Pharmacogenomic Approaches to Predicting Susceptibility to Neuroleptic Malignant Syndrome and Severe Anticholinergic Adverse Effects: A Multi-Modal Explainable AI Framework

Neuroleptic Malignant Syndrome (NMS) and severe anticholinergic adverse drug reactions (ADRs) are rare but life-threatening complications associated with antipsychotic pharmacotherapy.These conditions often arise unpredictably, posing significant challenges in psychiatric clinical practice.Current risk stratification approaches lack the granularity to account for complex interplays between genetic predispositions, pharmacological profiles, and individual patient characteristics.In this study, we introduce a comprehensive pharmacogenomic risk prediction framework that integrates synthetic cohort simulation, genotypic modelling, and state-of-the-art explainable artificial intelligence (XAI).Our platform simulates a diverse patient population with realistic demographic, clinical, and pharmacokinetic parameters, incorporating known pharmacogenomic markers such as CYP2D6 (rs3892097), COMT (rs4680), DRD2 (rs1800497), and HTR2A (rs6311).A deep learning model augmented with multi-head attention mechanisms is employed to capture latent interactions among features, while SHAP (SHapley Additive exPlanations) is used for local and global model interpretability.The system demonstrates that polygenic risk scores (PRS), combined with drug dosage and EPS history, significantly improve predictive granularity, particularly for identifying high-risk cases.Notably, CYP2D6 and COMT polymorphisms emerged as dominant predictors for NMS and severe anticholinergic responses.Evaluation metrics, including confusion matrices, precision-recall curves, and ROC analysis, highlight the model's capacity to differentiate reaction severities, though perfor-How to cite this paper: