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doi: 10.1111/joim.12226

Application of routine electronic health record databases for pharmacogenetic research A. Yasmina1,2, V. H. M. Deneer3, A. H. Maitland-van der Zee1, T. P. van Staa1,4, A. de Boer1 & O.H. Klungel1 From the 1Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands; 2Department of Pharmacology and Therapeutics, Faculty of Medicine, Lambung Mangkurat University, Banjarmasin, Indonesia; 3Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands; and 4London School of Hygiene & Tropical Medicine, London, UK

Abstract. Yasmina A, Deneer VHM, Maitland-van der Zee AH, van Staa TP, de Boer A, Klungel OH (Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands; Lambung Mangkurat University, Banjarmasin, Indonesia; Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands; London School of Hygiene & Tropical Medicine, London, UK). Application of routine electronic health record databases for pharmacogenetic research. (Review). J Intern Med 2014; 275: 590– 604. Inter-individual variability in drug responses is a common problem in pharmacotherapy. Several factors (non-genetic and genetic) influence drug responses in patients. When aiming to obtain an

Variable drug responses are common phenomena and responsible for suboptimal benefit–risk ratios in individual patients or groups of patients. Drug effects (beneficial and harmful) are determined by a combination of non-genetic and genetic factors. Non-genetic factors include the nature and severity of disease, patient characteristics (e.g., age, race, nutritional status, lifestyle, kidney function, liver function, heart function), concomitant diseases, concomitant drugs or food intake (e.g., grapefruit juice), patient compliance and the quality of prescribing and dispensing of the drug [1–4]. Genetic factors include a combination of multiple genes that encode the proteins involved in the pharmacokinetics and pharmacodynamics of drugs and disease processes [1, 3–8]. Even after the rigorous regulations and various stages of development a new drug has to go through before marketing, reports on the variability of drug responses in populations are common. There are several explanations for these ‘knowledge

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optimal benefit–risk ratio of medicines and with the emergence of genotyping technology, pharmacogenetic studies are important for providing recommendations on drug treatments. Advances in electronic healthcare information systems can contribute to increasing the quality and efficiency of such studies. This review describes the definition of pharmacogenetics, gene selection and study design for pharmacogenetic research. It also summarizes the potential of linking pharmacoepidemiology and pharmacogenetics (along with its strengths and limitations) and provides examples of pharmacogenetic studies utilizing electronic health record databases. Keywords: electronic health records, pharmacogenetics.

gaps’. Clinical trials have strict inclusion criteria and rarely include the elderly, pregnant women, breastfeeding women, and patients with multiple comorbidities. Furthermore, the duration of clinical trials is relatively short and the number of subjects limited. Therefore, discovering rare adverse effects occurring during chronic use of a particular drug is difficult [9]. Different ethnicities are not always represented in clinical trials. Patients from different ethnic groups may have different capabilities for metabolizing drugs based on the different occurrence of polymorphisms in genes encoding the drug-metabolizing enzymes [2]. These examples show that drugs that have been admitted to the market need to be monitored for their effectiveness and safety in the real-life populations. When aiming for an optimal benefit–risk ratio for every patient, the principal idea is to provide drugs with the best efficacy and highest safety to each patient, thereby resulting in a lower prevalence of

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morbidity and mortality. There has been eagerness to individualize therapy based on the genetic inheritance of patients [10–13]. The emergence of advanced technology in molecular biology and genetics has brought deeper understanding of the pathophysiology of diseases and mechanisms of action of drugs as well as the factors influencing them. Pharmacogenetics and, later, pharmacogenomics, have facilitated the potential for the individualization of treatment, particularly for diseases with variability in treatment effect, such as acute coronary syndrome (ACS), depression, cancer and diabetes mellitus (DM). The availability of genetic data for determining drug choices and drug regimens for these diseases is becoming more important. As a result, it has become more feasible to implement this in clinical practice. Genetic data can be obtained routinely and incorporated into patient-information databases. They may serve as materials for pharmacogenetic studies and also as a basis for treatment decision-making in clinical practice [14]. This article addresses the potential of routine healthcare databases as data sources for pharmacogenetic studies. The first part describes the definition of pharmacogenetics and its role in drug responses. The second part discusses identification of relevant genes from the aspect of gene discovery and methodology. The last part gives a short definition of pharmacoepidemiology and its link to pharmacogenetics, the health record databases that are available, along with the advantages and disadvantages of using routine health record databases for pharmacogenetic studies. Pharmacogenetic research The term ‘pharmacogenetics’ was introduced by Vogel, who defined it as ‘the inherited difference in drug response’ [5]. Pharmacogenetics focuses on the gene variants affecting the metabolism, disposition and target of the drug as well as the diseasecausal pathway, and studies the effect of these factors on the efficacy and safety of the drug [3, 4, 6, 15]. The information obtained from pharmacogenetics may help in reducing the problems of the variability of drug responses, potentially resulting in a decrease in the prevalence of treatment failure and adverse events [15]. Pharmacogenomics focuses on a genome-wide search of genes to enhance drug discovery and optimize drug treatment based on the genetic information of

individual patients [3]. Recently, the terms ‘pharmacogenetics’ and ‘pharmacogenomics’ have been used interchangeably. A gene contains DNA that determines the arrangement of amino acids to produce proteins. Mutations and polymorphisms in DNA may result in the production of proteins with altered function, poor binding, disrupted stability, inappropriate expression and a failure to respond to signals; indeed, the protein may not be produced at all. Mutations and polymorphisms may occur on genes encoding proteins for transporters or carriers in absorption, distribution and excretion processes, drug-metabolizing enzymes and drug targets [1, 16]. Therefore, genetic variants may affect drug responses through pharmacokinetic and pharmacodynamic pathways. Genotypes in the causal pathway of a disease may also influence drug responses [5, 6, 14, 17]. Numerous studies have evaluated the association between genes and the effects of drug treatment in terms of efficacy and safety. Not all studies have reached similar conclusions on these associations. This inconsistency may be related to differences in endpoints (e.g., intermediate outcomes such as cholesterol levels and clinical endpoints such as myocardial infarction), different nature of disease (different phenotype) as well as the polygenic nature of disease and of drug effects [3]. Lack of reproducibility can also result from a poor study design, small sample size, subgroup analysis, poorly matched control group, failure to detect linkage disequilibrium with adjacent loci, incorrect assumption on the genetic architecture and overinterpretation of data [18]. Several meta-analyses have confirmed drug–genetic interactions in the determination of disease outcomes. Key findings include the association between CYP2C9*2/*3 and increased risk of bleeding with warfarin [19, 20], the VKORC1 genetic polymorphism and requirement of a higher dose of warfarin [21] and the interaction between CYP2C19*2 and clopidogrel that causes increased cardiovascular events (cardiac death, myocardial infarction and stent thrombosis) [22–26]. Identification of relevant genes in pharmacogenetic research There are several challenges in pharmacogenetic research: the response of a single drug might be the result of a combination of multiple potential genetic variations; the effect of the genetic variant ª 2014 The Association for the Publication of the Journal of Internal Medicine Journal of Internal Medicine, 2014, 275; 590–604

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on drug responses might be small; the prevalence of the genetic variant might be low; there might be potential confounders (e.g., the population admixture that occurs if subjects from different populations have offspring together [4]); and the difficulty in determining which genes are important [17]. These issues are addressed from the perspective of gene selection as well as study design and data analyses. Gene selection As mentioned above, genetic variations can affect drug responses via three routes: pharmacokinetic, pharmacodynamic and disease-causal pathways. In the pharmacokinetic route, variations in genes encoding the proteins for absorption, metabolism and excretion may affect drug responses. If the metabolism and excretion of a drug are known, candidate genes can be selected for further evaluation. For instance, CYP2C9*2 and *3 are associated with increased bleeding upon warfarin use [19] and the CYP2C19 polymorphism has been associated with adverse events in patients with coronary artery diseases treated with clopidogrel [22–27]. In the pharmacodynamic route, genes encoding drug receptors may also have polymorphisms affecting drug responses. For instance, the VKORC1 genotype modifies the effect of the CYP2C9 genotype in increasing the risk of over-anticoagulation in patients receiving phenprocoumon [28]. Several diseases have a polygenetic origin. This feature may lead to variable drug responses. In one study, women who were carriers of the Factor V Leiden mutation had a higher risk of venous thromboembolism when using oral contraceptives (28.5 per 10 000 person-years) compared with women who did not use oral contraceptives (5.7 per 10 000 person-years) [29]. Previously, the effect of genetic polymorphisms on the metabolism and disposition of drugs was identified based on differences in the phenotype of individuals in the population. This research method was referred to as ‘forward’ or ‘functional’ genetics, whereby the search began with identification of susceptible responders and their families. Subsequently, genetic variants in responders and non-responders were compared. Nowadays, identification starts from the polymorphism followed by biochemical and clinical studies to 592

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associate it with the phenotypes. This process is referred to as “reverse genetics” [16] and has two approaches: candidate gene and genome-wide screening [17]. In the candidate-gene approach, dozens to thousands of single nucleotide polymorphisms (SNPs) within one or several genes are genotyped (including coding and non-coding sequences). Genes can be: ‘positional’ (have been proven in previous studies); ‘functional’ (i.e., based on function); known to be involved in pharmacokinetic and pharmacodynamic pathways; related to the disease or intermediate phenotype [30, 31]. This approach is hypothesis-driven. The advantage of this approach is that resources can be directed to several important genes and polymorphisms, and elicits a higher a priori chance of relevant drug– gene interactions being found. However, if the information of the genes from previous studies is incomplete, identifying every important genetic determinant in the genome may not be possible [16, 17, 32]. The genome-wide approach can be used to identify the genetic variants in the whole genome. By comparing the frequency of SNP markers between responders and non-responders in the population, each genetic determinant may be identified [16, 17]. In this approach, hypotheses are not needed. The genome-wide approach may be used to detect the SNPs in genes which were previously not considered to be candidate genes, or even the SNPs outside the genes. Replication studies (in vitro and in vivo) must be conducted to ascertain the true association. Functional studies are also needed to understand the underlying molecular mechanism of the association [30]. This approach is useful for studying complex diseases in which multiple genetic variations contribute to the risk of disease [33]. However, this approach is more expensive than the candidate-gene approach. It also necessitates considerable computational resources because of the large number of data to be analysed. Also, the power of the study is limited to detect only a common variant with a large effect [30]. Study design The randomized controlled trial (RCT) is the best study design to prove the causal association between a gene–drug interaction and the outcome of drug treatment [14, 30]. The limitations of this

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In statistical terms, a gene-exposure interaction is established if the effect of the genotype upon disease risk is dependent on exposure, or vice versa [35]. If only one factor is available, its effect upon disease risk is called the ‘main effect’. If two or more risk factors are available, the marginal effect of a risk factor is its average effect across all levels of the other risk factors. The joint effect may vary from less-than-additive to more-thanmultiplicative of the individual marginal effects [36].

between gemfibrozil and pioglitazone. Adaptive trial designs use interim analyses on already collected data to determine useful modification of an ongoing study (e.g., remove an inferior treatment/dose arm, re-estimate the sample size, exclude genetic subgroups, change the primary endpoint) to enhance the trial, which may result in better treatment for subjects in the trial, more efficient drug development and more economical use of resources [37, 40–42]. For assessment of the effectiveness of genotype-guided treatment compared with standard care, subjects are randomized to genotype-guided treatment or standard care [37]. This design was used in a study by Mallal et al. [43] that compared the use of prospective HLA-B*5701 screening with a standard-of-care approach of abacavir use without HLA-B*5701 screening for decreasing the risk of hypersensitivity to abacavir in adults infected with HIV-1 (PREDICT-1 study).

Randomized controlled trial

Observational studies

The RCT design can be used to evaluate new drug– gene interactions or to replicate already identified interactions. Furthermore, a RCT can be used to establish the benefit of genotype-guided treatment compared with standard care. For studying the modifying effect of genetic variants on patient response in a RCT, post hoc subgroup analyses, pre-planned a priori analyses or adaptive trial designs can be used [37].

Observational studies can be family-based or population-based [4]. A family study uses twins or siblings as subjects. A population study uses unrelated individuals as subjects. The power of these association studies is dependent upon allele frequency, disease prevalence, extent of linkage disequilibrium, sample size and effect size of the genetic variants [31, 44–46].

design are its requirement of a large sample size, its high cost and its relative inability to assess rare events [14]. Observational studies are the alternative option. Case–control, cohort, crosssectional, case-only and exposure-only designs are examples of observational studies that may be used for demonstrating drug–gene interactions [34].

Studies on gene–drug interactions in a RCT are conducted primarily with post hoc subgroup analyses. DNA samples are obtained from subjects in a Phase III clinical trial and then a retrospective study is conducted to search for genetic polymorphisms causing non-responses, adverse effects or toxicity. Patients provide written informed consent to permit blood sampling and for their DNA samples to be used in subsequent studies [14, 37]. An example of this type of design is a study by Maitland-van der Zee et al., [38] who studied the effects of polymorphisms in the anticoagulation genes F5 and F7 on the efficacy of pravastatin in reducing the risk of cardiovascular events. In preplanned a priori analyses, genotyping is conducted before randomization. This design is more efficient because a balanced size of subjects based on genotype can be selected [37]. Aquilante et al. [39] conducted a priori analyses in their study of the effect of the CYP2C8*3 allele polymorphism on pharmacokinetic variability in the interaction

Family study In this design, two types are commonly used: linkage studies and sibling-pair studies. A linkage study is conducted if there is a specific pattern of drug response in high-risk families [4]. A siblingpair study includes related subjects as controls (e.g., a sibling or cousin of the affected subjects). This avoids the bias of genetic admixture [47]. The ESPRIT study was a pharmacogenetic study that enrolled sibling pairs to evaluate responses to lisinopril [4]. The family study design is not often used for pharmacogenetic research due to practical problems: finding families in which the patients have been treated with the same drugs is difficult. Cohort design In this design, those who are exposed and not exposed are determined, and then followed over time. The incidence of the outcome between the two groups is compared. In the prospective cohort design, there is more control of subject ª 2014 The Association for the Publication of the Journal of Internal Medicine Journal of Internal Medicine, 2014, 275; 590–604

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selection compared with other observational studies [48] because both groups are selected early before the onset of outcome studied and followed up under the same conditions. This design is useful if multiple possible outcomes from one exposure require evaluation. The disadvantages of a prospective cohort design include the relatively high costs; larger sample sizes needed to achieve sufficient statistical power; the prolonged time period to discover the delayed effect of the exposure [18, 36, 49]. Harmsze et al. [50] conducted a cohort study on the interaction effect of the CYP2C19*2 polymorphism and combined use of dual antiplatelet therapy, proton pump inhibitors and calcium channel blockers upon on-treatment platelet reactivity and the atherothrombotic events in patients with established coronary artery disease scheduled for elective percutaneous coronary intervention with stent implantation. Another example of a pharmacogenetic cohort study is the Rotterdam Study by Elens

RCT

Cohort

Case–control design In this design, cases and controls are taken from a source population, and the history of exposure compared between the two groups. A case–control study is useful if multiple possible exposures in relation to an outcome, or a relatively rare outcome, need to be evaluated. The sample size for a case–control study is usually smaller than that for a cohort study. However, inappropriate selection of cases or controls may lead to selection bias [49]. A case–control design is the most suitable and frequently used design for analysing gene–drug interactions [14, 30, 52]. This is because of the stability of a genetic marker as

D + w/t D + var Pl/A + w/t Pl/A + var

Outcome (+)/(–)

w/t+ Pl/A w/t +D var +Pl/A var + D

Outcome (+)/(–)

w/t+D or var + D2

Outcome (+)/(–)

w/t + D w/t – D (or + A) var + D var – D (or + A)

Outcome (+)/(–)

Case-control

w/t + D w/t – D (or + A) var + D var – D (or + A)

Cross-sectional

w/t + D + Outcome w/t – D (or + A) - Outcome var + D + Outcome var – D (or + A) - Outcome

EHR database enriched with genotyping data from various sources

Case-only

Exposure-only

594

et al., [51] who studied the association between the CYP3A4 SNP and the efficacy of simvastatin. The Rotterdam Study was designed as a prospective cohort study but allowed for retrospective analyses, such as the study by Elens et al.

D (+) D (–) or A (+) D (+) D (–) or A (+)

w/t + D var + D

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Outcome (+) Outcome (–)

Outcome (+) + w/t Outcome (+) + var

Outcome (+)/(–)

Fig. 1 Designs of pharmacogenetics research. All the observational studies can be conducted both in population studies and family studies. A, active comparator; D, (experimental) drug; D2, another drug or dose adjustment EHR, electronic health records; Pl, placebo; RCT, randomized controlled trial; var, variant; w/ t, wild-type.

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Table 1 Designs in pharmacogenetic studies Study design Randomised controlled trial

Cohort

Advantage Able to ascertain causal inference between gene– drug interactions and treatment outcome

Disadvantage Large sample size needed

• • • • •

•

Optimal study design, where randomisation and blinding prevent bias and imbalance of confounders between groups

•

Costly

•

Relatively unable to assess rare outcomes

•

Able to ascertain time sequence between gene– drug interactions and treatment outcome Appropriate to study rare genetic polymorphisms or uncommonly used drugs

• •

Useful to evaluate multiple outcomes

• • • Case–control

•

Useful for studying multiple genetic determinants

•

Appropriate for study of rare outcomes

•

Crosssectional

Case-only

Exposureonly

•

Requires smaller sample size compared with cohort studies

•

•

Efficient in terms of time and cost

•

May be difficulties in choosing appropriate controls

•

Efficient in terms of time and resources

• •

•

•

Able to evaluate many exposures and outcomes simultaneously

•

Requires fewer cases compared with conventional case–control design

•

•

Can avoid difficulties in selecting controls

•

•

More efficient in terms of cost and time

•

•

No requirement to include a non-exposed group

•

Has relatively high cost unless conducted retrospectively Requires larger sample size compared with case– control studies Usually needs longer time to discover the effect of exposure with a considerable chance of loss-tofollow-up unless conducted retrospectively Prone to selection bias, information bias, and confounding Can be biased by population stratification and genetic admixture Not suitable for rare outcomes Prone to selection bias, information bias, and confounding Can be biased by population stratification and genetic admixture Not suitable for rare genetic polymorphisms or uncommonly used drugs

Unable to ascertain the time sequence and causal inference between gene–drug exposure and treatment outcome Can be biased by population stratification and genetic admixture Cannot estimate the main effects of genetic polymorphisms and drug exposure Prone to information bias and confounding Needs the assumptions of independence between genotype and drug exposure, and the outcome studied to be not common Unable to differentiate between the effect of genetic factors and the gene–drug exposure interactions

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the marker of exposure over time as well as its efficiency in the relatively small number of cases to be genotyped, easy recruitment of patients and the possibility of using late-onset disease as an outcome without the issue of follow-up. A gene– drug interaction effect is estimated from incident cases and unrelated controls in non-cases from the population. The advantage of this design is the ability to estimate the main effect of the gene and drug as well as the gene–drug interaction effect. However, this design is not suitable for rare genes or uncommonly used drugs [34]. Besides, population stratification and genetic admixture can be confounders when assessing a gene–drug interaction [34, 47, 52], and has caused identification of DNA variants proven to be spuriously associated with a disease [18]. Another potential problem is information bias [53], such as the misclassification of subjects in terms of previous exposure or misclassification of the disease because of the change in behaviour of patients related to the disease [54]. Several pharmacogenetic studies with a case– control design have been conducted. For instance, Peters et al. [55] investigated the genetic variants in a cholesterol-lowering pathway that affected the effectiveness of statins in reducing the risk of myocardial infarction. Cross-sectional design This design requires exposure and outcome to be measured simultaneously, resulting in the inability to ascertain the time sequence between exposure and the disease [4]. This design is less timeconsuming and can be used to evaluate many exposures and outcomes, but its inability in producing causal inferences has limited its use in pharmacogenetic research. One example of this design is a study by Biss et al., [56] who studied the variability of the requirement of warfarin dose in children based on VKORC1 and CYP2C9 genotypes and patient characteristics. Case-only design In this design, a gene–drug exposure interaction is determined only in cases. Cases without genetic variants are considered to be the ‘pseudocontrol’ group and cases with genetic variants are considered to be the ‘pseudocase’ group. Both are compared in terms of their drug exposure, and non-exposed pseudocases are the reference. The

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case-only design improves the statistical power of a study to identify a gene–drug exposure interaction with the assumption that there is independence between genotype and drug exposure, and that the outcome studied is not common. This design requires fewer cases compared with the traditional case–control design [57, 58]. The disadvantage of this design is that the main effect of genetic and drug exposure cannot be estimated [52, 57]. The case-only design is also prone to bias and confounding, particularly if there is misclassification of exposure [36]. A study by Lynch et al. [59] used the case-only design to investigate the association between the RYR3 polymorphism and four antihypertensive drug classes in modifying the risk of coronary heart disease and heart failure in high-risk hypertensive patients. Exposure-only design This design is used to identify individuals who carry a risk of suffering from serious adverse effects or decreased efficacy by including only individuals who underwent drug treatment. The advantage of this design is that there is no requirement to include a non-exposed group. However, the disadvantage is that differentiation between the effect of genetic factors and the gene– drug exposure interaction is not possible [36, 37]. An example of this design are the analyses conducted in a study by Mega et al. [60] on data from the TRITON-TIMI 38 trial, which investigated the association between the CYP2C19 genetic variant and cardiovascular outcomes in ACS patients treated with clopidogrel. A systematic review and meta-analysis by Holmes et al. clearly demonstrated the disadvantage of the exposure-only design. In exposure-only studies, a significantly increased risk of cardiovascular events was found among those on clopidogrel who were poor metabolizers for CYP2C19, whereas studies that included a non-exposed (control) group did not find an interaction between poor metabolizers of CYP2C19 and the effect of clopidogrel on cardiovascular events [61]. Summary of these study designs is shown in Fig. 1, and their advantages and disadvantages are listed in Table 1. An illustrative example of a pharmacogenetic case–control study based on electronic health record databases is shown in Box 1.

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Box 1 Illustrative example of a pharmacogenetic study Genetic variability within the cholesterol-lowering pathway and the effectiveness of statins in reducing the risk of myocardial infarction This study investigated the effect of SNPs in genes involved in the cholesterol-lowering pathway of statins in decreasing the risk of myocardial infarction (MI). It was a case–control study, nested in a cohort of patients in the Utrecht Cardiovascular Pharmacogenetics (UCP) study. Patients from the PHARMO database who received a prescription for an antihypertensive drug and/or a cholesterol-lowering drug or had a total cholesterol level >5.0 mmol L 1 were included. Cases were patients hospitalized for a first MI. Controls were those without a MI and matched to cases by age (2 years), sex, region and index date of MI. Patients were contacted through their community pharmacies and provided written informed consent (explicitly for saliva collection, genotyping and storage of material). They were asked to complete a questionnaire to provide information regarding confounders. Determinants were statin exposure and 209 SNPs in 27 genes. A total of 668 cases and 1217 controls were included. Ten SNPs in 8 genes interacted significantly with the effect of statins. The most significant was the effect of SCARB1 rs4765615 in reducing the risk of MI (GG: OR 0.31, 95% CI = 0.19–0.51; and AG: OR 0.29, 95% CI = 0.21–0.41). Other genes were PCSK9 (rs10888896 and rs505151), ABCG5 (rs4245786 and rs1864815), LIPC rs16940379, ABCA1 rs4149264, PPARG rs2972164, LRP1 rs715948 and SOAT1 rs2493121. The Heart and Vascular Health Study (HVH) in Washington was used to replicate these findings in the 5 SNPs available, but none were statistically significant. Peters BJ, Pett H, Klungel OH, et al. Genetic variability within the cholesterol lowering pathway and the effectiveness of statins in reducing the risk of MI. Atherosclerosis 2011; 217(2): 458–464.

Potential of using electronic health record databases for pharmacogenetic research Pharmacoepidemiology uses epidemiological methods to evaluate the use and effect of drugs in selected patient groups or the general population. Pharmacoepidemiology provides supplemental information on drugs with better quantification of the incidence of beneficial and adverse effects with higher precision, including patients not considered in pre-marketing clinical studies and patients with comorbidities and concomitant drugs. Pharmacoepidemiology also contributes to gaining new knowledge on the beneficial and adverse effects not discovered previously in premarketing clinical studies, drug utilization patterns and cost-effectiveness of drug use [9]. The contribution of genetic variants in drug responses is the link that draws pharmacoepidemiology and pharmacogenetics together. Pharmacoepidemiology uses epidemiological methods to explain the variability of drug responses in daily practice by the evaluation of gene–drug interactions in pharmacogenetic studies. This knowledge can be used to individualize pharmacotherapy

based on the genetic makeup of individual patients. Pharmacoepidemiology and pharmacogenetics ultimately aim to contribute to optimizing the benefit–risk ratio of drug treatment. Patient data stored in electronic health record databases are being used to evaluate the intended and unintended effects of drugs. With the emergence of pharmacogenetics, it is becoming more important that genetic data are incorporated into these health record databases by, for example, linking these databases to banks of biosamples [7] or by collecting samples in patients who have contributed data to the database. Genetic data obtained from patients are their private property, so written informed consent must be obtained in accordance with the laws and guidelines of a particular country. Examples of large databases that have been used for pharmacogenetic research are the Group Health Cooperative (GHC) database [62], the PharmacoMorbidity Record Linkage System (PHARMO) database in the Netherlands [63], the Tayside Medicines Monitoring Unit (MEMO) database in Scotland [64] and the Clinical Practice Research Datalink (CPRD), formerly known as the General Practice Research ª 2014 The Association for the Publication of the Journal of Internal Medicine Journal of Internal Medicine, 2014, 275; 590–604

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Database (GPRD), in the United Kingdom [65]. These databases have been linked to biobanks in various pharmacogenetic studies. Table 2 gives examples of pharmacogenetic studies using these electronic health record databases. The GHC database is an electronic health record system with integrated best-practice alerts, decision supports and electronic prescribing for patients in Washington State and northern Idaho. Psaty et al. [66] used the GHC database to study the interaction between alpha-adducin and a diuretic on the risk of the first non-fatal myocardial infarction or stroke.

healthcare records of >10 million patients. The CPRD database has been linked using a TTP to other datasets, including hospital admission data, death certificates and disease registries. There are plans to also link CPRD to the biobankUK project. Patients can be recruited for pharmacogenetic studies through their GPs [75]. A recent study recruited 76 statin users with above-normal levels of creatine phosphokinase and 372 controls [70]. Several models can be employed when using electronic health record databases for pharmacogenetic research (Fig. 2). The strengths and limitations in using these databases for pharmacogenetic studies are summarized in Table 3.

The PHARMO database links drug-dispensing histories from Dutch community pharmacies to the national registrations of hospital discharges (LMR) from 1985 including patients representative of the general Dutch population [55]. The Utrecht Cardiovascular Pharmacogenetics (UCP) study established a biobank for a population at high cardiovascular risk (hypertensive and/or hypercholesterolemic and/or diabetic patients) from the PHARMO database. This database was used by Peters et al. to study the association of ten SNPs in relation to the effect of statins on the occurrence of acute myocardial infarction [55, 71]. More recently, the Dutch Mondriaan project established an Information and Communication Technology infrastructure for linkage and enrichment of routine health care databases. Using privacy-enhancing technologies such as application of a trusted third party (TTP), the research data are strictly separated from personal identifying information [72].

Large electronic medical record databases are useful data sources for studying drug responses in populations. The size of the population in the database will often be adequate for identification of genetic variants with modest effects. Subjects can be stratified based on their diverse background (including ethnicities). These databases can also provide detailed information on patients who can be ‘followed’ routinely over time. Depending on the database, up-to-date data on patients can be obtained without effort because data are collected routinely. A cohort can be chosen from the database rapidly. Data are available with relatively low costs, resulting in improved efficiency for answering important and acute problems in drug use. Data entry into databases is undertaken by those who are (mostly) not the researchers themselves, so observer bias can be avoided.

Since 1989, MEMO has been a university-based database of the population of Tayside, Scotland. It contains healthcare data, including data on all dispensed prescriptions. Prescription records are linked to hospitalization records [73]. The MEMO database has been linked to the Genetics of Diabetes Audit and Research Tayside (GoDARTS) biobank (which combines prescribing, biochemistry and phenotypic historical data with genetic data of patients with type-2 DM) in a study which showed that a genetic variant in transcription factor 7-like 2 (TCF7L2) affected the response to sulfonylurea [67].

However, the use of healthcare databases for pharmacogenetic studies has limitations. Mostly, the electronic healthcare databases have been developed for other purposes (e.g., for surveillance of drug use in pharmacies, efficient management of resources in healthcare institutions, reimbursement claims or enhancing healthcare quality) so not all the information of patients is available in the databases. Sometimes, for databases with different coding systems [76], patient information is sometimes not verifiable. Different coding and clinical terminologies used in each database has led to difficulties in comparing data between countries (or even between local databases).

The CPRD database is an electronic medical database containing comprehensive data from general practitioners (GPs) in primary care and is a representative sample of the entire population of the United Kingdom [74]. It includes the anonymized 598

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To be useful, these databases should have the complete entries of inpatients, outpatients and emergency patients, including information on their diagnosis, investigatory examinations (laboratory

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Table 2 Examples of pharmacogenetic studies using electronic health record databases Reference

Type of database

Drug–gene interaction

Finding

Harmsze

Hospital-based records linked to

CYP2C19*2/clopidogrel, (also

Carriers of CYP2C19*2 who also

et al. [50]

community pharmacy records,

studying calcium channel

used a calcium channel blocker

and de novo collection of DNA

blockers and proton pump

showed increased on-treatment

inhibitors) and on-treatment

platelet reactivity while on

platelet reactivity and risk of

clopidogrel and increased risk of

atherothrombotic events in

the composite atherothrombotic

clopidogrel-treated patients

endpoint (OR 3.1, 95% CI = 1.1– 9.0)

Peters et al. [55]

Community pharmacy records

Genes in cholesterol-lowering

10 SNPs in 8 genes (SCARB1,

linked to hospital records and

pathway/statins and risk of

PCSK9, ABCG5, LIPC, ABCA1,

enrichment by de novo

acute myocardial infarction in

PPARG, LRP1, SOAT1)

collection of DNA and

antihypertensive and/or

influenced the effectiveness of

questionnaire information on

hypolipidemic drug users

statin treatment; the most

risk factors

significant interaction was for SCARB1 rs4765615, in which carriers of the homozygous A allele had less benefit from statins (OR 0.64, 95% CI = 0.41 –0.98) compared with those with one or two G alleles

Psaty et al. [66]

Health maintenance (pharmacy,

Alpha-adducin/diuretics and

Carriers of the adducin Trp460

hospital, family physician) and

risk of first nonfatal myocardial

variant allele who had diuretics

de novo collection of DNA and

infarction or stroke in

had a lower risk of combined

questionnaires on risk factors

antihypertensive drug users

outcome of myocardial infarction and stroke compared with non-carriers (OR 0.45, 95% CI = 0.26–0.79), and this was not confounded by traditional cardiovascular risk factors

Pearson et al. [67]

University-based dispensing

TCF7L2/sulfonylurea and failure

Carriers of rs12255372 TT

records linked to hospital

to reach target HbA1c C variant is a

general practitioners (CPRD)

with serum CPK levels of >4-

significant risk factor for statin-

with sample collection

times the upper normal limit

induced myopathy (OR 2.08, 95% CI = 1.35–3.23)

ACE, angiotensin converting enzyme, CI, confidence interval, CPK, creatine phosphokinase, CPRD, Clinical Practice Research Datalink, DNA, deoxyribonucleic acid, OR, odds ratio, SNP, single-nucleotide polymorphism.

PGx study Define RQ & Study Population 1 3 Select subjects EHR • GP records • Pharmacy records • Hospital records • Claims database

Contact patients • Obtain informed consent • Collect data o Interview o Questionnaire o Biological samples

2

PGx study Define RQ & Study Population

Model 1

1 3

PGx study Define RQ & Study Population

Biobanks • Specific study • Population survey • Hospital routine genotyping

1 3

Recruit patients • Obtain informed consent • Collect data o Interview o Questionnaire o Biological samples

Select subjects

2

• • • •

EHR GP records Pharmacy records Hospital records Claims database

2

• • • •

EHR GP records Pharmacy records Hospital records Claims database

Model 3

Model 2

Fig. 2 Several models in using electronic health records in pharmacogenetic research. Numbers in each model represent the sequential steps. Model 1: Subjects are chosen from EHR, and then lifestyle risk factors and genotype information are obtained from patients after they gave informed consent [55, 66, 70]. Model 2: Lifestyle risk factors and genotyping data are obtained from patients in a pharmacogenetic study, and then drugs and disease information are obtained from EHR [50]. Model 3: Subjects are selected from biobanks (from large cohort/case-control/randomized controlled studies, population health survey or routine genotyping data in hospitals), which provide lifestyle risk factors and genotyping data, and then drugs and disease information are obtained from EHR [51, 67–69]. GP, general practitioner; EHR, electronic health records; PGx, pharmacogenetics; RQ, research question. 600

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Table 3 Strengths and limitations of the use of electronic health record databases for pharmacogenetic studies Strength

• • •

Large sample size

Limitation

•

Extensive and up-todate longitudinal data Routine collection of data

•

Relatively inexpensive

•

Minimal observer bias

• • •

Variable comprehensiveness of databases and incomplete phenotypic descriptions Phenotypes not always verifiable Different coding system/ clinical terminology Ethical, legal and social implications

and radiological tests) and pharmacological as well as non-pharmacological treatments. The drugs in question have to be prescribed in substantial number to ensure adequate power in statistical analyses. Data from databases may need to be combined with background data obtained from patient interviews, including information on lifestyle and family history. The weakness of information obtained from databases is that these databases usually do not include less than moderate-to-severe diseases [77]. Availability and access to genetic data, drug-exposure data and information on disease and outcome are crucial to pharmacogenetic research. An ‘ideal’ database is a comprehensive electronic healthcare database containing regularly updated, verifiable, reliable data, with easy access for researchers. However, the ‘ideal’ database is not available yet. A potential solution to this is the linkage between available electronic health record databases and biobanks. Biobanks are built for collecting biological samples for research use. Biobanks can be constructed by collecting DNA samples from populations (or routinely from patients in hospital), complete with data on disease history, lifestyle and all potentially important health-relevant information. Pharmacogenetic researchers can formulate research questions and extract data directly from complete biobanks. Another alternative is choosing subjects from an electronic health record database, de-identifying patient data to remove personal identifiers and then linking the de-identified data to DNA samples from biobanks [78]. Linking healthcare databases to genotype data in biobanks also brings the issue of ethical, legal,

social and financial implications [4, 6]. Ethical issues predominate in this respect, particularly in databases where genetic information is not collected routinely. These issues sometimes come from the misunderstanding of the use of genetic testing for gene–drug interactions. Patients (and their lawyers) as well as health practitioners need to be informed about the difference between genetic testing for predicting rare, severe disease (disease susceptibility) and tests for predicting drug responses. Most people fear gene testing for disease susceptibility because of the fear of discrimination (by employer or health insurance company) and stigmatization [79]. Fear of genetic discrimination in the USA has been one of the greatest barriers to the integration of genetic tests in clinical practice [80]. With passing of the Genetic Information Nondiscrimination Act (GINA) in 2008 (a Federal law that prohibits discrimination in health insurance and employment based on genetic information [81]), this fear is expected to be reduced. Access to individual genetic data and public SNP data may lead to the identification of the individual because only a small set of SNPs contain information that can be used to identify a specific person [82]. Several genetic variants which actually determine the drug response may also determine the predisposition to disease. Therefore, use of genetic information for pharmacogenetic studies needs the assurance that privacy and confidentiality issues have been addressed with the owners of the genetic data which, in this case, are the patients who use the drugs. Written informed consent (in general or specific to the research objectives) must be obtained if samples are taken for genotyping. Patients must be informed that their DNA samples may be used for future research. There are also concerns that the result of routine genotyping will be used by insurance companies or employers, depriving the patients from insurance coverage and job opportunities [17]. Although the cost for genotyping is decreasing, until now, there are few adequate studies on the cost-effectiveness of routine genotyping [1]. This lack of evidence has been one of the causes of considerable reluctance in adopting routine genotyping in clinical practice. Other causes include the lack of information and guidelines on routine genotyping (and the further action needed) on the part of physicians [83] and the lack of institutional/healthcare system support [84]. Providing user-friendly information and training physicians ª 2014 The Association for the Publication of the Journal of Internal Medicine Journal of Internal Medicine, 2014, 275; 590–604

601

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on genotyping for drug responses (through the leaflets in the packages of genotyping tests and genotype-related drugs or through official guidelines from professional medical communities) will increase acceptance from medical professionals and healthcare systems. The Royal Dutch Association for the Advancement of Pharmacy is establishing the Pharmacogenetics Working Group to develop pharmacogenetic-based dosing recommendations for 53 drugs associated with genes coding for CYP2D6, CYP2C19, CYP2C9, TPMT, DPD, VKORC1, UGT1A1, HLA-B44, HLA-B*5701, CYP3A5 and factor V Leiden [10]. Availability of point-of-care genetic tests also helps in facilitating utilization for timely clinical decision-making. Last but not least, acceptance by medical communities will be enhanced by adequately powered pharmacogenetic studies, along with the cost-effectiveness of the genotyping, not only conducted with a clinical-trial design, but also in the ‘real’ clinical situation. This is where healthcare databases will have an important role. Furthermore, in addition to their use in observational studies, healthcare databases may also be used in cluster randomized trials between sites using routine genotyping and those not using one. Databases can supply adequate pre-intervention data to enable balance in the baseline demographic and clinical characteristics between groups, thereby resulting in less selection bias, more efficient sample size and less requirement of extensive adjustment of covariates [85]. Conclusion Availability of high-quality electronic health record databases involving extensive information on patients is important for providing reliable data for pharmacogenetic research. Furthermore, access to DNA samples through de novo collection or linkage with existing biobanks makes electronic health record databases a valuable resource for pharmacogenetic studies. Patient identifiers are needed to validate individual data which, through privacyenhancing technologies (such as application of a TTP), can be protected. In the future, when routine genotyping is more common (preferably after it has been assessed in terms of cost-effectiveness), it may be incorporated directly in the electronic healthcare database, giving an ideal data source for studying the factors determining the variability of drug responses. Conflict of interest statement No conflict of interests is declared. 602

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