Deep Reinforcement Learning-Driven Efficacy-Toxicity Balance Optimization Strategy for Personalized Drug Combination in Cancer Patients
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Abstract
Precision medicine demands the careful optimization of dose regimens in cancer therapy, particularly when adjusting doses to balance therapeutic effectiveness against adverse toxicities. In this study, we introduce a deep reinforcement learning (DRL) framework that leverages multimodal patient data to optimize personalized drug combination strategies, aiming to maximize efficacy while minimizing toxicity. The DRL agent employs multi-objective reward functions to identify optimal treatment strategies by integrating genomic, clinical, and pharmacokinetic data through an advanced feature engineering pipeline. This approach was evaluated in a cohort of 2,847 cancer patients encompassing a diverse range of tumor types. Experimental results demonstrate that the algorithm improved predicted treatment response by 23.4% compared to conventional methods, while reducing serious adverse events by 18.7%. These findings highlight a significant advancement in computational approaches for personalized therapy optimization, providing clinically interpretable outputs to guide patient-specific treatment decisions.
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