A Drug-Gene Interaction (DGI) is an association between a drug and a genetic variant that may affect a patient’s response to drug treatment. The selection of which DGIs to include in the PREDICT program starts with published evidence and FDA guidance. All implemented DGIs are reviewed by medical and pharmacy experts, including final approval by Vanderbilt’s Pharmacy and Therapeutics (P&T) subcommittees.
Patients are identified for genetic testing based on their medical history and the likelihood that genetic information may be useful for drug prescribing in the future. Once tested, a patient’s results from the analyzed genes that have been approved as actionable will be made available for clinical decision support.
Depending on the results of the test, dosage adjustments for specific drugs prescribed to the patient or alternative medications may be indicated. For management decisions regarding the clinical care of a patient, see the “Genomic Indicators” section of “Patient Summary” or “PREDICT” results within “Labs” in EPIC. If a provider orders a medication for which a patient has an actionable genotype, a best practice advisory message will fire to alert the provider and provide clinical recommendations.
Specific Drug Genome Interactions
SUPPORTING EVIDENCE FOR PHARMACOGENETIC TESTING
Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project.
Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL, Ramirez AH, Delaney JT, Bowton E, Brothers K, Johnson K, Crawford DC, Schildcrout J, Masys DR, Dilks HH, Wilke RA, Clayton EW, Shultz E, Laposata M, McPherson J, Jirjis JN, Roden DM. Clin Pharmacol Ther. 2012 Jul;92(1):87-95.
Abstract: 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.
Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping.
Schildcrout JS, Denny JC, Bowton E, Gregg W, Pulley JM, Basford MA, Cowan JD, Xu H, Ramirez AH, Crawford DC, Ritchie MD, Peterson JF, Masys DR, Wilke RA, Roden DM. Clin Pharmacol Ther. 2012 Aug;92(2):235-42
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.