Automated active learning to optimize hydrogel drug release profiles

Hydrogels are widely used in drug delivery due to their biocompatibility and tunable release properties. However, optimizing hydrogel formulations to the desired release of therapeutics remains experimentally intensive. In this study, we developed an automated, high-throughput and machine learning (ML)-guided framework to efficiently optimize alginate formulations for drug delivery. Using a liquid handling robot, we initially prepared a diverse seed library of 120 alginate hydrogel formulations loaded with bovine serum albumin (BSA) and measured their release profiles. A Gaussian process regression (GPR) ML model was trained to predict cumulative release across time, enabling implicit modeling of release curves. Feature importance analysis using Shapley additive explanations (SHAP) identified time, alginate molecular weight, and concentration as dominant factors influencing release kinetics. Through Bayesian optimization and active learning, we iteratively selected and tested new formulations, progressively reaching a near zero-order release. Finally, the top-performing BSA-optimized formulations were directly applied to the sustained release of chondroitinase ABC single-enzyme nanoparticles (chABC-SENs), achieving near-zero-order release with no further optimizations. This study demonstrates a scalable, data-driven strategy for hydrogel formulation optimization and highlights the potential of ML to accelerate the development of controlled release systems for sensitive and valuable therapeutics.