Generalized concentration addition accurately predicts estrogenic potentials of mixtures and environmental samples containing partial agonists

Cell-based bioanalytical tools are considered one alternative to overcome limitations of sensitivities of instrumental, analytical chemistry for monitoring estrogenic chemicals in the environment. Because these tools also reflect non-additive interactions of chemicals in mixtures, their outcomes often deviate from outcomes of chemical analytical approaches that assume additivity, e.g. the concentration addition (CA) model. Often this is because CA is unable to adequately represent effects of partial agonists, i.e. estrogens with lesser efficacies compared to 17β-estradiol. A generalized concentration addition (GCA) model has been proposed to address this shortcoming. In the present study, we investigated effects of mixtures of isomers of nonylphenol as partial model agonists in a cell-based estrogenicity assay. Whether the GCA model was able to more accurately predict the outcomes of these and previously published mixture experiments was evaluated, as well as the potency of a set of comprehensively characterized sewage effluent samples, compared to CA. If samples contained partial agonists, the GCA model consistently predicted potencies of mixtures and extracts of environmental samples more accurately than did the CA model. These findings enable more accurate estimations of potencies of estrogenicity explained by concentrations of agonists and partial agonists, thus significantly improving the ability to identify causative chemicals. •Predictive power of the generalized concentration addition (GCA) was evaluated.•Accuracy of GCA was greater compared to the EEF concept.•An improved workflow to derive EEQs in environmental samples using GCA is presented.•This approach can significantly improve risk and hazard assessments of estrogens.•GCA improves identifications of causative chemicals in environmental samples.