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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

Model predictions for three volunteers from one iteration of the Markov chain and the associated measurements: (a) urine predictions and data, (b) CV predictions and data, (c) CX predictions and data.
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fig10: Model predictions for three volunteers from one iteration of the Markov chain and the associated measurements: (a) urine predictions and data, (b) CV predictions and data, (c) CX predictions and data.

Mentions: Calibration using data from (reliable) CV measurements satisfied this assumption. Model predictions, from one iteration of parameters from the MCMC algorithm and corresponding measurements for three volunteers (A, C and D), are shown in Figure 10(a). Given that the PBPK model could predict the observed biomarker profile suggests that the model adequately describes the inhalation and subsequent excretion of m-xylene from the blood.


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)

Model predictions for three volunteers from one iteration of the Markov chain and the associated measurements: (a) urine predictions and data, (b) CV predictions and data, (c) CX predictions and data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig10: Model predictions for three volunteers from one iteration of the Markov chain and the associated measurements: (a) urine predictions and data, (b) CV predictions and data, (c) CX predictions and data.
Mentions: Calibration using data from (reliable) CV measurements satisfied this assumption. Model predictions, from one iteration of parameters from the MCMC algorithm and corresponding measurements for three volunteers (A, C and D), are shown in Figure 10(a). Given that the PBPK model could predict the observed biomarker profile suggests that the model adequately describes the inhalation and subsequent excretion of m-xylene from the blood.

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