Implementing comprehensive pharmacogenomics in a community hospital-associated primary care setting.

BACKGROUND: Pharmacogenomics (PGx) is an emerging field. Many drug-gene interactions are known but not yet routinely addressed in clinical practice. Therefore, there is a significant gap in care, necessitating development of implementation strategies. OBJECTIVE: The objective of the study was to assess the impact of implementing a PGx practice model which incorporates comprehensive pharmacogenomic risk evaluation, testing and medication optimization administered by 7 PGx-certified ambulatory care pharmacists embedded across 30 primary care clinic sites. METHODS: Pharmacogenomic services were implemented in 30 primary care clinics within the Cincinnati, Ohio area. Patients are identified for pharmacogenomic testing using a clinical decision support tool (CDST) that is fully integrated in the electronic medical record (EMR) or by provider designation (e.g., psychotropic drug failure). Pharmacogenomic testing is performed via buccal swab using standardized clinic processes. Discrete data results are returned directly into the EMR/CDST for review by PGx-certified ambulatory care pharmacists. Recommendations and prescriptive changes are then discussed and implemented as a collaborative effort between pharmacist, primary care provider, specialists, and patient. RESULTS: A total of 422 unique interactions were assessed by the embedded ambulatory care PGx pharmacists (N = 7) during this interim analysis. About half (213) were pharmacogenomic interactions, and of these, 124 were actionable. When an intervention was actionable, 82% of the time a change in medication was recommended. The underlying reasons for recommending therapy alterations were most commonly ineffective therapy (43%), adverse drug reaction prevented (34%), or adverse drug reaction observed (13%). CONCLUSION: Variations in drug metabolism, response, and tolerability can negatively impact patient outcomes across many disease states and treatment specialties. Incorporation of pharmacogenomic testing with accessible clinical decision support into the team-based care model allows for a truly comprehensive review and optimization of medications. Our initial analysis suggests that comprehensive PGx testing should be considered to enhance medication safety and efficacy in at-risk patients.

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.

Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug-Gene, Drug-Drug-Gene, and Drug-Gene-Gene Interaction Risks in a Large Patient Population.

Utilizing pharmacogenomic (PGx) testing and integrating evidence-based guidance in drug therapy enables an improved treatment response and decreases the occurrence of adverse drug events. We conducted a retrospective analysis to validate the YouScript® PGx interaction probability (PIP) algorithm, which predicts patients for whom PGx testing would identify one or more evidence-based, actionable drug-gene, drug-drug-gene, or drug-gene-gene interactions (EADGIs). PIP scores generated for 36,511 patients were assessed according to the results of PGx multigene panel testing. PIP scores versus the proportion of patients in whom at least one EADGI was found were 22.4% vs. 22.4% (p = 1.000), 23.5% vs. 23.4% (p = 0.6895), 30.9% vs. 29.4% (p = 0.0667), and 27.3% vs. 26.4% (p = 0.3583) for patients tested with a minimum of 3-, 5-, 14-, and 25-gene panels, respectively. These data suggest a striking concordance between the PIP scores and the EAGDIs found by gene panel testing. The ability to identify patients most likely to benefit from PGx testing has the potential to reduce health care costs, enable patient access to personalized medicine, and ultimately improve drug efficacy and safety.