National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA.
Syst Biol Reprod Med. 2012 Feb;58(1):3-9. doi: 10.3109/19396368.2011.652288.
A predictive model of reproductive toxicity, as observed in rat multigeneration reproductive (MGR) studies, was previously developed using high throughput screening (HTS) data from 36 in vitro assays mapped to 8 genes or gene-sets from Phase I of USEPA ToxCast research program, the proof-of-concept phase in which 309 toxicologically well characterized chemicals were testing in over 500 HTS assays. The model predicted the effects on male and female reproductive function with a balanced accuracy of 80%. In a theoretical examination of the potential impact of the model, two case studies were derived representing different tiered testing scenarios to: 1) screen-out chemicals with low predicted probability of effect; and 2) screen-in chemicals with a high probability of causing adverse reproductive effects. We define 'testing cost efficiency' as the total cost divided by the number of positive chemicals expected in the definitive guideline toxicity study. This would approach $2.11 M under the current practice. Under case study 1, 22% of the chemicals were screened-out due to low predicted probability of adverse reproductive effect and a misclassification rate of 12%, yielding a test cost efficiency of $1.87 M. Under case study 2, 13% of chemicals were screened-in yielding a testing cost efficiency of $1.13 M per test-positive chemical. Applying the model would also double the total number of positives identified. It should be noted that the intention of the case studies is not to provide a definitive mechanism for screening-in or screening-out chemicals or account for the indirect costs of misclassification. The case studies demonstrate the customizability of the model as a tool in chemical testing decision-making. The predictive model of reproductive toxicity will continue to evolve as new assays become available to fill recognized biological gaps and will be combined with other predictive models, particularly models of developmental toxicity, to form an initial tier to an overarching integrated testing strategy.
先前，我们利用美国环保署毒性预测筛选项目第一阶段（概念验证阶段）的高通量筛选数据，建立了一个预测大鼠多代生殖毒性的模型，该模型基于 36 项体外试验，映射到 8 个基因或基因集。这是 USEPA ToxCast 研究计划的第一阶段，该阶段共测试了 309 种具有良好毒理学特征的化学物质，这些化学物质在 500 多项高通量筛选试验中进行了测试。该模型预测雄性和雌性生殖功能的准确率为 80%。在对该模型的潜在影响进行的理论研究中，我们提出了两种案例研究，代表了不同的分层测试情景：1）筛选出预测效应低的化学物质；2）筛选出高概率导致不良生殖效应的化学物质。我们将“测试成本效率”定义为总成本除以预期在明确指导毒性研究中呈阳性的化学物质的数量。在当前实践下，这将接近 2110 万美元。在案例研究 1 中，由于预测的不良生殖效应的概率低，有 22%的化学物质被筛选出来，而误分类率为 12%，测试成本效率为 1870 万美元。在案例研究 2 中，筛选出 13%的化学物质，测试阳性化学物质的测试成本效率为 1130 万美元。应用该模型还可以将阳性识别数量增加一倍。需要注意的是，案例研究的目的不是提供筛选化学物质的明确机制，也不是为了考虑误分类的间接成本。这些案例研究表明，该生殖毒性预测模型是一种可定制的化学测试决策工具。随着新试验的出现，该生殖毒性预测模型将继续发展，以填补已知的生物学空白，并将与其他预测模型，特别是发育毒性模型相结合，形成一个全面综合测试策略的初始层次。
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