Active Learning-Based Prediction of Drug Combination Efficacy

Combination therapy, which involves the use of multiple drugs, has emerged as a promising approach to cancer treatment. However, traditional combination therapy development is constrained by the vast experimental design space, requiring exhaustive testing of drug ratios, concentrations, and encapsulation strategies. In this study, we present a computational intelligence method combining active learning and fine-grid optimization to predict the efficacy of drug combinations, focusing on dual-drug-loaded polymeric nanoparticles for cancer therapy. Our approach harnesses Gaussian Process Regression to predict both drug efficacy and associated uncertainty, enabling rapid identification of optimal conditions with only 25% of the experimental effort. This method was successfully applied to optimize dual-drug systems, including doxorubicin and docetaxel, demonstrating significant reductions in experimental workload without compromising precision. Our study has demonstrated the potential of AI-driven methodologies in overcoming the challenges posed by traditional experimental designs in the drug delivery field.