Abstract

The pregnane X receptor (PXR) is a drug/xenobiotic-activated transcription factor of crucial importance for major cytochrome P450 xenobiotic-metabolizing enzymes (CYP) expression and regulation in the liver and the intestine. One of the major target genes regulated by PXR is the cytochrome P450 enzyme (CYP3A4), which is the most important human drug-metabolizing enzyme. In addition, PXR is supposed to be involved both in basal and/or inducible expression of many other CYPs, such as CYP2B6, CYP2C8, 2C9 and 2C19, CYP3A5, CYP3A7, and CYP2A6. Interestingly, the dynamics of PXR-mediated target genes regulation has not been systematically studied and we have only a few mechanistic mathematical and biologically based models describing gene expression dynamics after PXR activation in cellular models. Furthermore, few indirect mathematical PKPD models for prediction of CYP3A metabolic activity in vivo have been built based on compartmental models with respect to drug–drug interactions or hormonal crosstalk. Importantly, several negative feedback loops have been described in PXR regulation. Although current mathematical models propose these adaptive mechanisms, a comprehensive mathematical model based on sufficient experimental data is still missing. In the current review, we summarize and compare these models and address some issues that should be considered for the improvement of PXR-mediated gene regulation modelling as well as for our better understanding of the quantitative and spatial dynamics of CYPs expression.

Highlights

  • Nuclear receptors (NRs), which are ligand- or hormon-activated transcription factors, are essential in the organism in many processes, such as hormonal regulation, homeostasis of endogenous compounds and ions, the regulation of both intermediary and xenobiotic metabolism, and the regulation of cell differentiation, development, and proliferation [1]

  • The mouse PXR (mPXR) is primarily located in the cytosol of untreated liver cells, where mPXR binds with the cytoplasmic Constitutive androstane receptor (CAR) retention protein (CCRP) and the heat shock protein 90 (Hsp90) to form a protein complex [11]

  • Employing the mathematical modelling and a large set of experimental data from the literature, the authors predicted degradation rate constants for CYP3A4 mRNA and proteins in primary human hepatocytes and showed a mechanistic application of the model in the examination of molecular aspects of pregnane X receptor (PXR)-mediated CYP3A4 regulation after a single-dose treatment. Two extrapolations of this basic model are further presented: An indirect effect dynamic model, which assumes that hepatic CYP3A4 protein levels depend dynamically on the actual liver unbound fraction concentration of rifampicin, and a static model, where hepatic CYP3A4 levels depend on the average steady state concentrations of rifampicin in plasma

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Summary

Introduction

Nuclear receptors (NRs), which are ligand- or hormon-activated transcription factors, are essential in the organism in many processes, such as hormonal regulation, homeostasis of endogenous compounds and ions, the regulation of both intermediary and xenobiotic metabolism, and the regulation of cell differentiation, development, and proliferation [1]. An understanding of the regulation of drug-metabolizing enzymes expression and activity in response to drug administration, leading to drug–drug interactions (DDIs), is nowadays an essential issue in rational pharmacotherapy These interactions influence the degree of absorption or elimination of medications and can modify the therapeutic or toxicological response to a drug [19,20]. We focus on mechanistically and biologically based cellular models that concentrate on molecular aspects of PXR-mediated regulation of the most important target gene, CYP3A4, or its rodent orthologues

Pregnane X Receptor Characterization
PXR Localization
PXR Transcriptional Machinery
Ligands of the Pregnane X Receptor
Brief Overview of Physiological Functions of PXR
Compartmental Models
Discussion
Findings
Feedback Loops
Conclusions
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