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多变量分析α-乳糖一水合物的材料性质、工艺参数与片剂拉伸强度之间的关系。

Multivariate analysis of relationships between material properties, process parameters and tablet tensile strength for alpha-lactose monohydrates.

机构信息

University of Tromsø, Department of Pharmacy, Tromsø, Norway.

出版信息

Eur J Pharm Biopharm. 2009 Nov;73(3):424-31. doi: 10.1016/j.ejpb.2009.08.005. Epub 2009 Aug 19.

Abstract

The present work describes an approach to quantify relationships between the material properties of various alpha-lactose monohydrate grades (alphaLM), process parameters (punch velocity, lubricant fraction) and the tablet tensile strength (TS). Milled, sieved, agglomerated and spray-dried alphaLMs were studied. Each material was tableted (11 mm flat punches, constant true volume of 0.2833 cm(3)) on a compaction simulator at a pressure of 104.4+/-0.1 MPa. The force-displacement data was analyzed by applying a combination of compression descriptors (derived from Kawakita and Heckel equations, work-related parameters). The relationships were evaluated and quantified by principal component analysis (PCA) and partial least square regression (PLS-1). PCA verified fundamental relationships between different powder and compression properties of studied materials. It was found that the compression descriptors Kawakita '1/b' and WoC were sufficient to distinguish the tested alphaLM-grades, even in combination with different lubricant fraction or by utilizing different punch velocities; the identified descriptors correlated with TS. These relationships were quantified by PLS-1. Finally, TS were successfully predicted for all alphaLM with the help of separate optimized PLS-1 models. The present study shows an approach how to extract relevant information about tableting behavior from a limited amount of material.

摘要

本工作描述了一种量化各种α-乳糖一水合物(αLM)等级的材料性质、工艺参数(冲头速度、润滑剂分数)与片剂拉伸强度(TS)之间关系的方法。研究了研磨、筛分、团聚和喷雾干燥的αLMs。每种材料都在压实模拟器上以 104.4±0.1 MPa 的压力用 11 毫米平冲头(恒定的真实体积为 0.2833 cm(3))压制成片剂。通过应用组合压缩描述符(源自 Kawakita 和 Heckel 方程、与工作相关的参数)对力-位移数据进行分析。通过主成分分析(PCA)和偏最小二乘回归(PLS-1)对关系进行评估和量化。PCA 验证了研究材料不同粉末和压缩性质之间的基本关系。结果发现,即使在结合不同润滑剂分数或使用不同冲头速度的情况下,压缩描述符 Kawakita '1/b' 和 WoC 也足以区分测试的αLM 等级;所识别的描述符与 TS 相关。这些关系通过 PLS-1 进行量化。最后,借助单独优化的 PLS-1 模型,成功地对所有αLM 进行了 TS 预测。本研究展示了一种如何从有限数量的材料中提取压片行为相关信息的方法。

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