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Predicting the Activation of the Androgen Receptor by Mixtures of Ligands Using Generalized Concentration Addition.

ABSTRACT: Concentration/dose addition is widely used for compounds that act by similar mechanisms. But it cannot make predictions for mixtures of full and partial agonists for effect levels above that of the least efficacious component. As partial agonists are common, we developed generalized concentration addition, which has been successfully applied to systems in which ligands compete for a single binding site. Here, we applied a pharmacodynamic model for a homodimer receptor system with 2 binding sites, the androgen receptor, that acts according to the classic homodimer activation model: Each cytoplasmic monomer protein binds ligand, undergoes a conformational change that relieves inhibition of dimerization, and binds to DNA response elements as a dimer. We generated individual dose-response data for full (dihydroxytestosterone, BMS564929) and partial (TFM-4AS-1) agonists and a competitive antagonist (MDV3100) using reporter data generated in the MDA-kb2 cell line. We used the Schild method to estimate the binding affinity of MDV3100. Data for individual compounds fit the homodimer pharmacodynamic model well. In the presence of a full agonist, the partial agonist had agonistic effects at low effect levels and antagonistic effects at high levels, as predicted by pharmacological theory. The generalized concentration addition model fits the empirical mixtures data-full/full agonist, full/partial agonist, and full agonist/antagonist-as well or better than relative potency factors or effect summation. The ability of generalized concentration addition to predict the activity of mixtures of different types of androgen receptor ligands is important as a number of environmental compounds act as partial androgen receptor agonists or antagonists.

SUBMITTER: Schlezinger JJ

PROVIDER: S-EPMC7548291 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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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Predicting the Activation of the Androgen Receptor by Mixtures of Ligands Using Generalized Concentration Addition.

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