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口服吸入药物气雾剂级联撞击器数据的体外-体内相关性

In vitro-in vivo correlation of cascade impactor data for orally inhaled pharmaceutical aerosols.

作者信息

Chow Michael Yee Tak, Tai Waiting, Chang Rachel Yoon Kyung, Chan Hak-Kim, Kwok Philip Chi Lip

机构信息

Advanced Drug Delivery Group, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, New South Wales 2006, Australia.

Advanced Drug Delivery Group, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, New South Wales 2006, Australia.

出版信息

Adv Drug Deliv Rev. 2021 Oct;177:113952. doi: 10.1016/j.addr.2021.113952. Epub 2021 Aug 27.

Abstract

In vitro-in vivo correlation is the establishment of a predictive relationship between in vitro and in vivo data. In the context of cascade impactor results of orally inhaled pharmaceutical aerosols, this involves the linking of parameters such as the emitted dose, fine particle dose, fine particle fraction, and mass median aerodynamic diameter to in vivo lung deposition from scintigraphy data. If the dissolution and absorption processes after deposition are adequately understood, the correlation may be extended to the pharmacokinetics and pharmacodynamics of the delivered drugs. Correlation of impactor data to lung deposition is a relatively new research area that has been gaining recent interest. Although few in number, experiments and meta-analyses have been conducted to examine such correlations. An artificial neural network approach has also been employed to analyse the complex relationships between multiple factors and responses. However, much research is needed to generate more data to obtain robust correlations. These predictive models will be useful in improving the efficiency in product development by reducing the need of expensive and lengthy clinical trials.

摘要

体外-体内相关性是建立体外数据与体内数据之间的预测关系。在口服吸入药物气雾剂的级联撞击器结果的背景下,这涉及将诸如发射剂量、细颗粒剂量、细颗粒分数和质量中值空气动力学直径等参数与闪烁扫描数据中的体内肺沉积联系起来。如果沉积后的溶解和吸收过程得到充分理解,这种相关性可以扩展到所递送药物的药代动力学和药效学。撞击器数据与肺沉积的相关性是一个相对较新的研究领域,最近受到了关注。虽然数量很少,但已经进行了实验和荟萃分析来检验这种相关性。还采用了人工神经网络方法来分析多个因素与反应之间的复杂关系。然而,需要进行更多的研究以生成更多数据来获得可靠的相关性。这些预测模型将有助于通过减少昂贵且冗长的临床试验需求来提高产品开发效率。

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