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Concentration addition, independent action and generalized concentration addition models for mixture effect prediction of sex hormone synthesis in vitro.

Hadrup N, Taxvig C, Pedersen M, Nellemann C, Hass U, Vinggaard AM - PLoS ONE (2013)

Bottom Line: Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data.In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects.In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals.

View Article: PubMed Central - PubMed

Affiliation: Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Søborg, Denmark.

ABSTRACT
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.

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The effect of Mixture 2 and its constituents on estradiol levels in H295R cells.H295R cells were incubated with chemicals or Mixture 2 in concentrations ranging from 0.04 to 30 µM for 48 h. The cell medium was next isolated and estradiol was measured by DELFIA. Data are mean ± SD. A p-value of less than 0.05 was considered significant, and in case of significance a sigmoidal curve fit (black line) was applied with a 95% confidence band (black dotted lines). The calculated contribution of each chemical is illustrated on the graph of the mixture data (abbreviated as “calculate” in the graph). This contribution is established by shifting the regression line of single chemical effects to the right along the x-axis by the reciprocal of its ratio in the mixture.
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pone-0070490-g007: The effect of Mixture 2 and its constituents on estradiol levels in H295R cells.H295R cells were incubated with chemicals or Mixture 2 in concentrations ranging from 0.04 to 30 µM for 48 h. The cell medium was next isolated and estradiol was measured by DELFIA. Data are mean ± SD. A p-value of less than 0.05 was considered significant, and in case of significance a sigmoidal curve fit (black line) was applied with a 95% confidence band (black dotted lines). The calculated contribution of each chemical is illustrated on the graph of the mixture data (abbreviated as “calculate” in the graph). This contribution is established by shifting the regression line of single chemical effects to the right along the x-axis by the reciprocal of its ratio in the mixture.

Mentions: The effects of Mixture 2 and its constituent chemicals on estradiol were the following (Fig. 7): A decrease was found for epoxiconazole (EC50: 0.48 µM, Emax: 8%), prochloraz (EC50: 0.044 µM, Emax: 1%), tebuconazole (EC50: 4.0 µM, Emax: 41%) and Mixture 2 (EC50: 1.1 µM, Emax: 12%). Increased estradiol levels were found for mancozeb (EC50: 5.7 µM Emax: 284%) and procymidone (EC50: 8.0 µM, Emax: 278%).


Concentration addition, independent action and generalized concentration addition models for mixture effect prediction of sex hormone synthesis in vitro.

Hadrup N, Taxvig C, Pedersen M, Nellemann C, Hass U, Vinggaard AM - PLoS ONE (2013)

The effect of Mixture 2 and its constituents on estradiol levels in H295R cells.H295R cells were incubated with chemicals or Mixture 2 in concentrations ranging from 0.04 to 30 µM for 48 h. The cell medium was next isolated and estradiol was measured by DELFIA. Data are mean ± SD. A p-value of less than 0.05 was considered significant, and in case of significance a sigmoidal curve fit (black line) was applied with a 95% confidence band (black dotted lines). The calculated contribution of each chemical is illustrated on the graph of the mixture data (abbreviated as “calculate” in the graph). This contribution is established by shifting the regression line of single chemical effects to the right along the x-axis by the reciprocal of its ratio in the mixture.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3750043&req=5

pone-0070490-g007: The effect of Mixture 2 and its constituents on estradiol levels in H295R cells.H295R cells were incubated with chemicals or Mixture 2 in concentrations ranging from 0.04 to 30 µM for 48 h. The cell medium was next isolated and estradiol was measured by DELFIA. Data are mean ± SD. A p-value of less than 0.05 was considered significant, and in case of significance a sigmoidal curve fit (black line) was applied with a 95% confidence band (black dotted lines). The calculated contribution of each chemical is illustrated on the graph of the mixture data (abbreviated as “calculate” in the graph). This contribution is established by shifting the regression line of single chemical effects to the right along the x-axis by the reciprocal of its ratio in the mixture.
Mentions: The effects of Mixture 2 and its constituent chemicals on estradiol were the following (Fig. 7): A decrease was found for epoxiconazole (EC50: 0.48 µM, Emax: 8%), prochloraz (EC50: 0.044 µM, Emax: 1%), tebuconazole (EC50: 4.0 µM, Emax: 41%) and Mixture 2 (EC50: 1.1 µM, Emax: 12%). Increased estradiol levels were found for mancozeb (EC50: 5.7 µM Emax: 284%) and procymidone (EC50: 8.0 µM, Emax: 278%).

Bottom Line: Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data.In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects.In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals.

View Article: PubMed Central - PubMed

Affiliation: Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Søborg, Denmark.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus