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Generalized Concentration Addition Model Predicts Glucocorticoid Activity Bioassay Responses to Environmentally Detected Receptor-Ligand Mixtures.

ABSTRACT: Many glucocorticoid receptor (GR) agonists have been detected in waste and surface waters domestically and around the world, but the way a mixture of these environmental compounds may elicit a total glucocorticoid activity response in water samples remains unknown. Therefore, we characterized 19 GR ligands using a CV1 cell line transcriptional activation assay applicable to water quality monitoring. Cells were treated with individual GR ligands, a fixed ratio mixture of full or partial agonists, or a nonequipotent mixture with full and partial agonists. Efficacy varied (48.09%-102.5%) and potency ranged over several orders of magnitude (1.278 × 10-10 to 3.93 × 10-8 M). Concentration addition (CA) and response addition (RA) mixtures models accurately predicted equipotent mixture responses of full agonists (r2 = 0.992 and 0.987, respectively). However, CA and RA models assume mixture compounds produce full agonist-like responses, and therefore they overestimated observed maximal efficacies for mixtures containing partial agonists. The generalized concentration addition (GCA) model mathematically permits < 100% maximal responses, and fell within the 95% confidence interval bands of mixture responses containing partial agonists. The GCA, but not CA and RA, model predictions of nonequipotent mixtures containing both full and partial agonists fell within the same statistical distribution as the observed values, reinforcing the practicality of the GCA model as the best overall model for predicting GR activation. Elucidating the mechanistic basis of GR activation by mixtures of previously detected environmental GR ligands will benefit the interpretation of environmental sample contents in future water quality monitoring studies.

SUBMITTER: Medlock Kakaley E

PROVIDER: S-EPMC6709530 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Generalized Concentration Addition Model Predicts Glucocorticoid Activity Bioassay Responses to Environmentally Detected Receptor-Ligand Mixtures.

Medlock Kakaley Elizabeth E Cardon Mary C MC Gray L Earl LE Hartig Phillip C PC Wilson Vickie S VS

Toxicological sciences : an official journal of the Society of Toxicology 20190301 1

Many glucocorticoid receptor (GR) agonists have been detected in waste and surface waters domestically and around the world, but the way a mixture of these environmental compounds may elicit a total glucocorticoid activity response in water samples remains unknown. Therefore, we characterized 19 GR ligands using a CV1 cell line transcriptional activation assay applicable to water quality monitoring. Cells were treated with individual GR ligands, a fixed ratio mixture of full or partial agonists, ...[more]

PMID: 30535411

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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.

Dataset Information

Generalized Concentration Addition Model Predicts Glucocorticoid Activity Bioassay Responses to Environmentally Detected Receptor-Ligand Mixtures.

Publications

Generalized Concentration Addition Model Predicts Glucocorticoid Activity Bioassay Responses to Environmentally Detected Receptor-Ligand Mixtures.

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