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Drug Discovery Today � Volume 00, Number 00 �August 2011 REVIEWS
Pharmacogenetics: past, present andfutureMunir Pirmohamed
The Wolfson Centre for Personalised Medicine, Department of Pharmacology, University of Liverpool,
Block A: Waterhouse Buildings, 1–5 Brownlow Street, Liverpool, UK L69 3GL
The subject area of pharmacogenetics, also known as
pharmacogenomics, has a long history. Research in this area has led to
fundamental discoveries, which have helped our understanding of the
reasons why individuals differ in the way they handle drugs, and
ultimately in the way they respond to drugs, either in terms of efficacy or
toxicity. However, not much of this knowledge has been translated into
clinical practice, most drug–gene associations that have some evidence
of clinical validity have not progressed to clinical settings. Advances in
genomics since 2000, including the ready availability of data on the
variability of the human genome, have provided us with unprecedented
opportunities to understand variability in drug responses, and the
opportunity to incorporate this into patient care. This is only likely to
occur with a systematic approach that evaluates and overcomes the
different translational gaps in taking a biomarker from discovery to
clinical practice. In this article, I explore the history of
pharmacogenetics, appraise the current state of research in this area, and
finish off with suggestions for progressing in the field in the future.
IntroductionThe term pharmacogenetics was coined by the German Pharmacologist Friedrich Vogel [1] in
1959, two years after Arno Motulsky [2] wrote his seminal paper on how ‘. . .drug reactions. . .may
be considered pertinent models for demonstrating the interaction of heredity and environment
in the pathogenesis of disease’. Pharmacogenetics can be defined as the study of the variability in
drug response because of heredity. In 1997, Marshall introduced the term ‘pharmacogenomics’
[3]. Both terms are used interchangeably; however, the latter term, phamacogenomics, signifies
that we have the knowledge and technology to evaluate the whole genome and we have the
ability to interrogate multiple genes on drug response, rather than having to concentrate on a
single gene at a time [4]. Although there are constant debates in the literature as to which term
should be used, both refer to the need to improve the way we use drugs, to change the current
‘trial-and-error’ approach to one where we can be more precise as to how a patient is going to
Munir Pirmohamed
qualified in Medicine in
1985, and obtained a PhD
in Pharmacology in 1993.
He was awarded a Personal
Chair in Clinical Pharma-
cology at The University of
Liverpool in 2001, and in
2007, was appointed to the
NHS Chair of Pharmacogenetics. He is also Head of
Department of Molecular and Clinical Pharmacology
and Director of the Wolfson Centre for Personalised
Medicine. Professor Pirmohamed is a Member of the
Commission on Human Medicines and Chair of its
Pharmacovigilance Expert Advisory Group. His main
area of research is in pharmacogenetics and drug
safety, where he has published over 250 articles.
Please cite this article in press as: M. Pirmohamed, Pharmacogenetics: past, present and future, Drug Discov Today (2011), doi:10.1016/j.drudis.2011.08.006
E-mail address: munirp@liv.ac.uk.
1359-6446/06/$ - see front matter � 2011 Published by Elsevier Ltd. doi:10.1016/j.drudis.2011.08.006 www.drugdiscoverytoday.com 1
DRUDIS-877; No of Pages 10
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respond to a drug, where efficacy is maximised and toxicity is
minimised. However, the transition from empirical approaches to
better precision in drug therapy is not going to be easy, and will
require a consolidated approach that will involve expertise from
all sectors. It is also important to mention at the outset that
genetics and genomics are not the sole determinants of drug
response. Many other factors have to be taken into account
including clinical and environmental factors. A combinatory
approach evaluating all factors, including disease subphenotypes,
is going to be crucial if we are going to succeed in personalising or
stratifying drug therapy.
History of pharmacogeneticsThe first example of a pharmacogenetic trait was described by
Pythagoras [5] (Table 1), now known as favism; this is where
certain Mediterranean populations can develop red blood cell
haemolysis by eating fava beans [6]. This is owing to a deficiency
of glucose-6-phosphate dehydrogenase (G6PD), the commonest
human enzyme deficiency in the world, affecting approximately
600 million people. There are at least 140 variants that have been
identified [6], most of them are rare and have different clinical
effects. G6PD deficiency is still important with respect to prescrib-
ing drugs; the recently introduced uricosuric drug rasburicase
contains a warning about G6PD deficiency in its label [7]. Also,
the combination antimalarial chlorproguanil-dapsone (Lapdap)
Please cite this article in press as: M. Pirmohamed, Pharmacogenetics: past, present and futu
TABLE 1
Historical overview of important advances which have either had, odetermining drug responseAdapted from [4]
Year Individual(s) Landmark
510 BC Pythagoras Recognition of the dang
characterised to be bec
1866 Mendel Establishment of the ru
1906 Garrod Publication of ‘Inborn E
1932 Snyder Characterisation of the
1956 Alving et al. Discovery of glucose-6-p
1957 Motulsky Further refined the con
metabolism could expla
1957 Kalow and Genest Characterisation of seru
1957 Vogel Coined the term pharm
1960 Price Evans Characterisation of acet
1962 Kalow Publication of ‘Pharmac
1977/79 Mahgoub et al. and
Eichelbaum et al.
Discovery of the polym
1988 Gonzalez et al. Characterisation of the
hydroxylase, later terme
1988–2000 Various Identification of specificand phase II drug meta
2001–2003 Public–private partnership Completion of the initia
2003 The International
HapMap Project
Completion of map of h
2006 Reddon et al. Global map of copy num
2007 Wellcome TrustCase–Control Consortium
Genome-wide associatio
2011 1000 genomes project A map of human genom
2 www.drugdiscoverytoday.com
drug had to be withdrawn owing to a higher risk of anaemia in
G6PD deficient patients in Africa [8].
Phenotype-driven assessment of variation in drug metabolising
enzyme genes was the hallmark of research undertaken from the
end of the 1950s to the end of the 1980s [9]. This usually requires
the administration of a probe drug and the measurement of the
ratio between the probe drug and its metabolite, the ratio being
used to depict whether the individual had an absolute or partial
deficiency of an enzyme. Such techniques were used to define an
individual’s N-acetylation capacity as slow or fast acetylators (an
example of a phase II enzyme), whereas debrisoquine hydroxyla-
tion was used to define the activity of the phase I cytochrome P450
enzyme, later named as CYP2D6 (Fig. 1). Phenotypic assessment of
drug metabolising enzyme capacity is still used as a research tool,
for example defining the relationship between genotype and in
vivo phenotype [10], and through the use of a cocktail of probe
drugs that enables simultaneous assessment of multiple P450
enzymes [11]. There is an advantage to understanding the phe-
notype of a particular gene because it enables the identification of
many polymorphisms, even those that have not been discovered,
and determination of phenocopy (where there is no functional
polymorphism in the gene, but the function is decreased because
of the co-administration of a drug that inhibits that enzyme).
However, disadvantages include the labour intensive nature of the
techniques, the associated cost, the low throughput and the fact
re, Drug Discov Today (2011), doi:10.1016/j.drudis.2011.08.006
r are likely to have, an impact on identifying genetic factors in
Refs.
ers of ingesting fava beans, later
ause of deficiency of G6PD
[88]
les of heredity [89]
rrors of Metabolism’
‘phenylthiourea nontaster’ as an autosomal recessive trait [90]
hosphate dehydrogenase deficiency [91]
cept that inherited defects of
in individual differences in drug response
[2]
m cholinesterase deficiency [92]
acogenetics [1]
ylator polymorphism [93]
ogenetics – Heredity and the Response to Drugs’ [94]
orphism in debrisoquine hydroxylase [95,96]
genetic defect in debrisoquine
d CYP2D6
[12]
polymorphisms in various phase Ibolising enzymes, and latterly in drug transporters
l draft and complete sequence of the human genome [97,98]
uman genome sequence variation [99]
ber variation [100]
n in 14,000 cases in seven diseases [33]
e variation based on population-scale genome sequencing [101]
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Extensivemetabolizers
Intermediatemetabolizers
Poormetabolizers
Ultrarapidmetabolizers
5-10% 80-65% 5-10%
orororor
90
80
70
60
50
40
30
20
10
00.01 0.1 1 10 100
20-50150-100
MR
MR=12.6Nu
mb
er o
f p
atie
nts
>250-500
Nortriptyline (mg)
Nortriptyline dose requirement (mg day-1 )
10-15%
Genotype
Phenotype
Frequency(Caucasians)
FIGURE 1
Phenotype–genotype correlation for the CYP2D6 polymorphism. For phenotype determination, individuals were given a probe drug, such as debrisoquine, and
the ratio of the metabolite-parent drug used to determine the metaboliser status. Genetic advances have enabled an assessment of the genotype–phenotype
correlation, including the identification of individuals with more than two copies of the gene, so called ultra-rapid metabolisers (top of the figure). The bottom partof the figure shows the predicted dose requirements of the antidepressant nortriptyline in individuals with different polymorphisms in the CYP2D6 gene.
Reproduced, with permission, from Ref. [9].
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that in some cases, the probe substance might not be specific for
the one enzyme.
The advent of molecular biological techniques enabled phar-
macogenetics to enter a new era where the phenotypic assessments
could be directly related to nucleotide substitutions (and other
variants) in the causative genes. Leading the way here was the
molecular characterisation of the defects underlying the debriso-
quine hydroxylase or CYP2D6 polymorphism [12]. At present, over
80 variants have been described in the CYP2D6 gene, detailed on
the P450 allele website (Home Page of the Human Cytochrome
P450 (CYP) Allele Nomenclature Committee; http://www.cypalle-
les.ki.se/). Interestingly, the gene comprises variants that lead to
both deficient and reduced activity [13], in addition to the ampli-
fication of the gene that can lead to individuals with between 3
and 13 copies of the gene [14]. This leads to the ultra-rapid
metaboliser phenotype, which shows an interesting north–south
geographical distribution with the highest incidence of CYP2D6
ultra-rapid metabolisers being found in Ethiopia [15]. CYP2D6 is
responsible for the metabolism of approximately 25% of drugs
[16], with poor metabolisers being at risk of toxicity (e.g. meto-
prolol causing bradycardia) or lack of efficacy (e.g. through the
reduced formation of active metabolites as seen with codeine
leading to poor analgesic efficacy and tamoxifen resulting in
higher breast cancer recurrence rate) [17]. There have been many
case reports and case series of CYP2D6 polymorphisms leading to
alteration in drug response; however, none of the drug response
phenotypes associated with CYP2D6 polymorphisms have made it
Please cite this article in press as: M. Pirmohamed, Pharmacogenetics: past, present and futu
to clinical practice. There are many reasons for this (discussed
below) as evidenced by the systematic reviews on the role of
CYP2D6 polymorphisms as determinants of response to selective
serotonin reuptake inhibitors [18] and anti-psychotics [19], both
of which concluded the need for more research in this area.
Although the wide availability of PCR-based techniques enabled
molecular assessment of many genes, predominantly the drug
metabolising enzyme genes, most studies were still largely limited
to single genes, and often single variants within that gene. The
advent of pharmacogenomics truly began this century following
the completion of the human genome in 2003, and the ready
availability of new genotyping and sequencing technologies,
which have enabled the assessment of the whole genome [9].
Table 1 highlights some of the major advances that have occurred
this century. These are covered in more detail in the following
sections; although the crucial question is still whether the infor-
mation available to us and these technologies can be harnessed in
such a way to enable for translation into clinical practice for the
benefit of patients.
Pharmacogenetics todayMost commentators and researchers agree that despite many
decades of advances in pharmacogenetics, few tests (genotype
or phenotype) have made it to clinical practice [20]. Although
this is not unique to pharmacogenetics in that the concept of ‘lost
in translation’ has been described for many scientific fields [21], it
nevertheless represents a worry. There are many reasons for the
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BOX 1
Possible reasons for lack of translation ofpharmacogenetic findings into clinical practice
Inadequate sample sizes
Poor clinical phenotyping
Poor study designs
Poor genotyping strategies
Inadequate assessment of co-existing clinical and environmental
determinants
Lack of collaboration between groups
Inadequate funding
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lack of translation into clinical practice (Box 1), and these all need
to be tackled in a comprehensive and systematic manner to
improve clinical translation.
A survey of pharmacogenetic/genomic literature shows that
since 2000, there have been an increasing number of publications
annually [22] (Fig. 2). However, worryingly, the majority of these
have been reviews rather than primary papers. Even when primary
clinical studies have been undertaken, they have often been far
from ideal (Box 1).
The most significant pharmacogenetic findings, including those
that have either led to implementation into clinical practice and/
or a change in the drug label or summary of product character-
istics, are shown in Table 2 [17]. As can be seen, even within this
list, clinical translation for many of the tests has been patchy with
many areas subject to a great deal of controversy. For example,
with clopidogrel and CYP2C19 polymorphisms, although there is
consistent evidence to implicate the variant CYP2C19*2 allele in
predisposing to stent thrombosis, the evidence for adverse cardi-
ovascular outcomes following stenting or in those patients with
acute coronary syndrome who have not been stented is less clear
cut [23]. Furthermore, there are many proponents who suggest
that pharmacodynamic platelet aggregation tests would be more
Please cite this article in press as: M. Pirmohamed, Pharmacogenetics: past, present and futu
1400
1200
1000
800
600
400
Y pear of pu
Nu
mb
er o
f p
ub
licat
ion
s
200
0
1962
1965
1970
1975
1980
1985
FIGURE 2
Publications on pharmacogenetics and/or pharmacogenomics each year between
PubMed between the years 1957–2011 and include articles in all languages.
4 www.drugdiscoverytoday.com
informative than static genetic tests, such as CYP2C19 polymorph-
ism analysis [24]. The situation is further compounded by the fact
that:
(i) There is also a lack of agreement on which platelet function
test to use [25,26].
(ii) There is insufficient evidence at present as to whether
polymorphisms in other genes besides CYP2C19 (e.g. ABCB1
and paraoxonase) are also important in defining therapy
and/or dose [27].
(iii) It is unclear what dosing strategy should be used in those
patients with either one or two variants in the CYP2C19 gene
for both loading and maintenance to further improve the
efficacy of clopidogrel [24,28,29].
(iv) The role of genotype based drug choice and/or drug dose
with respect to clopidogrel, and its use, in comparison with
the newer anti-platelet agents, such as prasugrel and
ticagrelor is unclear [30].
Genome-wide association studiesAlthough there are still many candidate gene studies being per-
formed, the advent of genome-wide association studies (GWAS)
has added an impetus to identify novel pharmacogenetic associa-
tions that might have greater potential clinical use in the future.
The bar to publishing GWAS is certainly higher than that observed
in the past with candidate gene studies. This has been helped by
guidelines produced by journals on GWAS, including the need for
‘replication sets’ of patients [31], which hopefully will reduce the
problem of the ‘winners curse’. Multi-centre collaborations have
also been facilitated to increase sample sizes; a typical example is
the international serious adverse event consortium (iSAEC), which
is a collaboration between the pharmaceutical industry, regulators
and academia (http://www.saeconsortium.org/). A review of the
GWAS undertaken in pharmacogenomics was published in 2010
by Daly [32].
The initial GWAS published in complex diseases, particularly
those from The Wellcome Trust Case–Control Consortium
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200
000
2005
1990
1995
2010
1962 and 2011 (up to June). The figures were compiled from a search of
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TABLE 2
The most significant genetic predictors of drug response
Organ or system involved Associated gene/allele Drug/drug response phenotype
BloodRed blood cells G6PD Primaquine and others
Neutrophils TPMT*2 Azathioprine/6MP-induced neutropenia
UGT1A1*28 Irintotecan-induced neutropeniaPlatelets CYP2C19*2 Stent thrombosis
Coagulation CYP2C9*2, *3, VKORC1 Warfarin dose-requirement
Brain and peripheral nervous systemCNS depression CYP2D6*N Codeine-related sedation and respiratory depressionAnaesthesia Butyrylcholinesterase Prolonged apnoea
Peripheral nerves NAT-2 Isoniazid-induced peripheral neuropathy
Drug hypersensitivity HLA-B*5701 Abacavir hypersensitivity
HLA-B*1502 Carbamazepine-induced Stevens Johnson syndrome (in some Asian groups)HLA-A*3101 Carbamazepine-induced hypersensitivity in Caucasians and Japanese
HLA-B*5801 Allopurinol-induced serious cutaneous reactions
Drug-induced liver injury HLA-B*5701 Flucloxacillin
HLA-DRB1*1501-DQB1*0602 Co-amoxiclav
HLA-DRB1*1501-DQB1*0602 LumiracoxibHLA-DRB1*07-DQA1*02 Ximelagatran
HLA-DQA1*0201 Lapatinib
InfectionHIV-1 infection CCR5 Maraviroc efficacyHepatitis C infection IL28B Interferon-alpha efficacy
MalignancyBreast cancer CYP2D6 Response to tamoxifen
Chronic myeloid leukaemia BCR-ABL Imatinib and other tyrosine kinase inhibitorsColon cancer KRAS Cetuximab efficacy
GI stromal tumours c-kit Imatinib efficacy
Lung cancer EGFR Gefitinib efficacy
EML4-ALK Crizotinib efficacyMalignant melanoma BRAF V600E Vemurafenib efficacy
MuscleGeneral anaesthetics Ryanodine receptor Malignant hyperthermia
Statins SLCO1B1 Myopathy/rhabdomyolysis
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(WTCCC) [33], were undertaken on at least 2000 cases. Subse-
quently in many studies, such as Type II diabetes, the sample size
has been increased to more than 40,000 [34]. Although at least 38
loci have been identified, few have exceeded relative risks of 1.5,
and are therefore unlikely to be used as genetic predictive tests
[35]. For example, with Type II diabetes, the genetic loci identified
add less than 5% to risk prediction that can be determined by
clinical factors alone [36].
In pharmacogenetics, it would have been difficult for most
phenotypes to obtain sample sizes in excess of 2000, especially
for rare adverse events. Fortunately, even with the small number of
GWAS undertaken for drug response to date, it seems that the
genetic effect size is much greater than that seen for complex
diseases [32]. GWAS with sample sizes as low as 22 have produced
highly significant findings [37]. A typical example of a successful
GWAS is with statin-induced myopathy. The SEARCH collabora-
tive undertook a GWAS in 80 subjects with definite or incipient
myopathy with 80 mg/day of simvastatin [38]. An association was
found with rs4363657 single nucleotide polymorphism (SNP) in
SLCO1B1, an influx membrane transporter responsible for the
transport of some statins. The association was replicated in
patients on 40 mg of simvastatin, and has subsequently also been
replicated by other investigators [39,40]. Although this association
seems to be important for simvastatin-induced myopathy,
Please cite this article in press as: M. Pirmohamed, Pharmacogenetics: past, present and futu
whether it is also important for the other statins, still requires
further study [41].
As with GWAS in complex diseases, a finding that might not
show clinical value might still be of use in identifying the mechan-
ism(s) of action of the drug. For example, the glycaemic response
to metformin, a first line therapy for Type 2 diabetes mellitus, has
recently been shown to be linked to SNPs near the ataxia telan-
giectasia mutated gene [42], which is involved in cell cycle control
and DNA repair. This provides novel insights into the mechanism
of action of metformin [42], a tantalising link between diabetes
and cancer [43], and at least a partial explanation for the role of
metformin as an anti-tumour agent [44].
Archetypal examplesOwing to space constraints, the different areas highlighted in
Table 2 will not all be discussed in detail. Below are short sum-
maries of three areas where different strategies have been used to
identify genetic predictors of drug response and/or aid clinical
implementation.
Warfarin pharmacogeneticsWarfarin is a widely used oral anticoagulant, which has a narrow
therapeutic index. Individual daily dose requirements vary from
0.5 mg to 20 mg/day, with over-anticoagulation, as measured by
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the international normalized ratio, predisposing to bleeding [45].
Indeed, warfarin appears within the top three prescribed drugs for
causing adverse drug reaction (ADR)-related hospital admission in
most epidemiological studies [46]. Although there are many clin-
ical factors that lead to the variability in daily dose requirements,
most studies worldwide have now shown that:
(i) CYP2C9 genetic polymorphisms, particularly the *2 and *3
variants, which are associated with reduced catalytic activity
of CYP2C9, account for approximately 15% of the variability
in dose requirement [47]. This is consistent with the fact that
CYP2C9 is the main P450 isoform responsible for the
metabolism of S-warfarin, the more active enantiomer of
warfarin [45].
(ii) VKORC1 genetic polymorphisms account for approximately
25% of the variability in dose requirement [47] consistent
with the fact that warfarin inhibits VKORC1 to inhibit the
vitamin K-dependent activation of clotting factors II, VII, IX
and X [45].
Taken together, age and BMI, together with the genetic
factors can account for approximately 50% of the variation in
daily dose requirements for warfarin [47]. This has led to the
development of many dosing algorithms, including the IWPC
algorithm, which represents a collaboration of approximately 21
groups worldwide [48], and a change in the warfarin drug label
by the US Food and Drug Administration (FDA) in 2007, and the
subsequent introduction of dosing tables in 2010 [49,50]. How-
ever, despite the consistency of the evidence, and the label
change, genotype guided prescribing for warfarin is not reim-
bursed in the USA, and has not been recommended in clinical
guidelines [51]. To aid clinical implementation, there are at least
five clinical trials ongoing globally, including EU-PACT in
Europe [52], and COAG [53], GIFT [54] and WARFARIN in the
USA. In the meantime, new oral anticoagulants, such as the oral
thrombin inhibitor dabigatran [55], and the oral Xa inhibitor
rivaroxaban [56], have been or are about to be licensed. The
advantage of these drugs is that the anticoagulation is much
more predictable and thus there is no need for monitoring, and
they have been shown to be equally or more effective than
warfarin. However, there are disadvantages including the cost,
lack of a pharmacodynamic biomarker and lack of an antidote.
Whether these new anticoagulants will supplant warfarin or
whether a stratified approach to anticoagulation, particularly
in AF, will be required is unclear.
Human leukocyte antigen and immune-mediated adverse drugreactionsImmune-mediated or hypersensitivity ADRs account for 8% of all
the admissions that are drug related [57]. The immune nature of
these reactions has for many years led to a search for genetic
predisposition within the major histocompatibility complex on
chromosome 6. The older studies in the literature did identify
some associations but these were not clinically used [58]. More
recently, with the availability of improved genotyping and
sequencing technologies, it has been possible to type patients to
four digits, which has led to some remarkable associations [37,59],
some of them have been identified using genome-wide scanning,
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despite the availability of small patient numbers [60,61] (Table 2).
The most successful of these has been with abacavir hypersensi-
tivity, where the association with HLA-B*5701 has led to drug label
changes and incorporation into clinical guidelines, and wide-
spread adoption into clinical guidelines with the result that the
incidence of abacavir hypersensitivity has decreased [62]. The
challenge faced by research in this area is to define what type of
evidence will be acceptable to clinicians, regulators and patients
for the demonstration of clinical value, and the procedures that
will be required for clinical implementation.
IL28B and response to interferon-a in hepatitis CAt least 3% of the world’s population is infected with hepatitis C.
Interferon (IFN)-a together with ribavarin form the mainstay of
therapy, but the response, measured as sustained virologic
response (SVR) at 24 and 48 weeks, is variable. Hepatitis C virus
(HCV) genotype 1 responds more poorly than genotypes 2 and 3,
whereas viral load is also a determinant of response. Patient
predictors of response include age, sex, weight, the presence of
liver fibrosis and adherence to therapy [63]. Three GWAS in
patients infected with HCV genotype 1 undertaken in the USA,
Japan and Australia demonstrated that SNPs in the vicinity of the
IL28B gene were associated with response to therapy [64–66].
Patients with the CC genotype at rs12979860 are more likely to
have SVR than patients with CT and TT genotypes, with the
kinetics of viral response also showing a difference between the
genotypes [67]. The effect of IL28B SNPs has also been demon-
strated in HIV co-infected patients [68], and on spontaneous viral
clearance [63]. In patients infected with genotypes 2 and 3, IL28B
SNPs seem to have a greater effect only in those who were not
negative for HCV RNA after four weeks of therapy [69]. IL28B
encodes a lambda type of IFN, which has antiviral activity, but the
actual mechanism by which variation in the IL28B gene affects
response to therapy is unclear [63]. Genotyping for IL28B now
seems to be used by many hepatitis C clinics, and also seems to be
increasingly investigated even in trials involving new anti-hepa-
titis C agents. A quick search of the clinical trials databases shows
that there are at least 12 studies of hepatitis C where IL28B
genotype is being investigated (ClinicalTrials.gov; http://www.cli-
nicaltrials.gov; accessed June 2011).
Pharmacogenomics: the futureGiven the apt quote from the Danish physicist Niels Bohr (1885–
1962), ‘Prediction is very difficult, especially about the future’, I cer-
tainly do not want to predict the future of pharmacogenomics.
Rather, I would like to make some general points, which is a from a
personal perspective on where I see the opportunities and chal-
lenges that lie ahead for researchers in this area. This is not meant
to represent an exhaustive list of recommendations. But I hope
that it stimulates some discussion so that other perspectives can be
added to this debate.
‘‘The best way to predict the future is to invent it’’Alan Kay, American Computer Scientist
First and foremost, pharmacogenomics is one of the many
‘-omics’ technologies (Fig. 3), each of which could add to our
ability to predict disease, improve the phenotyping of disease and
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FIGURE 3
A word cloud depicting the many different -omics terms.
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predict drug response. These technologies, to a greater or lesser
extent, are all likely to be important in realising the promise of
personalised or stratified medicine. Clearly physicians have been
personalising therapies for many decades, largely based on clinical
predictors, but our ability to do this is crude. The judicious use of
these technologies, in combination with clinical factors, is likely
to improve our ability to predict drug response. A scan of the
literature will reveal differing views on the probable impact of
personalised medicine on the future practice of clinical medicine
[70]. Although there is a great deal of hype, there is also an equal
amount of pessimism. Both of these viewpoints can potentially be
disruptive, and a more realistic perspective of the opportunities,
and of the challenges, and how to optimally meet these, is required
to enter a real, and hopefully prolonged, period of productivity.
Second, there is a need to improve our phenotyping strategies.
Poor phenotyping has contributed to difficulties in replication of
associations between different studies. For example, in patients
with extrapyramidal adverse effects from antipsychotics, different
phenotypic manifestations, such as parkinsonism, dystonia and
tardive dyskinesia have been lumped together [19]. Similar issues
have also been identified with idiosyncratic reactions. An initiative
in this area by the iSAEC is the phenotype standardisation project
[71], which has now published standardised phenotypes for drug-
induced skin injury [71] and drug-induced liver injury (DILI) [72].
Phenotyping does not only depend on clinical criteria or conven-
tional diagnostic tests. For example, in cancer, it is becoming
increasingly clear that reliance on histology inevitably leads
to the same treatment for tumours that differ considerably in
their molecular characteristics at genomic, transcriptomic and
proteomic levels [73]. New trials that enable for segmentation
of patients based on their transcriptomic profile are currently
being conducted, for example iSPY2 in breast cancer [74]. Such
Please cite this article in press as: M. Pirmohamed, Pharmacogenetics: past, present and futu
developments have also led to regulatory approval of MammaPrint
by the FDA [75], which predicts the likelihood of breast cancer
recurrence within 5–10 years of the initial diagnosis, based on a
microarray analysis of a panel of 70 genes.
Third, a key issue with studies in the past has been inadequate
sample size. Therefore, there is a need to collaborate across regio-
nal, national and international borders. In this genomic era, the
importance of such collaboration was shown by the WTCCC
which by using whole genome SNP analysis, led to the identifica-
tion of many new loci for seven common diseases [33]. In phar-
macogenomics, this is now beginning to happen. For instance, the
Canadian Pharmacogenomics Network for Drug Safety has estab-
lished a surveillance network in 17 Canadian hospitals to identify
specific ADRs where clinical data can be linked to biological
samples [76]. This has already led to some significant findings,
for example with cisplatin-induced deafness [77]. Similarly, the
iSAEC has fostered international collaboration in the area of
serious ADRs, which has also led to some significant publications
[59,78,79]. The initial phase of the iSAEC program is now being
followed by a more global effort largely dedicated to two areas,
drug-induced liver reactions (being led by the international DILI
consortium) and serious skin reactions (led by the international
consortium on drug hypersensitivity [ITCH]). The focus on serious
ADRs is predicated by the fact that these are by their very nature
relatively rare, and it is therefore difficult for one centre to accrue
enough cases to evaluate genetic predisposition at genome-wide
level. However, it is also important to note that even with these
consortia, for serious ADRs, it is rare to collect more than a couple
of hundred cases. Fortunately, the genetic effect size being
detected in these studies is much greater than that seen for
complex diseases, highlighting the fact that sticking to dogma
established through research in complex diseases that several
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thousand patients are needed for GWAS could hamper our ability
to move forward and seize the opportunities, not only with GWAS
but also through sequencing using the next generation technol-
ogies. To this end, it is also perhaps important for researchers to
consider evaluating extreme phenotypes to identify genetic pre-
disposition when only small numbers of patients are available [5].
In addition to forming consortia, we also need to explore novel
ways of identifying patients, and biobanking samples. Of impor-
tance here will be the use of electronic health records which, if set up
correctly, will provide us with an unprecedented opportunity to
identify and recruit patients with both common and rare pheno-
types [80]. It is encouraging to note that this is already being pursued
by many researchers [42,62]. In the USA, this has led to the devel-
opment of the Electronic Medical Records and Genomics (eMERGE)
network which is a consortium of biorepositories linked to electro-
nic medical records with the aim of identifying and implementing
genomic biomarkers into clinical practice [81]. A further develop-
ment of this is routine biobanking of samples collected through
clinical practice with subsequent linkage to the electronic
records. An example here is the BioVU programme (http://dbmi.
mc.vanderbilt.edu/research/dnadatabank.html) [82] where DNA
samples with a unique identification code can be linked to de-
identified information taken from the electronic medication record.
Genomic analysis of this resource has shown that it is possible to get
replication of genotype–phenotype associations across several dis-
eases [83], and identify new genomic predictors [84].
Fourth, without a robust evidence base it will be impossible to
implement genomics into clinical practice. This might seem an
obvious statement, but perhaps not fully appreciated by researchers.
What was considered to be adequate evidence in the past for clinical
implementation might not necessarily be adequate for modern
medicine. Many of the diagnostic tests we currently use in clinical
practice now have little evidence to support their use; however, it is
clear that this is no longer acceptable by current standards where a
much higher level of evidence is required [85]. This could partly be
related to genetic exceptionalism (the concept that genetic infor-
mation is inherently unique and should be treated differently in law
than other forms of personal or medical information), but not
completely because the same standards are being applied to protein
biomarkers. Given the requirements for evidence, it is important for
researchers to be aware of translational gaps [85], and develop their
Please cite this article in press as: M. Pirmohamed, Pharmacogenetics: past, present and futu
8 www.drugdiscoverytoday.com
programme to overcome these translational hurdles using the most
the effective research and study designs possible. Evidence gener-
ated from randomised controlled trials (RCT) is often regarded as the
gold standard, however, it is important to remember that not all
RCTs are perfect, and conversely, not all observational studies are
imperfect. Rather it is important to consider the evidence base in its
entirety so that the quality of decision making is not diminished
[86], but its efficiency is enhanced from clinical, economic and
societal perspectives.
An important aspect to consider as part of developing the evi-
dence base is implementation, which is not particularly well
researched with respect to biomarkers [85]. There are many different
aspects to this, including the ability to undertake and interpret test
results in clinical practice, the underlying educational requirements
of healthcare staff and patients, ethical, legal and social issues, and
the societal effects of introducing new genetic biomarkers (e.g.
exacerbation of health inequalities). Furthermore, the implementa-
tion of personalised medicine will lead to closer working between
academia and industry, although the business models for this will
vary. For example, for a new drug–diagnostic combination, the
business model for licensing and adoption into clinical practice
will clearly be different from that of an older drug, which is off
patent, where a new biomarker is identified. The involvement of the
diagnostics industry will be crucial particularly for the latter sce-
nario, but there are significant challenges [87].
ConclusionThere is general acceptance that the field of pharmacogenomics is
going to be one of first areas to impact on clinical care following
the completion of the human genome. However, although there
are many opportunities, there are also significant challenges,
which will require a multidisciplinary effort, not only within
healthcare, but also within the commercial sector. There is a need
to build upon recent successes; however, this is going to require
funding, and indeed of all the ‘-omics’ terms (Fig. 3), ‘economics’
will be the ultimate driver.
AcknowledgementsMunir Pirmohamed wishes to thank the Department of Health
(NHS Chair of Pharmacogenetics), the Wellcome Trust, MRC, EU-
FP7 and the Wolfson Foundation for their support.
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