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用于肺部药物输送研究的创新临床前模型。

Innovative preclinical models for pulmonary drug delivery research.

机构信息

Médecine Intensive Réanimation, CHRU Tours, Tours, France.

Centre d'étude des Pathologies Respiratoires, INSERM, Tours, France.

出版信息

Expert Opin Drug Deliv. 2020 Apr;17(4):463-478. doi: 10.1080/17425247.2020.1730807. Epub 2020 Feb 23.

Abstract

: Pulmonary drug delivery is a complex field of research combining physics which drive aerosol transport and deposition and biology which underpins efficacy and toxicity of inhaled drugs. A myriad of preclinical methods, ranging from to and , can be implemented.: The present review covers mathematical and computational fluid dynamics modelization of aerosol deposition, cascade impactor technology to estimated drug delivery and deposition, advanced cell culture methods and associated aerosol exposure, lung-on-chip technology, modeling, inhaled drug delivery, lung imaging, and longitudinal pharmacokinetic analysis.: No single preclinical model can be advocated; all methods are fundamentally complementary and should be implemented based on benefits and drawbacks to answer specific scientific questions. The overall best scientific strategy depends, among others, on the product under investigations, inhalation device design, disease of interest, clinical patient population, previous knowledge. Preclinical testing is not to be separated from clinical evaluation, as small proof-of-concept clinical studies or conversely large-scale clinical big data may inform preclinical testing. The extend of expertise required for such translational research is unlikely to be found in one single laboratory calling for the setup of multinational large-scale research consortiums.

摘要

肺部药物输送是一个复杂的研究领域,结合了驱动气溶胶输送和沉积的物理学以及支撑吸入药物疗效和毒性的生物学。可以实施无数种临床前方法,从 到 和 。本文综述涵盖了气溶胶沉积的数学和计算流体动力学建模、级联撞击器技术以估计药物输送和沉积、先进的细胞培养方法和相关的气溶胶暴露、肺芯片技术、建模、吸入药物输送、肺部成像和纵向药代动力学分析。没有单一的临床前模型可以被提倡;所有方法都是根本互补的,应该根据其优缺点来实施,以回答具体的科学问题。最佳的整体科学策略取决于研究中的产品、吸入装置设计、感兴趣的疾病、临床患者人群、先前的知识。临床前测试不应与临床评估分开,因为小的概念验证临床研究或相反的大规模临床大数据可能会为临床前测试提供信息。这种转化研究所需的专业知识范围不太可能在一个单一的实验室中找到,这需要建立跨国大规模研究联盟。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7e5/8083945/2ca5f69da073/nihms-1684478-f0001.jpg

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