Munir PirmohamedDavid Weatherall Chair of Medicine and

NHS Chair of PharmacogeneticsDepartment of Molecular and Clinical Pharmacology

University of Liverpool

Genomics of Adverse Drug Reactions: The Need for a

Multi-Functional Approach

Adverse Drug Reactions: Classification ON TARGET REACTIONS

Predictable from the known primary or secondary pharmacology of the drug

Clear dose-dependence relationship within the individual

OFF TARGET REACTIONS Not predictable from a knowledge

of the basic pharmacology of the drug and can exhibit marked inter-individual susceptibility

Complex dose-dependence

Outline

Phenotyping Sample sizes Genomic approaches The path to clinical translation Genetic exceptionalism

Genetic Contribution

Many factors predispose to adverse drug reactions, many of which are environmental and clinical

We do not know the overall genetic contribution to the occurrence of adverse reactions

The genetic effect will vary according to drug and reaction

Nature Nurture

ADRs account for:• 6.5% of all hospital

admissions• 15% rate in in-patients• 8000 NHS Beds in the UK

Deep Phenotyping

ADRs can affect any organ system, can be of any severity – MIMIC OF DISEASE

Important to be aware of the phenotypic heterogeneity – link between clinicians and genomics experts

Although overall burden of ADRs is high, the incidence of individual ADRs may be low or rare in many instances – so patient identification can be difficult (cf. Type 2 Diabetes)

Power of Studies

Many pharmacogenetics studies in the past had small sample sizes, compunded by poor phenotype

Led to low effect sizes with lack of replication in independent cohorts

But since ADRs may be uncommon, it will never be possible to attain samples sizes seen in complex diseases International consortia Electronic medical records

Toxic epidermal necrolysis1 in million per year

InTernational Consortium on Drug Hypersensitivity (ITCH)

EU

Australia

Canada

US

Brazil

Croatia

Norway

EUDRAGENE

Sponsored by theInternationalSerious Adverse Event Consortium (iSAEC)

12 international centres 50 UK centres 1500 patients

Electronic Medical Records: Clinical Practice Research Datalink

Previously GPRD

12 million patient records (March 2011)Increased to 52 million with the transition to CPRD

Feasibility study using statin myopathy as paradigm 641,703 patients prescribed a statin 127,209 with concurrent CPK measurement

The R&D Governance Burden

Statin myopathyIdentified via CPRDLink to DNA samples

132 R&D approvals

1. Implicated SNP is in the SLCO1B1 gene (transporter)2. Shown with simvastatin 40mg and 80mg

Genotype Frequency

n T/T T/C C/C pPer C-allele OR

(95%CI)All Statins (n=448)

Tolerant 372 0.70 0.27 0.03 - -All Myopathy 76 0.53 0.39 0.08 0.005 2.08 (1.35-3.23)Severe Myopathy 23 0.35 0.44 0.21 0.0003 4.47 (1.84-10.84)

Simvastatin Only (n=281)

Tolerant 222 0.66 0.32 0.02 - -All Myopathy 59 0.49 0.42 0.09 0.014 2.13 (1.29-3.54)

<40mg/day 24 0.63 0.37 0.00 0.997 1.03 (0.45-2.36)≥40mg/day 35 0.40 0.46 0.14 0.0002 3.23 (1.74-5.99)

Severe Myopathy 18 0.28 0.50 0.22 0.0004 4.97 (2.16-11.43)<40mg/day 5 0.40 0.60 0.00 0.778 1.84 (0.34-9.86)≥40mg/day 13 0.23 0.46 0.31 0.0004 6.28 (2.38-16.60)

Statin Myopathy GWAS

All myopathy (n=128) vs. WTCCC2 (unimputed)

SLCO1B1

Severe myopathy (n=32) vs. WTCCC2 (unimputed)

SLCO1B1

Carbamazepine Hypersensitivity

More complicated than abacavir hypersensitivity

Different phenotypes Skin (mild → blistering) Liver Systemic (DRESS)

Predisposition varies with ethnicity and phenotype HLA-B*1502 (Chinese) HLA-A*3101 (Caucasian)

N

C

NH2

O

CPT, 2012

HLA-B*1502

Liverpool22 patients with HSS

• Replicated in Japanese, Chinese, South Korean, Canadian and EU populations

• NNT = 47• SmPC/drug label

changed (for information)

• Patient and clinician preferences

• Cost effectiveness• 55% likelihood

• Cluster RCT being planned

Whole Genome Sequencing in CBZ Hypersensitivity

N= 48 (28 CBZ-induced severe hypersensitivity and 20 tolerant controls)

HLA-A* Loci Using NGS data

• 30 HLA-A* loci typed • 18 HLA-A* alleles identified• 40% CBZ hypersensitive

patients are A*31:01 positive

Rare Variant Pathway Analysis

Name p-value #Genes #Variants #Cases #Controls Gene Name

Graft-versus-Host Disease Signaling 3.96E-04 4 75 27 0HLA-A, HLA-DRB1, HLA-DRB5, KIR2DL1/KIR2DL3

Antigen Presentation Pathway 7.75E-04 3 61 26 0 HLA-A, HLA-DRB1, HLA-DRB5

Crosstalk between Dendritic Cells and Natural Killer Cells 1.08E-03 4 75 27 0HLA-A, HLA-DRB1, HLA-DRB5, KIR2DL1/KIR2DL3

Mitotic Roles of Polo-Like Kinase 3.55E-03 3 82 28 0 ANAPC5, CDC27, SLK

Type I Diabetes Mellitus Signaling 4.57E-03 4 82 26 0HLA-A, HLA-DRB1, HLA-DRB5, MAP2K3

Autoimmune Thyroid Disease Signaling 7.50E-03 3 61 26 0 HLA-A, HLA-DRB1, HLA-DRB5

B Cell Development 9.20E-03 2 45 23 0 HLA-DRB1, HLA-DRB5

Cytotoxic T Lymphocyte-mediated Apoptosis of Target Cells 1.25E-02 3 61 26 0 HLA-A, HLA-DRB1, HLA-DRB5

Inhibition of Matrix Metalloproteases 1.28E-02 2 23 20 0 MMP24, TIMP2

OX40 Signaling Pathway 1.47E-02 3 61 26 0 HLA-A, HLA-DRB1, HLA-DRB5

Allograft Rejection Signaling 1.69E-02 3 61 26 0 HLA-A, HLA-DRB1, HLA-DRB5

Estrogen Receptor Signaling 2.11E-02 3 88 28 0 CTBP2, MED13L, NCOR1

Communication between Innate and Adaptive Immune Cells 2.37E-02 3 61 26 0 HLA-A, HLA-DRB1, HLA-DRB5IL-17 Signaling 4.56E-02 2 62 27 0 MAP2K3, MUC5B

IL-4 Signaling 4.84E-02 2 45 23 0 HLA-DRB1, HLA-DRB5

T Cells in Carbamazepine Hypersensitivity: HLA-A*31:01+ patient

Gender

Age Time to reaction (days)

Details of reaction Time since reaction (years)

Rechallenge HLA-A genotype

Comments

female 74 6 Generalized rash, raised liver enzymes, fever, eosinophilia, lymphocytosis →Hypersensitivity syndrome

22 No A*11:01/ A*31:01

Previously experienced allergic reaction to Cotrimoxazol

Clinical data

Lymphocyte transformation test

Carbamazepine-Responsive T-cell clones

Clones tested

(n)

Specific clones (n)

Proliferation (cpm) CD phenotype (%)

control CBZ (25μg/ml) CD4+ CD8+ CD4+

CD8+

947 67 5,525.8(±18,928.0)

34,418.8(±43,632.5) 35 37 28

Specificity and Phenotype

IFNγ IL-13 Perforin Granz.B FasL

0

CBZ

a) CD4+ TCC

IFNγ IL-13 Perforin Granz.B FasL

0

CBZ

b) CD8+ TCC

Secretion of cytokines and cytolytic molecules

HLA Restriction of CBZ-Specific TCCMHC restriction of CD4+ (a) and CD8+ (b) TCC

* p = 0.03

ns

b) CD8+ (n=3)* p = 0.03a) CD4+ (n=3)

ns

HLA class II restriction of CD4+ TCC HLA A31 restriction of CD8+ TCC

** p = 0.004

p = 0.008

n = 3* p = 0.03n = 3

Hierarchy of EvidenceWhat type of evidence is required for demonstration of clinical utility?

Technology-Based Reduction in the Burden of ADRs: The Case of Abacavir Hypersensitivity

Clinical phenotype

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

Uptake of HLA-B*5701 in Different Continents

0

1000

2000

3000

4000

5000

6000

7000

8000

J 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

CombivirKivexaTruvadaHLA*

Drug label changed before prospective

study

Two prospective studies did not contradict previous data from retrospective studies

Evidence standards differ between non-genetic and genetic tests 3 examples given:

Drug exposure Prevention of adverse drug reactions Health technology assessment

Drug Exposure: Differential Evidential Standards

Example: Aztreonam SmPC “after an initial usual dose, the dosage of aztreonam should be halved

in patients with estimated creatinine clearances between 10 and 30 mL/min/1.73 m2”

Many different examples in hepatic and renal impairment with dose instructions based on PK studies and occasionally PK-PD modelling

No need for RCTs – in fact, would be impractical

However, a genetic polymorphism leading to same degree of change in drug exposure is often ignored and/or RCT data are required for implementation

Differential Evidence Standards

Unfamiliarity with genetic tests

Lack of experience in interpretation

Perceived cost of genetic testing

Lack of availability of tests

Poor turnaround time

recommendations on dosing evaluation in patients with polymorphisms in known metabolic pathways

Summary

Prediction of adverse drug reactions (safety biomarker)

Insights into mechanisms of the adverse drug reaction

Poste, Nature, 2011

“Hierarchies of evidence should be replaced by accepting—indeed embracing—a diversity of approaches.....

...It is a plea to investigators to continue to develop and improve their methods; to decision makers to avoid adopting entrenched positions about the nature of evidence; and for both to accept that the interpretation of evidence requires judgment.”

AcknowledgementsAnn Daly (Newcastle University)Panagiotis Deloukas (Sanger Institute)SERIOUS ADVERSE EVENT CONSORTIUMEPIGENEU-PACTFDAFunders: Dept of Health (NHS Chair of Pharmacogenetics)MRC, WT, DH, NIHR, EU-FP7

The University of Liverpool• B Kevin Park• Ana Alfirevic• Maike Lichtenfels• Dean Naisbitt• Ben Francis• Dan Carr