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The following HTML text is provided to enhance online readability. Many aspects of typography translate only awkwardly to HTML. Please use the page image as the authoritative form to ensure accuracy. obtained by using the traditional approach of dose-response modeling based only on tumor data. Physiological modeling of metabolic interactions Frédéric Y. Bois INERIS, Verneuil en Halatte, France Purpose: Modeling metabolic interactions between chemicals can be a formidable task in model development. This presentation demonstrates a new approach and the capabilities of new tools to facilitate that development. Methods: Individual models of metabolic pathways are automatically merged and coupled to a template physiologically based pharmacokinetic (PBPK) model by using the GNU MCSim software. The global model generated is very efficient and able to simulate the interactions between a theoretically unlimited number of substances. Development time increases only linearly with the number of substances considered while the number of possible interactions increases exponentially. Results: An example of application of the approach to the prediction of the kinetics of a mixture of 30 arbitrary chemicals is shown. The qualitative and quantitative behavior of the corresponding pathway network is analyzed by using Monte Carlo simulations. In our example, the number of significant interactions, given the uncertainty and variability in the pharmacokinetics and metabolism of those substances, is much lower than the theoretically possible number of interactions. Conclusion: The integrative approach to interaction modeling is efficient and can be extended beyond metabolic interactions. It relies on the availability of specific data on the rate constants of individual reactions. Such data could be obtained through unconventional experiments in enzyme kinetics or through ab initio chemical modeling of enzymatic reactions. We are currently exploring both approaches. The role of oxysterols in a computational steroidogenesis model of human H295R cells to improve predictability of biochemical responses to endocrine disruptors M. Breen,1,2 M.S. Breen,3 A.L. Lloyd,1 and R.B. Conolly2 Steroids, which have an important role in a wide range of physiological
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