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模型开发和预测在连续制药过程中的共研磨操作中的粒度分布、密度和可压碎性。

Model development and prediction of particle size distribution, density and friability of a comilling operation in a continuous pharmaceutical manufacturing process.

机构信息

Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, NJ, USA.

Laboratory of Pharmaceutical Process Analytical Technology, Ghent University, Belgium.

出版信息

Int J Pharm. 2018 Oct 5;549(1-2):271-282. doi: 10.1016/j.ijpharm.2018.07.056. Epub 2018 Aug 1.

DOI:10.1016/j.ijpharm.2018.07.056
PMID:30076889
Abstract

The comilling process plays an important role in solid oral dosage manufacturing. In this process, the granulated products are comminuted to the required size distribution through collisions created from a rotating impeller. In addition to predicting particle size distribution, there is a need to predict other critical quality attributes (CQAs) such as bulk density and tapped density, as these impact tablet compaction behavior. A comprehensive modeling approach to predict the CQAs is needed to aid continuous process modeling in order to simulate interaction with the tablet press operation. In the current work, a full factorial experiment design is implemented to understand the influence of granule strength, impeller speed and residual moisture content on the CQAs. A population balance modeling approach is applied to predict milled particle size distribution and a partial least squares modeling approach is used to predict bulk and tapped density of the milled granule product. Good agreement between predicted and experimental CQAs is achieved. An R value of 0.9787 and 0.7633 is obtained when fitting the mean particle diameters of the milled product and the time required to mill the granulated material respectively.

摘要

制粒过程在固体制剂生产中起着重要作用。在该过程中,通过旋转叶轮产生的碰撞将颗粒状产品粉碎至所需的粒径分布。除了预测粒径分布外,还需要预测其他关键质量属性(CQAs),如堆密度和振实密度,因为这些属性会影响片剂的压缩行为。需要采用全面的建模方法来预测 CQAs,以帮助连续过程建模,从而模拟与压片机操作的相互作用。在当前的工作中,采用完全析因实验设计来理解颗粒强度、叶轮速度和残余水分含量对 CQAs 的影响。应用颗粒群平衡建模方法来预测粉碎后的粒径分布,应用偏最小二乘建模方法来预测粉碎后的颗粒产品的堆密度和振实密度。预测的 CQAs 与实验结果吻合良好。当拟合粉碎产品的平均粒径和粉碎颗粒材料所需的时间时,分别获得了 0.9787 和 0.7633 的 R 值。

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