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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) CV, most influential, (b) CV, fixed, measured, and remaining most influential, (c) CV, most influential, using unreliable data.
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Related In: Results  -  Collection


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fig8: 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) CV, most influential, (b) CV, fixed, measured, and remaining most influential, (c) CV, most influential, using unreliable data.

Mentions: The best estimates of exposure λ were obtained using the reliable CV data from three volunteers (Data from the fourth volunteer were not used due to problems with taking blood samples.) although the 95% interval for λ was widest using the reliable CV data, which can be explained by the smaller number of volunteers and measurements (compared with data from CXPPM and Curine). The central estimate of λ was close to the target exposure of 37–39 ppm (Figure 8(a) and Table 5, reliable data, most influential), and model predictions were consistent with the observed biomarker profile (discussed later on in results). There was a small improvement in the central estimate of λ after making use of measured parameters x (comprising of QPC, Pba, and body weight) for each subject; the 95% interval for λ (Figure 8(b) and Table 5, reliable data, most influential including measured parameters) was shifted upward although the width of the interval was unchanged.


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) CV, most influential, (b) CV, fixed, measured, and remaining most influential, (c) CV, most influential, using unreliable data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: 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) CV, most influential, (b) CV, fixed, measured, and remaining most influential, (c) CV, most influential, using unreliable data.
Mentions: The best estimates of exposure λ were obtained using the reliable CV data from three volunteers (Data from the fourth volunteer were not used due to problems with taking blood samples.) although the 95% interval for λ was widest using the reliable CV data, which can be explained by the smaller number of volunteers and measurements (compared with data from CXPPM and Curine). The central estimate of λ was close to the target exposure of 37–39 ppm (Figure 8(a) and Table 5, reliable data, most influential), and model predictions were consistent with the observed biomarker profile (discussed later on in results). There was a small improvement in the central estimate of λ after making use of measured parameters x (comprising of QPC, Pba, and body weight) for each subject; the 95% interval for λ (Figure 8(b) and Table 5, reliable data, most influential including measured parameters) was shifted upward although the width of the interval was unchanged.

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