Abstract

A physiological model was previously constructed to facilitate extrapolation of surrogates for the effects of 2,3,7,8-tetrachlorodibenzo- p-dioxin (TCDD) in rat liver to doses comparable to human environmental exposures. The model included induction of P450 isozymes and suggested the presence of multiple binding sites with different affinities for the TCDD-liganded Ah receptor at CYP1A1 dioxin responsive elements. The model also indicated that protein synthesis on the mRNA template exhibited saturation kinetics with respect to message levels. In the present work the earlier model was revised to include the increased proteolysis of the Ah receptor on binding TCDD, more realistic representations of gene transcription and mRNA translation, and different stability for each mRNA. The revised model includes multiple TCDD-liganded Ah receptor binding sites for CYP1A1 and CYP1B1 genes, a lag of 0.2 day for production of mRNA and induced proteins, and stabilization of mRNA by a poly(A) tail. The model reproduced the transient depletion of the Ah receptor subsequent to binding ligand and the dose–response of the receptor in rats treated with biweekly oral doses of TCDD in corn oil. The model reproduced tissue TCDD concentrations observed for several dosing scenarios. Such robustness indicates the utility of the model in estimating internal dose. The model also reproduced the observed dose–response patterns for mRNA and protein for CYP1A1, CYP1A2, and CYP1B1 after repeated dosing. Neither of the two dissociation constants for the Ah receptor bound to the CYP1B1 gene is negligible, supporting the assumption of multiple response elements for this gene. The poorer induction of CYP1B1 was predicted to be due to lower affinity of the dioxin responsive elements for binding the liganded Ah receptor, suggesting the involvement of other regulatory factors, and a shorter poly(A) tail on CYP1B1 mRNA, leading to a shorter lifetime. Saturation in the kinetics of protein synthesis was linked to the limited number of ribosomes that could bind to each message molecule, resulting in fewer ribosomes bound per message at higher doses. Predicted induction at low doses was found to vary widely with the assumptions used in the construction of a model. More detailed descriptions of biological processes might provide more reliable predictions of enzyme induction.

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