The distribution of the active pharmaceutical ingredient (API) within polymer-based controlled release drug products is a critical quality attribute (CQA). It is crucial for the development of such products, to be able to accurately characterize phase distributions in these products to evaluate performance and microstructure (Q3) equivalence. In this study, polymer, API, and porosity distributions in poly(lactic-co-glycolic acid) (PLGA) microspheres were characterized using a combination of focused ion beam scanning electron microscopy (FIB-SEM) and quantitative artificial intelligence (AI) image analytics. Through in-depth investigations of nine different microsphere formulations, microstructural CQAs were identified including the abundance, domain size, and distribution of the API, the polymer, and the microporosity. 3D models, digitally transformed from the FIB-SEM images, were reconstructed to predict controlled drug release numerically. Agreement between the in vitro release experiments and the predictions validated the image-based release modelling method. Sensitivity analysis revealed the dependence of release on the distribution and size of the API particles and the porosity within the polymeric microspheres, as captured through FIB-SEM imaging. To our knowledge, this is the first report showing that microstructural CQAs in PLGA microspheres derived from imaging can be quantitatively and predictively correlated with formulation and manufacturing parameters.

基于聚合物的控释药物产品中活性药物成分 (API) 的分布是关键质量属性 (CQA)。对于此类产品的开发而言，能够准确表征这些产品中的相分布以评估性能和微观结构 (Q3) 等效性至关重要。在本研究中，聚乳酸共聚物中的聚合物、API 和孔隙率分布结合使用聚焦离子束扫描电子显微镜 (FIB-SEM) 和定量人工智能 (AI) 图像分析对 -乙醇酸 (PLGA) 微球进行表征。通过对九种不同微球配方的深入研究，确定了微结构 CQA，包括 API、聚合物和微孔率的丰度、域大小和分布。从 FIB-SEM 图像进行数字转换的 3D 模型被重建，以数字方式预测受控药物释放。体外协议发布实验和预测验证了基于图像的发布建模方法。敏感性分析揭示了释放对 API 颗粒的分布和大小以及聚合物微球内的孔隙率的依赖性，如通过 FIB-SEM 成像捕获的那样。据我们所知，这是第一份报告表明从成像中获得的 PLGA 微球中的微结构 CQA 可以与配方和制造参数进行定量和预测相关。