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Developing Predictive Approaches to Characterize Adaptive Responses of the Reproductive Endocrine Axis to Aromatase Inhibition: Computational Modeling
Citation:
Breen, M., Dan Villeneuve, G. Ankley, D. Bencic, M. Breen, K. Watanabe, A. Lloyd, AND R. Conolly. Developing Predictive Approaches to Characterize Adaptive Responses of the Reproductive Endocrine Axis to Aromatase Inhibition: Computational Modeling. Presented at Society of Toxicology, March 10 - 14, 2013.
Impact/Purpose:
This abstract will be presented at the Society of Toxicology meeting March 10-14, 2013, San Antonio, TX
Description:
Exposure to endocrine disrupting chemicals can affect reproduction and development in both humans and wildlife. We developed a mechanistic mathematical model of the hypothalamic-pituitary-gonadal (HPG) axis in female fathead minnows to predict dose-response and time-course (DRTC) behaviors for endocrine effects of a well-defined aromatase inhibitor, fadrozole (FAD). The model includes a regulatory feedback that mediates adaptive responses to endocrine stress by controlling the secretion of a generic gonadotropin (LH/FSH) from the hypothalamic-pituitary complex. Plasma 17β-estradiol (E2) and ovarian cytochrome P450 (CYP) 19A aromatase mRNA data from two time-course experiments, each of which included both an exposure and a depuration phase, and plasma E2 data from 4 days exposure experiment were used to develop and evaluate the model. Model parameters were estimated using E2 concentrations for 0, 0.5, and 3 µg/L FAD concentrations, and good fits to these data were obtained. The model accurately predicted CYP19A mRNA fold changes for controls and three FAD doses (0, 0.5, or 3 µg/L), and venous E2 dose-response during FAD exposure on day 4. Comparing the model-predicted DRTC with experimental data provided insight into how the feedback control mechanisms embedded in the HPG axis mediate these changes: adaptive changes in plasma E2 levels occurring during exposure and “overshoot” occurring post-exposure. This study demonstrates the value of mechanistic computational modeling to examine and predict the possible dynamic behaviors. This abstract does not necessarily reflect US Environmental Protection Agency policy.