Mechanism-aware and explainable artificial intelligence for drug release prediction in polymeric drug delivery systems

Abstract

Predicting drug release from polymer-based delivery systems remains challenging because release behavior depends on nonlinear interactions among formulation composition, polymer properties, drug characteristics, processing conditions, and time. This study develops an interpretable artificial intelligence framework for predicting cumulative drug release from PLGA nanoparticle formulations prepared by nanoprecipitation. A curated dataset containing 4,909 formulation–release observations was used to integrate formulation-informed feature engineering, temporal representation learning, symbolic regression, latent-space analysis, and explainable machine learning. The proposed framework achieved R2 = 0.9977, RMSE = 1.56 × 10−4, and MAE = 1.31 × 10−4 on the training data, while the independent test set yielded R2 = 0.9975, RMSE = 1.64 × 10−4, and MAE = 1.31 × 10−4. Y-scrambling produced substantially lower R2 values of 0.0636–0.0714, providing evidence that the observed predictive performance is not readily reproduced after randomization of the response. Temporal autoencoder representations further revealed structured organization of release trajectories in a low-dimensional latent space. Nevertheless, the high predictive accuracy should be interpreted cautiously because the dataset contains repeated observations associated with formulation–release trajectories and includes release time as an input-related variable; therefore, random observation-level splitting may provide more favorable estimates than formulation-level or study-level external validation. The framework provides a computational basis for formulation screening and hypothesis generation, but experimentally validated release experiments and prospective external datasets remain necessary before practical deployment.