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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

Lowry plot of the eFAST quantitative measure. The total effect of a parameter STi comprised the main effect Si (black bar) and any interactions with other parameters (grey bar) given as a proportion of variance. The ribbon, representing variance due to parameter interactions, is bounded by the cumulative sum of main effects (lower bold line) and the minimum of the cumulative sum of the total effects (upper bold line), (a) CV at 3 hours, (b) CXPPM at 3 hours, (c) Curine at 5 hours.
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fig6: Lowry plot of the eFAST quantitative measure. The total effect of a parameter STi comprised the main effect Si (black bar) and any interactions with other parameters (grey bar) given as a proportion of variance. The ribbon, representing variance due to parameter interactions, is bounded by the cumulative sum of main effects (lower bold line) and the minimum of the cumulative sum of the total effects (upper bold line), (a) CV at 3 hours, (b) CXPPM at 3 hours, (c) Curine at 5 hours.

Mentions: Figures 6(a), 6(b), and 6(c) are typical Lowry plots for CV and CXPPM at 3 hours and Curine at 5 hours used to select the most influential parameters. The total effect of a parameter STi comprised the main effect Si (black bar) and any interactions with other parameters (grey bar) given as a proportion of variance. The ribbon, representing variance due to parameter interactions, is bounded below by the cumulative sum of main effects (lower bold line) and above by the minimum of the cumulative sum of the total effects and one minus the sum of the main effects that were not included (upper bold line). The most influential parameters were selected by reading across from the 95% variance point on the y-axis to the upper bold line and then down to the x-axis. All parameters to the left of this point were selected and used in the dose reconstruction. The most influential parameters at the latter time points of five hours for CV and CXPPM and eight hours Curine were selected in the same way (Lowry plots not shown). In Table 4 the most influential parameters at the latter time points are listed alongside those from the earlier time points. The parameters in bold are those that only become influential at the latter time points. These were added to the parameters that were influential at the earlier time points in order to ensure that prior distributions were assigned to all influential parameters across the entire time period of interest. The measured parameters listed in Table 1 are also listed in Table 3 to indicate when they were used in each simulation and are italicised in Table 4 to indicate when they contributed to variance of dose metric.


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)

Lowry plot of the eFAST quantitative measure. The total effect of a parameter STi comprised the main effect Si (black bar) and any interactions with other parameters (grey bar) given as a proportion of variance. The ribbon, representing variance due to parameter interactions, is bounded by the cumulative sum of main effects (lower bold line) and the minimum of the cumulative sum of the total effects (upper bold line), (a) CV at 3 hours, (b) CXPPM at 3 hours, (c) Curine at 5 hours.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Lowry plot of the eFAST quantitative measure. The total effect of a parameter STi comprised the main effect Si (black bar) and any interactions with other parameters (grey bar) given as a proportion of variance. The ribbon, representing variance due to parameter interactions, is bounded by the cumulative sum of main effects (lower bold line) and the minimum of the cumulative sum of the total effects (upper bold line), (a) CV at 3 hours, (b) CXPPM at 3 hours, (c) Curine at 5 hours.
Mentions: Figures 6(a), 6(b), and 6(c) are typical Lowry plots for CV and CXPPM at 3 hours and Curine at 5 hours used to select the most influential parameters. The total effect of a parameter STi comprised the main effect Si (black bar) and any interactions with other parameters (grey bar) given as a proportion of variance. The ribbon, representing variance due to parameter interactions, is bounded below by the cumulative sum of main effects (lower bold line) and above by the minimum of the cumulative sum of the total effects and one minus the sum of the main effects that were not included (upper bold line). The most influential parameters were selected by reading across from the 95% variance point on the y-axis to the upper bold line and then down to the x-axis. All parameters to the left of this point were selected and used in the dose reconstruction. The most influential parameters at the latter time points of five hours for CV and CXPPM and eight hours Curine were selected in the same way (Lowry plots not shown). In Table 4 the most influential parameters at the latter time points are listed alongside those from the earlier time points. The parameters in bold are those that only become influential at the latter time points. These were added to the parameters that were influential at the earlier time points in order to ensure that prior distributions were assigned to all influential parameters across the entire time period of interest. The measured parameters listed in Table 1 are also listed in Table 3 to indicate when they were used in each simulation and are italicised in Table 4 to indicate when they contributed to variance of dose metric.

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