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.

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.

Effect of dry matter content on the microbial community and on the effectiveness of a microbial inoculant to improve the aerobic stability of corn silage.

Silage inoculants are commonly used as a tool to improve the fermentation and aerobic stability of corn silage fed to dairy cows. However, their effectiveness can be inconsistent. Our objective was to determine the effect of the dry matter (DM) content of freshly chopped whole-plant corn on its microbial community as affected by an inoculant containing Lentilactobacillus hilgardii, Lentilactobacillus buchneri, and Pediococcus pentosaceus on improving the aerobic stability of silage.

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.