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The Importance of Biological Realism in Dioxin Risk Assessment Models Michael
Authors:C Kohn  George W Lucier  Christopher J Portier
Institution:Laboratory of Quantitative and Computational Biology, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709.;Laboratory of Biochemical Risk Analysis, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709.
Abstract:Mechanistic mathematical models of hepatocarcinogenesis in the female rat were constructed to investigate possible relationships among the Ah, estrogen, and EGF receptors in TCDD hepato-carcinogenicity. Each model generates dose-response curves for the expression of biomarker liver proteins CYP1A1, CYP1A2, and residual plasma membrane EGF receptor consequent to exposure to TCDD. The shapes of the response curves were strongly dependent on the assumed mechanisms of constitutive expression of these proteins. Assuming a constant level of the hepatic Ah receptor, a sigmoidal dose-response of hepatic CYP1A1 to total liver TCDD was computed. However, inclusion of induction of the Ah receptor by TCDD in a physiologically realistic dosimetric model produced a linear low-dose response of CYP1A1. This behavior was computed to arise from the net effect of sublinear response of CYP1A1 mRNA to the concentration of the Ah-TCDD complex and supralinear response of the protein concentration to the mRNA level, illustrating the importance of biological realism in dose-response modeling. The dosimetric model also computed effects of TCDD on the hepatic estradiol concentration and consequent effects on the binding capacity of the EGF receptor and suggests plausible mechanisms for tumor promotion by TCDD. Setting circulating estradiol levels in the model to values typical of the male rat indicated possible sources of the differences in the responses of the EGF receptor and in development of tumors in the two sexes.
Keywords:2  3  7  8–Tetrachlorodibenzo-p-dioxin  mathematical models  gene expression  carcinogenesis
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