Citation:

Green, E., N. Evans, AND E. MedlockKakaley. Application of the Generalized Concentration Addition Model to Predict In Vitro Responses of Tertiary Mixtures of Glucocorticoid Ligands. Society of Toxicology - Virtual, N/A, Virtual, March 12 - 26, 2021.

Impact/Purpose:

Prescription and endogenous glucocorticoid ligands are frequently reported in environmental surface water, sewage treatment plant influent, wastewater effluent, hospital effluent, and industrial effluent due to the inability of wastewater treatment plants to effectively eliminate these bioactive compounds, leading to potentially harmful exposures. As a result, it is increasingly imperative to predict adverse outcomes of exposure and expedite water quality testing. An emerging focus of toxicological research aims to improve these methods by developing computational models to predict joint-responses of chemical mixtures. In this study, we expand the application of the generalized concentration addition model and demonstrate its ability to accurately predict the summed response produced by tertiary mixtures containing multiple partial agonists. This broadened application could save time, resources, and costs associated with experimental studies to determine joint-responses of more complex mixtures accurately representing environmental concentrations.

Description:

Glucocorticoid receptor (GR) ligands, both endogenous and pharmaceuticals, are frequently detected in the environment and exhibit high potency at low concentrations. Prolonged exposure alters expression of protein encoding genes involved in metabolism, immunity, and development; suppresses immune function; impacts endocrine signaling; and decreases reproductive success in exposed aquatic populations. We previously demonstrated the generalized concentration addition (GCA) model most accurately predicts in vitro responses of mixtures of two GR ligands. Contrary to the concentration addition (CA) model, the GCA model accounts for <100% maximal efficacy of partial agonists. Previously, the model was applied to mixtures containing a single partial agonist; therefore, we aim to apply the GCA model to mixtures containing multiple partial GR agonists and compare the model predictions to CA model predictions and observed in vitro responses. We used natural and synthetic glucocorticoids commonly detected in surface water and wastewater effluent including dexamethasone, fluticasone propionate, triamcinolone acetonide, clobetasol propionate, prednisolone, 21-hydroxyprogesterone, corticosterone, and aldosterone. Using in vitro observed EC50, Hill slope, relative potency factor, and maximum efficiency values, we applied GCA and CA models to four equipotent and non-equipotent tertiary mixtures containing full and partial agonists. All mixtures were modeled at half-log concentrations. Equipotent mixture results were graphed with GraphPad Prism 7.00 while non-equipotent mixtures were modeled using an 8x8 factorial matrix design and graphed with SigmaPlot 14.0. GCA modeled responses predict chemical mixtures containing partial agonists exhibit a lower response at saturating ligand concentrations compared to CA modeled responses. Chemical mixtures containing only full agonists are predicted to exhibit similar responses by GCA and CA models. These modeled predictions are consistent with previous in vitro observations of tertiary mixtures containing one partial agonist and will be compared to future in vitro studies of tertiary mixtures with multiple partial agonists. Environmental mixtures contain many compounds, including multiple partial agonists, but the accuracy of the GCA model to predict the effects of GR partial agonist mixtures suggests it may be applied to more complex mixtures accurately representing environmental exposures.

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