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用于监测固体口服制剂连续制造过程中控状态的过程分析技术

PAT for monitoring the state of control in continuous manufacturing of solid oral dosage forms.

作者信息

Movilla-Meza Nathaly A, Patel Dhavalkumar S, Méndez Rafael, Romañach Rodolfo J

机构信息

Department of Chemistry, University of Puerto Rico at Mayaguez, PO Box 9000, Mayaguez, Puerto Rico, 00681, USA.

Department of Chemical Engineering, University of Puerto Rico at Mayaguez, Mayaguez, Puerto Rico, USA.

出版信息

Anal Bioanal Chem. 2025 Jul 21. doi: 10.1007/s00216-025-06012-w.

Abstract

Process analytical technology plays an important role in maintaining the state of control during pharmaceutical manufacturing processes. This review discusses different studies where continuous manufacturing runs were monitored up to 120 h. This set of studies have used near infrared spectroscopy to determine the drug concentration and confirm whether the pharmaceutical products are within specifications. The performance of these methods was evaluated through validations that included accuracy, precision, linearity, and robustness tests. The importance of the repeatability study is emphasized in this review. Different sampling locations have been shown to be useful for real-time monitoring. Process analytical technology and residence time distribution models have enabled the detection of non-conforming material and its diversion from the manufacturing process. The use of variographic analysis for improving process analytical technology implementation is discussed, since it is the only method that differentiates process variability from sampling and analytical errors. The studies discussed in this review have contributed to the improvement of the continuous manufacturing processes, providing a high level of product and process understanding, product quality, and reliability.

摘要

过程分析技术在药品生产过程中维持控制状态方面发挥着重要作用。本综述讨论了不同的研究,这些研究对长达120小时的连续生产运行进行了监测。这一系列研究使用近红外光谱法来测定药物浓度,并确认药品是否符合规格。通过包括准确度、精密度、线性和稳健性测试在内的验证来评估这些方法的性能。本综述强调了重复性研究的重要性。已表明不同的采样位置对于实时监测很有用。过程分析技术和停留时间分布模型能够检测不合格物料及其从生产过程中的转移。讨论了使用变差函数分析来改进过程分析技术的实施,因为它是唯一能区分过程变异性与采样和分析误差的方法。本综述中讨论的研究有助于改进连续生产过程,提供高水平的产品和过程理解、产品质量及可靠性。

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