Svetič Sandi, Medved Laura, Korasa Klemen, Vrečer Franc
KRKA, d. d., 8501 Novo Mesto, Slovenia.
Faculty of Pharmacy, University of Ljubljana, 1000 Ljubljana, Slovenia.
Pharmaceutics. 2024 Dec 1;16(12):1538. doi: 10.3390/pharmaceutics16121538.
Active pharmaceutical ingredient (API) content is a critical quality attribute (CQA) of amorphous solid dispersions (ASDs) prepared by spraying a solution of APIs and polymers onto the excipients in fluid bed granulator. This study presents four methods for quantifying API content during ASD preparation. Raman and three near-infrared (NIR) process analysers were utilized to develop methods for API quantification. Four partial least squares (PLS) models were developed using measurements from three granulation batches, with an additional batch used to evaluate model predictability. Models performance was assessed using metrics such as root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), residual prediction deviation (RPD), and others. Off-line and at-line NIR models were identified as suitable for process control applications. Additionally, at-line Raman measurements effectively predicted the endpoint of the spraying phase. To the best of authors' knowledge, this is the first study focused on monitoring API content during fluidized bed granulation (FBG) used for ASD preparation. The findings provide novel insights into the application of Raman and NIR process analysers with PLS modelling for monitoring and controlling ASD preparation processes.
活性药物成分(API)含量是通过将API和聚合物溶液喷雾到流化床制粒机中的辅料上制备的无定形固体分散体(ASD)的关键质量属性(CQA)。本研究提出了四种在ASD制备过程中定量API含量的方法。利用拉曼光谱仪和三种近红外(NIR)过程分析仪开发了API定量方法。使用三个制粒批次的测量数据建立了四个偏最小二乘(PLS)模型,并使用另外一个批次来评估模型的可预测性。使用预测均方根误差(RMSEP)、交叉验证均方根误差(RMSECV)、残差预测偏差(RPD)等指标评估模型性能。离线和在线近红外模型被确定适用于过程控制应用。此外,在线拉曼测量有效地预测了喷雾阶段的终点。据作者所知,这是第一项专注于监测用于ASD制备的流化床制粒(FBG)过程中API含量的研究。这些发现为拉曼光谱仪和近红外过程分析仪结合PLS建模在监测和控制ASD制备过程中的应用提供了新的见解。