Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication.

Journal Article (Journal Article)

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 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (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 area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN-AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) 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.

Full Text

Duke Authors

Cited 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

Published Date

  • October 2019

Published In

Volume / Issue

  • 106 / 4

Start / End Page

  • 855 - 865

PubMed ID

  • 31012492

Pubmed Central ID

  • PMC6739122

Electronic International Standard Serial Number (EISSN)

  • 1532-6535

Digital Object Identifier (DOI)

  • 10.1002/cpt.1482


  • eng

Conference Location

  • United States