Unmanaged Pharmacogenomic and Drug Interaction Risk Associations with Hospital Length of Stay among Medicare Advantage Members with COVID-19: A Retrospective Cohort Study

Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

Comparison of targeted vs. expanded pharmacogenomic testing: What are we missing?

BACKGROUND

Pharmacogenomics (PGx) is used as a medication management strategy by a small but growing number of institutions. PGx allows prescribers to individually treat patients concordant with their genes. Recent litigation for preventable PGx-mediated adverse events highlights the need to accelerate PGx implementation for patient safety. Genetic variations cause drug metabolism, transport, and target changes, affecting medication response and tolerability. PGx testing often consists of targeted testing aimed at specific gene-drug pairs or disease states. Conversely, expanded panel testing can evaluate all known actionable gene-drug interactions, enhancing proactive clarity regarding patient response.

OBJECTIVES

Evaluate the divergence of targeted PGx testing with a single gene-drug pair test (cardiac), a two-gene panel, and a focused psychiatric panel compared to expanded PGx testing.

METHODS

An expanded PGx panel (≥25 genes) was compared to a single gene-drug pair test of CYP2C19/clopidogrel, a dual gene test of CYP2C19/CYP2D6, a 7-gene psychiatric list, and a 14-gene psychiatric panel to inform specific depression and pain management drugs. The expanded panel provided a baseline to evaluate total PGx variations compared to those possibly missed by targeted testing.

RESULTS

Targeted testing did not identify up to 95% of total PGx gene-drug interactions discovered. The expanded panel reported all gene-drug interactions for any medication with Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance or U.S. Food and Drug Administration (FDA) labeling for that gene. Single gene CYP2C19/clopidogrel testing missed or did not report on ∼95% of total interactions, CYP2C19/CYP2D6 testing missed or did not report ∼89%, and the 14-gene panel missed or did not report on ∼73%. The 7-gene list missed ∼20% of discovered potential PGx interactions but was not designed to identify gene-drug interactions.

CONCLUSIONS

Targeted PGx testing for limited genes or by specialty may miss or not report significant portions of PGx gene-drug interactions. This can lead to potential patient harm from the missed interactions and subsequent failed therapies and/or adverse reactions.

Assessment of a Manual Method versus an Automated, Probability-Based Algorithm to Identify Patients at High Risk for Pharmacogenomic Adverse Drug Outcomes in a University-Based Health Insurance Program

We compared patient cohorts selected for pharmacogenomic testing using a manual method or automated algorithm in a university-based health insurance network. The medication list was compiled from claims data during 4th quarter 2018. The manual method selected patients by number of medications by the health system’s list of medications for pharmacogenomic testing. The automated method used YouScript’s pharmacogenetic interaction probability (PIP) algorithm to select patients based on the probability that testing would result in detection of one or more clinically significant pharmacogenetic interactions. A total of 6916 patients were included. Patient cohorts selected by each method differed substantially, including size (manual n = 218, automated n = 286) and overlap (n = 41). The automated method was over twice as likely to identify patients where testing may reveal a clinically significant pharmacogenetic interaction than the manual method (62% vs. 29%, p < 0.0001). The manual method captured more patients with significant drug–drug or multi-drug interactions (80.3% vs. 40.2%, respectively, p < 0.0001), higher average number of significant drug interactions per patient (3.3 vs. 1.1, p < 0.0001), and higher average number of unique medications per patient (9.8 vs. 7.4, p < 0.0001). It is possible to identify a cohort of patients who would likely benefit from pharmacogenomic testing using manual or automated methods.