Generalized concentration addition predicts joint effects of aryl hydrocarbon receptor agonists with partial agonists and competitive antagonists.
Generalized concentration addition predicts joint effects of aryl hydrocarbon receptor agonists with partial agonists and competitive antagonists.
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DOI：
10.1289/ehp.0901312
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发表时间：
2010-05
影响因子：
10.4
通讯作者：
Webster TF
中科院分区：
文献类型：
--
作者：
Howard GJ;Schlezinger JJ;Hahn ME;Webster TF
预测组合暴露的预期结果对风险评估至关重要。毒性当量因子（TEF）法用于分析二恶英类化合物的联合效应，是浓度相加法的特例。然而，TEF方法假设单个药剂是具有平行剂量-反应曲线的完全芳烃受体（AhR）激动剂，而许多混合物包括部分激动剂。我们评估了广义浓度相加（GCA）预测完全AhR激动剂与部分激动剂或竞争性拮抗剂组合的效果的能力。我们测量了应用AhR配体的二元组合后H1G1.1c3细胞中AhR依赖性基因表达的激活。将完全激动剂（2，3，7，8-四氯二苯并-对-二恶英或2，3，7，8-四氯二苯并呋喃）与完全激动剂（3，3 ′，4，4 ′，5-五氯联苯）、部分激动剂（2，3，3 ′，4，4 ′-五氯联苯或高良姜素）或拮抗剂（3，3 ′-二吲哚基甲烷）组合。通过TEF和GCA方法对组合效应进行建模，并使用非参数统计检验评估建模响应面与实验数据的拟合优度。对于两种完全激动剂的混合物，GCA和TEF模型同样很好地拟合实验数据。在所有其他情况下，GCA拟合实验数据显着优于TEF模型。TEF模型过度预测AhR配体在最高浓度组合下的作用。在较低浓度下，GCA和TEF方法之间的差异取决于部分激动剂的功效。GCA代表了包括部分激动剂或竞争性拮抗剂配体的混合物的加和性的更准确的定义。
Predicting the expected outcome of a combination exposure is critical to risk assessment. The toxic equivalency factor (TEF) approach used for analyzing joint effects of dioxin-like chemicals is a special case of the method of concentration addition. However, the TEF method assumes that individual agents are full aryl hydrocarbon receptor (AhR) agonists with parallel dose–response curves, whereas many mixtures include partial agonists. We assessed the ability of generalized concentration addition (GCA) to predict effects of combinations of full AhR agonists with partial agonists or competitive antagonists. We measured activation of AhR-dependent gene expression in H1G1.1c3 cells after application of binary combinations of AhR ligands. A full agonist (2,3,7,8-tetrachlorodibenzo-p-dioxin or 2,3,7,8-tetrachlorodibenzofuran) was combined with either a full agonist (3,3′,4,4′,5-pentachlorobiphenyl), a partial agonist (2,3,3′,4,4′-pentachlorobiphenyl or galangin), or an antagonist (3,3′-diindolylmethane). Combination effects were modeled by the TEF and GCA approaches, and goodness of fit of the modeled response surface to the experimental data was assessed using a nonparametric statistical test. The GCA and TEF models fit the experimental data equally well for a mixture of two full agonists. In all other cases, GCA fit the experimental data significantly better than the TEF model. The TEF model overpredicts effects of AhR ligands at the highest concentration combinations. At lower concentrations, the difference between GCA and TEF approaches depends on the efficacy of the partial agonist. GCA represents a more accurate definition of additivity for mixtures that include partial agonist or competitive antagonist ligands.
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影响因子：
3.5
作者：
Peters, A. K.;Leonards, P. E.;Van den Berg, M.
通讯作者：
Van den Berg, M.
DOI：
10.1006/faat.1995.1116
发表时间：
1995-08-01
期刊：
FUNDAMENTAL AND APPLIED TOXICOLOGY
影响因子：
--
作者：
HARPER, N;CONNOR, K;SAFE, S
通讯作者：
SAFE, S
类黄酮Galangin是CYP1A1活性的抑制剂，也是芳基烃受体的激动剂/拮抗剂。
影响因子：
8.8
作者：
Ciolino, HP;Yeh, GC
通讯作者：
Yeh, GC
影响因子：
3.8
作者：
Hestermann, EV;Stegeman, JJ;Hahn, ME
通讯作者：
Hahn, ME
影响因子：
3.8
作者：
FernandezSalguero, PM;Hilbert, DM;Gonzalez, FJ
通讯作者：
Gonzalez, FJ