Adverse drug reactions (ADRs) remain a major clinical challenge and a leading cause of morbidity and mortality worldwide. Among them, severe cutaneous adverse drug reactions (SCARs), including Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), represent life-threatening immune-mediated hypersensitivity responses strongly associated with specific human leukocyte antigen (HLA) alleles. Despite well-established pharmacogenetic associations, current diagnostic strategies remain largely retrospective and lack predictive capability for novel drug–HLA risk pairs. Here, we present an integrative network-informed machine learning framework for predicting HLA-associated SCAR risk by combining pharmacogenomic features, drug chemical structure, and topological descriptors derived from drug–drug and drug–symptom interaction networks. An Extreme Gradient Boosting (XGBoost) classifier trained on integrated HLA allele and drug features, labeled using curated HLA–SCAR associations, achieved an accuracy of 0.860 ± 0.005, an F1-score of 0.689 ± 0.010, with an area under the receiver operating characteristic curve (AUROC) of 0.922 ± 0.003 and an area under the precision–recall curve (AUPRC) of 0.768 ± 0.007. Notably, several predicted positive associations absent from the training data corresponded to biologically plausible and literature-supported cases, including carbamazepine—HLA-B*15:11, supporting the model’s ability to generalize beyond known associations. Molecular docking provides structural evidence for the predicted associations, highlighting allele-specific binding patterns underlying these results. Overall, our results demonstrate that network-informed machine learning provides a proactive and integrative approach to SCAR risk prediction and may support early risk stratification and personalized drug safety assessment in precision medicine.
Ponduan et al. (Fri,) studied this question.