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Reconstruction of Exposure to m-Xylene from Human Biomonitoring Data Using PBPK Modelling, Bayesian Inference, and Markov Chain Monte Carlo Simulation.

McNally K, Cotton R, Cocker J, Jones K, Bartels M, Rick D, Price P, Loizou G - J Toxicol (2012)

Bottom Line: There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals.We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene.We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures.

View Article: PubMed Central - PubMed

Affiliation: Health and Safety Laboratory, Harpur Hill, Buxton, Derbyshire SK17 9JN, UK.

ABSTRACT
There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure.

No MeSH data available.


Related in: MedlinePlus

Comparison of estimated posterior distributions for 4-hour inhalation exposure to m-xylene. Posterior distributions were estimated by updating the most influential parameters or by fixing the measured parameters and updating the remaining most influential: (a) CXPPM, full parameter set, (b) CXPPM, most influential.
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fig9: Comparison of estimated posterior distributions for 4-hour inhalation exposure to m-xylene. Posterior distributions were estimated by updating the most influential parameters or by fixing the measured parameters and updating the remaining most influential: (a) CXPPM, full parameter set, (b) CXPPM, most influential.

Mentions: Exposure reconstruction using the entire CXPPM dataset resulted in a posterior distribution for λ that was concentrated on a range well below the “true” value (16.0, 15.8, 11.4, 21.4, mean and median, 2.5% and 97.5%, resp.). The simulation was repeated after excluding the measurements made at 4 hours 1 minute (i.e., one minute after exiting the exposure facility). Exclusion of these measurements is justified because, during exposure and immediately after leaving the source of exposure, the concentration of solvent in breath reflects the portion of the inhaled concentration that has not been absorbed, therefore, is not representative of circulating blood solvent levels. However, the posterior distribution for λ was not much improved after excluding the first measurement (Figure 9(a)) and Table 5, most influential) although still well below the target exposure. The model that included measured anthropometric parameters had an almost identical central estimate of exposure (Figure 9(b) and Table 5, most influential, including measured parameters, x). The error σ was similarly large for both of these models indicating that, even at the estimated exposures, the fit to the measurements (discussed in Section 3.6) was poor.


Reconstruction of Exposure to m-Xylene from Human Biomonitoring Data Using PBPK Modelling, Bayesian Inference, and Markov Chain Monte Carlo Simulation.

McNally K, Cotton R, Cocker J, Jones K, Bartels M, Rick D, Price P, Loizou G - J Toxicol (2012)

Comparison of estimated posterior distributions for 4-hour inhalation exposure to m-xylene. Posterior distributions were estimated by updating the most influential parameters or by fixing the measured parameters and updating the remaining most influential: (a) CXPPM, full parameter set, (b) CXPPM, most influential.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3376947&req=5

fig9: Comparison of estimated posterior distributions for 4-hour inhalation exposure to m-xylene. Posterior distributions were estimated by updating the most influential parameters or by fixing the measured parameters and updating the remaining most influential: (a) CXPPM, full parameter set, (b) CXPPM, most influential.
Mentions: Exposure reconstruction using the entire CXPPM dataset resulted in a posterior distribution for λ that was concentrated on a range well below the “true” value (16.0, 15.8, 11.4, 21.4, mean and median, 2.5% and 97.5%, resp.). The simulation was repeated after excluding the measurements made at 4 hours 1 minute (i.e., one minute after exiting the exposure facility). Exclusion of these measurements is justified because, during exposure and immediately after leaving the source of exposure, the concentration of solvent in breath reflects the portion of the inhaled concentration that has not been absorbed, therefore, is not representative of circulating blood solvent levels. However, the posterior distribution for λ was not much improved after excluding the first measurement (Figure 9(a)) and Table 5, most influential) although still well below the target exposure. The model that included measured anthropometric parameters had an almost identical central estimate of exposure (Figure 9(b) and Table 5, most influential, including measured parameters, x). The error σ was similarly large for both of these models indicating that, even at the estimated exposures, the fit to the measurements (discussed in Section 3.6) was poor.

Bottom Line: There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals.We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene.We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures.

View Article: PubMed Central - PubMed

Affiliation: Health and Safety Laboratory, Harpur Hill, Buxton, Derbyshire SK17 9JN, UK.

ABSTRACT
There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure.

No MeSH data available.


Related in: MedlinePlus