Pharmacogenetics: past, present and future

REVIEWS

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

Reviews  KEYNOTE REVIEW

Pharmacogenetics: past, present and future Munir 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.

Munir Pirmohamed qualified in Medicine in 1985, and obtained a PhD in Pharmacology in 1993. He was awarded a Personal Chair in Clinical Pharmacology 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.

Introduction The 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

E-mail address: [email protected].

852

www.drugdiscoverytoday.com

1359-6446/06/$ - see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.drudis.2011.08.006

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 pharmacogenetics The 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 prescribing drugs; the recently introduced uricosuric drug rasburicase contains a warning about G6PD deficiency in its label [7]. Also, the combination antimalarial chlorproguanil-dapsone (Lapdap)

REVIEWS

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 hydroxylation 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 phenotype 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

TABLE 1

Historical overview of important advances which have either had, or are likely to have, an impact on identifying genetic factors in determining drug responseAdapted from [4] Year

Individual(s)

Landmark

Refs.

510 BC

Pythagoras

Recognition of the dangers of ingesting fava beans, later characterised to be because of deficiency of G6PD

[88]

1866

Mendel

Establishment of the rules of heredity

[89]

1906

Garrod

Publication of ‘Inborn Errors of Metabolism’

1932

Snyder

Characterisation of the ‘phenylthiourea nontaster’ as an autosomal recessive trait

[90]

1956

Alving et al.

Discovery of glucose-6-phosphate dehydrogenase deficiency

[91]

1957

Motulsky

Further refined the concept that inherited defects of metabolism could explain individual differences in drug response

[2]

1957

Kalow and Genest

Characterisation of serum cholinesterase deficiency

[92]

1957

Vogel

Coined the term pharmacogenetics

[1]

1960

Price Evans

Characterisation of acetylator polymorphism

[93]

1962

Kalow

Publication of ‘Pharmacogenetics – Heredity and the Response to Drugs’

[94]

1977/79

Mahgoub et al. and Eichelbaum et al.

Discovery of the polymorphism in debrisoquine hydroxylase

[95,96]

1988

Gonzalez et al.

Characterisation of the genetic defect in debrisoquine hydroxylase, later termed CYP2D6

[12]

1988–2000

Various

Identification of specific polymorphisms in various phase I and phase II drug metabolising enzymes, and latterly in drug transporters

2001–2003

Public–private partnership

Completion of the initial draft and complete sequence of the human genome

[97,98]

2003

The International HapMap Project

Completion of map of human genome sequence variation

[99]

2006

Reddon et al.

Global map of copy number variation

[100]

2007

Wellcome Trust Case–Control Consortium

Genome-wide association in 14,000 cases in seven diseases

[33]

2011

1000 genomes project

A map of human genome variation based on population-scale genome sequencing

[101]

www.drugdiscoverytoday.com

853

Reviews  KEYNOTE REVIEW

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

REVIEWS

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

Genotype

Phenotype

or

or

or

or

Ultrarapid metabolizers

Extensive metabolizers

Intermediate metabolizers

Poor metabolizers

Frequency (Caucasians)

5-10%

80-65%

10-15%

5-10%

Reviews  KEYNOTE REVIEW

90

Number of patients

80 70 60 50 40 30

MR=12.6

20 10 0 0.01

0.1

1

10

>250-500

100

MR

Nortriptyline dose requirement (mg day-1)

150-100

20-50

Nortriptyline (mg) Drug Discovery Today

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 part of 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 pharmacogenetics 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 debrisoquine 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.cypalleles.ki.se/). Interestingly, the gene comprises variants that lead to both deficient and reduced activity [13], in addition to the amplification 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. metoprolol 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 854

www.drugdiscoverytoday.com

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 information 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 today Most 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

REVIEWS

BOX 1

informative than static genetic tests, such as CYP2C19 polymorphism 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].

Possible reasons for lack of translation of pharmacogenetic 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

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 characteristics, 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 cardiovascular 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

Genome-wide association studies Although there are still many candidate gene studies being performed, the advent of genome-wide association studies (GWAS) has added an impetus to identify novel pharmacogenetic associations 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

1400

Number of publications

1200 1000 800 600 400

10 20

05 20

20 00

95 19

90 19

85 19

0 19 8

75 19

0 19 7

19 6

0

2 19 65

200

Year of p publication Drug Discovery Today

FIGURE 2

Publications on pharmacogenetics and/or pharmacogenomics each year between 1962 and 2011 (up to June). The figures were compiled from a search of PubMed between the years 1957–2011 and include articles in all languages. www.drugdiscoverytoday.com

855

Reviews  KEYNOTE REVIEW

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

REVIEWS

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

TABLE 2

The most significant genetic predictors of drug response Organ or system involved Blood Red blood cells Neutrophils Reviews  KEYNOTE REVIEW

Platelets Coagulation

Associated gene/allele

Drug/drug response phenotype

G6PD TPMT*2 UGT1A1*28 CYP2C19*2 CYP2C9*2, *3, VKORC1

Primaquine and others Azathioprine/6MP-induced neutropenia Irintotecan-induced neutropenia Stent thrombosis Warfarin dose-requirement

Brain and peripheral nervous system CNS depression CYP2D6*N Butyrylcholinesterase Anaesthesia Peripheral nerves NAT-2

Codeine-related sedation and respiratory depression Prolonged apnoea Isoniazid-induced peripheral neuropathy

Drug hypersensitivity

HLA-B*5701 HLA-B*1502 HLA-A*3101 HLA-B*5801

Abacavir hypersensitivity Carbamazepine-induced Stevens Johnson syndrome (in some Asian groups) Carbamazepine-induced hypersensitivity in Caucasians and Japanese Allopurinol-induced serious cutaneous reactions

Drug-induced liver injury

HLA-B*5701 HLA-DRB1*1501-DQB1*0602 HLA-DRB1*1501-DQB1*0602 HLA-DRB1*07-DQA1*02 HLA-DQA1*0201

Flucloxacillin Co-amoxiclav Lumiracoxib Ximelagatran Lapatinib

CCR5 IL28B

Maraviroc efficacy Interferon-alpha efficacy

Malignant melanoma

CYP2D6 BCR-ABL KRAS c-kit EGFR EML4-ALK BRAF V600E

Response to tamoxifen Imatinib and other tyrosine kinase inhibitors Cetuximab efficacy Imatinib efficacy Gefitinib efficacy Crizotinib efficacy Vemurafenib efficacy

Muscle General anaesthetics Statins

Ryanodine receptor SLCO1B1

Malignant hyperthermia Myopathy/rhabdomyolysis

Infection HIV-1 infection Hepatitis C infection Malignancy Breast cancer Chronic myeloid leukaemia Colon cancer GI stromal tumours Lung cancer

(WTCCC) [33], were undertaken on at least 2000 cases. Subsequently 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 collaborative 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, 856

www.drugdiscoverytoday.com

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 mechanism(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 telangiectasia 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 examples Owing to space constraints, the different areas highlighted in Table 2 will not all be discussed in detail. Below are short summaries of three areas where different strategies have been used to identify genetic predictors of drug response and/or aid clinical implementation.

Warfarin pharmacogenetics Warfarin 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

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 clinical 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]. However, despite the consistency of the evidence, and the label change, genotype guided prescribing for warfarin is not reimbursed 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 drug reactions Immune-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,

REVIEWS

despite the availability of small patient numbers [60,61] (Table 2). The most successful of these has been with abacavir hypersensitivity, where the association with HLA-B*5701 has led to drug label changes and incorporation into clinical guidelines, and widespread 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 C At 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 demonstrated 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-hepatitis 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.clinicaltrials.gov; accessed June 2011).

Pharmacogenomics: the future Given the apt quote from the Danish physicist Niels Bohr (1885– 1962), ‘Prediction is very difficult, especially about the future’, I certainly 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 challenges 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 www.drugdiscoverytoday.com

857

Reviews  KEYNOTE REVIEW

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

REVIEWS

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

Reviews  KEYNOTE REVIEW Drug Discovery Today

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 druginduced skin injury [71] and drug-induced liver injury (DILI) [72]. Phenotyping does not only depend on clinical criteria or conventional 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 858

www.drugdiscoverytoday.com

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 regional, 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 identification of many new loci for seven common diseases [33]. In pharmacogenomics, this is now beginning to happen. For instance, the Canadian Pharmacogenomics Network for Drug Safety has established 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

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 technologies. To this end, it is also perhaps important for researchers to consider evaluating extreme phenotypes to identify genetic predisposition 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 importance 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 phenotypes [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 development of the Electronic Medical Records and Genomics (eMERGE) network which is a consortium of biorepositories linked to electronic medical records with the aim of identifying and implementing genomic biomarkers into clinical practice [81]. A further development 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 deidentified 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 diseases [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 information 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

REVIEWS

programme to overcome these translational hurdles using the most the effective research and study designs possible. Evidence generated 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 evidence 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 implementation 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 scenario, but there are significant challenges [87].

Conclusion There 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.

Acknowledgements Munir Pirmohamed wishes to thank the Department of Health (NHS Chair of Pharmacogenetics), the Wellcome Trust, MRC, EUFP7 and the Wolfson Foundation for their support.

References 1 Vogel, F. (1959) Moderne probleme der Humangenetik. Ergeb. Inn. Med. Kinderheilkd. 12, 52–125 2 Motulsky, A.G. (1957) Drug reactions enzymes, and biochemical genetics. J. Am. Med. Assoc. 165, 835–837 3 Marshall, A. (1997) Genset-Abbott deal heralds pharmacogenomics era. Nat. Biotechnol. 15, 829–830 4 Pirmohamed, M. (2001) Pharmacogenetics and pharmacogenomics. Br. J. Clin. Pharmacol. 52, 345–347 5 Nebert, D.W. et al. (2008) From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions. Drug Metab. Rev. 40, 187–224 6 Cappellini, M.D. and Fiorelli, G. (2008) Glucose-6-phosphate dehydrogenase deficiency. Lancet 371, 64–74 7 Oldfield, V. and Perry, C.M. (2006) Rasburicase: a review of its use in the management of anticancer therapy-induced hyperuricaemia. Drugs 66, 529–545 8 Luzzatto, L. (2010) The rise and fall of the antimalarial Lapdap: a lesson in pharmacogenetics. Lancet 376, 739–741

9 Meyer, U.A. (2004) Pharmacogenetics – five decades of therapeutic lessons from genetic diversity. Nat. Rev. Genet. 5, 669–676 10 Michael, M. et al. (2011) Docetaxel pharmacokinetics and its correlation with two in vivo probes for cytochrome P450 enzymes: the C(14)-erythromycin breath test and the antipyrine clearance test. Cancer Chemother. Pharmacol. 11 Turpault, S. et al. (2009) Pharmacokinetic assessment of a five-probe cocktail for CYPs 1A2, 2C9, 2C19, 2D6 and 3A. Br. J. Clin. Pharmacol. 68, 928–935 12 Gonzalez, F.J. et al. (1988) Characterization of the common genetic-defect in humans deficient in debrisoquine metabolism. Nature 331, 442–446 13 Niwa, T. et al. (2011) Comparison of cytochrome P450 2D6 and variants in terms of drug oxidation rates and substrate inhibition. Curr. Drug Metab. 14 Ingelman-Sundberg, M. et al. (1999) Polymorphic human cytochrome P450 enzymes: an opportunity for individualized drug treatment. Trends Pharmacol. Sci. 20, 342–349 15 Aklillu, E. et al. (1996) Frequent distribution of ultrarapid metabolizers of debrisoquine in an ethiopian population carrying duplicated and multiduplicated functional CYP2D6 alleles. J. Pharmacol. Exp. Ther. 278, 441–446

www.drugdiscoverytoday.com

859

Reviews  KEYNOTE REVIEW

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

REVIEWS

Reviews  KEYNOTE REVIEW

16 Pirmohamed, M. and Park, B.K. (2001) Genetic susceptibility to adverse drug reactions. Trends Pharmacol. Sci. 22, 298–305 17 Sim, S.C. and Ingelman-Sundberg, M. (2011) Pharmacogenomic biomarkers: new tools in current and future drug therapy. Trends Pharmacol. Sci. 32, 72–81 18 Thakur, M. et al. (2007) Review of evidence for genetic testing for CYP450 polymorphisms in management of patients with nonpsychotic depression with selective serotonin reuptake inhibitors. Genet. Med. 9, 826–835 19 Fleeman, N. et al. (2011) Cytochrome P450 testing for prescribing antipsychotics in adults with schizophrenia: systematic review and meta-analyses. Pharmacogenomics J. 11, 1–14 20 Roden, D.M. and Tyndale, R.F. (2011) Pharmacogenomics at the tipping point: challenges and opportunities. Clin. Pharmacol. Ther. 89, 323–327 21 Lenfant, C. (2003) Shattuck lecture – clinical research to clinical practice – lost in translation? N. Engl. J. Med. 349, 868–874 22 Holmes, M.V. et al. (2009) Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies. PLoS One 4, E7960 23 Mega, J.L. et al. (2010) Reduced-function CYP2C19 genotype and risk of adverse clinical outcomes among patients treated with clopidogrel predominantly for PCI: a meta-analysis. JAMA 304, 1821–1830 24 Bonello, L. et al. (2010) Impact of loading dose adjustment on platelet reactivity in homozygotes of the 2C19 2*loss of function polymorphism. Int. J. Cardiol. 145, 165–166 25 Azam, S.M. and Jozic, J. (2009) Variable platelet responsiveness to aspirin and clopidogrel: role of platelet function and genetic polymorphism testing. Transl. Res. 154, 309–313 26 Fitzgerald, R. and Pirmohamed, M. (2011) Aspirin resistance: effect of clinical, biochemical and genetic factors. Pharmacol. Ther. 130, 213–225 27 Mega, J.L. et al. (2010) Genetic variants in ABCB1 and CYP2C19 and cardiovascular outcomes after treatment with clopidogrel and prasugrel in the TRITON-TIMI 38 trial: a pharmacogenetic analysis. Lancet 376, 1312–1319 28 Gladding, P. et al. (2009) Pharmacogenetic testing for clopidogrel using the rapid INFINITI analyzer: a dose-escalation study. JACC Cardiovasc. Interv. 2, 1095–1101 29 Price, M.J. et al. (2011) Standard- vs high-dose clopidogrel based on platelet function testing after percutaneous coronary intervention: the GRAVITAS randomized trial. JAMA 305, 1097–1105 30 Yin, T. and Miyata, T. (2011) Pharmacogenomics of clopidogrel: evidence and perspectives. Thromb. Res. (Epub ahead of print) 31 Little, J. et al. (2009) STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med. 6, E22 32 Daly, A.K. (2010) Genome-wide association studies in pharmacogenomics. Nat. Rev. Genet. 11, 241–246 33 Wellcome Trust Case Control Consortium, (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 34 Voight, B.F. et al. (2010) Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 35 Billings, L.K. and Florez, J.C. (2010) The genetics of type 2 diabetes: what have we learned from GWAS? Ann. N.Y. Acad. Sci. 1212, 59–77 36 Talmud, P.J. et al. (2010) Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: whitehall II prospective cohort study. BMJ 340, B4838 37 McCormack, M. et al. (2011) HLA-A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans. N. Engl. J. Med. 364, 1134–1143 38 Link, E. et al. (2008) SLCO1B1 variants and statin-induced myopathy – a genomewide study. N. Engl. J. Med. 359, 789–799 39 Donnelly, L.A. et al. (2011) Common nonsynonymous substitutions in SLCO1B1 predispose to statin intolerance in routinely treated individuals with type 2 diabetes: a go-DARTS study. Clin. Pharmacol. Ther. 89, 210–216 40 Voora, D. et al. (2009) The SLCO1B1*5 genetic variant is associated with statininduced side effects. J. Am. Coll. Cardiol. 54, 1609–1616 41 Brunham, L.R. et al. (2011) Differential effect of the rs4149056 variant in SLCO1B1 on myopathy associated with simvastatin and atorvastatin. Pharmacogenomics J. (Epub ahead of print) 42 Zhou, K. et al. (2011) Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat. Genet. 43, 117–120 43 Birnbaum, M.J. and Shaw, R.J. (2011) Genomics: drugs, diabetes and cancer. Nature 470, 338–339 44 Li, D. (2011) Metformin as an antitumor agent in cancer prevention and treatment. J. Diabetes (Epub ahead of print) 45 Wadelius, M. and Pirmohamed, M. (2007) Pharmacogenetics of warfarin: current status and future challenges. Pharmacogenomics J. 7, 99–111 46 Pirmohamed, M. (2006) Warfarin: almost 60 years old and still causing problems. Br. J. Clin. Pharmacol. 62, 509–511

860

www.drugdiscoverytoday.com

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011

47 Jonas, D.E. and McLeod, H.L. (2009) Genetic and clinical factors relating to warfarin dosing. Trends Pharmacol. Sci. 30, 375–386 48 Klein, T.E. et al. (2009) Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360, 753–764 49 Lesko, L.J. (2008) The critical path of warfarin dosing: finding an optimal dosing strategy using pharmacogenetics. Clin. Pharmacol. Ther. 84, 301–303 50 Finkelman, B.S. (2011) Genetic warfarin dosing: tables versus algorithms. J. Am. Coll. Cardiol. 57, 612–618 51 Ansell, J. et al. (2008) Pharmacology and management of the vitamin K antagonists: American college of chest physicians evidence-based clinical practice guidelines (8th Edition). Chest 133, 160S–198S 52 van Schie, R.M. et al. (2009) Genotype-guided dosing of coumarin derivatives: the European pharmacogenetics of anticoagulant therapy (EU-PACT) trial design. Pharmacogenomics 10, 1687–1695 53 French, B. et al. (2010) Statistical design of personalized medicine interventions: the Clarification of Optimal Anticoagulation through Genetics (COAG) trial. Trials 11, 108 54 Do, E.J. et al. (2011) Genetics informatics trial (GIFT) of warfarin to prevent deep vein thrombosis (DVT): rationale and study design. Pharmacogenomics J. (Epub ahead of print) 55 Connolly, S.J. et al. (2009) Dabigatran versus warfarin in patients with atrial fibrillation. N. Engl. J. Med. 361, 1139–1151 56 Cleland, J.G. et al. (2011) Clinical trials update from the American Heart Association meeting 2010: EMPHASIS-HF, RAFT, TIM-HF, Tele-HF, ASCEND-HF, ROCKET-AF, and PROTECT. Eur. J. Heart Fail. 13, 460–465 57 Gomes, E.R. and Demoly, P. (2005) Epidemiology of hypersensitivity drug reactions. Curr. Opin. Allergy Clin. Immunol. 5, 309–316 58 Pirmohamed, M. (2006) Genetic factors in the predisposition to drug-induced hypersensitivity reactions. AAPS J. 8, E20–E26 59 Daly, A.K. et al. (2009) HLA-B*5701 genotype is a major determinant of druginduced liver injury due to flucloxacillin. Nat. Genet. 41, 816–819 60 Alfirevic, A. and Pirmohamed, M. (2010) Drug-induced hypersensitivity reactions and pharmacogenomics: past, present and future. Pharmacogenomics 11, 497–499 61 Alfirevic, A. and Pirmohamed, M. (2011) Drug induced hypersensitivity and the HLA complex. Pharmaceuticals 4, 69–90 62 Pirmohamed, M. (2010) Pharmacogenetics of idiosyncratic adverse drug reactions. Handb. Exp. Pharmacol. 477–491 63 Afdhal, N.H. et al. (2011) Hepatitis C pharmacogenetics: state of the art in 2010. Hepatology 53, 336–345 64 Ge, D. et al. (2009) Genetic variation in IL28B predicts hepatitis C treatmentinduced viral clearance. Nature 461, 399–401 65 Tanaka, Y. et al. (2009) Genome-wide association of IL28B with response to pegylated interferon-alpha and ribavirin therapy for chronic hepatitis C. Nat. Genet. 41, 1105–1109 66 Suppiah, V. et al. (2009) IL28B is associated with response to chronic hepatitis C interferon-alpha and ribavirin therapy. Nat. Genet. 41, 1100–1104 67 Thompson, A.J. et al. (2010) Interleukin-28B polymorphism improves viral kinetics and is the strongest pretreatment predictor of sustained virologic response in genotype 1 hepatitis C virus. Gastroenterology 139, 120–129.E18 68 Rauch, A. et al. (2010) Genetic variation in IL28B is associated with chronic hepatitis C and treatment failure: a genome-wide association study. Gastroenterology 138, 1338–1945 1345 E1–E7 69 Mangia, A. (2011) Individualizing treatment duration in hepatitis C virus genotype 2/3-infected patients. Liver Int. 31, 36–41 70 Ma, Q. and Lu, A.Y. (2011) Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacol. Rev. 63, 437–459 71 Pirmohamed, M. et al. (2011) Phenotype standardization for immune-mediated drug-induced skin injury. Clin. Pharmacol. Ther. 89, 896–901 72 Aithal, G.P. et al. (2011) Case definition and phenotype standardization in druginduced liver injury. Clin. Pharmacol. Ther. 89, 806–815 73 Simpson, P.T. et al. (2011) Application of molecular findings to the diagnosis and management of breast disease: recent advances and challenges. Hum. Pathol. 42, 153–165 74 Barker, A.D. et al. (2009) I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther. 86, 97–100 75 Mehta, R. et al. (2011) Personalized medicine: the road ahead. Clin. Breast Cancer 11, 20–26 76 Ross, C.J. et al. (2010) The Canadian Pharmacogenomics Network for Drug Safety: a model for safety pharmacology. Thyroid 20, 681–687 77 Ross, C.J. et al. (2009) Genetic variants in TPMT and COMT are associated with hearing loss in children receiving cisplatin chemotherapy. Nat. Genet. 41, 1345–1349

78 Lucena, M.I. et al. (2011) Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles. Gastroenterology 141, 338–347 79 Shen, Y. et al. (2011) Genome-wide association study of serious blistering skin rash caused by drugs. Pharmacogenomics J. (Epub ahead of print) 80 Wilke, R.A. et al. (2011) The emerging role of electronic medical records in pharmacogenomics. Clin. Pharmacol. Ther. 89, 379–386 81 McCarty, C.A. et al. (2011) The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genomics 4, 13 82 Roden, D.M. et al. (2008) Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin. Pharmacol. Ther. 84, 362–369 83 Ritchie, M.D. et al. (2010) Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am. J. Hum. Genet. 86, 560–572 84 Denny, J.C. et al. (2010) Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation 122, 2016–2021 85 Pirmohamed, M. (2010) Acceptance of biomarker-based tests for application in clinical practice: criteria and obstacles. Clin. Pharmacol. Ther. 88, 862–866 86 Rawlins, M. (2008) De testimonio: on the evidence for decisions about the use of therapeutic interventions. Lancet 372, 2152–2161 87 Opar, A. (2011) Bridging the drug-diagnostic divide. Nat. Rev. Drug Discov. 10, 323–324 88 Nebert, D.W. (1999) Pharmacogenetics and pharmacogenomics: why is this relevant to the clinical geneticist? Clin. Genet. 56, 247–258

REVIEWS

¨ ber Plflanzenhybriden. Verhandlungen des 89 Mendel, G. (1866) Versuche u ¨ nn, Bd. IV fu ¨ r das Jahr 1865. Abhandlungen 3–47 naturforschenden Vereines in Bru 90 Snyder, L.H. (1932) Studies in human inheritance IX. The inheritance of human deficiency in man. Ohio J. Sci. 32, 436–468 91 Alving, A.S. et al. (1956) Enzymatic deficiency in primaquine-sensitive erythrocytes. Science 124, 484–485 92 Kalow, W. and Genest, K. (1957) A method for the detection of atypical forms of human serum cholinesterase. Determination of dibucaine numbers. Can. J. Biochem. Physiol. 35, 339–346 93 Price-Evans, D.A. et al. (1960) Genetic control of isoniazid metabolism in man. Br. Med. J. 2, 485–491 94 Kalow, W. (1962) Pharmacogenetics – Heredity and Responses to Drugs. W. B Saunders 95 Mahgoub, A. et al. (1977) Polymorphic hydroxylation of debrisoquine in man. Lancet 2, 584–586 96 Eichelbaum, M. et al. (1979) Defective N-oxidation of sparteine in man, a new pharmacogenetic defect. Eur. J. Clin. Pharmacol. 16, 183–187 97 Lander, E.S. et al. (2001) Initial sequencing and analysis of the human genome. Nature 409, 860–921 98 Venter, J.C. et al. (2001) The sequence of the human genome. Science 291, 1304–1351 99 International HapMap Consortium, (2003) The international HapMap project. Nature 426, 789–796 100 Redon, R. et al. (2006) Global variation in copy number in the human genome. Nature 444, 444–454 101 1000 Genomes Project Consortium, (2010) A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073

www.drugdiscoverytoday.com

861

Reviews  KEYNOTE REVIEW

Drug Discovery Today  Volume 16, Numbers 19/20  October 2011