Institute of Pharmaceutical Technology and Biopharmaceutics, Technische Universität Braunschweig, Braunschweig 38106, Germany; Department of Orals Development, Merck Healthcare KGaA, Darmstadt 64293, Germany.
Department of Orals Development, Merck Healthcare KGaA, Darmstadt 64293, Germany.
Eur J Pharm Sci. 2024 Sep 1;200:106836. doi: 10.1016/j.ejps.2024.106836. Epub 2024 Jun 18.
Principal component analysis (PCA) and partial least squares regression (PLS) were combined in this study to identify key material descriptors determining tabletability in direct compression and roller compaction. An extensive material library including 119 material descriptors and tablet tensile strengths of 44 powders and roller compacted materials with varying drug loads was generated to systematically elucidate the impact of different material descriptors, raw API and filler properties as well as process route on tabletability. A PCA model was created which highlighted correlations between different powder descriptors and respective characterization methods and, thus, can enable reduction of analyses to save resources to a certain extent. Subsequently, PLS models were established to identify key material attributes for tabletability such as density and particle size but also surface energy, work of cohesion and wall friction, which were for the first time demonstrated by PLS as highly relevant for tabletability in roller compaction and direct compression. Further, PLS based on extensive material characterization enabled the prediction of tabletability of materials unknown to the model. Thus, this study highlighted how PCA and PLS are useful tools to elucidate the correlations between powder and tabletability, which will enable more robust prediction of manufacturability in formulation development.
本研究将主成分分析（PCA）和偏最小二乘回归（PLS）相结合，以确定直接压缩和滚压过程中决定可压性的关键材料描述符。建立了一个广泛的材料库，包括 119 个材料描述符和 44 种粉末以及不同药物载量的滚压材料的片剂拉伸强度，以系统地阐明不同材料描述符、原料药和赋形剂性质以及工艺路线对可压性的影响。创建了一个 PCA 模型，突出了不同粉末描述符之间的相关性及其各自的表征方法，从而可以在一定程度上减少分析以节省资源。随后，建立了 PLS 模型来识别可压性的关键材料属性，如密度和粒径，但也包括表面能、内聚功和壁摩擦，这是首次通过 PLS 证明它们在滚压和直接压缩中与可压性高度相关。此外，基于广泛的材料特性的 PLS 还能够预测模型未知的材料的可压性。因此，本研究强调了 PCA 和 PLS 如何有助于阐明粉末和可压性之间的相关性，这将能够更稳健地预测制剂开发中的可制造性。
