Use of a Physiologically Based Pharmacokinetic Model to Identify Exposures Consistent With Human Biomonitoring Data for Chloroform

TLDR

Biomonitoring data quantify chemicals or metabolites in biological matrices, providing evidence of human exposure to environmental chemicals. This study uses a combined PBPK–exposure model for showering to estimate chloroform intake concentrations from measured blood and exhaled breath levels, aiming to clarify the link between biomonitoring data and environmental exposure. The model was validated against three controlled showering studies, then used to simulate a residential exposure regimen combining inhalation, ingestion, and dermal routes, followed by sensitivity and Monte Carlo analyses and reverse dosimetry to estimate exposure distributions consistent with observed concentrations. Simulations revealed that inhalation and dermal exposure contribute substantially to total chloroform exposure, and that variability in exposure conditions—especially shower duration—drives more variation in blood and breath concentrations than pharmacokinetic factors.

Abstract

Biomonitoring data provide evidence of human exposure to environmental chemicals by quantifying the chemical or its metabolite in a biological matrix. To better understand the correlation between biomonitoring data and environmental exposure, physiologically based pharmacokinetic (PBPK) modeling can be of use. The objective of this study was to use a combined PBPK model with an exposure model for showering to estimate the intake concentrations of chloroform based on measured blood and exhaled breath concentrations of chloroform. First, the predictive ability of the combined model was evaluated with three published studies describing exhaled breath and blood concentrations in people exposed to chloroform under controlled showering events. Following that, a plausible exposure regimen was defined combining inhalation, ingestion, and dermal exposures associated with residential use of water containing typical concentrations of chloroform to simulate blood and exhaled breath concentrations of chloroform. Simulation results showed that inhalation and dermal exposure could contribute substantially to total chloroform exposure. Next, sensitivity analysis and Monte Carlo analysis were performed to investigate the sources of variability in model output. The variability in exposure conditions (e.g., shower duration) was shown to contribute more than the variability in pharmacokinetics (e.g., body weight) to the predicted variability in blood and exhaled breath concentrations of chloroform. Lastly, the model was used in a reverse dosimetry approach to estimate distributions of exposure consistent with concentrations of chloroform measured in human blood and exhaled breath.