Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine Learning Approach with Multi-Trial Replication

AUTHORS

Athreya AP , Neavin D , Carrillo-Roa T , Skime M , Biernacka J , Frye MA , Rush AJ , Wang L , Binder EB , Iyer RK , Weinshilboum RM , Bobo WV , . Clinical pharmacology and therapeutics. 2019 4 23; ().

PMID: 31012492 [PubMed].

ABSTRACT

We set out to determine whether machine learning-based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 Caucasian MDD outpatients treated with citalopram/escitalopram in the PGRN-AMPS (n = 398), STAR*D (n = 467), and ISPC (n = 165) trials. GWAS for PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified SNPs in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with AUC > 0.7 (p<0.04) in PGRN-AMPS patients, with comparable prediction accuracies >69% (p<0.05) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers. This article is protected by copyright. All rights reserved.

Tags: alumni publications 2019