Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder

Abstract

Introduction: The efficacy of antidepressants for patients with major depressive disorder (MDD) varies from individual to individual, making the prediction of therapeutic outcomes difficult. Better methods for predicting antidepressant outcomes are needed. However, complex interactions between biological, psychological, and environmental factors affect outcomes, presenting immense computational challenges for prediction. Using machine learning (ML) techniques with pharmacogenomics data provides one pathway toward individualized prediction of therapeutic outcomes of antidepressants.

Areas covered: This report systematically reviews the methods, results, and limitations of individual studies of ML and pharmacogenomics for predicting response and/or remission with antidepressants in patients with MDD. Future directions for research and pragmatic considerations for the clinical implementation of ML-based pharmacogenomic algorithms are also discussed.

Expert opinion: ML methods utilizing pharmacogenomic and clinical data demonstrate promising results for predicting short-term antidepressant response. However, predictions of antidepressant treatment outcomes depend on contextual factors that ML algorithms may not be able to capture. As such, ML-driven prediction is best viewed as a companion to clinical judgment, not its replacement. Successful implementation and adoption of methods predicting antidepressant response warrants provider education about ML and close collaborations between computing scientists, pharmacogenomic experts, health system engineers, laboratory medicine experts, and clinicians.

Keywords: Machine learning; antidepressant; artificial intelligence; deep learning; depression; genomics; major depressive disorder; outcome; pharmacogenomics; prediction.