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, due to the lack of custom modeling and analysis tailored to LAI data. 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 hours, classification of release profile types, and prediction of complete release profiles. These three experiments investigate the contribution and control of LAI material characteristics in early and complete drug release profiles. A strong correlation (>0.65) is observed between the true and predicted drug release in 72 hours, while a 0.87 F1-score is obtained in classifying release profile types. A time-independent ML framework predicts delayed biphasic and triphasic curves with better performance than current time-dependent approaches. Shapley additive explanations reveal the relative influence of material characteristics during early 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.

翻译：聚合物基长效注射剂（LAIs）通过实现可控药物递送，改变了慢性疾病的治疗模式，从而降低了给药频率并延长了治疗持续时间。要实现LAIs中的可控药物释放，需要对其复杂的潜在物理化学性质进行广泛优化。机器学习（ML）能够通过建模LAI特性与药物释放之间的复杂关系，加速LAI的研发。然而，由于缺乏针对LAI数据定制的建模与分析，近期的ML研究对调控药物释放的关键特性提供了有限的信息。本文提出了一种新颖的数据转换与可解释ML方法，通过预测24、48和72小时的早期药物释放、释放曲线类型的分类以及完全释放曲线的预测，从321种LAI配方中综合出可操作的信息。这三项实验研究了LAI材料特性在早期和完全药物释放曲线中的贡献与控制作用。在72小时的真实与预测药物释放之间观察到强相关性（>0.65），同时在释放曲线类型分类中获得了0.87的F1分数。一个时间无关的ML框架在预测延迟双相和三相曲线方面，表现优于当前的时间依赖方法。Shapley加性解释揭示了材料特性在早期及完全释放过程中的相对影响，填补了先前体外研究和基于ML的研究中的若干空白。这种新颖的方法与发现可为科研人员优化LAI的药物释放动力学提供定量策略与建议。模型实现的源代码已公开提供。