool [email protected]. The drugs don’t work. ....... they just make it worse. - PowerPoint PPT PresentationTRANSCRIPT
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Dr Richard FitzGeraldMolecular & Clinical PharmacologyInstitute of Translational Medicine
University of Liverpool
Same Medicine, Different ResultPharmacogenetics: Where Are We Now?
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The drugs don’t work.......
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....... they just make it worse.
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The problem: variability
‘If it were not for the great variability among individuals, medicine might as well be a science and not an art.’
Sir William Osler, 1892
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Pythagoras (6th Century B.C.)
“…..be far from fava beans consumptions”
Met death in Ancient Italy because he refused to cross a field of beans
Many theories: Contained souls Looked like testicles flatulence Medical reason
Fava beans
RBChaemolysis
FAVISM
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‘Chemical Individuality’
First suggested by Sir Archibald Garrod that genetics may affect chemical transformations
He used the example of alkaptonuria (1902)
‘One gene, one enzyme’
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Types of Genetic Variation
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Drug Response: a complex trait?
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The early years: one gene, one disease
Robert Smith investigated debrisoquine (a commercially available anti-hypertensive)
He took the tablet, along with most of his laboratory staff
He collapsed and became markedly hypotensive. Nobody else did.
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CYP2D6 Major Alleles
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Nortriptyline pharmacogenetics
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Codeine phosphate
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Drug metabolising enzymes
Most DME have clinically relevant polymorphismsThose with changes in drug effects are separated from pie.
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Azathioprine
6-Mercaptopurine
6-thioinosine nucleotide
6-thioguaninenucleotides
Thiouricacid 6-Me MP
TPMTXanthineoxidase
HGPRT
IMPDH
Immunosupression Clinical benefit
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TPMT (Thiopurine methyltransferase)
Allelic polymorphism
HighTPMT89%
IntermediateTPMT11%
LowTPMT1/300
?very highTPMT
Severe BoneMarrow
Suppression
High riskof marrow
suppression
Low risk Low risk? poor
responders
-+ clinical response
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PGx: current applications
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Abacavir Hypersensitivity
Nucleoside analogue Reverse transcriptase
inhibitor Hypersensitivity 5% Fever, skin rash, gastro-
intestinal symptoms, eosinophilia within 6 weeks
Re-challenge results in a more serious reaction
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Abacavir Hypersensitivity
Clinical phenotype Causal chemical
Association with HLA-B*5701
Clinical genotype
CH2OH
H2N
N
NN
N
NH
Incidence before and after testing for HLA-B*5701
Country Pre testing Post testing Reference
Australia 7% <1% Rauch et al, 2006
France 12% 0% Zucman et al, 2007
UK (London) 7.8% 2% Waters et al, 2007
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PGx: effects on drug usage
0
1000
2000
3000
4000
5000
6000
7000
8000J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D
2005 2006 2007
0
50
100
150
200
250
300
350
Combivir
Kivexa
Truvada
HLA*
Data from RLBUHT courtesy of Prof Saye Khoo
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PREDICT-1
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Abacavir Genetics: Why so Rapidly Implemented?
Implemented even before RCT evidence In some cases, observational study designs may provide adequate
evidence
Successful implementation was because of several factors: Good and replicated evidence of a large genetic effect size Clinician community amenable to rapid change in clinical practice Vocal and knowledgeable patient lobby
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Carbamazepine-induced hypersensitivity reactions5% of patients on carbamazepine (CBZ) develop hypersensitivity reactions10% in prospective SANAD study (UK)
Clinical manifestations Maculopapular exanthema
usually mild
Hypersensitivity reaction (HSS)1/1000 patients
Fever, hepatitis, eosinophilia
Stevens-Johnson syndrome Toxic epidermal necrolysis 5-30% fatality rate
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FDA warning
PATIENTS WITH ASIAN ANCESTRY SHOULD BE SCREENED FOR THE PRESENCE OF HLA-B*1502 PRIOR TO INITIATING TREATMENT WITH Carbamazepine.
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To prospectively identify subjects at risk for SJS4877 CBZ naive subjects from 23 hospitals The Taiwan SJS Consortium
HLA-B*1502 testing → 0 incidence of SJS/TEN
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University of Liverpool (SANAD, EUDRAGENE, Swiss, WT Sanger, Harvard)
EPIGEN Consortium (Ireland, Duke University, UCL, Belgium)
Faculty of 1000 -top 2% of published articles in biology and medicine American Academy of Neurology meeting- voted as one of the top
articles in neurology this year
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22 patients with HSS 43 patients with MPE2691 healthy control subjects 1296 healthy control subjects
McCormack et al. NEJM 2011
HLA-A*3101 HLA-A*3101
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P= P=0.03
P=8 x10-7
P=8 x10-5
P=1x10-7
Pooled analysis of case-control studies
McCormack et al. NEJM 2011
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GWAS identifies HLA-A*3101 allele as a genetic risk factor for CBZ-induced cutaneous adverse drug reactions in Japanese population
HLA-A*3101
Ozeki et al. Hum Mol Genet 2011
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Conclusions
HLA-A*3101 - a prospective marker for CBZ hypersensitivity
Associated with several phenotypes Further work needed to enable clinical use Need for consortia Possibility of rare variants and CNVs (exome-sequencing/WGS) Mechanistic studies to follow genetics
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Flucloxacillin-Induced Cholestatic Hepatitis: Whole Genome Scan
Illumina 1 million SNP arrayStrong (P=10-30) association with SNP in LD with HLA-B*5701Weaker association with novel marker on chromosome 3 (p < 1.4 x 10-8 ) Weak association with copy number polymorphism
Performed in collaboration with the Serious Adverse Event ConsortiumPerformed in collaboration with the Serious Adverse Event Consortium
Daly at al, 2009
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1. Implicated SNP is in the SLCO1B1 gene (transporter)2. Shown with simvastatin 40mg and 80mg3. C variant may account for 60% of the cases of myopathy
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Clopidogrel Pharmacogenetics
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Stent Thromb HR 2.61; 95% CI 1.61-4.37, P<0.00001
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All events: HR 1.57; 95% CI 1.13-2.16, P=0.006
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Conclusions
Clear adverse effect of the CYP2C19*2 polymorphism on clinical and pharmacodynamic outcomes PD Meta-analysis limited by multiple outcome measures
Potential utility in CYP2C19*2 as marker of clopidogrel non-response and risk of adverse outcome
Translation into clinical practice Increase dose of clopidogrel from 75mg/day to 150mg/day
– Evidence from CURRENT-OASIS 7 trial– Bleeding risk
Use of alternative anti-platelet drugs (Prasugrel, Ticagrelor)– Better platelet inhibition– Higher rates of bleeding (+ other adverse effects)– Benefit may be only seen in those with the CYP2C19*2 allele– Cost
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Warfarin: a more complex variation
Widely used drug
A variety of acute/chronic indications
Large numbers of patients
6% of all patients over 80 years of age
Narrow therapeutic index
Drug interactions and alcohol
Efficacy
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• Bleeding complications:10-24 per 100-patient years
• 10% of all ADR-related hospital admissions
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The clinical phenotype
10-50 fold variability in dose requirements
Increased age; decreased requirements 8% decrease in warfarin dose per decade Enhanced responsiveness (PD) Reduced clearance (PK)
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Warfarin and metabolism by Warfarin and metabolism by CYP2C9CYP2C9
CYP2C9*1 Wild Type Arg144 Ile359
CYP2C9*2 Arg144 Cys
: interaction with cytochrome
P450 reductase
CYP2C9*3 Ile359 Leu
: substrate binding site
: affects Km, Vmax
Steward et al, Pharmacogenetics (1997), 7, 361-367
Variant alleles have 5-12% of the activity of wild-type
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Warfarin and pharmacokinetics
CYP2C9 genotype
Number of patients
Aggregate mean dose (mg)
CYP2C9*1*1 639 5.5
CYP2C9*1*2 207 4.5
CYP2C9*1*3 109 3.4
CYP2C9*2*2 7 3.6
CYP2C9*2*3 11 2.7
CYP2C9*3*3 5 1.6
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Warfarin and pharmacodynamics
Polymorphisms in vitamin K epoxide reductase (VKOR)C1
Associated reductions in warfarin dose Accounts for greater variance in dose than CYP2C9 Variation in genes encoding γ-glutamyl carboxylase
and factors II, VII and X
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Genetic and Environmental Factors and Dose Requirements of Warfarin
VKORC1 SNP rs 2359612 vs. warfarin dose
05
101520253035404550
A A A G G G
(n=29) (n=96) (n=75)
mg/
wee
k
Independent effects of VKORC1 and CYP2C9:
VKORC1: p<0.0001, r2 = 0.29
CYP2C9: p=0.0003, r2 = 0.11
Wadelius et al. 2005
Age: p<0.0001, r2 = 0.10
Body weight: p=0.0018, r2 = 0.05
55%
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GENETIC Cytochrome P450
polymorphisms Vitamin K epoxide
reductase Phase II metabolising
genes Drug transporters Clotting factors Disease genes
ENVIRONMENTAL Sex Age Smoking Interacting drugs Alcohol Compliance Diet
Warfarin: multiple genes/factors
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Test interpretation
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The potential for complication
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Will pharmacogenetic testing be any better than more intensive INR monitoring?
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Pharmacogenetic algorithm was superior to clinical algorithm or fixed dosingGreatest benefit seen in 46% of the population who require either <3mg/day or >7mg/day
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Two Randomised Controlled Trials
COAG NIH-sponsored US trial 1200 patients Genetic algorithm vs clinical
algorithm %TIR as primary outcome
measure
EU-PACT EU FP7 sponsored EU trials 3 trials: warfarin,
phenprocoumon, acenocoumarol
900 patients in each (2700 total)
Final study design completed
%TIR as primary outcome measure
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Closing The Loop
Show anassociationShow an
associationReplicate the
associationReplicate the
association
Identify a variant
Identify a variant
Demonstrateclinical
validity andutility
Demonstrateclinical
validity andutility
Demonstratea positive
clinicaloutcome
Demonstratea positive
clinicaloutcome
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Pre-clinicalPre-clinical Phases I, II, IIIPhases I, II, III Phase IVPhase IV
Systems Biology
Minimise risk and maximize benefitUncertainty reduced but not abolished
Minimise risk and maximize benefitUncertainty reduced but not abolished
New technologies:PharmacogenomicsProteomicsMetabolomics
New technologies:PharmacogenomicsProteomicsMetabolomics
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Advances in Technologies
14 billion bases/day
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PGx and Prospective Utility
Drug development process Potential prospective use of PGx to enhance success Increase confidence US$1 billion to market a new drug Target discovery Proof of concept Candidate gene/whole genome association
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Current Status of Genetic Tests
“Today, there is no mechanism to ensure that genetic tests are supported by adequate evidencebefore they are marketed or that marketing claims for such tests are truthful and not misleading. Misleading claims about testsmay lead health-care providers and patients to make inappropriate decisions about whether to test or how to interpret test results.”
Science, 4 April 2008
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Personalised Medicines: The Future?
Many recent advancesHere to stay, and likely to be supported by increasing evidenceEvolutionary process, not revolutionaryLot of cynicism about personalised medicine approachesEvidence being required is much greater with other tests
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Personalised vs. Empirical Paradigms
Empirical (intuitive) medicine
Personalised (precision) medicine
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Terminology
Personalised MedicinePersonalised Medicine
Personal Medicine
not
• We cannot truly personalise medicines• No test or prediction rule will be 100%
effective
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“ What we know about the genome today is not enough for all the miracles many expect from this field. There’s a lot about what regulates the genes and how they interact that we still need to understand. We won’t have the answers by tomorrow.”
29th April 2008
Arno Motulsky