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利用近红外化学成像技术预测药物片剂的溶出行为。

Predicting the dissolution behavior of pharmaceutical tablets with NIR chemical imaging.

机构信息

Pfizer Industrial Research Chair on Process Analytical Technology in Pharmaceutical Engineering, Department of Chemical & Biotechnological Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada; Faculty of Pharmacy, Université Montpellier I, France.

Pfizer Industrial Research Chair on Process Analytical Technology in Pharmaceutical Engineering, Department of Chemical & Biotechnological Engineering, Université de Sherbrooke, Sherbrooke, Quebec, Canada.

出版信息

Int J Pharm. 2015;486(1-2):242-51. doi: 10.1016/j.ijpharm.2015.03.060. Epub 2015 Mar 30.

Abstract

Near infrared chemical imaging (NIRCI) is a common analytical non-destructive technique for the analysis of pharmaceutical tablets. This powerful process analytical technology provides opportunity to chemically understand the sample, and also to determine spatial distribution and size of ingredients in a tablet. NIRCI has been used to link disintegrant, excipients and active pharmaceutical ingredient (API) to tablet dissolution, as disintegrants play an important role in tablet disintegration, resulting in API dissolution. This article describes a specific methodology to predict API dissolution based on disintegrant chemical information obtained with NIRCI. First, several tablet batches with different disintegrant characteristics were produced. Then, NIRCI was successfully used to provide chemical images of pharmaceutical tablets. A PLS regression model successfully predicted dissolution profiles. These results show that NIRCI is a robust and versatile technique for measuring disintegrant properties in tablet formulations and predicting their effects on dissolution profiles. Thus, NIRCI could routinely complement and eventually replace dissolution testing by monitoring a critical material attribute: disintegrant content.

摘要

近红外化学成像(NIRCI)是一种常用于分析药物片剂的分析非破坏性技术。这种强大的过程分析技术不仅提供了对样品进行化学理解的机会,还可以确定片剂中成分的空间分布和大小。NIRCI 已被用于将崩解剂、赋形剂和活性药物成分(API)与片剂溶解联系起来,因为崩解剂在片剂崩解中起着重要作用,导致 API 溶解。本文描述了一种基于 NIRCI 获得的崩解剂化学信息来预测 API 溶解的特定方法。首先,生产了具有不同崩解剂特性的几个片剂批次。然后,成功地使用 NIRCI 提供了药物片剂的化学图像。PLS 回归模型成功地预测了溶解曲线。这些结果表明,NIRCI 是一种强大而通用的技术,可用于测量片剂配方中的崩解剂特性,并预测它们对溶解曲线的影响。因此,NIRCI 可以通过监测关键材料属性(崩解剂含量)来常规补充并最终取代溶解测试。

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