Generalized Concentration Addition Modeling Predicts Mixture Effects of Environmental PPARγ Agonists.
Generalized Concentration Addition Modeling Predicts Mixture Effects of Environmental PPARγ Agonists.
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广义浓度加成模型预测环境 PPARγ 激动剂的混合效应。
DOI：
10.1093/toxsci/kfw100
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Schlezinger,JenniferJ
中科院分区：
文献类型：
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作者：
Watt,James;Webster,ThomasF;Schlezinger,JenniferJ
大量的潜在的环境毒物的组合，有必要制定有效的战略，预测混合物的毒性作用。目前的做法强调使用浓度相加来预测共同暴露中内分泌干扰化学品的联合效应。广义浓度相加（GCA）是一种预测化学品共同暴露联合效应的方法，其优点是允许混合物组分具有疗效差异（即剂量-反应曲线最大值）。过氧化物酶体增殖物激活受体γ（过氧化物酶体增殖物激活受体γ）是一种核受体，在调节脂质体内平衡、胰岛素敏感性和骨质量方面发挥核心作用，并且是越来越多的环境毒物的靶点。在这里，我们测试了GCA在预测治疗（罗格列酮和非噻唑烷二酮部分激动剂）和环境PPARγ配体（使用EPA的ToxCast数据库识别的邻苯二甲酸酯化合物）的混合效应中的适用性。使用过氧化物酶体增殖物反应元件驱动的荧光素酶报告基因评估单个化合物和混合物对人PPARγ1的转录激活。使用个体剂量反应参数和GCA，我们生成了混合物对PPARγ激活的预测，并将这些预测与经验数据进行了比较。在高浓度下，GCA提供了一个更好的估计实验响应相比，3个替代模型：毒性等效因子，效果总和和独立的行动。这些替代品在该系统中的低浓度下为数据提供了合理的拟合。这些实验支持了GCA在内分泌干扰物混合物分析中的应用，并确立了PPARγ作为化学混合物进一步研究的重要靶点。
The vast array of potential environmental toxicant combinations necessitates the development of efficient strategies for predicting toxic effects of mixtures. Current practices emphasize the use of concentration addition to predict joint effects of endocrine disrupting chemicals in coexposures. Generalized concentration addition (GCA) is one such method for predicting joint effects of coexposures to chemicals and has the advantage of allowing for mixture components to have differences in efficacy (ie, dose-response curve maxima). Peroxisome proliferator–activated receptor gamma (PPARγ) is a nuclear receptor that plays a central role in regulating lipid homeostasis, insulin sensitivity, and bone quality and is the target of an increasing number of environmental toxicants. Here, we tested the applicability of GCA in predicting mixture effects of therapeutic (rosiglitazone and nonthiazolidinedione partial agonist) and environmental PPARγ ligands (phthalate compounds identified using EPA’s ToxCast database). Transcriptional activation of human PPARγ1 by individual compounds and mixtures was assessed using a peroxisome proliferator response element-driven luciferase reporter. Using individual dose-response parameters and GCA, we generated predictions of PPARγ activation by the mixtures, and we compared these predictions with the empirical data. At high concentrations, GCA provided a better estimation of the experimental response compared with 3 alternative models: toxic equivalency factor, effect summation and independent action. These alternatives provided reasonable fits to the data at low concentrations in this system. These experiments support the implementation of GCA in mixtures analysis with endocrine disrupting compounds and establish PPARγ as an important target for further studies of chemical mixtures.