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 II: Computational Modeling. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 133(2):234-47, (2013).

Impact/Purpose:

The molecular and biochemical response to environmental chemical exposures are major determinants of health risk. The NRC report Toxicology Testing in the 21st Century emphasizes that adaptive changes within organisms exposed to environmental stress can alter dose-response behaviors to minimize the effects of the stressors. To refine descriptions of dose-response behavior for risk assessments, better understanding of the adaptive mechanisms is needed. The main goal of our research was to develop a computational model of the HPG axis in fathead minnows that will help us to understand and characterize how the feedback regulatory loops in the axis generate adaptive responses to toxicant stress.

Description:

ABSTRACT 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 regulatory feedback that captures adaptive responses to endocrine stress by controlling the secretion of a generic gonadotropin (LH/FSH) from the hypothalamic-pituitary complex. To develop and evaluate the model, we used plasma 17-estradiol (E2) concentrations 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 a third 4-d study. Model parameters were estimated using E2 concentrations for 0, 0.5, and 3 j.lg/L FAD exposure 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, and 3 J.lg/L), and plasma E2 dose-response from the 4-d study. Comparing the model-predicted DRTC with experimental data provided insight into how the feedback control mechanisms embedd•ed in the HPG axis mediate these changes: specifically, 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 possible dynamic behaviors in perturbed systems. As this work progresses, we will obtain a refined understanding of how adaptive responses within the vertebrate HPG axis can affect DRTC behaviors for aromatase inhibitor and other types of endocrine active chemicals, and apply that knowledge in support of risk assessments.

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