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.

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.

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.

Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: A prospective pilot randomized controlled trial.

BACKGROUND: In polypharmacy patients under home health management, pharmacogenetic testing coupled with guidance from a clinical decision support tool (CDST) on reducing drug, gene, and cumulative interaction risk may provide valuable insights in prescription drug treatment, reducing re-hospitalization and emergency department (ED) visits. We assessed the clinical impact of pharmacogenetic profiling integrating binary and cumulative drug and gene interaction warnings on home health polypharmacy patients. METHODS AND FINDINGS: This prospective, open-label, randomized controlled trial was conducted at one hospital-based home health agency between February 2015 and February 2016. Recruitment came from patient referrals to home health at hospital discharge. Eligible patients were aged 50 years and older and taking or initiating treatment with medications with potential or significant drug-gene-based interactions. Subjects (n = 110) were randomized to pharmacogenetic profiling (n = 57). The study pharmacist reviewed drug-drug, drug-gene, and cumulative drug and/or gene interactions using the YouScript® CDST to provide drug therapy recommendations to clinicians. The control group (n = 53) received treatment as usual including pharmacist guided medication management using a standard drug information resource. The primary outcome measure was the number of re-hospitalizations and ED visits at 30 and 60 days after discharge from the hospital. The mean number of re-hospitalizations per patient in the tested vs. untested group was 0.25 vs. 0.38 at 30 days (relative risk (RR), 0.65; 95% confidence interval (CI), 0.32-1.28; P = 0.21) and 0.33 vs. 0.70 at 60 days following enrollment (RR, 0.48; 95% CI, 0.27-0.82; P = 0.007). The mean number of ED visits per patient in the tested vs. untested group was 0.25 vs. 0.40 at 30 days (RR, 0.62; 95% CI, 0.31-1.21; P = 0.16) and 0.39 vs. 0.66 at 60 days (RR, 0.58; 95% CI, 0.34-0.99; P = 0.045). Differences in composite outcomes at 60 days (exploratory endpoints) were also found. Of the total 124 drug therapy recommendations passed on to clinicians, 96 (77%) were followed. These findings should be verified with additional prospective confirmatory studies involving real-world applications in larger populations to broaden acceptance in routine clinical practice. CONCLUSIONS: Pharmacogenetic testing of polypharmacy patients aged 50 and older, supported by an appropriate CDST, considerably reduced re-hospitalizations and ED visits at 60 days following enrollment resulting in potential health resource utilization savings and improved healthcare. TRIAL REGISTRATION: ClinicalTrials.gov NCT02378220.

Cytochrome P-450 gene and drug interaction analysis in patients referred for pharmacogenetic testing.

PURPOSE: The results of a study of variant cytochrome P-450 (CYP) alleles and associated risks of drug-drug interactions (DDIs) and altered drug metabolism are reported. METHODS: The records of a pharmacogenetic testing laboratory were retrospectively analyzed to identify patients tested for polymorphisms of genes coding for five CYP isozymes important in drug metabolism (CYP2D6, CYP2C9, CYP2C19, CYP3A4, and CYP3A5) over a 16-month period. Based on the results of phenotyping, the patients were categorized by expected CYP isozyme activity (e.g., normal or poor metabolizer, expresser or nonexpresser). Using proprietary Web-based software, researchers analyzed phenotyping data and medication lists submitted by patients to determine the potential for DDIs, drug-gene interactions (DGIs), and drug-drug-gene interactions (DDGIs). RESULTS: In the mixed-race study population of more than 22,000 male and female patients (age range, 1-108 years; mean, 60 years), phenotypes associated with alterations of CYP metabolic pathways were common. Among patients in whom phenotypes for all five isozymes of interest were determined (n = 14,578), about 93% were not categorized as normal metabolizers of all five proteins. In many cases, potential interaction threats were rated by clinicians as severe enough to warrant implementation or consideration of a medication regimen change or dose adjustment. Analysis of patient-provided medication lists indicated frequent use of medications posing DDI, DGI, or DDGI risks. CONCLUSION: In a mixed-race population of over 20,000 U.S. patients, CYP gene polymorphisms associated with DDIs and other interaction threats were prevalent, and most individuals were not categorized as normal metabolizers of all five CYP isozymes of interest.