Biomathematics Seminar
Tuesday, 4/24/07

Speaker: Michael S. Breen
National Center for Computational Toxicology, 
U.S. Environmental Protection Agency, 
Office of Research and Development, 
Research Triangle Park, NC 27711

Title: Mechanistic Model of Steroid Biosynthesis to Predict Biochemical 
Response to Endocrine Active Chemicals


Abstract:
Sex steroids, which have an important role in a wide range of physiological 
and pathological processes, are synthesized 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 computational model of the 
intraovarian metabolic network was developed to predict the synthesis 
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 with ovary explants from a small fish model, the 
fathead minnow. 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 (control) 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, and conversions of AD to T, and AD to estrone. 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 to help define mechanisms of 
actions for poorly characterized chemicals in support of predictive 
environmental risk assessments, and to screen drug candidates based on 
steroidogenic effects in the early phase of drug development. This research 
does not necessarily reflect US EPA policy.