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Nat Com:细胞检验预测化学毒性

摘要 : 本周《 Nature Communications》上发表的一项研究,发现可以通过基于细胞的方法来预测化学物质对人的毒性,而不需要做动物实验。

 本周《 Nature Communications》上发表的一项研究,发现可以通过基于细胞的方法来预测化学物质对人的毒性,而不需要做动物实验。这项研究演示了基于细胞的毒性模型,可能有助于开发出代替传统用动物实验测量化合物毒性的方法。

作为美国联邦政府主导的21世纪毒理学计划(Toxicology in the 21st Century, Tox21)的一部分,Ruili Huang 和她的同事们测试了超过1万种化学物质,尝试开发出更好的方法测试化合物的毒性,例如农药、工业化学品,食品添加剂和药品。他们测试了化学物质在15种不同浓度下和30个靶点反应时的活性,靶点包括人体细胞核受体或者细胞通路。作者们因此获得了超过5千万条数据。他们将数据和化学结构结合起来,创造了一些毒性模型,这些模型可以用于预测化学物质在动物或者人身上的不良健康结果。



Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization


Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ~10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure–activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing.

来源: Nature Communications 浏览次数:0


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