Polymer-based long-acting injectables (LAIs) have transformed the treatment of chronic diseases by enabling controlled drug delivery, thus reducing dosing frequency and extending therapeutic duration. Achieving controlled drug release from LAIs requires extensive optimization of the complex underlying physicochemical properties. Machine learning (ML) can accelerate LAI development by modeling the complex relationships between LAI properties and drug release. However, recent ML studies have provided limited information on key properties that modulate drug release, as existing approaches rely on time as a primary input feature, obscuring the independent contributions of material characteristics to release dynamics This paper presents a novel data transformation and explainable ML approach to synthesize actionable information from 321 LAI formulations by predicting early drug release at 24, 48, and 72 h, classifying release profile types, and predicting complete release profiles. These three experiments investigate the influence of LAI material characteristics in early and complete drug release profiles. A moderate correlation (0.37) is observed between the true and predicted drug release at 72 h, while an F1-score of 0.72 is obtained in classifying the types of drug release profiles. For the first time, we demonstrate that time-independent ML frameworks achieve equivalent performance to time-dependent approaches in predicting complete drug release profiles, including complex delayed biphasic and triphasic curves. Shapley additive explanations reveal the relative influence of material characteristics during early time points, between drug release profile classes and for complete release, which fill several gaps in previous in-vitro and ML-based studies. The novel approach and findings can provide a quantitative strategy and recommendations for scientists to optimize the drug-release dynamics of LAI. The source code for the model implementation is publicly available in
Robles et al. (Wed,) studied this question.