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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: [email protected].

1359-6446/06/$ - see front matter � 2011 Published by Elsevier Ltd. doi:10.1016/j.drudis.2011.08.006 www.drugdiscoverytoday.com 1

http://dx.doi.org/10.1016/j.drudis.2011.08.006

mailto:[email protected]


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REVIEWS Drug Discovery Today � Volume 00, Number 00 �August 2011

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]




Drug Discovery Today

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].


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


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


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.


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


blication


200

000

2005

1990

1995

2010

1962 and 2011 (up to June). The figures were compiled from a search of

http://www.saeconsortium.org/




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


(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,


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,



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


http://www.clinicaltrials.gov/

http://www.clinicaltrials.gov/





FIGURE 3

A word cloud depicting the many different -omics terms.


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


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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EYNOTEREVIEW

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



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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