Machine Learning for Predicting Drug Release Behavior of PLGA Microspheres

PLGA microspheres are widely used in long-acting drug formulations due to their ability to provide sustained release, improving patient adherence and reducing dosing frequency. However, drug release behavior is influenced by complex formulation and processing factors, making traditional trial-and-error development inefficient. This study leverages machine learning to predict drug release profiles from PLGA (poly(lactic-co-glycolic acid)) microsphere formulations. A dataset of 113 PLGA formulations containing small-molecule drugs and large-molecule peptides was collected from published literature. Multiple machine learning models were developed and compared. The best-performing model achieved an R² value of 0. 9415, a RMSE of 6.99% and a MAE of 4.35%, demonstrating strong predictive accuracy for in vitro drug release. Additionally, feature importance analysis was conducted, offering insights into key factors influencing release behavior and guiding the rational design of PLGA-based microspheres.