same medicine, different result pharmacogenetics: where are we now?

Dr Richard FitzGeraldMolecular & Clinical PharmacologyInstitute of Translational Medicine

University of Liverpool

Richard.Fitzgerald@liverpool.ac.uk

Same Medicine, Different ResultPharmacogenetics: Where Are We Now?

The drugs don’t work.......

....... they just make it worse.

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

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

‘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’

Modern pharmacogenetics

Types of Genetic Variation

Drug Response: a complex trait?

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.

CYP2D6 Major Alleles

Nortriptyline pharmacogenetics

Codeine phosphate

Drug metabolising enzymes

Most DME have clinically relevant polymorphismsThose with changes in drug effects are separated from pie.

Azathioprine

6-Mercaptopurine

6-thioinosine nucleotide

6-thioguaninenucleotides

Thiouricacid 6-Me MP

TPMTXanthineoxidase

HGPRT

IMPDH

Immunosupression Clinical benefit

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

PGx: current applications

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

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

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

PREDICT-1

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

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

FDA warning

PATIENTS WITH ASIAN ANCESTRY SHOULD BE SCREENED FOR THE PRESENCE OF HLA-B*1502 PRIOR TO INITIATING TREATMENT WITH Carbamazepine.

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

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

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

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

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

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

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

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

Clopidogrel Pharmacogenetics

Stent Thromb HR 2.61; 95% CI 1.61-4.37, P<0.00001

All events: HR 1.57; 95% CI 1.13-2.16, P=0.006

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

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

• Bleeding complications:10-24 per 100-patient years

• 10% of all ADR-related hospital admissions

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)

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

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

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

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%

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

Test interpretation

The potential for complication

Will pharmacogenetic testing be any better than more intensive INR monitoring?

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

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

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

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

Advances in Technologies

14 billion bases/day

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

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

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

Personalised vs. Empirical Paradigms

Empirical (intuitive) medicine

Personalised (precision) medicine

Terminology

Personalised MedicinePersonalised Medicine

Personal Medicine

not

• We cannot truly personalise medicines• No test or prediction rule will be 100%

effective

“ 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