ArticleClinical pharmacology and therapeutics2019
Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication.
Article in Clinical pharmacology and therapeutics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 6 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Genetic overlap and causality between depression and preterm birth: a large-scale genome-wide cross-trait analysis.Psychological medicine · 2025Pooled it
- Machine learning in the prediction of treatment response for emotional disorders: A systematic review and meta-analysis.Clinical psychology review · 2025Pooled it
- Quantifying Sample Representation in Global Pharmacogenomic Studies of Major Depressive Disorder: A Systematic Review.Clinical and translational science · 2025Pooled it
- Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder.Clinical and translational science · 2025Pooled it
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- Recent applications of quantitative systems pharmacology and machine learning models across diseases.Journal of pharmacokinetics and pharmacodynamics · 2022Pooled it
- Pharmaco-multiomics in major depressive disorder: a narrative review and proposed translational framework for difficult-to-treat and treatment-resistant depression.Frontiers in pharmacology · 2026Review
- Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning-Based Advanced Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Physician Perspectives on the Potential Benefits and Risks of Applying Artificial Intelligence in Psychiatric Medicine: Qualitative Study.JMIR mental health · 2025Article
- Pharmaco-Multiomics: A New Frontier in Precision Psychiatry.International journal of molecular sciences · 2025Review
- Clinical Pharmacology of Cytokine Release Syndrome with T-Cell-Engaging Bispecific Antibodies: Current Insights and Drug Development Strategies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Review
- Review
- Review
- Pharmacogenomic augmented machine learning in electronic health record alerts: A health system-wide usability survey of clinicians.Clinical and translational science · 2024Article
- Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment.World journal of psychiatry · 2024Review
- Consumer Health Informatics to Advance Precision Prevention.Yearbook of medical informatics · 2024Review
- PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service.Applied clinical informatics · 2024Article
- Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract.Microorganisms · 2024Review
- Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors.Frontiers in medicine · 2024Review
- Does Baseline Cognitive Function Predict the Reduction Rate in HDRS-17 Total Scores in First-Episode, Drug-Naïve Patients with Major Depressive Disorder?Neuropsychiatric disease and treatment · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.