Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication.
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 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.
Type
Journal articleSubject
HumansCitalopram
Serotonin Uptake Inhibitors
Genetic Markers
Remission Induction
Depressive Disorder, Major
Polymorphism, Single Nucleotide
Algorithms
Adult
Female
Male
Biomarkers, Pharmacological
Genome-Wide Association Study
Machine Learning
Pharmacogenomic Variants
Pharmacogenomic Testing
Clinical Decision Rules
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https://hdl.handle.net/10161/24806Published Version (Please cite this version)
10.1002/cpt.1482Publication Info
Athreya, Arjun P; Neavin, Drew; Carrillo-Roa, Tania; Skime, Michelle; Biernacka, Joanna;
Frye, Mark A; ... Bobo, William V (2019). Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning
Approach With Multi-trial Replication. Clinical pharmacology and therapeutics, 106(4). pp. 855-865. 10.1002/cpt.1482. Retrieved from https://hdl.handle.net/10161/24806.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Augustus John Rush
Adjunct Professor in the Department of Psychiatry and Behavioral Sciences

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