Biryol Derya, Nicolas Chantel I, Wambaugh John, Phillips Katherine, Isaacs Kristin
Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States; U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States.
Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, United States; U.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States.
Environ Int. 2017 Nov;108:185-194. doi: 10.1016/j.envint.2017.08.004. Epub 2017 Aug 31.
Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C) and chemical properties. The most predictive variables in the resulting model were C, molecular weight, log K, and food type (R=0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C based on the functional role of chemicals in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R=0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations. While these HT methods have large uncertainties (and thus may not be appropriate for assessments of single chemicals), they can provide critical refinement to aggregate exposure predictions used in risk-based chemical priority-setting.
在“ExpoCast计划”下，美国环境保护局（EPA）的研究人员开发了一种高通量（HT）框架，用于估算通过多种途径接触化学物质的总暴露量，以支持对化学物质进行快速优先级排序。在此，我们介绍估算从食品接触物质（FCS）迁移到食品中的化学物质的高通量暴露量的方法。这些方法包括将化学物质迁移的经验模型与从国家健康和营养检查调查（NHANES）的食物日记中得出的每日人群食物摄入量估计值相结合。通过将可用的迁移测量值拟合为温度、食物类型（即脂肪类、水性、酸性、酒精类）、FCS中初始化学物质浓度（C）和化学性质的函数，建立了平衡时迁移的线性回归模型。所得模型中最具预测性的变量是C、分子量、log K和食物类型（R = 0.71，p < 0.0001）。针对通过公开数据源确定存在于聚合物FCS中的1009种化学物质，预测了12种食物组（3种储存温度和食物类型的组合）基于迁移的浓度。该模型根据化学物质在FCS中的功能作用，用C的筛选水平估计值进行参数化。通过将这些浓度与来自NHANES的食物组每日摄入量相结合，估算了人群摄入化学物质的暴露量，单位为毫克/千克体重/天（mg/kg-BW/天）。通过将高通量FCS和消费品暴露量拟合到从NHANES生物监测推断出的暴露量，估算了1931种化学物质的校准总暴露量（R = 0.61，p < 0.001）；FCS和消费品途径暴露量均能显著预测推断出的暴露量。纳入FCS途径对预测暴露量与估计产生等于体外生物活性浓度的稳态血药浓度的暴露量之比有显著影响。虽然这些高通量方法存在很大的不确定性（因此可能不适用于单一化学物质的评估），但它们可为基于风险的化学物质优先级设定中使用的总暴露量预测提供关键的优化。
