The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems.

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.

Attitudes of clinicians following large-scale pharmacogenomics implementation.

Clinician attitudes toward multiplexed genomic testing may be vital to the success of translational programs. We surveyed clinicians at an academic medical center about their views on a large pharmacogenomics implementation, the PREDICT (Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment) program. Participants were asked about test ordering, major factors influencing use of results, expectations of efficacy and responsibility for applying results to patient care. Virtually all respondents (99%) agreed that pharmacogenomics variants influence patients' response to drug therapy. The majority (92%) favored immediate, active notification when a clinically significant drug-genome interaction was present. However, clinicians were divided on which providers were responsible for acting on a result when a prescription change was indicated and whether patients should be directly notified of a significant result. We concluded genotype results were valued for tailoring prescriptions, but clinicians do not agree on how to appropriately assign clinical responsibility for actionable results from a multiplexed panel.The Pharmacogenomics Journal advance online publication, 11 August 2015; doi:10.1038/tpj.2015.57.

Physician response to implementation of genotype-tailored antiplatelet therapy.

Physician responses to genomic information are vital to the success of precision medicine initiatives. We prospectively studied a pharmacogenomics implementation program for the propensity of clinicians to select antiplatelet therapy based on CYP2C19 loss-of-function variants in stented patients. Among 2,676 patients, 514 (19.2%) were found to have a CYP2C19 variant affecting clopidogrel metabolism. For the majority (93.6%) of the cohort, cardiologists received active and direct notification of CYP2C19 status. Over 12 months, 57.6% of poor metabolizers and 33.2% of intermediate metabolizers received alternatives to clopidogrel. CYP2C19 variant status was the most influential factor impacting the prescribing decision (hazard ratio [HR] in poor metabolizers 8.1, 95% confidence interval [CI] [5.4, 12.2] and HR 5.0, 95% CI [4.0, 6.3] in intermediate metabolizers), followed by patient age and type of stent implanted. We conclude that cardiologists tailored antiplatelet therapy for a minority of patients with a CYP2C19 variant and considered both genomic and nongenomic risks in their clinical decision-making.

Strategies for personalized medicine-based research and implementation in the clinical workflow.

The decoding of the human genome, paralleled by the development of high-throughput technologies to obtain large-scale molecular data (e.g., information on genetic variations, transcription levels, and metabolite concentrations) is providing new insights into the molecular basis of common diseases and the causes of variability in drug response. This article presents strategies to incorporate this new knowledge into research and clinical workflow.

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

Variants in ABCB1 and CYP2C19 have been identified as predictors of cardiac events during clopidogrel therapy initiated after myocardial infarction (MI) or percutaneous coronary intervention (PCI). In addition, PON1 has recently been associated with stent thrombosis. The reported effects of these variants have not yet been replicated in a real-world setting. We used BioVU, the Vanderbilt DNA repository linked to de-identified electronic health records (EHRs), to find data on patients who were on clopidogrel treatment after an MI and/or a PCI; among these, we identified those who had experienced one or more recurrent cardiac events while on treatment (cases, n = 225) and those who had not experienced any cardiac event while on treatment (controls, n = 468). We found that CYP2C19*2 (hazard ratio (HR) 1.54, 95% confidence interval (CI) 1.16-2.06, P = 0.003) and ABCB1 (HR 1.28, 95% CI 1.04-1.57, P = 0.018), but not PON1 (HR 0.91, 95% CI 0.73-1.12, P = 0.370), were associated with recurrent events. In this population, genetic signals for clopidogrel resistance in ABCB1 and CYP2C19 were replicated, supporting the use of EHRs for pharmacogenomic studies. Our data do not show an association between PON1 and recurrent cardiovascular events.

Development of a large-scale de-identified DNA biobank to enable personalized medicine.

Our objective was to develop a DNA biobank linked to phenotypic data derived from an electronic medical record (EMR) system. An "opt-out" model was implemented after significant review and revision. The plan included (i) development and maintenance of a de-identified mirror image of the EMR, namely, the "synthetic derivative" (SD) and (ii) DNA extracted from discarded blood samples and linked to the SD. Surveys of patients indicated general acceptance of the concept, with only a minority ( approximately 5%) opposing it. As a result, mechanisms to facilitate opt-out included publicity and revision of a standard "consent to treatment" form. Algorithms for sample handling and procedures for de-identification were developed and validated in order to ensure acceptable error rates (<0.3 and <0.1%, respectively). The rate of sample accrual is 700-900 samples/week. The advantages of this approach are the rate of sample acquisition and the diversity of phenotypes based on EMRs.