Predicting clozapine-induced adverse drug reaction biomarkers using machine learning

Clozapine is an atypical antipsychotic used for patients with treatment-resistant schizophrenia. This drug has serious adverse drug reactions (ADRs), including the risk of severe neutropenia (agranulocytosis). Patients who could benefit from clozapine may not be administered it due to concerns about monitoring ADRs. In addition, traditional toxicological assessments cannot predict clozapine-induced agranulocytosis. Predicting agranulocytosis could improve patient safety. Our study aimed to develop and validate machine learning (ML) models for predicting agranulocytosis in clozapine-prescribed patients using the Canada Vigilance Adverse Reaction Online Database (n = 9395 reports). We addressed the class imbalance (337 agranulocytosis-positive cases vs. 9058 agranulocytosis-negative cases) through systematically evaluating resampling techniques and selecting appropriate performance metrics for rare event prediction. Five ML algorithms were evaluated on a hold-out test set. The best-performing model was the Gradient Boosting with Synthetic Minority Over-sampling technique (GB-SMOTE), achieving recall (sensitivity) of 0.85, AUC-PR (area under the precision-recall (PR) curve) of 0.77, PPV (Positive Predictive Value) of 0.40 and a Matthews Correlation Coefficient of 0.56. SHAP feature analysis identified blood and lymphatic system disorders, leukocytosis, and neutropenia as the strongest predictors. Our results demonstrate the potential of ML for predicting clozapine-induced agranulocytosis and provide a framework for developing pharmacovigilance prediction models. This is clinically important and relevant to the management of schizophrenia, which remains a chronic disease with high morbidity and mortality.