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Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping.

ABSTRACT: 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.

SUBMITTER: Schildcrout JS

PROVIDER: S-EPMC3785311 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping.

Schildcrout J S JS Denny J C JC Bowton E E Gregg W W Pulley J M JM Basford M A MA Cowan J D JD Xu H H Ramirez A H AH Crawford D C DC Ritchie M D MD Peterson J F JF Masys D R DR Wilke R A RA Roden D M DM

Clinical pharmacology and therapeutics 20120627 2

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 Vand ...[more]

PMID: 22739144

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Project description:ObjectiveTo report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR).Patients and methodsWe used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR.ResultsThe prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance.ConclusionThis translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

Dataset Information

Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping.

Publications

Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping.

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