Development of a Quantitative Model of Pregnane X Receptor (PXR) Mediated Xenobiotic Metabolizing Enzyme Induction

Luke, Nicholas S.; DeVito, Michael J.; Shah, Imran; El‐Masri, Hisham A.

doi:10.1007/s11538-010-9508-5

Cited by 9 publications

(35 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of PXR, only three quantitative mechanistic mathematical models of PXR-mediated human CYP3A4 gene regulation in the liver have been published in the literature that incorporate data from humans or from human models [ 88 , 89 , 90 ]. In addition, one further model described rodent PXR regulation of its target genes (CYP3A1/2) in rats or in primary rat hepatocytes with the rodent PXR ligands dexamethasone, lithocholic acid, and pregnenalone-16α-carbonitrile [ 91 ].…”

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

“…The first quantitative (semi-)mechanistic biologically based compartmental model for PXR-mediated human CYP3A4 gene regulation has been described in the report by Luke et al [ 90 ]. A schematic representation of the modelled processes is given in Figure 2 , which can also be found in Luke et al [ 90 ].…”

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

“…This leads to mass-balance equations not only for rifampicin blood and liver concentrations, but for the inducible maximum elimination rate in the liver as well. The equation for rifampicin liver concentration contains, in addition to the terms in the work by Luke et al [ 90 ], a Michaelis–Menten term and a repeated-dose term.…”

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

See 2 more Smart Citations

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

Tebbens

Azar

Friedmann

et al. 2018

IJMS

View full text Add to dashboard Cite

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.

show abstract

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

Section: Mathematical Models Of Pxr Activation and Pxr-induced Genmentioning

confidence: 99%

See 1 more Smart Citation

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

Tebbens

Azar

Friedmann

et al. 2018

IJMS

View full text Add to dashboard Cite

show abstract

“…Our description of the liver is also simplistic and could be improved with a finer description of liver zonation-dependent metabolism (Andersen et al 1997; Sheikh-Bahaei et al 2009; Wambaugh and Shah 2010). It would not be difficult to extend our models with more enzymes and complex reactions mechanisms (including enzymatic induction) (Bois 2009c; Luke et al 2010) or specific early toxicity pathways.…”

Section: Discussionmentioning

confidence: 99%

A Mechanistic Modeling Framework for Predicting Metabolic Interactions in Complex Mixtures

Cheng

2011

Environ Health Perspect

View full text Add to dashboard Cite

Background: Computational modeling of the absorption, distribution, metabolism, and excretion of chemicals is now theoretically able to describe metabolic interactions in realistic mixtures of tens to hundreds of substances. That framework awaits validation.Objectives: Our objectives were to a) evaluate the conditions of application of such a framework, b) confront the predictions of a physiologically integrated model of benzene, toluene, ethylbenzene, and m-xylene (BTEX) interactions with observed kinetics data on these substances in mixtures and, c) assess whether improving the mechanistic description has the potential to lead to better predictions of interactions.Methods: We developed three joint models of BTEX toxicokinetics and metabolism and calibrated them using Markov chain Monte Carlo simulations and single-substance exposure data. We then checked their predictive capabilities for metabolic interactions by comparison with mixture kinetic data.Results: The simplest joint model (BTEX interacting competitively for cytochrome P450 2E1 access) gives qualitatively correct and quantitatively acceptable predictions (with at most 50% deviations from the data). More complex models with two pathways or back-competition with metabolites have the potential to further improve predictions for BTEX mixtures.Conclusions: A systems biology approach to large-scale prediction of metabolic interactions is advantageous on several counts and technically feasible. However, ways to obtain the required parameters need to be further explored.

show abstract

“…PBPK models will likely be merged with systems biology and virtual human models. The boundary between PK and PD actually tends to blur as metabolism becomes more and more integrated into detailed models of toxicity pathways when, for example, modeling enzymatic induction by xenobiotics (Bois 2010; Luke et al 2010a). The variability of the different components of those models will be directly informed by time series of genomic, proteomic, metabolomic data on the chemical species considered.…”

Section: Advances In In Silico Methods To Address Human Variabilitymentioning

confidence: 99%

Addressing Human Variability in Next-Generation Human Health Risk Assessments of Environmental Chemicals

Zeise

Bois

Chiu

et al. 2013

Environ Health Perspect

115

View full text Add to dashboard Cite

Background: Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment.Objective: Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum.Methods: This review was informed by a National Research Council workshop titled “Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making.” We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals.Discussion: One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification—and extent of characterization—of interindividual variability in the human population.Conclusions: Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.

show abstract

Development of a Quantitative Model of Pregnane X Receptor (PXR) Mediated Xenobiotic Metabolizing Enzyme Induction

Cited by 9 publications

References 38 publications

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

A Mechanistic Modeling Framework for Predicting Metabolic Interactions in Complex Mixtures

Addressing Human Variability in Next-Generation Human Health Risk Assessments of Environmental Chemicals

Contact Info

Product

Resources

About