Machine Learning-Based Identification of Clinical and Genetic Predictors of Antidepressant Non-Response in Bipolar Depression: A Simulation-Based Feature-Importance Study across BD-I and BD-II Subtypes

Antidepressant non-response in bipolar depression affects an estimated 40% - 55% of treated patients and is a major contributor to treatment chronification and pharmacological escalation. Although clinical, pharmacogenomic, and circadian correlates have been proposed, their combined predictive value remains uncertain. This study is a simulation-based proof of concept using a fully synthetic cohort and simulated outcomes; it does not constitute a validated clinical prediction. Because the full model includes 2-week MADRS information, it is interpreted as an early-treatment updating model rather than a purely pre-treatment model. The modelling pipeline was rerun using the final locked specification, and all numerical results reported in the manuscript are outputs from that completed rerun. In the held-out synthetic test set, the full ensemble achieved AUC = 0.958 (95% CI: 0.917 - 0.989), F1 = 0.906 (CI: 0.842 - 0.957), sensitivity = 0.906, and specificity = 0.924. XGBoost achieved the same point-estimate AUC (0.958) and a lower Brier score (0.072 versus 0.077). Feature-attribution analysis of the tree-based base learners identified genetic load, prior non-response, 2-week early response, chronic depressive course, and 5-HTTLPR S/S status as prominent predictors. These findings partly reflect the synthetic label-generating mechanism and should not be interpreted as evidence that any genotype is clinically deterministic. The study demonstrates the technical feasibility of combining simulated clinical, pharmacogenomic, circadian, and early-response information in a subtype-stratified modelling framework. External validation on observed bipolar depression cohorts, including a true baseline-only analysis, is required before clinical use.