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2007 CompTox Forum
Abstract - Mathematical Model of Steroidogenesis in Fathead Minnow Ovaries to Predict Biochemical Response to Endocrine Active Compounds
Michael S. Breen[a], Miyuki Breen[b], Daniel L. Villeneuve[c], Gerald T. Ankley[c] and Rory B. Conolly[a]
Michael S. Breen, Ph.D. (presenting author)
Biomedical Engineer
U.S. EPA, Office of Research and Development
National Center for Computational Toxicology
Mail Code: B205-01
Research Triangle Park, NC 27711
Phone: 919-541-9409
E-mail: breen.michael@epa.gov
Sex steroids, which have an important role in a wide range of physiological and pathological processes, are produced primarily in the gonads and adrenal glands through a series of enzyme mediated reactions. The activity of steroidogenic enzymes can be altered by a variety of endocrine active compounds (EAC), some of which are therapeutics and others that are environmental contaminants. A steady-state mathematical model of the intraovarian metabolic network was developed to predict the production and secretion of testosterone (T) and estradiol (E2), and their responses to EAC. Model predictions were compared to data from an in vitro steroidogenesis assay using fathead minnow ovary explants. Model parameters were estimated using an iterative optimization algorithm. Model-predicted concentrations of T and E2 closely correspond to the time-course data from baseline experiments and dose-response data from experiments with the EAC, fadrozole. A sensitivity analysis of the model parameters identified specific transport and metabolic processes that most influence the concentrations of T and E2, which included uptake of cholesterol into the ovary, secretion of androstenedione (AD) from the ovary, conversion of AD to T, and conversion of AD to estrone (E1). The sensitivity analysis also indicated the E1 pathway as the preferred pathway for E2 synthesis, as compared to the T pathway. Our study demonstrates the feasibility of using the steroidogenesis model to predict T and E2 concentrations, in vitro, while reducing model complexity with a steady-state assumption. This capability could be useful for environmental health and ecological assessments and pharmaceutical development with EAC.
This work was reviewed by the U.S. EPA and approved for publication but does not necessarily reflect Agency policy.