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An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes
Environment International ( IF 11.8 ) Pub Date : 2021-07-10 , DOI: 10.1016/j.envint.2021.106748
Yaoxing Wu 1 , Zidong Song 2 , John C Little 1 , Min Zhong 3 , Hongwan Li 4 , Ying Xu 5
Affiliation  

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The “source-to-outcome” local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.



中文翻译:

用于评估邻苯二甲酸酯及其替代品的人群规模风险的综合暴露和药代动力学建模框架

为了有效地将基于体外-in silico的方法纳入消费品安全的监管中,产品邻苯二甲酸盐浓度与体外实验之间的定量联系必须为一般人群建立生物活性数据。我们开发、评估并展示了一个建模框架,该框架集成了暴露和药代动力学模型,可将产品邻苯二甲酸酯浓度转化为邻苯二甲酸酯及其替代品的人口规模风险。开发了一种概率暴露模型,以根据邻苯二甲酸盐产品浓度、化学特性和人类活动生成多途径暴露的分布。通过马尔可夫链蒙特卡罗方法,使用贝叶斯分析开发了药代动力学模型来模拟群体毒代动力学。与全球研究相比,暴露和药代动力学模型均显示出良好的预测能力。整合暴露分布和药代动力学以预测内部剂量测定的群体分布。预测的分布与美国对尿代谢物的生物监测调查显示出合理的一致性。“从源头到结果”的局部敏感性分析显示,食品接触材料对己二酸二(2-乙基己基)酯(DEHA)、邻苯二甲酸二-2-乙基己基酯(DEHP)、二(异壬基)环己烷的身体负担影响最大-1,2-二羧酸酯 (DINCH) 和邻苯二甲酸二(2-丙基庚基)酯 (DPHP),而邻苯二甲酸二乙酯 (DEP) 的身体负担对个人护理产品中的浓度最为敏感。预测血浆浓度的上限显示与 ToxCast 没有重叠 尿代谢物的生物监测调查。“从源头到结果”的局部敏感性分析显示,食品接触材料对己二酸二(2-乙基己基)酯(DEHA)、邻苯二甲酸二-2-乙基己基酯(DEHP)、二(异壬基)环己烷的身体负担影响最大-1,2-二羧酸酯 (DINCH) 和邻苯二甲酸二(2-丙基庚基)酯 (DPHP),而邻苯二甲酸二乙酯 (DEP) 的身体负担对个人护理产品中的浓度最为敏感。预测血浆浓度的上限显示与 ToxCast 没有重叠 尿代谢物的生物监测调查。“从源头到结果”的局部敏感性分析显示,食品接触材料对己二酸二(2-乙基己基)酯(DEHA)、邻苯二甲酸二-2-乙基己基酯(DEHP)、二(异壬基)环己烷的身体负担影响最大-1,2-二羧酸酯 (DINCH) 和邻苯二甲酸二(2-丙基庚基)酯 (DPHP),而邻苯二甲酸二乙酯 (DEP) 的身体负担对个人护理产品中的浓度最为敏感。预测血浆浓度的上限显示与 ToxCast 没有重叠 而邻苯二甲酸二乙酯 (DEP) 的身体负担对个人护理产品中的浓度最为敏感。预测血浆浓度的上限显示与 ToxCast 没有重叠 而邻苯二甲酸二乙酯 (DEP) 的身体负担对个人护理产品中的浓度最为敏感。预测血浆浓度的上限显示与 ToxCast 没有重叠体外生物活性值。与相比于vitro--in体内外推(IVIVE)的方式,集成建模框架在测绘产品的邻苯二甲酸酯浓度的多路由的风险显著的优势,因而对以相对低的输入要求监管使用具有重要意义。与新方法的进一步整合将促进这些基于体外-in silico的风险评估,用于对含有同样广泛化学品的广泛产品进行风险评估。

更新日期:2021-07-12
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