Characterization of Pharmacogenetic Information in Food and Drug Administration Drug Labeling and the Table of Pharmacogenetic Associations.

BACKGROUND: The US Food and Drug Administration (FDA) recommends using only FDA-reviewed pharmacogenetic information to make prescribing decisions based on genetic test results. Such information is available in drug labeling and in the Table of Pharmacogenetic Associations ("Associations table"). OBJECTIVE: To compile a list of drug-gene pairs from drug labeling and the Associations table and categorize the pharmacogenetic information and clinical outcome associated with each drug-gene pair. METHODS: This was a cross-sectional analysis of pharmacogenetic information in the Associations table and individual drug labeling in March 2020. We used the Table of Pharmacogenomic Biomarkers in Drug Labeling to identify drug labels to review. We categorized the pharmacogenetic information for each drug-gene pair according to whether the purpose was to describe (1) polymorphisms affecting drug disposition (metabolism or transport), (2) polymorphisms affecting a direct drug target, (3) variants associated with adverse drug reaction (ADR) susceptibility, (4) variants associated with therapeutic failure, (5) a biomarker-defined indication, or (6) a biomarker-defined ADR. We also categorized the clinical outcome-efficacy, safety, or unknown-associated with each drug-gene pair. We reported counts and proportions of drug-gene pairs in each pharmacogenetic information and clinical outcome category. RESULTS: We identified 308 drug-gene pairs, of which 36% were associated with a biomarker-defined drug indication, 33% with polymorphic drug metabolism, and 28% with ADR susceptibility. Most drug-gene pairs (n = 267, 87%) were associated with an efficacy or safety-related outcome. CONCLUSION AND RELEVANCE: FDA-reviewed pharmacogenetic information is available for more than 300 drug-gene pairs and can help guide prescribing decisions.

Successful deployment of drug-disease interaction clinical decision support across multiple Kaiser Permanente regions.

OBJECTIVE: The study sought to develop a criteria-based scoring tool for assessing drug-disease knowledge base content and creation of a subset and to implement the subset across multiple Kaiser Permanente (KP) regions. MATERIALS AND METHODS: In Phase I, the scoring tool was developed, used to create a drug-disease alert subset, and validated by surveying physicians and pharmacists from KP Northern California. In Phase II, KP enabled the alert subset in July 2015 in silent mode to collect alert firing rates and confirmed that alert burden was adequately reduced. The alert subset was subsequently rolled out to users in KP Northern California. Alert data was collected September 2015 to August 2016 to monitor relevancy and override rates. RESULTS: Drug-disease alert scoring identified 1211 of 4111 contraindicated drug-disease pairs for inclusion in the subset. The survey results showed clinician agreement with subset examples 92.3%-98.5% of the time. Postsurvey adjustments to the subset resulted in KP implementation of 1189 drug-disease alerts. The subset resulted in a decrease in monthly alerts from 32 045 to 1168. Postimplementation monthly physician alert acceptance rates ranged from 20.2% to 29.8%. DISCUSSION: Our study shows that drug-disease alert scoring resulted in an alert subset that generated acceptable interruptive alerts while decreasing overall potential alert burden. Following the initial testing and implementation in its Northern California region, KP successfully implemented the disease interaction subset in 4 regions with additional regions planned. CONCLUSIONS: Our approach could prevent undue alert burden when new alert categories are implemented, circumventing the need for trial live activations of full alert category knowledge bases.