Schmidtke Robert, Schröder Daniela, Menth Judith, Staab Andrea, Braun Michael, Wagner Karl G
Pharmaceutical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach, Germany; Department of Pharmaceutical Technology, University of Bonn, Gerhard-Domagk-Straße 3, 53121 Bonn, Germany.
Pharmaceutical Development, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach, Germany.
Int J Pharm. 2017 May 15;523(1):366-375. doi: 10.1016/j.ijpharm.2017.03.054. Epub 2017 Mar 25.
The aim of this study was to provide a systematic evaluation of various compression models (Percolation, Kawakita, Exponential model) in respect to predict tablet́s solid fraction for direct compression mixtures, based on single component compression analysis. Four mixtures were compressed over a wide pressure range at various fractions of microcrystalline cellulose (MCC) and pre-agglomerated lactose monohydrate (LAC) to compare an adjusted Percolation, Kawakita and a simple Exponential model. Based on single compression analysis of the pure excipients and application of these models, it was possible to predict the solid fraction of all mixtures. The Kawakita model showed overall superior prediction accuracy, whereas the Percolation model resulted in the best fit for mixtures containing microcrystalline cellulose in a range of 72%-48%. Both models were in good agreement at residuals below 3%.
本研究的目的是基于单一组分压缩分析,对各种压缩模型(渗流模型、河合模型、指数模型)在预测直接压片混合物片剂固体分数方面进行系统评估。在微晶纤维素(MCC)和预团聚一水乳糖(LAC)的不同比例下,于较宽压力范围内对四种混合物进行压缩,以比较调整后的渗流模型、河合模型和简单指数模型。基于纯辅料的单压缩分析及这些模型的应用,能够预测所有混合物的固体分数。河合模型总体显示出更高的预测准确性,而渗流模型对含72%-48%微晶纤维素的混合物拟合效果最佳。两种模型在残差低于3%时吻合度良好。