An approach for assessing estrogen receptor-mediated interactions in mixtures of three chemicals: A pilot study

Abstract

Most studies investigating interactions among endocrine-active chemicals have been limited to binary mixtures. This study reports on the preliminary evaluation an in vitro MCF-7 cell ER-α reporter gene system, coupled with a statistical methodology adapted for assessing interactions within ternary (3-chemical) mixtures. Two mixtures were initially chosen for assessment of the in vitro system's ability to detect additivity (mixture A) as well as greater-than-additive (mixture B) responses. Mixture A was composed of 17β-estradiol (E2), ethinyl estradiol, and diethylstilbestrol and served as a control for additivity, whereas mixture B (E2, epidermal growth factor, insulin-like growth factor-I) was selected to model greater-than-additive interactions based on previous in vitro studies. After generating complete dose-response curves for each chemical, ternary mixtures were then tested in a full factorial design (4 concentrations per chemical, 64 treatment groups). A response surface was estimated using a nonlinear mixed model, and the observed responses were statistically analyzed for departures from the responses expected under the assumption of additivity. Mixture A exhibited additivity in vitro when the chemicals were present at concentrations in the linear range of their individual dose-response curves. For mixture B, in vitro analysis resulted in the additivity hypothesis being rejected (p < 0.001) because of a greater-than-additive interaction, as expected. A limited in vivo evaluation of mixture A was performed in the immature mouse uterotrophic assay (27 treatment groups), which agreed with the in vitro assessment of no significant departure from additivity (p = 0.903). These findings demonstrate the ability of this in vitro methodology to detect additive, greater-than-additive, and less-than-additive interactions within ternary mixtures, which now allows for the assessment of environmentally relevant mixtures.