You are here:
Computational Steroidogenesis Model To Predict Biochemical Responses to Endocrine Active Chemicals: Model Development and Cross Validation
Citation:
Breen, M., M. BREEN, N. Terasaki, M. Yamazaki, AND R. CONOLLY. Computational Steroidogenesis Model To Predict Biochemical Responses to Endocrine Active Chemicals: Model Development and Cross Validation. Presented at Triangle Consortium for Reproductive Biology, RTP, NC, February 23, 2008.
Impact/Purpose:
We are developing a dynamic mathematical model of the metabolic network of adrenal steroidogenesis to predict the synthesis and secretion of adrenocortical steroids (e.g. mineralocorticoids, glucocorticoids, androgens and estrogens), and the biochemical responses to EAC.
Description:
Steroids, which have an important role in a wide range of physiological 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 chemicals (EAC), some of which are therapeutics and others that are environmental contaminants. We are developing a dynamic mathematical model of the metabolic network of adrenal steroidogenesis to predict the synthesis and secretion of adrenocortical steroids (e.g. mineralocorticoids, glucocorticoids, androgens and estrogens), and the biochemical responses to EAC. We previously developed a deterministic model which describes the biosynthetic pathways for the conversion of cholesterol to adrenocortical steriods, and the kinetics for enzyme inhibition by the EAC, metyrapone. In this study, we extended our model for a multiple enzyme inhibitor, aminoglutethimide. Experiments were performed using H295R human adrenocarcinoma cells, and concentrations of 12 steriods were simultaneously measured with a newly developed LC/MS/MS method. We performed cross validation of our model for the baseline data across multiple experimental studies. Results show that the model simulation closely corresponds to the time-course baseline data. Our study demonstrates the feasibility of using the in silico mechanistic steroidogenesis model to predict the in vitro adrenocortical steroid concentrations using H295R cells. This capability could be useful to help define mechanisms of action for poorly characterized chemicals and mixtures in support of the H295R steroidogenesis screening system, and to screen drug candidates based on steroidogenic effects in the early phase of drug development.
URLs/Downloads:
Computational Steroidgenesis Model To Predict Biochemical Responses to Endocrine Active Chemicals: Model Development and Cross Validation (PDF, NA pp, 67 KB, about PDF)Computational Steroidogenesis Model To Predict Biochemical Responses to Endocrine Active Chemicals: Model Development and Cross Validation (poster) (PDF, NA pp, 206 KB, about PDF)