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Computational Model of Steroidogenesis in Human H295R Cells to Predict Biochemical Response to Endocrine Active Chemicals: Model Development for Metyrapone
Citation:
BREEN, M., M. Breen, N. Terasaki, M. Yamazaki, AND R. CONOLLY. Computational Model of Steroidogenesis in Human H295R Cells to Predict Biochemical Response to Endocrine Active Chemicals: Model Development for Metyrapone. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, 118(2):265-72, (2010).
Impact/Purpose:
We aimed to develop a mechanistic computational model of the metabolic network of adrenal steroidogenesis to predict the synthesis and secretion of adrenal steroids in human H295R cells, and their biochemical response to steroidogenic disrupting EAC.
Description:
BACKGROUND: An in vitro steroidogenesis assay using the human adrenocortical carcinoma cells H295R is being evaluated as a possible toxicity screening approach to detect and assess the impact of endocrine active chemicals (EAC) capable of altering steroid biosynthesis. Interpretation of these data and their quantitative use in human and ecological risk assessments can be enhanced with mechanistic computational models to help define mechanisms of action and obtain an improved understanding of the intracellular dose-response behavior. OBJECTIVES: We aimed to develop a mechanistic computational model of the metabolic network of adrenal steroidogenesis to predict the synthesis and secretion of adrenal steroids in human H295R cells, and their biochemical response to steroidogenic disrupting EAC. METHODS: We developed a deterministic model that describes the biosynthetic pathways for the conversion of cholesterol to adrenal steroids, and the kinetics for enzyme inhibition by metryrapone (MET), a model EAC. Model predictions were compared to data from an in vitro steroidogenesis assay using H295R cells. Model parameters were estimated using an optimization algorithm. RESULTS: Model predicted steroid concentrations in cells and culture medium correspond well to time-course experimental data from control and MET-exposed cells. A sensitivity analysis indicated the parameter uncertainties, and identified transport and metabolic processes that most influence the concentrations of the primary adrenal steroids, aldosterone and cortisol. CONCLUSIONS: Our study demonstrates the feasibility of using a mechanistic computational model of steroidogenesis to predict steroid concentrations, in vitro. This capability could be useful to help define mechanisms of action for poorly characterized chemicals and mixtures in support of predictive hazard and risk assessments with EAC.
