National Center for Computational Toxicology, United States Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States.
Environ Sci Technol. 2013 Aug 6;47(15):8479-88. doi: 10.1021/es400482g. Epub 2013 Jul 11.
The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research program to prioritize chemical inventories for potential hazard. Similar capabilities for estimating exposure potential would support rapid risk-based prioritization for chemicals with limited information; here, we propose a framework for high-throughput exposure assessment. To demonstrate application, an analysis was conducted that predicts human exposure potential for chemicals and estimates uncertainty in these predictions by comparison to biomonitoring data. We evaluated 1936 chemicals using far-field mass balance human exposure models (USEtox and RAIDAR) and an indicator for indoor and/or consumer use. These predictions were compared to exposures inferred by Bayesian analysis from urine concentrations for 82 chemicals reported in the National Health and Nutrition Examination Survey (NHANES). Joint regression on all factors provided a calibrated consensus prediction, the variance of which serves as an empirical determination of uncertainty for prioritization on absolute exposure potential. Information on use was found to be most predictive; generally, chemicals above the limit of detection in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure HTS can place risk earlier in decision processes. High-priority chemicals become targets for further data collection.
美国环境保护署(U.S. EPA)必须描述与数千种化学物质的制造和使用相关的人类健康和环境的潜在风险。高通量筛选(HTS)可用于生物活性,使 ToxCast 研究计划能够优先考虑化学物质的潜在危害。类似的估计暴露潜力的能力将支持对信息有限的化学品进行快速基于风险的优先排序;在这里,我们提出了一个高通量暴露评估框架。为了演示应用,我们进行了一项分析,通过与生物监测数据进行比较,预测了化学物质对人类的暴露潜力,并估计了这些预测的不确定性。我们使用远场质量平衡人体暴露模型(USEtox 和 RAIDAR)和室内和/或消费者使用的指标,对 1936 种化学物质进行了评估。这些预测与从国家健康和营养检查调查(NHANES)报告的 82 种化学物质的尿液浓度中贝叶斯分析推断的暴露情况进行了比较。对所有因素进行联合回归提供了校准的共识预测,其方差可作为基于绝对暴露潜力进行优先级排序的不确定性的经验确定。发现使用信息最具预测性;通常,在 NHANES 中检测限以上的化学物质具有消费者/室内使用。与危害 HTS 相结合,暴露 HTS 可以更早地将风险纳入决策过程。高优先级的化学物质成为进一步数据收集的目标。