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用于模拟辊压造粒-粉碎过程中颗粒粒度分布的多变量数学模型。

A Multi-variate Mathematical Model for Simulating the Granule Size Distribution in Roller Compaction-Milling Process.

机构信息

Integrated Material Science & Technology, Drug Product Development, Bristol-Myers Squibb, 556 Morris Avenue, Summit, New Jersey, 07901, USA.

出版信息

AAPS PharmSciTech. 2021 Mar 10;22(3):97. doi: 10.1208/s12249-021-01955-6.

Abstract

Granule size distribution (GSD) is one of the critical quality attributes in the roller compaction (RC) process. Determination of GSD for newly developed pharmaceutical compounds with unknown ribbon breakage behaviors at the RC milling step requires a quantitative insight into process parameters and ribbon attributes. Despite its pivotal role in mapping the process operating conditions to achieve desired granule size, limited work has been presented in literature with a focus on RC-milling modeling. In this study, a multi-variate mathematical model is presented to simulate the full size-distribution of granulated ribbons as a function of ribbon mechanical properties. Experimental data with a lab-scale oscillating milling apparatus were generated using ribbons made of various powder compositions. Model parameters were determined by fitting it to experimental data sets. Parameters obtained from the first step were correlated to ribbon Young's modulus. The model was validated by predicting GSD of data that were excluded in model development step. Predictive capabilities of the developed model were further explored by simulating GSD profiles of a granulated pharmaceutical excipient obtained at three different conditions of a real-scale Gerteis RC system. While maintaining the milling operating conditions similar to the lab-scale apparatus (i.e., screen size and spacing, and low rotor speed), the proposed modeling approach successfully predicted the GSD of roller compacted MCC powder as the model compound. This model can be alternatively utilized in conjunction with an RC model in order to facilitate the process understanding to obtain granule attributes as part of Quality-by-Design paradigm.

摘要

粒度分布(GSD)是辊压(RC)过程中的关键质量属性之一。对于在 RC 制粒步骤中具有未知带状断裂行为的新开发药物化合物,需要定量了解工艺参数和带状物属性,以确定 GSD。尽管它在将工艺操作条件映射到实现所需颗粒尺寸方面起着关键作用,但文献中针对 RC 制粒建模的工作有限。在这项研究中,提出了一种多变量数学模型,以模拟作为带状物机械性能函数的颗粒化带状物的全粒度分布。使用各种粉末成分制成的带状物,在实验室规模的振荡制粒设备上生成实验数据。通过将模型拟合到实验数据集来确定模型参数。从第一步获得的参数与带状物的杨氏模量相关联。通过预测排除在模型开发步骤之外的数据的 GSD 来验证模型。通过模拟在真实规模 Gerteis RC 系统的三种不同条件下获得的药物赋形剂的 GSD 曲线,进一步探索了开发模型的预测能力。在保持与实验室规模设备相似的制粒操作条件(即筛网尺寸和间距以及低转子速度)的情况下,该建模方法成功预测了作为模型化合物的 MCC 粉末的 RC 压缩 GSD。该模型可与 RC 模型结合使用,以便在获得颗粒属性作为质量源于设计范式的一部分时促进工艺理解。

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