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Pharmacogenovigilance: a pharmacogenomics pharmacovigilance program In this report, we review the importance of pharmacovigilance in detecting postmarketing adverse drug events and the potential for developing pharmacogenovigilance programs by integrating pharmacogenomics with pharmacovigilance. We propose to start developing such a program in primary healthcare systems that use basic features of electronic medical records and have access to large numbers of patients commonly prescribed drugs. Such programs, if carefully designed, may grow over time and hopefully enhance the collection and interpretation of useful data for the clinical applications of pharmacogenomics testing. KEYWORDS: adverse drug events n electronic medical records n pharmacogenomics n pharmacogenovigilance n pharmacovigilance n primary care

Pharmacovigilance Pharmacovigilance (PV), also known as Phase IV clinical trials or postmarketing surveillance, is defined by the WHO as ‘the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problems’ [1]. PV monitors the safety of drugs once they are approved for marketing, and provides a longitudinal evidence stream essential to ensure that patients receive drugs of acceptable benefit–risk profiles [2]. „„ Importance of PV Failure to effectively conduct PV studies may expose the population to serious adverse drug events (ADEs). ADEs may not only affect patient well-being but also impose a considerable medical and financial burden on the healthcare system. In the USA, ADEs are the 4th to 6th leading causes of death [3], and have been shown to extend the average hospital stay by 1.74 days [4]. Similar figures were found in other countries [5]. In addition, studies conducted among hospitalized patients in The Netherlands and Canada reported ADE rates of 5.6 and 24.1%, respectively [6,7]. Therefore, by timely detection of adverse outcomes associated with drug exposure, PV could be an area of major medical and economic benefits [8]. During drug development in the premarketing phase, safety data are systematically gathered in preclinical and clinical trials for discovery of signals of potential safety issues. A signal is defined by the WHO as ‘reported information on a possible causal relationship between an adverse event and a drug, the relationship being 10.2217/PGS.14.44 © 2014 Future Medicine Ltd

unknown or incompletely documented previously’ [1]. The most rigorous study designs conducted to determine presences of cause–effect relationships between drug exposure and outcome are double-blind randomized controlled Phase III clinical trials. However, these are not enough for evaluating the full safety profiles of drugs. The limited number of participating patients and the short duration of clinical trials make it difficult to detect signals of rare occurrence or with a long latency. Another important limitation is the assumed homogeneity of the characteristics of participants being tested. For instance in clinical trials, the sample is not necessarily representative of the entire population owing to exclusion of at-risk subpopulations; this includes patients with comorbid conditions and those taking concomitant medications. Therefore, PV is necessary in order to detect signals of rare occurrence, signals with a long latency and signals that are peculiar to specific populations [2].

Zeinab Awada1 & Nathalie Khoueiry Zgheib*,2 Biomedical Sciences, Faculty of Medicine, American University of Beirut, Beirut, Lebanon 2 Department of Pharmacology & Toxicology, Faculty of Medicine, American University of Beirut, PO Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon *Author for correspondence: Tel.: +1 350000 ext. 4846 [email protected] 1

„„ Methods used in PV PV studies can either be descriptive or analytical. Descriptive studies are hypotheses-generating studies that determine occurrence of druginduced serious events, whereas analytical studies validate generated hypotheses, detect causal relationships between drugs and events, and estimate the proportion of these events [2]. Passive postmarketing surveillance has been the dominant method of collecting information on drug safety after marketing. It relies on healthcare practitioners, patients and others to spontaneously report observed ADEs, errors and drug quality problems to the manufacturer or Pharmacogenomics (2014) 15(6), 845–856

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directly to the drug regulatory authorities [9]. This ADE spontaneous reporting system is used by several PV centers such as the US FDA and the international PV program of the WHO [10]. This mechanism has led to the discovery of major safety issues that have caused the withdrawal of many drugs from the marketplace such as terfenadine that was withdrawn from the market owing to cardiac arrhythmia and sudden death, and dipyrone because of agranulocytosis [11,12]. However, this spontaneous reporting approach is limited by a small reporting rate, only up to 6% of ADEs [13], and by inadequate data on each report, producing incomplete and unreliable data. Accordingly, drugs could be used for years by a large number of patients before risks become evident [14]. Therefore, the US FDA launched its Sentinel Initiative to improve drug safety monitoring. This initiative seeks to conduct active post­ marketing drug surveillance by using information gathered for other aims, such as data from insurance claims and electronic health records that contain relatively more complete and higher quality data than spontaneous reports. This Sentinel system allows detection and investigation of safety signals through generating, refining and evaluating signals in near real-time. Besides, this method overcomes the problem of underreporting or reporting biases and can be used to calculate ADE rates [9]. „„ Limitation of PV studies Despite active and analytical surveillance, modern PV study designs still have several limitations. Most importantly, they study the relationship between developed events and administered drugs; that is, they do not undertake proactive measures to prevent occurrence of serious events once a drug is administered. In addition, PV study designs fail to subclassify patients into high- and low-risk groups as some of the reported drug outcomes may be specific to certain subpopulations, such as the lower anti­ hypertensive efficacy of angiotensin-converting enzyme inhibitors in African–Americans when compared with Caucasians [15]. An additional limitation is that PV designs do not ascertain the causal relationship between a drug and an event as they do not assess the underlying mechanism of an ADE [16]. Therefore, PV studies require newer approaches/technologies that enable proactive prevention of ADEs, segregation of patients into risk categories, and interpretation of the mechanisms of drug toxicity and/or response. One way to address these limitations 846

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is by incorporating genomic analyses into PV programs.

Pharmacogenomics Genetic variations among patients may contribute to differences in drug response and tolerability. Pharmacogenetics and pharmacogenomics (PGx) are terms that are commonly used interchangeably for the application of molecular genetic approaches to understand interindividual differences in drug response [17]. Genetic variability may affect the clinical response to drugs as well as the frequency and or severity of ADEs in at least three ways. The first is through variants in genes involved in drug metabolism and/or transport whereby increased or decreased metabolism or transport of a drug may influence the plasma concentration of the drug and its active, inactive and toxic metabolites; hence altering drug response and possibly leading to the development of ADEs. A typical example is the increased sensitivity to the effects of drugs metabolized by CYP2C19, such as the antiulcer agent omeprazole in patients who are homozygous for the null allele of CYP2C19 (poor metabolizers) [18]. The second is through variants in genes encoding drug targets, such as the evidence of increased risk of bleeding with warfarin in patients with variants in the gene encoding VKORC1 [19]. The third is through variants in genes involved in unexpected drug effects. This includes hemolysis in patients with G6PD deficiency and who are exposed to rasburicase to reduce the risk of tumor necrosis syndrome or the antimalarial primaquine [20]. It is important to note that although some ADEs are associated with monogenic traits, whereby a drug’s pharmaco­k inetics and dynamics may be related to variants in a single gene, the overall clinical effects of most of the drugs are more likely to be influenced by multiple genes involved in metabolism, distribution, elimination and action of drugs [21]. The discovery of several common genetic variations associated with drug response and the increased availability of PGx tests has led the US FDA to include PGx data on more than 30 drug labels [22]. Nevertheless, detailed examination of many of these label changes revealed that most of them, such as that for clopidogrel, were premature because of a lack of clinical utility evidence and clear guidelines for the prescribing physician [23]. Accordingly, organizations such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Royal Dutch Association for the Advancement of future science group

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Pharmacy–Pharmacogenetics Working Group have also been working diligently to translate genetic test results into specific dose recommendations and guidelines (Supplementary Table 1; www.futuremedicine.com/doi/suppl/10.2217/ pgs.14.44) [3,22,24–35]. Interestingly, however, the CPIC experts distance themselves from any potential harm resulting from these guidelines. In addition, it seems that application of PGx to determine clinical decisions may improve risk over benefit ratio at the population level, but it does not guarantee improvement at the individual level. Hence further work is needed before the widely promoted personalized medicine becomes a reality [23].

Pharmacogenovigilance PGx research can fill the gap in PV studies since PV detects ADEs related to drugs, and PGx assists in understanding the underlying connections between drug exposure and development of these ADEs. Thus, PV monitors the hetero­geneity in population response to drugs, and PGx interprets this heterogeneity. Therefore, combining the two disciplines, PV and PGx, into pharmacogenovigilance (PgV), which is informing and guiding PV activities by PGx analyses [16], may lead to the detection of subpopulations whereby ADEs are more likely to occur. Distinguishing those subpopulations may allow pharmaceutical companies to market drugs for low-risk groups. Both PGx and PV may also permit population-level extrapolation of findings [16]. For instance, because a safety signal has been observed in patients who have G6PD deficiency, risks are considered greater in populations of African, south Asian, Middle Eastern and Mediterranean ancestry, where the prevalence of G6PD deficiency is relatively high [36]. On the other hand, PV can also fill the gap in PGx studies by enabling investigators to gather data whose practical and clinical value could then be established prospectively. Role of PV centers in integrating PGx PV centers can play an important role in conducting PGx analyses reactively in response to reported ADEs. van Puijenbroek et al. suggested that PV centers could develop a method capable of informing the reporting physicians and patients about possible involvement of a genetic polymorphism in the ADE, and recommended subsequent genotyping of patients based on this possibility [37]. They concluded that after identification of safety signals, PV centers can be a valuable starting point for PGx studies, since future science group

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data from different centers can be combined to evaluate the causal relationship between drug exposure and ADE. Unfortunately, the level of participation of physicians in genotyping their patients was not found to be very high (39.5%). In addition, a pilot study carried out by de Bruin and her colleagues in The Netherlands investigated the possibility of a PGx study in which cases were chosen from the database of a spontaneous reporting system for ADEs [38]. Despite the poor response from the surveyed doctors (seven out of 29 participated), it was concluded that spontaneous reports of ADEs may be used for PGx research. Nevertheless, methods should be improved to increase physicians’ participation. In addition, international collaborations may be required. Based on the above, we believe that PgV programs should be based on the active collection of ADEs rather than depend on spontaneous reports. In addition, PV centers should develop one step further and conduct PGx analyses prospectively and proactively. For instance, genomewide scanning or a large number of genetic polymorphisms in pathway genes that are potentially involved in drug elimination and mechanism of action can be performed upfront and linked with ADEs. After assessment of findings in multiple studies, genetic test results would be translated into therapeutic guidelines whereby treatment regimens or dosing can be modified in patients carrying high-risk genetic polymorphisms in order to prevent development of ADEs. This proposal is in line with that of the EMA that has fairly recently developed a concept paper proposing seven key aspects for producing guidelines on the use of PGx methods in the PV evaluation of drugs. Two of these aspects are: “early consideration of when postauthorization genomic data may need to be monitored or collected to confirm appropriate dose and comedications, as well as to provide information or advice based on identified genomic biomarkers” and “collection and storage of genomic material (e.g., DNA or other) during clinical trials and upon the occurrence of serious ADRs, lack of effectiveness postauthorization or unexpected worsening of the condition” [39].

Current PgV programs To our knowledge, the majority of PGx studies were based on retrospective analyses, and only a few results were tested prospectively such as the influence of MHC, class I, B*5701 (HLA-B*5701) genotyping on abacavir-induced hypersensitivity reactions [40], and the effect of www.futuremedicine.com

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CYP2C9 and VKORC1 genetic polymorphisms on warfarin toxicity and efficacy, although recent evidence from two prospectively conducted trials showed that the value of pretreatment genotyping for warfarin is still controversial [41,42]. Currently described PgV programs can be divided into two categories: those that perform genotyping before signal detection and those that perform targeted genotyping based on signal detection. „„ Genotyping before signal detection This involves candidate genes or high-throughput genotyping to allow for later association of this genetic information with treatmentrelated signals that develop during the span of a patient’s clinical care. PGx findings that are clinically actionable would be integrated into the patients’ electronic medical record (EMR) to inform treatment decisions. This approach has been used by few academic institutions such as the University of Florida Clinical and Translational Science Institute (FL, USA) with a focus on the hematological drug clopidogrel. The aim was to perform adequate sample and information processing to create alerts in the EMRs for clopidogrel prescriptions. The effectiveness of the EMR alerts, changes in prescriptions, and effect on efficacy and safety of drugs will be assessed during program implementation [43]. The St Jude PGx for kids (PG4KDS) is another program whereby high-throughput genotyping is being performed on children treated for acute lymphoblastic leukemia. So far the focus has been on genetic data for pharmacologically important genes, such as CYP2D6, CYP2C19, CYP2C9, VKORC1 and TPMT that have been shown to determine clinical prescribing decisions and to have known PGx interpretations. In addition, scalable PGx consult templates were developed because, as more studies are carried out, well-characterized PGx evidence for additional drugs may become available and previous interpretations may change. For example, the CYP2D6 PGx consult template is divided into five modular sections: phenotype, diplotype interpretation, dosing recommendation, activity score and an educational link, with each modular section containing diplotype-specific versions. Test results and consults that are of highpriority are highlighted, and high-risk pheno­ types are automatically put on the problem list in the EMR, enabling decision support alerts to appear when an interacting drug is prescribed. This system has several valuable benefits. For instance, as information changes, modification 848

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should be made to the related modular version only, without the need to change the whole template. Also, this modular approach facilitates the use of pre-emptive PGx screening, particularly high-throughput genotyping data in clinical practice, since hundreds of consult templates can be constructed before any results are entered into the EMR. These consults provide clinicians who find difficulty in understanding the clinical applicability of PGx data with clear diplotypebased clinical interpretations. However, building up consult templates is hampered by the absence of agreement on how to interpret many of these PGx test results [44]. „„ Signal detection followed by targeted genotyping This involves the selection for genotyping of patients who experienced ADEs. Blood could be obtained from the patients before the drug is administered or after the decision to genotype is made. This model is highly efficient as, for example, it led to the discovery of the contribution of the HLA-B region in the hyper­ sensitivity risk of the antiviral agent abacavir [45]. The Netherlands PV center also carried out this approach in studying the feasibility of informing the pharmacist or physician about the possible involvement of PGx in the pathogenesis of ADEs, hence the potential role for further genotyping [37]. An additional example is the nested case–control study design developed in the New Zealand Intensive Medicines Monitoring Program (NZIMMP) to link prescription event monitoring studies with PGx: almost complete records of all patients administered widely used drugs of a new class were obtained throughout the whole monitoring period (4–5 years). Patients who developed psychiatric or visual disturbances following COX-2 inhibitor use were selected from the IMMP monitoring database and matched with controls [46]. More recently, the Vanderbilt personalized medicine initiative, PREDICT project, the Canadian Pharmaco­ genomics Network for Drug Safety (CPNDS), and the International Serious Adverse Events Consortium (iSAEC) have been conducting genomic analyses on selected patients who suffer from serious ADEs with matched control patients [47–49]. The PREDICT project at Vanderbilt entails the implementation of prospective genotyping linked to an elaborate decision-support system to guide clinical care. The investigators initially started with CYP2C19 genotyping in patients with cardiac diseases and maintained on clopidogrel. Additional future science group

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drug–gene pairs were planned such as warfarin with CYP2C9 and VKORC1. Some of the lessons learnt from the program implementation were the need for commitment, collaboration, as well as patient cooperation and education. Probably the most challenging task is to stay updated with the evolving PGx evidence by carefully and continuously reviewing the literature, and accordingly refining the decision support rules [47]. The CPNDS program has developed an active ADE surveillance network across Canada. Clinicians, who are trained to identify patients with severe ADEs, document and upload accurate and comprehensive clinical information onto a secure server. Causality of ADEs is evaluated by these clinicians in addition to site investigators using both the Naranjo ADE probability scale and the WHO collaboration center for international drug monitoring causality assessment algorithm. The hiring of experienced active ADE surveillance clinicians allows this program to overcome a major limitation of other PGx programs, which is potentially missing crucial information about patient history, comorbid conditions, and concomitant medications. Since March 2010, the program has detected more than 2900 severe ADE cases, a number that is expected to increase with national and international collaboration. In order to increase the benefit from limited research funds, the program focuses on severe ADEs associated with mortality or lifelong disabling conditions. Interestingly, CPNDS has detected PGx biomarkers that are predictors of two severe ADEs: rs12201199 in the TPMT gene or rs9332377 in the COMT gene, in approximately 50% of patients who experienced hearing loss on cisplatin, and CYP2D6 gene duplication in cases of codeine-induced infant mortality. Further studies are ongoing to identify additional predictive biomarkers for cisplatininduced ototoxicity and to assess the benefits of prospective PGx testing [48]. As for the iSAEC, it has established international collaboration with academic and clinical networks to assemble expertise and sample sizes sufficient to carry on statistically meaningful association analyses. Program objectives include efforts to standardized ADE phenotypes and develop effective genotyping and computational methods. The consortium has identified genetic polymorphisms that are associated with increased risks of flucloxacillin-induced liver injury, amoxicillin/clavulanate-induced liver injury, and druginduced blistering skin rash [49]. The consortium also conducted a recent genome-wide association study of the largest drug-induced liver injury future science group

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cohort (783 European-derived patients with drug-induced liver injury due to >200 drugs and 3001 controls) that showed no significant genetic risk factors [50]. The study concluded that significant associations may be drug specific or involve rare variants that may only be covered by next-generation sequencing. Ongoing studies investigate the predictive variants of other ADEs such as drug-induced torsades de pointes, renal injury, nephrotoxicity, osteonecrosis of the jaw and excessive weight gain [49,51]. We believe that the PgV programs described above are very valuable but too few. There is, therefore, a need for the development of more programs and centers of the sort. In addition, we believe that efforts should be focused on primary care services. This is because primary care systems have access to large numbers of patients, and drug therapy is probably the most common medical intervention, and this is associated with a high frequency of ADEs [52,53].

PgV program in primary care „„ Program set up Figure 1 shows a schematic description of a proposed PgV program that entails the prospective and active collection of biological samples for genotyping, and ADE data from EMRs for signal detection. For a PgV program to be successful in primary care, the following resources are needed upon initial launching: healthcare professionals open to change, PGx experts with access to biological samples storage space and genotyping technologies, basic EMRs with some structured data, and basic bioinformatics and data mining abilities. Last but not least is the need for institutional support and startup funds for project initiation. The program can be sustained if it shows evidence of cost savings generated from decreased costs in healthcare. „„ Implementation steps Informed consent for blood storage & genetic testing

All patients of the primary care medical service are approached and informed about the program. Those who accept, provide written informed consent for clinical data collection and future contact. Whole blood (and potentially other biological samples if possible) is drawn and stored for future genotyping. ADE signal detection from EMRs

EMRs can play an important role in providing practice-based longitudinal datasets crucial for detection of ADE signals. EMRs are usually www.futuremedicine.com

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Society

Pharmacoeconomics

Patient Personalized prescribing

Informed consent

Healthcare practitioners

Analysis of clinical utility

Pharmacogenomics experts

Data collection

Molecular biologists

– Communication – Education – Drug prioritization – Phenotype standardization – Data analysis and applications

Clinical pharmacologists Clinical pharmacists

Blood sample

Clinical data

Data entry

Integration of results

Electronic medical records

Storage for future analysis

Data mining

A signal triggers genotyping or analysis of already available genotyping results Adverse drug events

Candidate polymorphisms/genes Genotyping

Genotyping results analyzed with adverse drug event

Pathway analysis Next-generation sequencing Whole-genome analysis

Pharmacovigilance

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Pharmacogenovigilance

Figure 1. Pharmacogenovigilance model.

composed of structured and unstructured data elements. Structured data at least include diagnoses, vital signs, laboratory results, medical history and current medications. Unstructured data include free-text progress notes [54]. 850

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The ability to detect serious signals of ADEs requires a large set of patients and an extensive EMR database. Yet, and due to the absence of enough funds and adequate infrastructure, one may need to prioritize and start with a limited future science group

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set of phenotypic definitions that are reported as per common standards and relatively easily retrievable from EMRs. In order to detect ADE signals, one can apply an approach whereby a set of predefined events are extracted from EMRs and observed for potential linkage with all possible drugs used in primary care. This approach focuses on events deemed important from a public health perspective. Trifiro et al. list five criteria of high-risk events of interest for intensive longitudinal monitoring: event-induced drug withdrawal, event-induced black-box warning, event-induced hospitalization, probability of event being drug-related, and event-induced mortality [55]. These include hematologic events, cutaneous events, liver and gastrointestinal events, cardiac and vascular events, neurologic and musculo­skeletal, psychiatric, renal events and multisystemic events (Table 1) [55,56]. To establish a successful causal relationship between a drug and an ADE or combination of drugs, a set of data should be provided accurately in the EMRs and efficiently extracted by data mining techniques. Naranjo et al. suggested nine theoretical and practical criteria for causality assessment including rechallenge [57]. Nevertheless in practice, rechallenge is considered unethical especially in the presence of alternative treatments. As described by Currow et al., of the nine criteria proposed by Naranjo and his colleagues, five were evaluated as applicable and useful in understanding whether the development of ADEs is causally related to a specific drug or combination of drugs [58]. To summarize, the temporal relationship between the drug and an event, exacerbation/attenuation of an event after cessation of the drug, alternative causes such as age or medical condition that could have caused or aggravated the event, similar events encountered upon the administration of the same or similar drugs, and objective evidence that confirms the presence of adverse events, should be accurately provided and retrieved from EMRs. Based on the above ana­lysis and data shown in Table 1, one can start by monitoring data that can be relatively easily retrievable from basic EMRs. These include: electronic laboratory data such as complete blood counts as markers of myelotoxicity and surrogates for bleeding, liver function tests as markers of hepatotoxicity, and serum creatinine as a marker of kidney injury. In addition, EMRs may include more specialized data such as electrocardiograms (ECG) that can be used for detection of QT prolongation, and echocardiography results that are crucial for, as an example, detection and follow-up of heart future science group

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failure. This is in contrast to physical exam and medical history data most of which is usually highly unstructured and difficult to analyze. Nevertheless, one may be able to detect changes in vital signs such as blood pressure. Drug prioritization

Although ultimately all drugs should be part of the PgV program, it is very labor intensive and costly to start with everything. Therefore, we propose to develop a list of a limited number of drugs that are commonly prescribed in primary care and for which already known toxicities can be detected by interpreting basic data points from EMRs. As discussed above, this list may include drugs that are already known to be associated with liver toxicity that can be detected by a simple increase in liver function tests, a test that is usually recorded in electronic laboratory data. Another approach would be to choose the most commonly prescribed drugs in primary care that are linked to PGx evidence for pre-emptive genotyping; the efficacy of such an approach may however be questionable because of the lack of strong PGx evidence [53,59]. One can also argue to start with commonly prescribed drugs that appear in the WHO core list of essential medicine [10]; yet we recommend that the program drug prioritization be not limited to that list, but rather include a more extensive number of drugs per class. This is because the WHO document lists only minimum medicine needs, and hence it does not include drugs, such as clopidogrel and nonmetformin oral hypoglycemics, that may be commonly prescribed and have been shown to be associated with ADEs (Table 1). Genetic testing & ana­lysis

After display of ADE signals in association with specific drugs, patients of interest can be genotyped for presence of target pathway genes such as polymorphisms in drug-metabolizing enzymes and transporters. In addition, and depending on available funding, one can shift from searches for specific SNPs in candidate genes to wholegenome scanning using high-throughput technologies and the revolutionary next-generation sequencing that has recently shifted the paradigm of genomics [17,60]. As shown in Figure 1, high-throughput genotyping can be performed upon initial blood sample collection, and then analyzed based on relevant signal detection. Knowledge integration, use & dissemination

A PgV program is successful only if funds and efforts spent culminate into more cost effective www.futuremedicine.com

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Table 1. Drugs or drug classes associated with highly scored adverse drug events. System

Event

Helpful tests

Drug/drug class

Hematologic

Hemolytic anemia

CBC

Cephalosporins, penicillin/penicillin derivatives, NSAIDs, quinine/ quinidine, probenecid, levofloxacin, mefoxin, carboplatin, oxaliplatin

Aplastic anemia/ pancytopenia

CBC

Sulfonamides, chloramphenicol, anti-inflammatory/antirheumatic drugs, anticonvulsants, antithyroids, oral hypoglycemic agents (sulfonylureas), antipsychotics, antimalarial drugs

Neutropenia

CBC

Analgesics/anti-inflammatory drugs, antipsychotics, antidepressants (imipramine, desipramine, mianserin, mirtazapine), anticonvulsants, antithyroid drugs, antiallergic agents, antibiotics, cardiovascular drugs, thiazide diuretics, cytotoxic drugs, heavy metals, miscellaneous drugs (allopurinol, colchicine, levodopa)

Thrombocytopenia

CBC

Penicillin, sulfonamides, NSAIDs, quinine, quinidine, heparin, anticonvulsants, antirheumatics, oral antidiabetic drugs, gold salts, diuretics, rifampicin, ranitidine, chemotherapeutic agents

Maculopapular erythematous eruptions

PE

Antibiotics (aminopenicillins, cephalosporins, sulfonamides), antiepileptic drugs, allopurinol

Bullous eruptions (SJS/Lyell’s syndrome)

PE

Antibiotics (aminopenicillins, sulfonamides), hydantoins, antiepileptics, barbiturates, benoxaprofen†, phenylbutazone, NSAIDs (isoxicam†, piroxicam), chlormezanone, allopurinol, amithiozone, nevirapine, amifostine

Acute liver injury

Liver function tests

Analgesic (acetaminophen), NSAIDs, antituberculosis agents (isoniazid), anti-HIV drugs (nevirapine, ritonavir), antidiabetics (troglitazone)

Acute pancreatitis

Serum amylase and lipase

Analgesics, NSAIDs, penicillins, HMG-CoA reductase inhibitors (statins), antiarrythmics, ACE inhibitors, oral contraceptives/HRT, loop and thiazide diuretics, antiepileptics

Upper gastrointestinal bleeding

PE

NSAIDs, thyocolchicoside, antiplatelet agents, anticoagulants

Acute MI

ECG, troponins I and T, CPK-MB

Calcium channel blockers, b-agonists, tricyclic antidepressants, NSAIDs

QT prolongation

ECG

Antiarrhythmic drugs, calcium channel blockers (bepridil†, prenylamine†, terolidine†), antipsychotics (sertindole†), antihistamines (terfenadine†), antimicrobials and antimalarials, serotonin 5-HT4 receptor agonist (cisapride†), serotonin 5-HT2A receptor antagonist (ketanserin), selective serotonin receptor inhibitors (fluoxetine, paroxetine), immunosuppressants

Cardiac valve fibrosis

Echocardiography

Ergot alkaloids, appetite suppressants, dopamine agonists, 5HT2B agonists

Venous thrombosis

Doppler ultrasound, MRI

NSAIDs, corticosteroids, neuroleptics, chemotherapeutic agents (bleomycin, cisplatin), heparin

Convulsions

EEG

Analgesics, NSAIDs, anesthetics, antibiotics, neuromuscular blockers, anticonvulsants, anticholinergics, antidepressants, antifungals, antivirals, antihistamines, antiparasitics, cardiovascular agents, antidiabetics

Peripheral neuropathy

PE

Antibiotics, antineoplastic drugs, cardiovascular drugs, hypnotics (methaqualone†), antirheumatics, anticonvulsants (phenytoin)

Extrapyramidal disorders

PE

Typical antipsychotics, antidepressants

Cutaneous

Digestive

Cardiovascular

Neurologic

Drugs that are in bold are also mentioned in Supplementary Table 1. † Withdrawn from the market. ACE: Angiotensin-converting enzyme; BUN: Blood urea nitrogen; BP: Blood pressure; CBC: Complete blood count; CPK-MB: MB isoenzyme of creatine phosphokinase; ECG: Electrocardiography; EEG: Electroencephalography; HMG-CoA: Hydroxyl-3-methylglutaryl coenzyme A; HRT: Hormone replacement therapy; MI: Myocardial infarction; NSAID: Nonsteroidal anti-inflammatory drug; PE: Physical examination; SJS: Stevens–Johnson syndrome. Data taken from [55,56].

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Table 1. Drugs or drug classes associated with highly scored adverse drug events (cont.). System

Event

Helpful tests

Drug/drug class

Neurologic (cont.)

Rhabdomyolysis

CK, myoglobinurea

Amphetamine, amphotericin B, antimalarials, CNS depressants, colchicine, salicylates, corticosteroids, antihistaminics, diuretics, fibrates, HMG-CoA reductase inhibitors (statins), isoniazid, laxatives, narcotics, phenytoin

Psychiatric

Confusion

PE

Anticholinergics (atropine, scopolamine), benzodiazepines (nitrazepam, flurazepam), opoid analgesics (pethidine), antiparkinsonian drugs (levodopa, selegiline), tricyclic antidepressants (amitriptyline, imipramine), antipsychotics (clozapine, thioridazine)

Depression/mania

PE

Antiviral agents, cardiovascular agents, retinoic acid derivatives, antidepressants, anticonvulsants, antimigraine agents, antipsychotics, hormonal agents, immunologic agents

Amnesias

PE

Sedative/hypnotics

Suicidal behavior/attempt

PE

Analgesics, antihistaminics, antibiotics, antivirals, antiinflammatory agents, antipsychotics, anxiolytics, chemotherapeutics, cardiovascular agents, muscle relaxants, antiobesity agents, hypnotics

Renal

Acute renal failure

BUN, creatinine

Antibiotics (methicillin, aminoglycosides, sulfonamides), antiviral agents (acyclovir), antifungal agents (amphotericin B), NSAIDs, ACE inhibitors, diuretics, immunosuppressants, antineoplastic agents

Multisystemic

Anaphylactic shock

BP

NSAIDs, analgesics, antibiotics, anesthetics, ACE inhibitors, desensitization agents

Drugs that are in bold are also mentioned in Supplementary Table 1. † Withdrawn from the market. ACE: Angiotensin-converting enzyme; BUN: Blood urea nitrogen; BP: Blood pressure; CBC: Complete blood count; CPK-MB: MB isoenzyme of creatine phosphokinase; ECG: Electrocardiography; EEG: Electroencephalography; HMG-CoA: Hydroxyl-3-methylglutaryl coenzyme A; HRT: Hormone replacement therapy; MI: Myocardial infarction; NSAID: Nonsteroidal anti-inflammatory drug; PE: Physical examination; SJS: Stevens–Johnson syndrome. Data taken from [55,56].

and safer healthcare. In the Vanderbilt PREDICT project design whereby patients treated with clopidogrel were genotyped using the VeraCode ADME core panel that involves 184 variants known to be related to drug response, although the estimated total expense was approximately $5 million in the first 2 years, investigators expected this program to ultimately generate cost savings through reduction in side effects associated with lack of drug efficacy and toxicity [47]. In addition, investigators from the same institution and program [53] came to the conclusion that 383 severe adverse events from six medications could have been avoided through a prospective genotyping program. Although economic ana­lysis was beyond the scope of this study, the authors suggested that such a program could ultimately generate cost savings through reduction in adverse events. These cost-saving projections were, however, questioned by Koch et al. mainly due to the lack of strong PGx evidence from appropriately designed clinical trials, and the potential underestimation of the role of additional not very well characterized factors that may play a role in interindividual variability in drug response [59]. Therefore, further future science group

research is needed to evaluate the potential ­cost–effectiveness of such programs. It is therefore advisable to initially spend efforts on building infrastructure and carrying out targeted low-throughput genotyping after detecting signals from the use of commonly prescribed drugs and major ADEs that are fairly easily retrievable from basic EMRs. Sample size calculation & study design

Before carrying out any power calculation for determining PgV study sample size, one has to know the frequency of the ADE(s) associated with the drug of interest (effect size) in addition to the genetic polymorphisms’ pattern of inheritance, linkage disequilibrium and minor allele frequency [61]. Given the limited frequency of most adverse events, a high rate of participants’ inclusion is needed. One has to also be vigilant for additional factors that may affect variability in drug response, the most important of which is phenoconversion arising from drug–drug interaction [23,62]. In addition, phenotype standardization is a crucial step before the implementation of any PgV program [51]. Finally, taking into account the low frequency of many alleles of interest and www.futuremedicine.com

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hence the very large number of patients required, classical clinical trial designs are difficult to apply in PGx studies, and alternative study designs such as PgV programs may be required [63]. Example of statins

Many pharmacotherapeutic guidelines have been issued on the relationship between genetic polymorphism(s) and clinical drug outcomes. One example is the CPIC guideline for SLCO1B1 rs4149056 and simvastatin-induced myopathy [24]. Therefore, a PgV approach can be conducted in a bidirectional way. First, patients on simvastatin can be asked to sign an informed consent for upfront genotyping and evaluation of the SLCO1B1 variant in relation to myopathy incidence. Second, and if any additional ADE such as hepatotoxicity is commonly encountered during treatment and is considered a signal, then further targeted or highthroughput genotyping can be performed or ana­ lysis of already available genotyping data (if done at the outset) can be carried out to unravel a new clinically important relationship between genetic polymorphisms and treatment outcome.

Challenges of PgV programs Despite the potential role and importance of PgV programs, there are some challenges and limitations that need to be addressed for successful implementation and sustainability. First, funding should be secured from both public and private sectors to cover salary support of faculty and staff, maintain blood and data banks, and purchase and maintain equipment for genetic analyses particularly if high-multiplexed genotyping is planned. Second, the accuracy and completeness of EMRs is paramount since factors such as drug–drug interactions among prescription and over-thecounter drugs may contribute to ADEs [37]. Third,

such programs require harmonious interactions, collaborations and understanding among several key players in a multidisciplinary group. Clinicians, geneticists, informatics specialists, pharmacists and program managers must be committed to applying the program [47,64]. Fourth, healthcare providers and researchers need to be educated on the potential clinical importance of PGx tests. Therefore, training programs are required to educate clinicians on the literature available and the clinical utility of these tests, and on how to interpret and act on them [43]. Finally, active debate should be generated on the ethical, legal and social issues of such programs [65]. Questions for discussion may include: is PgV for authorities or for individual patients as well? How open should informed consent be? Who can access the data? Can data and samples be shared? For how long should data and samples be stored?

Conclusion In this report, we review the importance of PV in detecting postmarketing ADEs and the potential for developing PgV programs by integrating PGx with PV. We propose to start developing such a program in primary healthcare systems that use basic features of EMRs and have access to large numbers of patients commonly prescribed drugs. These programs can also be accompanied by therapeutic drug monitoring of drugs of interest and scaled up to integrate emerging omics technologies and discoveries [8,66]. Such programs, if carefully designed, may grow over time and hopefully enhance the collection and interpretation of useful data for the clinical applications of PGx testing. Acknowledgements The authors are grateful to J Simaan and C Bauer for reviewing the manuscript.

Executive summary Pharmacovigilance ƒƒ Pharmacovigilance (PV) is necessary in order to detect signals of rare occurrence, signals with a long latency and signals that are peculiar to special populations. ƒƒ By timely detection of adverse outcomes associated with drug exposure, PV could be an area of major medical and economic benefit. ƒƒ Despite active and analytical surveillance, modern PV study designs still have several limitations, some of which can be addressed by incorporating genomic analyses into PV programs. Pharmacogenomics ƒƒ Genetic variations among patients may contribute to differences in drug response and tolerability. Pharmacogenomics (PGx) is the application of molecular genetic approaches in order to understand interindividual differences in drug response. Pharmacogenovigilance ƒƒ Combining the two disciplines, PV and PGx, into pharmacogenovigilance (PgV) entails informing and guiding PV activities by PGx analyses. ƒƒ Although few PgV programs currently exist, the authors believe that more efforts should be made in implementing PgV in primary care. ƒƒ The authors propose a model for a PgV program in primary care. They also discuss some of the potential challenges and limitations of such programs.

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Pharmacovigilance & pharmacogenomics

Financial & competing interests disclosure The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes

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