Combinatorial pharmacogenomic algorithm is predictive of sertraline metabolism in patients with major depressive disorder

Abstract

Pharmacogenomic testing can be used to guide medication selection in patients with major depressive disorder (MDD). Currently, there is no consensus on which gene or genes to consider in medication management. Here, we assessed the clinical validity of the combinatorial pharmacogenomic algorithm to predict sertraline blood levels in a subset of patients enrolled in the Genomics Used to Improve DEpression Decisions (GUIDED) trial. Patients who reported taking sertraline within ≤2 weeks of the screening blood draw were included. All patients received combinatorial pharmacogenomic testing, which included a weighted assessment of individual phenotypes for multiple pharmacokinetic genes relevant for sertraline (CYP2C19, CYP2B6, and CYP3A4). Sertraline blood levels were compared between phenotypes based on: 1) the pharmacokinetic portion of the combinatorial pharmacogenomic algorithm, and 2) individual genes. When evaluated separately, individual genes (for CYP2C19 and CYP2B6) and the combinatorial algorithm were significant predictors of sertraline blood levels. However, in multivariate analyses that included individual genes and the combinatorial pharmacogenomic algorithm, only the combinatorial pharmacogenomic algorithm remained a significant predictor of sertraline blood levels. These findings support the clinical validity of the combinatorial pharmacogenomic algorithm, in that it is a superior predictor of sertraline blood levels compared to individual genes.

Keywords: CPIC guidelines; Clinical validity; GeneSight; Medication Blood Levels; Pharmacokinetics.