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3D 细胞药物筛选分析:成像、图像分析和高通量分析中的挑战。

3D Cell-Based Assays for Drug Screens: Challenges in Imaging, Image Analysis, and High-Content Analysis.

机构信息

1 Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.

2 NEXUS Personalized Health Technologies, ETH Zürich, Switzerland.

出版信息

SLAS Discov. 2019 Jul;24(6):615-627. doi: 10.1177/2472555219830087. Epub 2019 Feb 28.

Abstract

The introduction of more relevant cell models in early preclinical drug discovery, combined with high-content imaging and automated analysis, is expected to increase the quality of compounds progressing to preclinical stages in the drug development pipeline. In this review we discuss the current switch to more relevant 3D cell culture models and associated challenges for high-throughput screening and high-content analysis. We propose that overcoming these challenges will enable front-loading the drug discovery pipeline with better biology, extracting the most from that biology, and, in general, improving translation between in vitro and in vivo models. This is expected to reduce the proportion of compounds that fail in vivo testing due to a lack of efficacy or to toxicity.

摘要

在早期临床前药物发现中引入更相关的细胞模型,结合高内涵成像和自动化分析,有望提高进入药物开发管道临床前阶段的化合物的质量。在这篇综述中,我们讨论了当前向更相关的 3D 细胞培养模型的转变,以及高通量筛选和高内涵分析所面临的挑战。我们提出,克服这些挑战将使药物发现管道能够更好地进行生物学前加载,从生物学中提取更多信息,并通常改善体外和体内模型之间的转化。这有望减少由于缺乏疗效或毒性而导致在体内测试中失败的化合物的比例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/501b/6589915/0c786871d73d/10.1177_2472555219830087-fig1.jpg

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