Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for in vitro to in vivo extrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDC evoked metabolic diseases.

内分泌干​​扰化合物 (EDC) 具有干扰内分泌信号通路的能力，因此对人类和野生动物构成持续威胁。受先前通过使用毒物基因组学数据改进化学危害识别的工作的启发，我们开发了一个面向基因组的数据空间，用于以计算机方式分析 EDC 的分子活性，并创建预测模型来识别和优先考虑 EDC。EDC 的预测模型，源自大鼠（体内和体外原代肝细胞）和人类（体外）的基因表达数据原代肝细胞和 HepG2)，达到 90% 以上的检测准确率。阴性测试集表明已知的更安全的化学品不会被预测为 EDC。大鼠体内时用于测试基于分类器实现精度大于75％在 体外对体内 外推。这项研究揭示了受 EDC 影响的关键代谢途径和基因，以及一组预测模型，这些模型利用这些途径以剂量/时间依赖性方式优先考虑 EDC，并预测 EDC 诱发的 代谢疾病。