Department of Psychiatry & Psychology, Mayo Clinic Florida, Jacksonville, FL, USA.
Center for Individualized Medicine, Mayo Clinic, Rochester, FL, USA.
Expert Rev Clin Pharmacol. 2022 Aug;15(8):927-944. doi: 10.1080/17512433.2022.2112949. Epub 2022 Aug 21.
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.
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.
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.
抗抑郁药治疗重度抑郁症（MDD）患者的疗效因人而异，这使得治疗结果的预测变得困难。需要更好的方法来预测抗抑郁药的结果。然而，生物、心理和环境因素之间的复杂相互作用会影响结果，这给预测带来了巨大的计算挑战。使用机器学习（ML）技术和药物基因组学数据为抗抑郁药治疗的个体化预测提供了一种途径。
本报告系统地回顾了使用 ML 和药物基因组学预测 MDD 患者抗抑郁药反应和/或缓解的个体研究的方法、结果和局限性。还讨论了基于 ML 的药物基因组学算法的研究方向和临床实施的实际考虑因素。
利用药物基因组学和临床数据的 ML 方法在预测短期抗抑郁反应方面显示出有希望的结果。然而，抗抑郁治疗结果的预测取决于 ML 算法可能无法捕捉到的情境因素。因此，最好将基于 ML 的预测视为临床判断的辅助手段，而不是其替代品。成功实施和采用预测抗抑郁反应的方法需要对 ML 进行提供者教育，并在计算科学家、药物基因组学专家、卫生系统工程师、实验室医学专家和临床医生之间进行密切合作。
