Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
J Pharm Pharmacol. 2020 Jan;72(1):132-148. doi: 10.1111/jphp.13186. Epub 2019 Nov 11.
Cornu Caprae Hircus (goat horn, GH), a medicinal animal horn, is frequently used in traditional Chinese medicine, and hydrolysis is one of the most important processes for GH pretreatment in pharmaceutical manufacturing. In this study, on-line Raman spectroscopy was applied to monitor the GH hydrolysis process by the development of partial least squares (PLS) calibration models for different groups of amino acids.
Three steps were considered in model development. In the first step, design of experiments (DOE)-based preprocessing method selection was conducted. In the second step, the optimal spectral co-addition number was determined. In the third step, sample selection or reconstruction methods based on hierarchical clustering analysis (HCA) were used to extract or reconstruct representative calibration sets from the pool of hydrolysis process samples and investigated for their ability to improve model performance.
This study has shown the feasibility of using on-line Raman spectral analysis for monitoring the GH hydrolysis process based on the designed measurement system and appropriate model development steps.
The proposed Raman-based calibration models are expected to be used in GH hydrolysis process monitoring, leading to more rapid material information acquisition, deeper process understanding, more accurate endpoint determination and thus better product quality consistency.
鹿茸(GH)是一种药用动物角,在中药中经常使用,水解是 GH 在制药生产中预处理的最重要过程之一。在这项研究中,通过为不同组的氨基酸开发偏最小二乘(PLS)校准模型,应用在线拉曼光谱法来监测 GH 水解过程。
模型开发分为三个步骤。第一步,进行基于实验设计(DOE)的预处理方法选择。第二步,确定最佳光谱叠加数量。第三步,使用基于层次聚类分析(HCA)的样品选择或重建方法,从水解过程样品池中提取或重建具有代表性的校准集,并研究其改善模型性能的能力。
本研究表明,基于设计的测量系统和适当的模型开发步骤,使用在线拉曼光谱分析监测 GH 水解过程是可行的。
预计所提出的基于拉曼的校准模型将用于 GH 水解过程监测,从而更快地获取材料信息、更深入地了解过程、更准确地确定终点,从而更好地保持产品质量的一致性。