| Summary: | Fei Sun,1 Bing Xu,1,2 Yi Zhang,1 Shengyun Dai,1 Chan Yang,1 Xianglong Cui,1 Xinyuan Shi,1,2 Yanjiang Qiao1,2 1Research Center of Traditional Chinese Medicine Information Engineering, School of Chinese Materia Medica, Beijing University of Chinese Medicine, 2Key Laboratory of Manufacture Process Control and Quality Evaluation of Chinese Medicine, Beijing, People’s Republic of China Abstract: The quality of Chinese herbal medicine tablets suffers from batch-to-batch variability due to a lack of manufacturing process understanding. In this paper, the Panax notoginseng saponins (PNS) immediate release tablet was taken as the research subject. By defining the dissolution of five active pharmaceutical ingredients and the tablet tensile strength as critical quality attributes (CQAs), influences of both the manipulated process parameters introduced by an orthogonal experiment design and the intermediate granules’ properties on the CQAs were fully investigated by different chemometric methods, such as the partial least squares, the orthogonal projection to latent structures, and the multiblock partial least squares (MBPLS). By analyzing the loadings plots and variable importance in the projection indexes, the granule particle sizes and the minimal punch tip separation distance in tableting were identified as critical process parameters. Additionally, the MBPLS model suggested that the lubrication time in the final blending was also important in predicting tablet quality attributes. From the calculated block importance in the projection indexes, the tableting unit was confirmed to be the critical process unit of the manufacturing line. The results demonstrated that the combinatorial use of different multivariate modeling methods could help in understanding the complex process relationships as a whole. The output of this study can then be used to define a control strategy to improve the quality of the PNS immediate release tablet. Keywords: Panax notoginseng saponins, PNS immediate release tablet, pharmaceutical process understanding, partial least squares, orthogonal projections to latent structures, multiblock partial least squares, quality by design
|
|---|
