Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening
Predictive Model of Rat Reproductive Toxicity from ToxCast High Throughput Screening
复制标题
DOI：
10.1095/biolreprod.111.090977
复制
发表时间：
2011-08-01
影响因子：
3.6
通讯作者：
Dix, David J.
中科院分区：
文献类型：
--
作者：
Martin, Matthew T.;Knudsen, Thomas B.;Dix, David J.
美国环境保护署的ToxCast研究项目使用高通量筛选（HTS）分析大量化学物质的生物活性并预测其毒性。ToxCast第一阶段测试了309种具有良好特征的化学物质，进行了500多次分析，用于广泛的分子靶点和细胞反应。在第一阶段的309种环境化学物质中，256种与相关毒性参考数据库中高质量的大鼠多代生殖毒性研究有关。生殖毒性物质在这里被定义为达到生殖最低观察到的不良影响水平低于500 mg kg（-1）天（-1）。86种化学物质被确定为大鼠的生殖毒性物质，其中68种具有足够的体外生物活性来建立模型。评估每项试验与确定的生殖毒物的单变量关联。显著相关的测定与基因集相关联，并用于随后的预测建模。通过线性判别分析和五重交叉验证，建立了一个稳健、稳定的预测模型，能够分别以77% +/- 2%（平均+/- SEM）和74% +/- 5%（平均+/- SEM）的训练和检验交叉验证平衡精度识别啮齿动物生殖毒物。使用21种化学物质的外部验证集，该模型的准确率为76%，进一步表明该模型有可能在几乎没有危害信息的情况下优先考虑数千种环境化学物质。该模型的生物学特征包括甾体和非甾体核受体、细胞色素P450酶抑制、G蛋白偶联受体和细胞信号通路读出——这些机制信息提示了体外HTS在风险评估中的额外靶向、综合测试策略和潜在应用。
The U. S. Environmental Protection Agency's ToxCast research program uses high throughput screening (HTS) for profiling bioactivity and predicting the toxicity of large numbers of chemicals. ToxCast Phase I tested 309 well-characterized chemicals in more than 500 assays for a wide range of molecular targets and cellular responses. Of the 309 environmental chemicals in Phase I, 256 were linked to high-quality rat multigeneration reproductive toxicity studies in the relational Toxicity Reference Database. Reproductive toxicants were defined here as having achieved a reproductive lowest-observed-adverse-effect level of less than 500 mg kg(-1) day(-1). Eight-six chemicals were identified as reproductive toxicants in the rat, and 68 of those had sufficient in vitro bioactivity to model. Each assay was assessed for univariate association with the identified reproductive toxicants. Significantly associated assays were linked to gene sets and used for the subsequent predictive modeling. Using linear discriminant analysis and fivefold cross-validation, a robust and stable predictive model was produced capable of identifying rodent reproductive toxicants with 77% +/- 2% and 74% +/- 5% (mean +/- SEM) training and test cross-validation balanced accuracies, respectively. With a 21-chemical external validation set, the model was 76% accurate, further indicating the model's potential for prioritizing the many thousands of environmental chemicals with little to no hazard information. The biological features of the model include steroidal and nonsteroidal nuclear receptors, cytochrome P450 enzyme inhibition, G protein-coupled receptors, and cell signaling pathway readouts-mechanistic information suggesting additional targeted, integrated testing strategies and potential applications of in vitro HTS to risk assessment.