Suppr超能文献

基于干细胞的肺芯片:两全其美?

Stem cell-based Lung-on-Chips: The best of both worlds?

机构信息

Emulate, Inc., Boston, MA, United States of America.

Emulate, Inc., Boston, MA, United States of America.

出版信息

Adv Drug Deliv Rev. 2019 Feb 1;140:12-32. doi: 10.1016/j.addr.2018.07.005. Epub 2018 Jul 25.

Abstract

Pathologies of the respiratory system such as lung infections, chronic inflammatory lung diseases, and lung cancer are among the leading causes of morbidity and mortality, killing one in six people worldwide. Development of more effective treatments is hindered by the lack of preclinical models of the human lung that can capture the disease complexity, highly heterogeneous disease phenotypes, and pharmacokinetics and pharmacodynamics observed in patients. The merger of two novel technologies, Organs-on-Chips and human stem cell engineering, has the potential to deliver such urgently needed models. Organs-on-Chips, which are microengineered bioinspired tissue systems, recapitulate the mechanochemical environment and physiological functions of human organs while concurrent advances in generating and differentiating human stem cells promise a renewable supply of patient-specific cells for personalized and precision medicine. Here, we discuss the challenges of modeling human lung pathophysiology in vitro, evaluate past and current models including Organs-on-Chips, review the current status of lung tissue modeling using human pluripotent stem cells, explore in depth how stem-cell based Lung-on-Chips may advance disease modeling and drug testing, and summarize practical consideration for the design of Lung-on-Chips for academic and industry applications.

摘要

呼吸系统疾病,如肺部感染、慢性炎症性肺病和肺癌,是导致发病率和死亡率的主要原因之一,全球每六个人中就有一人因此死亡。由于缺乏能够捕捉疾病复杂性、高度异质性疾病表型以及患者药代动力学和药效动力学的人类肺部临床前模型,因此更有效的治疗方法的开发受到阻碍。两种新技术,器官芯片和人类干细胞工程的融合,有可能提供这种急需的模型。器官芯片是微工程仿生组织系统,可重现人类器官的机械化学环境和生理功能,而人类干细胞的生成和分化方面的同步进展则有望为个性化和精准医疗提供可再生的患者特异性细胞供应。在这里,我们讨论了在体外模拟人类肺部病理生理学的挑战,评估了过去和当前的模型,包括器官芯片,回顾了使用人类多能干细胞进行肺组织建模的现状,深入探讨了基于干细胞的肺芯片如何推进疾病建模和药物测试,并总结了用于学术和工业应用的肺芯片设计的实际考虑因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1aab/7172977/0941b05c9713/ga1_lrg.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验