Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization.
Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization.
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DOI：
10.1038/ncomms10425
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发表时间：
2016-01-26
影响因子：
16.6
通讯作者：
Simeonov A
中科院分区：
文献类型：
--
作者：
Huang R;Xia M;Sakamuru S;Zhao J;Shahane SA;Attene-Ramos M;Zhao T;Austin CP;Simeonov A
靶向特异性、机制导向的体外试验是传统动物毒理学研究的一种有前途的替代方法。在这里，我们报告了Tox 21努力的第一次全面分析，这是一项大规模的化学品体外毒性筛选。我们在15种浓度下对10，000种化学物质进行了三次重复测试，并对一组核受体和应激反应途径进行了检测，产生了超过5000万个数据点。通过测定的结构相似性和活性谱相似性进行的化合物聚类揭示了可用于生成机制假设的结构-活性关系。我们应用结构信息和活性数据，使用基于聚类的方法建立72个体内毒性终点的预测模型。基于体外测定数据的模型在预测人类毒性终点方面比动物毒性更好，而结构和活性数据的组合产生比单独使用结构或活性数据更好的模型。我们的研究结果表明，在体外活性概况可以作为签名的化合物的毒性机制，并用于优先进行更深入的毒理学测试。 大规模的体外试验可能会减少在动物中进行的毒理学试验的数量。在这里，Huang等人报告了一个包含大约10，000种化学物质体外测试结果的大型数据集，并使用这些数据创建可以潜在预测人体毒性的模型。
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. Large-scale in vitro assays may reduce the number of toxicological tests carried out in animals. Here, Huang et al. report a large dataset containing results of in vitro tests of approximately 10,000 chemicals, and use these data to create models that can potentially predict toxicity in humans.