Modelling defined mixtures of environmental oestrogens found in domestic animal and sewage treatment effluents using an in vitro oestrogen-mediated transcriptional activation assay (T47D-KBluc).

There is growing concern of exposure of fish, wildlife and humans to water sources contaminated with oestrogens and the potential impact on reproductive health. Environmental oestrogens can come from various sources including concentrated animal feedlot operations (CAFO), municipal waste, agricultural and industrial effluents. US EPA's drinking water contaminant candidate list 3 (CCL3) includes several oestrogenic compounds. Although these contaminants are currently not subject to any proposed or promulgated national primary drinking water regulations, they are known or anticipated to occur in public water systems and may require future regulation under the Safe Drinking Water Act. Using an in vitro transcriptional activation assay, this study evaluated oestrogens from CCL3 both individually and as a seven oestrogen mixture (fixed ray design) over a broad range of concentrations, including environmentally relevant concentrations. Log EC(50) and Hillslope values for individual oestrogens were as follows: estrone, -11.92, 1.283; estradiol-17α, -9.61, 1.486; estradiol-17β, 11.77, 1.494; estriol, -11.14, 1.074; ethinyl estradiol-17α, -12.63, 1.562; Mestranol, -11.08, 0.809 and Equilin, -11.48, 0.946. In addition, mixtures that mirrored the primary oestrogens found in swine, poultry and dairy CAFO effluent (fixed-ratio ray design), and a ternary mixture (4 × 4 × 4 factorial design) of oestrogens found in hormone replacement therapy and/or oral contraceptives were tested. Mixtures were evaluated for additivity using both the concentration addition (CA) model and oestrogen equivalence (EEQ) model. For each of the mixture studies, a broad range of concentrations were tested, both above and below environmentally relevant concentrations. Results show that the observed data did not vary consistently from either the CA or EEQ predictions for any mixture. Therefore, either the CA or EEQ model should be useful predictors for modelling oestrogen mixtures.