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A QUANTITATIVE MODEL FOR XENOBIOTIC METABOLIZING ENZYME (XME) INDUCTION REGULATED BY THE PREGNANE X RECEPTOR (PXR)
Citation:
LUKE, N. S., M. J. DEVITO, I. A. SHAH, AND H. A. EL-MASRI. A QUANTITATIVE MODEL FOR XENOBIOTIC METABOLIZING ENZYME (XME) INDUCTION REGULATED BY THE PREGNANE X RECEPTOR (PXR). Presented at Society of Toxicology Annual Meeting, Seattle, WA, March 16 - 20, 2008.
Impact/Purpose:
To develop a quantitative model predicts fold level inductions of mRNA and protein of CYP3A4 in response to PXR activation.
Description:
The nuclear receptor, PXR, is an integral part of the regulation of hepatic metabolism. It has been shown to regulate specific CYPs (phase I drug-metabolizing enzymes) as well as certain phase II drug metabolism activities, including UDP-glucuronosyl transferase (UGT), sulfotransferase (SULT), and glutathione S-transferase (GST). With its large and flexible ligand-binding domain, PXR can be activated by a broad range of relatively small, hydrophobic exogenous compounds. Upon activation, PXR partners with the Retinoid X Receptor (RXR) to form a heterodimer. The newly formed heterodimer binds to an appropriate DNA response element, causing increased transcription. This leads to an induction in the level of XMEs. These mechanistic steps are included in a biologically-based mathematical model. The quantitative model predicts fold level inductions of mRNA and protein of CYP3A4 in response to PXR activation. Initial sensitivity analysis of the model suggests that CYP3A4 levels are most affected by the transcription rate, translation rate, and CYP3A4 degradation rate. Parameter values were either based on information reported in the literature or optimized to available in vitro data. Model predictions are compared to literature data of CYP3A4 mRNA and protein levels in response to various doses of rifampicin, a known PXR activator. This initial modeling effort will be expanded to include other relevant nuclear receptors. Further calibration and evaluation of the current and expanded model will be performed using computational tools and in vitro and in vivo experiments utilizing different environmentally relevant chemicals. (This abstract does not reflect EPA policy)