Abstract
Global sensitivity analysis (SA) was used during the development phase of a binary chemical physiologically based pharmacokinetic (PBPK) model used for the analysis of m-xylene and ethanol co-exposure in humans. SA was used to identify those parameters which had the most significant impact on variability of venous blood and exhaled m-xylene and urinary excretion of the major metabolite of m-xylene metabolism, 3-methyl hippuric acid. This analysis informed the selection of parameters for estimation/calibration by fitting to measured biological monitoring (BM) data in a Bayesian framework using Markov chain Monte Carlo (MCMC) simulation. Data generated in controlled human studies were shown to be useful for investigating the structure and quantitative outputs of PBPK models as well as the biological plausibility and variability of parameters for which measured values were not available. This approach ensured that a priori knowledge in the form of prior distributions was ascribed only to those parameters that were identified as having the greatest impact on variability. This is an efficient approach which helps reduce computational cost.
Highlights
Exposures to chemicals in occupational and non-occupational settings have been linked to an increased incidence of disease (Rappaport, 2012; Rappaport et al, 2014)
Pharmacokinetic interactions can lead to a change in tissue dose of chemicals during exposure to a mixture compared with single exposures (Krishnan and Brodeur, 1994)
In each figure Panel A shows the main effects of parameters with no inhibition, and Panel B with inhibition by ethanol
Summary
Exposures to chemicals in occupational and non-occupational settings have been linked to an increased incidence of disease (Rappaport, 2012; Rappaport et al, 2014). Understanding the relationship between exposure and disease typically requires a human health risk assessment (RA; Sahmel et al, 2010). Such an assessment must include translation of ambient exposure concentration to a relevant, biologically effective dose that may, in turn, be related to response (Bessems and Geraets, 2013; Geraets et al, 2014). Pharmacokinetic interactions can lead to a change in tissue dose of chemicals during exposure to a mixture compared with single exposures (Krishnan and Brodeur, 1994). Krishnan et al (2002) demonstrated that with the application of PBPK modeling the extrapolation of binary interactions to more complex mixtures is possible and that this may afford a basis for a quantitative chemical mixture RA methodology
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