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Genomics of Adverse Drug
Reactions: The Need for a
Multi-Functional Approach
Munir Pirmohamed
David Weatherall Chair of Medicine and
NHS Chair of Pharmacogenetics
Department of Molecular and Clinical Pharmacology
University of Liverpool
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 interindividual 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
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 necrolysis
1 in million per year
InTernational Consortium on Drug Hypersensitivity (ITCH)
12 international centres
 50 UK centres
 1500 patients
EUDRAGENE

EU
Australia
Canada
Norway
Sponsored by the
International
Serious Adverse Event
Consortium (iSAEC)
US
Croatia
Brazil
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 myopathy
Identified via CPRD
Link 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
All Statins
(n=448)
Simvastatin Only
(n=281)
Tolerant
n
372
T/T T/C
0.70 0.27
C/C
0.03
p
-
Per C-allele OR
(95%CI)
-
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)
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
N
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)
C
O
NH2
CPT, 2012
HLA-B*1502
Liverpool
22 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
HLA-A, HLA-DRB1, HLA-DRB5,
KIR2DL1/KIR2DL3
Graft-versus-Host Disease Signaling
3.96E-04
4
75
27
0
Antigen Presentation Pathway
7.75E-04
3
61
26
0
Crosstalk between Dendritic Cells and Natural Killer Cells
1.08E-03
4
75
27
0
HLA-A, HLA-DRB1, HLA-DRB5
HLA-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
0
HLA-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
IL-17 Signaling
2.37E-02
4.56E-02
3
2
61
62
26
27
0
0
HLA-A, HLA-DRB1, HLA-DRB5
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
Clinical data
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
Lymphocyte transformation test
Carbamazepine-Responsive T-cell clones
Specificity and Phenotype
Clones
tested
(n)
Specific
clones
(n)
947
67
Proliferation (cpm)
CD phenotype (%)
control
CBZ
(25μg/ml)
CD4+
CD8+
CD4+
CD8+
5,525.8
(±18,928.0)
34,418.8
(±43,632.5)
35
37
28
Secretion of cytokines and cytolytic molecules
a) CD4+ TCCIFNγ
IL-13
Perforin Granz.B
FasL
IL-13
Perforin Granz.B
FasL
0
CBZ
b) CD8+ TCC IFNγ
0
CBZ
HLA Restriction of CBZ-Specific TCC
MHC restriction of CD4+ (a) and CD8+ (b) TCC
a) CD4+ (n=3)
b) CD8+ (n=3)
* p = 0.03
* p = 0.03
ns
ns
HLA class II restriction of CD4+ TCC
HLA A31 restriction of CD8+ TCC
n=3
n=3
p = 0.008
** p = 0.004
* p = 0.03
Hierarchy of Evidence
What type of evidence is required
for demonstration of clinical utility?
Technology-Based Reduction in the Burden of
ADRs: The Case of Abacavir Hypersensitivity
NH
N
H2N
Clinical genotype
N
N
Association with
HLA-B*5701
N
CH2OH
Clinical phenotype
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
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.”
Acknowledgements
The University of Liverpool
• B Kevin Park
• Ana Alfirevic
• Maike Lichtenfels
• Dean Naisbitt
• Ben Francis
• Dan Carr
Ann Daly (Newcastle University)
Panagiotis Deloukas (Sanger Institute)
SERIOUS ADVERSE EVENT CONSORTIUM
EPIGEN
EU-PACT
FDA
Funders: Dept of Health (NHS Chair of
Pharmacogenetics)
MRC, WT, DH, NIHR, EU-FP7