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Concentration Addition Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro

机译:体内性激素合成混合效应预测的浓度加成独立作用和广义浓度加成模型

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摘要

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals.
机译:伴随着人类暴露于多种化学物质。可以想象无数的组合及其剂量。因此,对于毒理学风险评估,需要使用有关单一化学物质的知识对混合物影响进行数学预测。我们研究了浓度增加(CA),独立作用(IA)和广义浓度增加(GCA)模型的优缺点。首先,我们测量了单一化学物质及其混合物对H295R细胞中类固醇合成的影响。然后将单一化学数据应用于模型;计算混合物效应的预测值,并将其与实验混合物数据进行比较。混合物1包含根据人体暴露水平调整比例的环境化学物质。混合物2是含有五种农药的效价调节混合物。睾丸激素作用的预测与实验混合物1数据一致。相反,观察到混合物2对该激素的作用有拮抗作用。混合物中所含的化学物质仅发挥有限的最大作用。这阻碍了CA和IA模型的预测,而GCA模型可用于预测完整的剂量反应曲线。关于对孕酮和雌二醇的影响,某些化学物质具有刺激作用,而其他化学物质具有抑制作用。这三种模型不适用于这种情况,无法进行任何预测。最后,计算了单一化学物质对混合物效果的预期贡献。丙草胺是混合物的主要驱动力,但不是唯一的驱动力,这表明单独使用一种化学品并不会对混合物产生影响。总之,在预测睾丸激素作用方面,GCA模型似乎优于CA和IA模型。确定了一种化学品起相反作用的情况,对此模型无法应用。另外,数据表明,在非效价调整的混合物中,效果不能总是由单一化学品来解释。

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