Predicting clopidogrel response using DNA samples linked to an electronic health record

Creation and Validation of an EMR-based Algorithm for Identifying Major Adverse Cardiac Events while on Statins

Statin medications are often prescribed to ameliorate a patient's risk of cardiovascular events due in part to cholesterol reduction. We developed and evaluated an algorithm that can accurately identify subjects with major adverse cardiac events (MACE) while on statins using electronic medical record (EMR) data. The algorithm also identifies subjects experiencing their first MACE while on statins for primary prevention. The algorithm achieved 90% to 97% PPVs in identification of MACE cases as compared against physician review. By applying the algorithm to EMR data in BioVU, cases and controls were identified and used subsequently to replicate known associations with eight genetic variants. We replicated 6/8 previously reported genetic associations with cardiovascular diseases or lipid metabolism disorders. Our results demonstrated that the algorithm can be used to accurately identify subjects with MACE and MACE while on statins. Consequently, future e studies can be conducted to investigate and validate the relationship between statins and MACE using real-world clinical data.

Secondary use of clinical data: the Vanderbilt approach

The last decade has seen an exponential growth in the quantity of clinical data collected nationwide, triggering an increase in opportunities to reuse the data for biomedical research. The Vanderbilt research data warehouse framework consists of identified and de-identified clinical data repositories, fee-for-service custom services, and tools built atop the data layer to assist researchers across the enterprise. Providing resources dedicated to research initiatives benefits not only the research community, but also clinicians, patients and institutional leadership. This work provides a summary of our approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others assembling similar services and infrastructure.

Validation and enhancement of a computable medication indication resource (MEDI) using a large practice-based dataset

Linking medications with their indications is important for clinical care and research. We have recently developed a freely-available, computable medication-indication resource, called MEDI, which links RxNorm medications to indications mapped to ICD9 codes. In this paper, we identified the medications and diagnoses for 1.3 million individuals at Vanderbilt University Medical Center to evaluate the medication coverage of MEDI and then to calculate the prevalence for each indication for each medication. Our results demonstrated MEDI covered 97.3% of medications recorded in medical records. The "high precision subset" of MEDI covered 93.8% of recorded medications. No significant prescription drugs were missed by MEDI. Manual physician review of random patient records for four example medications found that the MEDI covered the observed indications, and confirmed the estimated prevalence of these medications using practice information. Indication prevalence information for each medication, previously unavailable in other public resources, may improve the clinical usability of MEDI. We believe MEDI will be useful for both clinical informatics and to aid in recognition of phenotypes for electronic medical record-based research.

Antiplatelet drug interactions with proton pump inhibitors

INTRODUCTION

Non-aspirin antiplatelet agents (e.g., clopidogrel, prasugrel, ticagrelor) are commonly prescribed for the prevention of recurrent cardiovascular events among patients with acute coronary syndromes (ACS) and/or those undergoing percutaneous coronary intervention (PCI). In addition, combination therapy with proton pump inhibitors (PPIs) is often recommended to attenuate gastrointestinal bleeding risk, particularly during dual antiplatelet therapy (DAPT) with clopidogrel and aspirin. Importantly, a pharmacological interaction between clopidogrel and some PPIs has been proposed based on mutual CYP450-dependent metabolism, but available evidence is inconsistent.

AREAS COVERED

This article provides an overview of the currently approved antiplatelet agents and PPIs, including their metabolic pathways. Additionally, the CYP450 isoenzyme at the center of the drug interaction, CYP2C19, is described in detail, and the available evidence on both the potential pharmacological interaction and influence on clinical outcomes are summarized and evaluated.

EXPERT OPINION

Although concomitant DAPT and PPI use reduces clopidogrel active metabolite levels and ex vivo-measured platelet inhibition, the influence of the drug interaction on clinical outcomes has been conflicting and largely reported from non-randomized observational studies. Despite this inconsistency, a clinically important interaction cannot be definitively excluded, particularly among patient subgroups with higher overall cardiovascular risk and potentially among CYP2C19 loss-of-function allele carriers.

Pharmacogenomics and cardiovascular disease

Variability in drug responsiveness is a sine qua non of modern therapeutics, and the contribution of genomic variation is increasingly recognized. Investigating the genomic basis for variable responses to cardiovascular therapies has been a model for pharmacogenomics in general and has established critical pathways and specific loci modulating therapeutic responses to commonly used drugs such as clopidogrel, warfarin, and statins. In addition, genomic approaches have defined mechanisms and genetic variants underlying important toxicities with these and other drugs. These findings have not only resulted in changes to the product labels but also have led to development of initial clinical guidelines that consider how to facilitate incorporating genetic information to the bedside. This review summarizes the state of knowledge in cardiovascular pharmacogenomics and considers how variants described to date might be deployed in clinical decision making.

Automated extraction of clinical traits of multiple sclerosis in electronic medical records

Objectives

The clinical course of multiple sclerosis (MS) is highly variable, and research data collection is costly and time consuming. We evaluated natural language processing techniques applied to electronic medical records (EMR) to identify MS patients and the key clinical traits of their disease course.

Materials and methods

We used four algorithms based on ICD-9 codes, text keywords, and medications to identify individuals with MS from a de-identified, research version of the EMR at Vanderbilt University. Using a training dataset of the records of 899 individuals, algorithms were constructed to identify and extract detailed information regarding the clinical course of MS from the text of the medical records, including clinical subtype, presence of oligoclonal bands, year of diagnosis, year and origin of first symptom, Expanded Disability Status Scale (EDSS) scores, timed 25-foot walk scores, and MS medications. Algorithms were evaluated on a test set validated by two independent reviewers.

Results

We identified 5789 individuals with MS. For all clinical traits extracted, precision was at least 87% and specificity was greater than 80%. Recall values for clinical subtype, EDSS scores, and timed 25-foot walk scores were greater than 80%.

Discussion and conclusion

This collection of clinical data represents one of the largest databases of detailed, clinical traits available for research on MS. This work demonstrates that detailed clinical information is recorded in the EMR and can be extracted for research purposes with high reliability.

Clopidogrel and warfarin pharmacogenetic tests: what is the evidence for use in clinical practice?

PURPOSE OF REVIEW

To review the most promising genetic markers associated with the variability in the safety or efficacy of warfarin and clopidogrel and highlight the verification and validation initiatives for translating clopidogrel and warfarin pharmacogenetic tests to clinical practice.

RECENT FINDINGS

Rapid advances in pharmacogenetics, continuous decrease in genotyping cost, development of point-of-care devices and the newly established clinical genotyping programs at several institutions hold the promise of individualizing clopidogrel and warfarin based on genotype. Guidelines have been established to assist clinicians in prescribing clopidogrel or warfarin dose based on genotype. However, the clinical utility of clopidogrel and warfarin is still limited. Accordingly, large randomized clinical trials are underway to define the role of clopidogrel and warfarin pharmacogenetics in clinical practice.

SUMMARY

Pharmacogenetics has offered compelling evidence toward the individualization of clopidogrel and warfarin therapies. The rapid advances in technology make the clinical implementation of clopidogrel and warfarin pharmacogenetics possible. The clinical genotyping programs and the ongoing clinical trials will help in overcoming some of the barriers facing the clinical implementation of clopidogrel and warfarin pharmacogenetics.

Applied pharmacogenomics in cardiovascular medicine

Interindividual heterogeneity in drug response is a central feature of all drug therapies. Studies in individual patients, families, and populations over the past several decades have identified variants in genes encoding drug elimination or drug target pathways that in some cases contribute substantially to variable efficacy and toxicity. Important associations of pharmacogenomics in cardiovascular medicine include clopidogrel and risk for in-stent thrombosis, steady-state warfarin dose, myotoxicity with simvastatin, and certain drug-induced arrhythmias. This review describes methods used to accumulate and validate these findings and points to approaches--now being put in place at some centers--to implementing them in clinical care.

Electronic health record design and implementation for pharmacogenomics: a local perspective

PURPOSE

The design of electronic health records to translate genomic medicine into clinical care is crucial to successful introduction of new genomic services, yet there are few published guides to implementation.

METHODS

The design, implemented features, and evolution of a locally developed electronic health record that supports a large pharmacogenomics program at a tertiary-care academic medical center was tracked over a 4-year development period.

RESULTS

Developers and program staff created electronic health record mechanisms for ordering a pharmacogenomics panel in advance of clinical need (preemptive genotyping) and in response to a specific drug indication. Genetic data from panel-based genotyping were sequestered from the electronic health record until drug-gene interactions met evidentiary standards and deemed clinically actionable. A service to translate genotype to predicted drug-response phenotype populated a summary of drug-gene interactions, triggered inpatient and outpatient clinical decision support, updated laboratory records, and created gene results within online personal health records.

CONCLUSION

The design of a locally developed electronic health record supporting pharmacogenomics has generalizable utility. The challenge of representing genomic data in a comprehensible and clinically actionable format is discussed along with reflection on the scalability of the model to larger sets of genomic data.

PON1 Q192R genotype influences clopidogrel responsiveness by relative platelet inhibition instead of on-treatment platelet reactivity

BACKGROUND

paraoxonase-1 (PON1) was recently identified as the crucial enzyme for clopidogrel bioactivation, with PON1 Q192R (rs662) polymorphism determining the clopidogel antiplatelet efficacy. However, subsequent studies showed controversies over the findings. This study aimed to evaluate the impact of PON1 Q192R in parallel to that of CYP2C19*2 (rs4244285) on clopidogrel responsiveness in a cohort of Chinese patients with unstable angina pectoris.

MATERIAL AND METHODS

One hundred and eighty Chinese-Han patients diagnosed with unstable angina pectoris and treated with clopidogrel were consecutively recruited. Clopidogrel responsiveness, measured by relative platelet inhibition {RI=[(pretreatment aggregation-posttreatment aggregation at 5days)/(pretreatment aggregation)] x100%}, was assessed in relation to PON1 Q192R and CYP2C19*2 genotypes. RI values were stratified into four quartiles, with patients in quartile 1 defined as individuals of clopidogrel non-responsiveness. The contributions of PON1 Q192R and CYP2C19*2 to on-treatment platelet reactivity (OTPR) at 5days maintenance dose of clopidogrel were also evaluated.

RESULTS

For PON1 Q192R genotypes, RI values were significantly lower in patients with QR and RR alleles than in patients with QQ alleles (p=0.01). OTPR values at 5days maintenance dose of clopidogrel were similar across all the PON1 Q192R genotypes (p=0.41). PON1 192 QR and RR conferred increased risks for clopidogrel non-responsiveness [OR 3.64; 95% CI (1.21-10.92), p=0.02]. For CYP2C19*2 genotypes, compared to CYP2C19*1/*1 wild type carriers, CYP2C19*2 carriers showed a significantly higher OTPR (p=0.009), and a trend for lower RI values (p=0.06). An increased risk for clopidogrel non-responsiveness was found in patients with CYP2C19*2 genotype [OR 2.02; 95% CI (1.03-3.96), p=0.04].

CONCLUSIONS

Both PON1 Q192R and CYP2C19*2 genotypes influence clopidogrel responsiveness, with the impact of PON1 Q192R mainly on relative platelet inhibition instead of OTPR of clopidogrel.

Pharmacogenetics and cardiovascular disease--implications for personalized medicine

The past decade has seen tremendous advances in our understanding of the genetic factors influencing response to a variety of drugs, including those targeted at treatment of cardiovascular diseases. In the case of clopidogrel, warfarin, and statins, the literature has become sufficiently strong that guidelines are now available describing the use of genetic information to guide treatment with these therapies, and some health centers are using this information in the care of their patients. There are many challenges in moving from research data to translation to practice; we discuss some of these barriers and the approaches some health systems are taking to overcome them. The body of literature that has led to the clinical implementation of CYP2C19 genotyping for clopidogrel, VKORC1, CYP2C9; and CYP4F2 for warfarin; and SLCO1B1 for statins is comprehensively described. We also provide clarity for other genes that have been extensively studied relative to these drugs, but for which the data are conflicting. Finally, we comment briefly on pharmacogenetics of other cardiovascular drugs and highlight β-blockers as the drug class with strong data that has not yet seen clinical implementation. It is anticipated that genetic information will increasingly be available on patients, and it is important to identify those examples where the evidence is sufficiently robust and predictive to use genetic information to guide clinical decisions. The review herein provides several examples of the accumulation of evidence and eventual clinical translation in cardiovascular pharmacogenetics.

Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice

The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.

Enabling genomic-phenomic association discovery without sacrificing anonymity

Health information technologies facilitate the collection of massive quantities of patient-level data. A growing body of research demonstrates that such information can support novel, large-scale biomedical investigations at a fraction of the cost of traditional prospective studies. While healthcare organizations are being encouraged to share these data in a de-identified form, there is hesitation over concerns that it will allow corresponding patients to be re-identified. Currently proposed technologies to anonymize clinical data may make unrealistic assumptions with respect to the capabilities of a recipient to ascertain a patients identity. We show that more pragmatic assumptions enable the design of anonymization algorithms that permit the dissemination of detailed clinical profiles with provable guarantees of protection. We demonstrate this strategy with a dataset of over one million medical records and show that 192 genotype-phenotype associations can be discovered with fidelity equivalent to non-anonymized clinical data.

Personalized medicine: is it a pharmacogenetic mirage?

The notion of personalized medicine has developed from the application of the discipline of pharmacogenetics to clinical medicine. Although the clinical relevance of genetically-determined inter-individual differences in pharmacokinetics is poorly understood, and the genotype-phenotype association data on clinical outcomes often inconsistent, officially approved drug labels frequently include pharmacogenetic information concerning the safety and/or efficacy of a number of drugs and refer to the availability of the pharmacogenetic test concerned. Regulatory authorities differ in their approach to these issues. Evidence emerging subsequently has generally revealed the pharmacogenetic information included in the label to be premature. Revised drugs labels, together with a flurry of other collateral activities, have raised public expectations of personalized medicine, promoted as 'the right drug at the right dose the first time.' These expectations place the prescribing physician in a dilemma and at risk of litigation, especially when evidence-based information on genotype-related dosing schedules is to all intent and purposes non-existent and guidelines, intended to improve the clinical utility of available pharmacogenetic information or tests, distance themselves from any responsibility. Lack of efficacy or an adverse drug reaction is frequently related to non-genetic factors. Phenoconversion, arising from drug interactions, poses another often neglected challenge to any potential success of personalized medicine by mimicking genetically-determined enzyme deficiency. A more realistic promotion of personalized medicine should acknowledge current limitations and emphasize that pharmacogenetic testing can only improve the likelihood of diminishing a specific toxic effect or increasing the likelihood of a beneficial effect and that application of pharmacogenetics to clinical medicine cannot adequately predict drug response in individual patients.

Using linked data for mining drug-drug interactions in electronic health records

By nature, healthcare data is highly complex and voluminous. While on one hand, it provides unprecedented opportunities to identify hidden and unknown relationships between patients and treatment outcomes, or drugs and allergic reactions for given individuals, representing and querying large network datasets poses significant technical challenges. In this research, we study the use of Semantic Web and Linked Data technologies for identifying drug-drug interaction (DDI) information from publicly available resources, and determining if such interactions were observed using real patient data. Specifically, we apply Linked Data principles and technologies for representing patient data from electronic health records (EHRs) at Mayo Clinic as Resource Description Framework (RDF), and identify potential drug-drug interactions (PDDIs) for widely prescribed cardiovascular and gastroenterology drugs. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study patient health outcomes as well as enabling genome-guided drug therapies and treatment interventions.

Omics and drug response

A new generation of technologies commonly named omics permits assessment of the entirety of the components of biological systems and produces an explosion of data and a major shift in our concepts of disease. These technologies will likely shape the future of health care. One aspect of these advances is that the data generated document the uniqueness of each human being in regard to disease risk and treatment response. These developments have reemphasized the concept of personalized medicine. Here we review the impact of omics technologies on one key aspect of personalized medicine: the individual drug response. We describe how knowledge of different omics may affect treatment decisions, namely drug choice and drug dose, and how it can be used to improve clinical outcomes.

Insights from genome-wide association studies of drug response

Early genome-wide association studies (GWAS) using relatively small samples have identified both rare and common genetic variants with large impact on severe adverse drug reactions, dosing, and efficacy. Here we outline the challenges and recent successes of the GWAS approach in disease genetics and the ways in which these can be applied to pharmacogenomics for biological discovery, determination of heritability, and personalized treatment. We highlight that the genetic architecture of drug efficacy reflects a complex trait yet that of adverse drug reactions more closely mirrors the architecture of Mendelian diseases and how this difference affects future study design. Given that multiple layers of biological data are increasingly available on large samples from biorepositories linked to electronic medical records, GWAS will remain a key component of the systems biology approach to uncovering small to moderate genetic determinants of drug response; these discoveries should move us closer to a personalized approach to health care.

ABCB1 C3435T polymorphism and response to clopidogrel treatment in coronary artery disease (CAD) patients: a meta-analysis

BACKGROUND

A number of investigators have evaluated the association between the ABCB1 polymorphism and clopidogrel responding, but the results have been inconclusive. To examine the risk of high platelet activity and poor clinical outcomes associated with the ABCB1 C3435T polymorphism in CAD patients on clopidogrel, all available studies were included in the present meta-analysis.

METHODS

We performed a systematic search of PubMed, Scopus and the Cochrane library database for eligible studies. Articles meeting the inclusion criteria were comprehensively reviewed, and the available data were accumulated by the meta-analysis.

RESULTS

It was demonstrated that the ABCB1 C3435T variation was associated with the risk of early major adverse cardiovascular events (MACE) (T vs. C OR, 1.34; 95% CI, 1.10 to 1.62; P=0.003; TT vs. CC: OR, 1.77; 95% CI, 1.19 to 2.63; P=0.005; CT + TT vs.CC: OR, 1.48; 95% CI, 1.06 to 2.06; P=0.02) and the polymorphism was also associated with the risk of the long-term MACE in patients on clopidogrel LD 300 mg (T vs. C: OR, 1.28; 95% CI, 1.10 to 1.48; P=0.001; TT vs. CC: OR, 1.59; 95% CI, 1.19 to 2.13; P=0.002; CT + TT vs.CC: OR, 1.39; 95% CI, 1.08 to 1.79; P=0.01). The comparison of TT vs. CC was associated with a reduction in the outcome of bleeding (TT vs. CC: OR, 0.51; 95% CI, 0.40 to 0.66; P<0.00001). However, the association between ABCB1 C3435T polymorphism and platelet activity and other risk of poor clinical outcomes was not significant.

CONCLUSIONS

The evidence from our meta-analysis indicated that the ABCB1 C3435T polymorphism might be a risk factor for the MACE in patients on clopidogrel LD 300 mg, and that TT homozygotes decreased the outcome of bleeding compared with CC homozygotes.

Electronic medical records as a tool in clinical pharmacology: opportunities and challenges

The development and increasing sophistication of electronic medical record (EMR) systems hold the promise of not only improving patient care but also providing unprecedented opportunities for discovery in the fields of basic, translational, and implementation sciences. Clinical pharmacology research in the EMR environment has only recently started to become a reality, with EMRs becoming increasingly populated, methods to mine drug response and other phenotypes becoming more sophisticated, and links being established with DNA repositories.

Genetic determinants of response to cardiovascular drugs

PURPOSE OF REVIEW

To survey genetic variation contributing to variable responsiveness and toxicity to important cardiovascular drugs and highlight recent developments in the field of cardiovascular pharmacogenomics and personalized medicine.

RECENT FINDINGS

Previously recognized pharmacogenomic associations with drug efficacy have been further validated (e.g. with clopidogrel and warfarin) and shown to influence clinically important outcomes. The clinical significance of variants modulating toxicity (e.g. SLCO1B1 with simvastatin) has also been confirmed. The genetic contribution to variable efficacy and toxicity of other important classes of cardiovascular drugs, such as beta-blockers, is becoming increasingly recognized. Prospective trials testing whether the use of genomic information improves clinical care are underway. Guidance based on the most well-established pharmacogenomic findings has appeared in prescribing labeling and is in the early stages of being implemented into routine clinical care.

SUMMARY

Clinically validated gene variants that modulate responsiveness to cardiovascular drugs continue to be discovered and validated. Early steps are underway to translate these discoveries into clinical care.

Influence of the paraoxonase-1 Q192R genetic variant on clopidogrel responsiveness and recurrent cardiovascular events: a systematic review and meta-analysis

BACKGROUND

A poor biological response to clopidogrel is associated with an increased risk of major cardiovascular ischemic events (MACE). Paraoxonase 1 (PON1) enzyme activity is modulated by the PON1-Q192R variant (rs662) and was recently suggested to be strongly involved in clopidogrel bioactivation, but the influence of the PON1-Q192R variant on the risk of MACE in clopidogrel-treated patients is controversial.

OBJECTIVES

To determine whether the PON1-Q192R variant influences clopidogrel biological responsiveness and the risk of MACE in patients treated with clopidogrel.

METHODS

Systematic review and meta-analysis of studies of the association between the PON1-Q192R polymorphism and the biological response to clopidogrel and/or the risk of MACE during clopidogrel administration.

RESULTS

Seventeen studies were included. In the 12 studies of the biological response to clopidogrel (n = 5302 patients), there was no significant difference between 192QQ and 192QR + 192RR subjects, whatever the laboratory method used (global mean standardized difference = 0.10 [-0.06; 0.25], P = 0.22). Eleven studies assessed the risk of MACE, four using a case-control design (n = 2739 patients) and seven a prospective design (n = 5353 patients). Overall, MACE occurred in 19% of patients in case-control studies and in 6% of patients in prospective cohort studies, with no significant difference between 192QQ and 192QR + 192RR patients (OR = 1.28 [0.97; 1.68], P = 0.08). Similar results were obtained when study design was taken into account. Heterogeneity was mainly driven by one publication.

CONCLUSIONS

This meta-analysis suggests that the PON1-Q192R polymorphism has no major impact on the risk of MACE and does not alter the biological response to clopidogrel in clopidogrel-treated patients.

Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping

Routine integration of genotype data into drug decision making could improve patient safety, particularly if many relevant genetic variants can be assayed simultaneously before prescribing the target drug. The frequency of opportunities for pharmacogenetic prescribing and the potential adverse events (AEs) mitigated are unknown. We examined the frequency with which 56 medications with known outcomes influenced by variant alleles were prescribed in a cohort of 52,942 medical home patients at Vanderbilt University Medical Center (VUMC). Within a 5-year window, we estimated that 64.8% (95% confidence interval (CI): 64.4-65.2%) of individuals were exposed to at least one medication with an established pharmacogenetic association. Using previously published results for six medications with severe, well-characterized, genetically linked AEs, we estimated that 383 events (95% CI, 212-552) could have been prevented with an effective preemptive genotyping program. Our results suggest that multiplexed, preemptive genotyping may represent an efficient alternative approach to current single-use ("reactive") methods and may also improve safety.

Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project

The promise of "personalized medicine" guided by an understanding of each individual's genome has been fostered by increasingly powerful and economical methods to acquire clinically relevant information. We describe the operational implementation of prospective genotyping linked to an advanced clinical decision-support system to guide individualized health care in a large academic health center. This approach to personalized medicine entails engagement between patient and health-care provider, identification of relevant genetic variations for implementation, assay reliability, point-of-care decision support, and necessary institutional investments. In one year, approximately 3,000 patients, most of whom were scheduled for cardiac catheterization, were genotyped on a multiplexed platform that included genotyping for CYP2C19 variants that modulate response to the widely used antiplatelet drug clopidogrel. These data are deposited into the electronic medical record (EMR), and point-of-care decision support is deployed when clopidogrel is prescribed for those with variant genotypes. The establishment of programs such as this is a first step toward implementing and evaluating strategies for personalized medicine.

Predicting warfarin dosage in European-Americans and African-Americans using DNA samples linked to an electronic health record

AIM

Warfarin pharmacogenomic algorithms reduce dosing error, but perform poorly in non-European-Americans. Electronic health record (EHR) systems linked to biobanks may allow for pharmacogenomic analysis, but they have not yet been used for this purpose.

PATIENTS & METHODS

We used BioVU, the Vanderbilt EHR-linked DNA repository, to identify European-Americans (n = 1022) and African-Americans (n = 145) on stable warfarin therapy and evaluated the effect of 15 pharmacogenetic variants on stable warfarin dose.

RESULTS

Associations between variants in VKORC1, CYP2C9 and CYP4F2 with weekly dose were observed in European-Americans as well as additional variants in CYP2C9 and CALU in African-Americans. Compared with traditional 5 mg/day dosing, implementing the US FDA recommendations or the International Warfarin Pharmacogenomics Consortium (IWPC) algorithm reduced error in weekly dose in European-Americans (13.5-12.4 and 9.5 mg/week, respectively) but less so in African-Americans (15.2-15.0 and 13.8 mg/week, respectively). By further incorporating associated variants specific for European-Americans and African-Americans in an expanded algorithm, dose-prediction error reduced to 9.1 mg/week (95% CI: 8.4-9.6) in European-Americans and 12.4 mg/week (95% CI: 10.0-13.2) in African-Americans. The expanded algorithm explained 41 and 53% of dose variation in African-Americans and European-Americans, respectively, compared with 29 and 50%, respectively, for the IWPC algorithm. Implementing these predictions via dispensable pill regimens similarly reduced dosing error.

CONCLUSION

These results validate EHR-linked DNA biorepositories as real-world resources for pharmacogenomic validation and discovery.