Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium; Oral Solid Dosage, Drug Product Development, Pharmaceutical Development and Manufacturing Sciences, Pharmaceutical Research and Development, Division of Janssen Pharmaceutica, Johnson & Johnson, Turnhoutseweg 30, B-2340 Beerse, Belgium.
Laboratory of Pharmaceutical Technology, Department of Pharmaceutics, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium.
Int J Pharm. 2018 Oct 5;549(1-2):476-488. doi: 10.1016/j.ijpharm.2018.08.015. Epub 2018 Aug 11.
Based on characterization of a wide range of fillers and APIs, thirty divergent blends were composed and subsequently compressed on a rotary tablet press, varying paddle speed and turret speed. The tablet weight variability was determined of 20 grab samples consisting of each 20 tablets. Additionally, the bulk residence time, ejection force, pre-compression displacement, main compression force, die fill fraction and feed frame fill fraction were determined during each run. Multivariate data analysis was applied to investigate the relation between the process parameters, blend characteristics, product and process responses. Blends with metoprolol tartrate as API showed high ejection forces. This behavior could be linked to the high wall friction value of metoprolol tartrate. The main responses related to the die filling could be predicted via a PLS model based on blend characteristics. Tablet weight variability was highly correlated with the variability on pre-compression displacement and main compression force. A good predictive model for tablet weight variability was obtained taking the porosity, wall friction angle, flowability, density, compressibility and permeability into account. Additionally, turret speed and paddle speed were included in the calibration of the model. The applied approach can save resources (material, time) during early drug product development.
基于对各种填充剂和原料药的特性进行表征，共制备了 30 种不同的混合物，然后在旋转压片机上进行压缩，改变桨叶速度和转塔速度。从每个 20 片的 20 个随机样本中测定片剂重量差异。此外，在每个运行过程中还测定了散装停留时间、顶出力、预压位移、主压压力、模腔填充分数和送料器填充分数。应用多元数据分析研究了工艺参数、混合物特性、产品和工艺响应之间的关系。含有酒石酸美托洛尔作为原料药的混合物显示出较高的顶出力。这种行为可能与酒石酸美托洛尔的高壁摩擦值有关。基于混合物特性，可以通过 PLS 模型预测与模腔填充相关的主要响应。片剂重量差异与预压位移和主压压力的差异高度相关。考虑到孔隙率、壁摩擦角、流动性、密度、可压缩性和渗透性，建立了一个用于预测片剂重量差异的良好预测模型。此外，在模型校准中还包括了转塔速度和桨叶速度。该方法可在药物产品开发的早期节省资源（材料、时间）。
