ArticleDrug delivery and translational research2026
Predicting drug release from polymeric long-acting injectables using a machine learning approach to decode formulation-performance relationships.
Article in Drug delivery and translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Polymeric long-acting injectables (LAIs) are among the most impactful yet least predictable drug delivery systems, as their performance emerges from a widely unresolved combination of polymer microstructure, manufacturing history, and assay conditions. To address this challenge, we introduce a hybrid machine learning framework that combines data-driven prediction with mechanistically relevant release theory to systematically decode LAI performance. Using a curated dataset of 1,537 literature-derived release profiles and 35 descriptors, our random forest model achieved robust performance with a mean (R² = 0.86 ± 0.16). Interpretable SHAP analysis confirmed a critical insight: the release apparatus can shape measured kinetics as strongly as the formulation itself, highlighting a major hurdle in current standardization. The framework's prospective utility was examined through validation using the commercial product Risperdal Consta
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