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多元数据分析评估常用的压缩描述符。

Multivariate data analysis to evaluate commonly used compression descriptors.

机构信息

Heinrich Heine University Duesseldorf, Institute of Pharmaceutics and Biopharmaceutics, Universitaetsstraße 1, 40225 Duesseldorf, Germany.

Heinrich Heine University Duesseldorf, Institute of Pharmaceutics and Biopharmaceutics, Universitaetsstraße 1, 40225 Duesseldorf, Germany.

出版信息

Int J Pharm. 2023 Apr 25;637:122890. doi: 10.1016/j.ijpharm.2023.122890. Epub 2023 Mar 27.

Abstract

Numerous studies elucidated material behavior based on compression analyses. Especially compressibility, compactibility and tabletability were in the focus of these investigations. In the present study, a comprehensive multivariate data analysis was performed using principal component analysis method. Twelve pharmaceutically used excipients were selected for direct compression tableting and subsequent evaluation of several compression analyses. Material properties, tablet properties, tableting parameters and parameters from compression analyses were used as input variables. The materials could successfully be grouped using principal component analysis. Of the tableting parameters, the compression pressure showed the greatest influence on the results. The tabletability was found to be the most important compression analysis in the material characterization. Compressibility and compactibility only played a minor role in the evaluation. Some important insights have been gained for a deeper understanding of the tableting process using the multivariate approach to evaluate the variety of compression data.

摘要

许多研究基于压缩分析阐明了材料性能。特别是可压缩性、可压缩性和可压片性是这些研究的重点。在本研究中,使用主成分分析方法进行了全面的多变量数据分析。选择了 12 种药用辅料进行直接压片,并对几种压缩分析进行后续评估。材料特性、片剂特性、压片参数以及压缩分析参数用作输入变量。使用主成分分析可以成功地对材料进行分组。在压片参数中,压缩压力对结果的影响最大。在材料特性表征中,可压片性被认为是最重要的压缩分析。可压缩性和可压缩性在评估中仅起次要作用。使用多变量方法评估各种压缩数据,可以更深入地了解压片过程,从而获得一些重要的见解。

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