Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Søborg, Denmark.
PLoS One. 2013 Aug 22;8(8):e70490. doi: 10.1371/journal.pone.0070490. eCollection 2013.
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.
人类同时暴露于众多化学物质中。可以想象出无数的组合和剂量。为了进行毒理学风险评估，使用关于单一化学物质的知识，对混合物的效应进行数学预测是理想的。我们研究了浓度加和（CA）、独立作用（IA）和广义浓度加和（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。然后将单一化学物质数据应用于模型中；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 包含根据人类暴露水平调整比例的环境化学物质。混合物 2 是一种效力调整混合物，其中包含五种农药。预测的睾酮效应与实验混合物 1 的数据相符。相比之下，观察到混合物 2 对这种激素的作用存在拮抗作用。混合物中含有仅发挥有限最大效应的化学物质。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。关于对孕激素和雌二醇的影响，一些化学物质具有刺激作用，而另一些则具有抑制作用。在这种情况下，这三种模型均不适用，无法进行预测。最后，计算了单一化学物质对混合物效应的预期贡献。百菌清是混合物的主要但不是唯一驱动力，这表明单一化学物质本身并不是混合物效应的唯一原因。总之，对于预测睾酮效应，GCA 模型似乎优于 CA 和 IA 模型。确定了一种情况，其中存在作用相反的化学物质，而模型无法应用。此外，数据表明，在非效力调整混合物中，单一化学物质不能总是解释混合物的效应。
