| Computational Toxicology of Chloroform: Reverse Dosimetry Using Bayesian Inference, Markov Chain Monte Carlo Simulation, and Human Biomonitoring Data Michael A. Lyons,1,2,3 Raymond S.H. Yang,1,2 Arthur N. Mayeno,1,2 and Brad Reisfeld1,2,3 1Quantitative and Computational Toxicology Group, 2Department of Environmental and Radiological Health Sciences, and 3Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, USA Abstract
Background: One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available. Objectives: We demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III) , and for which no corresponding exposure data were collected. Methods: We used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis. Results: Posterior distributions for exposure indicate that 95% of the population represented by the NHANES III data had likely chloroform exposures ≤ 67 µg/L in tap water and ≤ 0.02 µg/L in ambient household air. Conclusions: Our results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure–health evaluation–risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data. Key words: Bayesian, biomonitoring, chloroform, Markov chain Monte Carlo, MC, MCMC, Monte Carlo, PBPK, reverse dosimetry. Environ Health Perspect 116:1040–1046 (2008) . doi:10.1289/ehp.11079 available via http://dx.doi.org/ [Online 26 April 2008]
Address correspondence to B. Reisfeld, 1370 Campus Delivery, Colorado State University, Fort Collins, CO 80523 USA. Telephone: (970) 491-1019. Fax: (970) 491-7369. E-mail: brad.reisfeld@colostate.edu We thank F. Bois, D. Marino, and T. Covington for their advice and assistance with MCSim and Markov chain Monte Carlo modeling. This study was supported by the National Institute of Environmental Health Sciences through grants K25 ES11146 and K25 ES012909 and the U.S. Environmental Protection Agency through STAR Research Assistance Agreement R833451. The authors declare they have no competing financial interests. Received 16 November 2007 ; accepted 24 April 2008.
Correction In the abstract of the original manuscript published online, the units for chloroform exposures in tap water were presented as milligrams per liter instead of micrograms per liter. They have been corrected here.
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