An analysis of exposure rate effects for benzene using a physiologically based pharmacokinetic model

Bois, Frédéric Y.; Paxman, Dalton G.

doi:10.1016/0273-2300(92)90044-a

Cited by 28 publications

(19 citation statements)

References 30 publications

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“…The amount of metabolites (hydroquinone, catechol, and muconaldehyde) formed was 20% higher after the 15-min exposure at the higher level than after the 8-h exposure at the lower level. Differences between the model predictions (Bois and Paxman, 1992) and the empirical data of Sabourin, et al (1989a,b) may be related, at least in part, to the higher benzene exposure levels (50, 150, and 600 ppm) used by Sabourin, et al A number of investigators have suggested that covalent binding of benzene metabolites to cellular macromolecules is related to benzene's mechanism of toxicity, although the relationship between adduct formation and toxicity is not clear. Benzene metabolites have been found to form covalent adducts with proteins from blood in humans (Bechtold, et al, 1992b).…”

Section: Metabolismmentioning

confidence: 86%

ATSDR evaluation of health effects of benzene and relevance to public health

et al. 2008

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As part of its mandate, the Agency for Toxic Substances and Disease Registry (ATSDR) prepares toxicological profiles on hazardous chemicals found at Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) National Priorities List (NPL) sites that have the greatest public health impact. These profiles comprehensively summarize toxicological and environmental information. This article constitutes the release of portions of the Toxicological Profile for Benzene. The primary purpose of this article is to provide public health officials, physicians, toxicologists, and other interested individuals and groups with an overall perspective on the toxicology of benzene. It contains descriptions and evaluations of toxicological studies and epidemiological investigations and provides conclusions, where possible, on the relevance of toxicity and toxicokinetic data to public health.

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Section: Metabolismmentioning

confidence: 86%

ATSDR evaluation of health effects of benzene and relevance to public health

et al. 2008

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“…Parameters k 1 and k 2 were sampled from wide uniform distributions, with informative bounds given by Segel (1975, p. 32). For k 3 we used a narrower range because we had reasonable prior values for benzene, toluene, ethylbenzene, and m -xylene V max (Bois and Paxman 1992; Dennison et al 2003; Tardif et al 1997) and for the total quantity of CYP2E1 in liver, hence for its total liver concentration [CYP2E1] total (Carlile et al 1997; Seaton et al 1995). …”

Section: Methodsmentioning

confidence: 99%

A Mechanistic Modeling Framework for Predicting Metabolic Interactions in Complex Mixtures

Cheng

2011

Environ Health Perspect

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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.

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“…We again used Monte Carlo simulations and reverted to a continuous measure of fit, log-likelihood, which has several disadvantages as men- Observed Dat Value Figure 10. Predictions made by the best-fitting simulation run versus observed experimental values (3,4,(11)(12)(13). For a perfect fit, the points would fall on the identity line.…”

Section: Methodological Versus Biological Accomplishmentsmentioning

confidence: 99%

Parameter variability and the interpretation of physiologically based pharmacokinetic modeling results.

Spear

1994

Environ Health Perspect

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For the past several years we have been working with models of benzene distribution and metabolism, principally in the rat, but more recently in humans. Our biologically related objectives have been primarily to assist our laboratory-based colleagues in their quest for understanding of the mechanisms by which benzene exerts its toxic action. A secondary goal has been to develop or adapt models useful in risk assessment applications. We have also had methodological goals that relate to applications of sensitivity analysis on the one hand, but more fundamentally to the connection between experimental data and model structure and parameterization. This paper presents an overview of our work in these areas -Environ Health Perspect 102(Suppl 11): 61-6 (1994)

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An analysis of exposure rate effects for benzene using a physiologically based pharmacokinetic model

Cited by 28 publications

References 30 publications

ATSDR evaluation of health effects of benzene and relevance to public health

ATSDR evaluation of health effects of benzene and relevance to public health

A Mechanistic Modeling Framework for Predicting Metabolic Interactions in Complex Mixtures

Parameter variability and the interpretation of physiologically based pharmacokinetic modeling results.

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