Computational toxicology of chloroform: reverse dosimetry using Bayesian inference, Markov chain Monte Carlo simulation, and human biomonitoring data.
BACKGROUND
One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available.


OBJECTIVE
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 < or = 67 microg/L [corrected] in tap water and < or = 0.02 microg/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.
Lyons
Michael A
MA
Quantitative and Computational Toxicology Group, Colorado State University, Fort Collins, CO 80523, USA.
Yang
Raymond S H
RS
Mayeno
Arthur N
AN
Reisfeld
Brad
B
D000393:Air Pollutants / Q000032:analysis / Q000097:blood / Q000633:toxicity*; D001499:Bayes Theorem; D002725:Chloroform / Q000032:analysis / Q000097:blood / Q000633:toxicity*; D019295:Computational Biology; D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D014874:Water Pollutants, Chemical / Q000032:analysis / Q000097:blood / Q000633:toxicity*
D000393:Air Pollutants; D014874:Water Pollutants, Chemical; D002725:Chloroform