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高内涵方法在抗蠕虫药物筛选中的应用。

High-content approaches to anthelmintic drug screening.

机构信息

Department of Pathobiological Sciences, University of Wisconsin -, Madison, WI, USA.

Department of Pathobiological Sciences, University of Wisconsin -, Madison, WI, USA; Department of Chemistry, University of Wisconsin -, Oshkosh, WI, USA.

出版信息

Trends Parasitol. 2021 Sep;37(9):780-789. doi: 10.1016/j.pt.2021.05.004. Epub 2021 Jun 3.

Abstract

Most anthelmintics were discovered through in vivo screens using animal models of infection. Developing in vitro assays for parasitic worms presents several challenges. The lack of in vitro life cycle culture protocols requires harvesting worms from vertebrate hosts or vectors, limiting assay throughput. Once worms are removed from the host environment, established anthelmintics often show no obvious phenotype - raising concerns about the predictive value of many in vitro assays. However, with recent progress in understanding how anthelmintics subvert host-parasite interactions, and breakthroughs in high-content imaging and machine learning, in vitro assays have the potential to discern subtle cryptic parasite phenotypes. These may prove better endpoints than conventional in vitro viability assays.

摘要

大多数驱虫药是通过使用感染动物模型的体内筛选发现的。开发寄生虫的体外检测方法存在一些挑战。缺乏体外生命周期培养方案需要从脊椎动物宿主或载体中收获蠕虫,限制了检测的通量。一旦蠕虫从宿主环境中取出,现有的驱虫药通常没有明显的表型-这引起了对许多体外检测的预测价值的关注。然而,随着人们对驱虫药如何颠覆宿主-寄生虫相互作用的理解的最新进展,以及在高内涵成像和机器学习方面的突破,体外检测有可能辨别出微妙的隐蔽寄生虫表型。这些可能比传统的体外生存能力检测更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81d8/8364491/72ddc426af62/nihms-1711456-f0001.jpg

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