AI-enabled multi-omics pharmacogenomic modeling guides resistance-aware multitarget optimization of venetoclax...

Acute myeloid leukemia (AML) remains difficult to treat because profound molecular heterogeneity enables rapid adaptive resistance to targeted therapy. We developed an AI-driven pharmacogenomic pipeline that integrates genomic, transcriptomic, epigenetic, and clinical features to predict patient-specific response to venetoclax and translate resistance-associated signals into actionable therapeutic hypotheses. Across internal and external cohorts, the deep learning model showed strong discrimination and generalization (AUROC 0.84-0.90), produced calibrated probabilities, and stratified overall survival independent of standard prognostic factors. Explainable modeling (SHAP) identified mechanistically coherent drivers of sensitivity and resistance: high BCL2 and apoptotic priming favored response, whereas MCL1 upregulation, TP53 disruption, and RAS/MAPK activation were dominant resistance programs. Translating these findings into therapy design, network-based modeling and in silico perturbation prioritized rational combinations expected to block escape routes, including venetoclax plus MCL1 inhibition (e.g., AZD5991-class inhibitors), venetoclax plus FLT3 inhibition (e.g., gilteritinib-class agents) in signaling-driven disease, and venetoclax plus p53-axis modulation (e.g., MDM2 inhibition) in TP53-altered contexts. Structural candidate evaluation using ensemble docking provided supportive drug-target interaction evidence for prioritized dependencies. Together, these results establish a clinically interpretable, resistance-aware AI framework for precision optimization of venetoclax-based combination therapy in AML.