MECHANISTIC MODEL OF STEROIDOGENESIS IN FISH OVARIES TO PREDICT BIOCHEMICAL RESPONSE TO ENDOCRINE ACTIVE CHEMICALS Michael S. Breen, 1 Miyuki Breen, 2 Daniel L. Villeneuve, 3 Gerald T. Ankley, 3 Rory B. Conolly 1 1 National Center for Computational Toxicology, U.S. EPA, Research Triangle Park, NC, USA, 2 Biomathematics Program, Department of Statistics, North Carolina State University, Raleigh, NC, USA 3 Mid-Continent Ecology Division, U.S. EPA, Duluth, MN, USA 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 action for poorly characterized chemicals in support of predictive environmental risk assessments. EFFECTS OF ENDOCRINE ACTIVE COMPOUNDS ON HPG AXIS Feedback control system of hypothalamus-pituitary-gonadal (HPG) axis regulates synthesis and secretion of sex steroid hormones (estradiol (E2), testosterone (T)) by release of gonadotropin releasing hormone (GnRH) from hypothalamus, and luteinizing hormone (LH) and follicle stimulationg hormone (FSH) from pituitary OVARIAN STEROIDOGENESIS MODEL Mathematical model based on in vitro experimental design with two compartments: culture medium and ovary tissue. Transport pathways include ovary uptake of cholesterol (CHOL) and fadrozole (FAD; endocrine active compound), and secretion of androstenedione (AD), estrone (E1), testosterone (T) and estradiol (E2). Metabolic pathway includes conversion of CHOL into T and E2 with specific enzyme inhibition by FAD. For steady-state model, T and E2 medium concentrations are independent of 9 processes (black arrows), and dependent on 11 processes (white arrows). IN VITRO STEROIDOGENESIS EXPERIMENTS WITH OVARY EXPLANTS Dissect fish ovary Incubate ovary in medium supplemented with cholesterol Collect medium at multiple time points over 31.5 hr Measure medium concentrations of testosterone (T) and estradiol (E2) using radioimmunoassay Small fish culture facilityFathead minnows Good evidence steroidogenic pathway is operating at steady-state during experiments Steady-state assumption reduces model complexity R 2 = 0.94 R 2 = 0.98 Dynamic Mass Balances PARAMETER ESTIMATION ASSESSMENT OF MODEL FIT Baseline (medium only) Study Fadrozole Study CONCLUSION Steroidogenesis model can predict testosterone and estradiol concentrations, in vitro, while reducing model complexity with a steady-state assumption Sensitivity analysis indicated E1 pathway as preferred pathway for E2 synthesis This capability could be useful for predictive environmental risk assessments, and screening drug candidates based on steroidogenic effects in early phase of drug development DISCLAIMER This work was reviewed by the U.S. EPA and approved for publication but does not necessarily reflect Agency policy. RELATIVE SENSITIVITY ANALYSIS Cost function: Applied an iterative optimization algorithm. Simultaneously estimated parameters using data from baseline and fadrozole-exposure studies STEADY-STATE ANALYSIS Relative sensitivities for model outputs, testosterone (a) and estradiol (b) in medium, are plotted as function of 11 model parameters for control (no fadrozole) and the lowest, middle, and highest fadrozole concentrations. Negative values indicate an inverse relationship between a parameter change and resulting model output change; positive values indicate a direct relationship. Magnitudes indicate degree to which changes in parameter values lead to changes in model outputs; percentage change of model output for a given percentage change of parameter. Comparison of model ‑ predicted and dose ‑ response data after a 14.5 hr incubation of ovary explants with fadrozole. Model-predicted testosterone and estradiol concentrations in the medium were plotted as a function of fadrozole concentration, and compared with mean concentrations measured from on control and five fadrozole concentrations. Comparison of model ‑ predictions with time ‑ course data from baseline experiments. Model ‑ predicted concentrations of testosterone and estradiol in the medium were plotted as a function of time, and compared with mean concentrations measured at six time points. = measured testosterone conc. in medium for d th FAD dose at i th time = model-predicted testosterone conc. in medium = measured estradiol conc. in medium for d th FAD dose at i th time = model-predicted estradiol conc. in medium = measured fadrozole conc. in medium for d th FAD dose where: Set differential equations to zero to yield algebraic equations Determined analytical solution for medium concentrations of testosterone, C T,med, and estradiol, C E2,med (not shown): Metabolic Pathway with Inhibition by Fadrozole Estimated Parameters Ovary Uptake of Cholesterol and FadrozoleSecretion of Testosterone and Estradiol First-order Enzyme Kinetics with Inhibition by Fadrozole k 0 k 15 k 10 k 18 k 19 k 9 k 11 k 12 k 13 k 16 k pg ml -1 hr -1 Partition coefficient (dimensionless) hr * 3.2* hr -1 pg ml -1 FAD inhibition constants First-order enzyme kinetics and transport rates Competitive enzyme inhibition by fadrozole where: Ovary: Medium: Net uptake rate Net metabolic rate where: * Literature values from fish experiments x * * * High sensitivity for T High sensitivity for E2 High dose-dependent sensitivity for E2 ** * High sensitivity for T High sensitivity for E2 High dose-dependent sensitivity for E2