Literature DB >> 21782697

PBPK modeling of complex hydrocarbon mixtures: gasoline.

James E Dennison1, Melvin E Andersen, Ivan D Dobrev, Moiz M Mumtaz, Raymond S H Yang.   

Abstract

Petroleum hydrocarbon mixtures such as gasoline, diesel fuel, aviation fuel, and asphalt liquids typically contain hundreds of compounds. These compounds include aliphatic and aromatic hydrocarbons within a specific molecular weight range and sometimes lesser amounts of additives, and often exhibit qualitatively similar pharmacokinetic (PK) and pharmacodynamic properties. However, there are some components that exhibit specific biological effects, such as methyl t-butyl ether and benzene in gasoline. One of the potential pharmacokinetic interactions of many components in such mixtures is inhibition of the metabolism of other components. Due to the complexity of the mixtures, a quantitative description of the pharmacokinetics of each component, particularly in the context of differing blends of these mixtures, has not been available. We describe here a physiologically-based pharmacokinetic (PBPK) modeling approach to describe the PKs of whole gasoline. The approach simplifies the problem by isolating specific components for which a description is desired and treating the remaining components as a single lumped chemical. In this manner, the effect of the non-isolated components (i.e. inhibition) can be taken into account. The gasoline model was based on PK data for the single chemicals, for simple mixtures of the isolated chemicals, and for the isolated and lumped chemicals during gas uptake PK experiments in rats exposed to whole gasoline. While some sacrifice in model accuracy must be made when a chemical lumping approach is used, our lumped PK model still permitted a good representation of the PKs of five isolated chemicals (n-hexane, benzene, toluene, ethylbenzene, and o-xylene) during exposure to various levels of two different blends of gasoline. The approach may be applicable to other hydrocarbon mixtures when appropriate PK data are available for model development.

Entities:  

Year:  2004        PMID: 21782697     DOI: 10.1016/j.etap.2003.10.003

Source DB:  PubMed          Journal:  Environ Toxicol Pharmacol        ISSN: 1382-6689            Impact factor:   4.860


  5 in total

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2.  Modeling and analysis of personal exposures to VOC mixtures using copulas.

Authors:  Feng-Chiao Su; Bhramar Mukherjee; Stuart Batterman
Journal:  Environ Int       Date:  2013-12-12       Impact factor: 9.621

3.  Application of physiologically based pharmacokinetic models in chemical risk assessment.

Authors:  Moiz Mumtaz; Jeffrey Fisher; Benjamin Blount; Patricia Ruiz
Journal:  J Toxicol       Date:  2012-03-19

4.  Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction.

Authors:  Jingtao Lu; Michael-Rock Goldsmith; Christopher M Grulke; Daniel T Chang; Raina D Brooks; Jeremy A Leonard; Martin B Phillips; Ethan D Hypes; Matthew J Fair; Rogelio Tornero-Velez; Jeffre Johnson; Curtis C Dary; Yu-Mei Tan
Journal:  PLoS Comput Biol       Date:  2016-02-12       Impact factor: 4.475

5.  A phased approach for assessing combined effects from multiple stressors.

Authors:  Charles A Menzie; Margaret M MacDonell; Moiz Mumtaz
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  5 in total

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