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RECONSTRUCTING POPULATION EXPOSURES FROM DOSE BIOMARKERS: INHALATION OF TRICHLOROETHYLENE (TCE) AS A CASE STUDY
Citation:
Sohn, M. D., T. E. McKone, AND J N. Blancato. RECONSTRUCTING POPULATION EXPOSURES FROM DOSE BIOMARKERS: INHALATION OF TRICHLOROETHYLENE (TCE) AS A CASE STUDY. JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY 14(3):204-213, (2004).
Impact/Purpose:
Research will be conducted to develop and apply integrated microenvironmental, and physiologically-based pharmacokinetic (PBPK) exposure-dose models and methods (that account for all media, routes, pathways and endpoints). Specific efforts will focus on the following areas:
1) Develop the Exposure Related Dose Estimating Model (ERDEM) System.
Includes: Updating the subsystems and compartments of the ERDEM models with those features needed for modeling chemicals of interest to risk assessors;
Designing and implementing the graphical user interface for added features.
Refining the exposure interface to handle various sources of exposure information;
Providing tools for post processing as well as for uncertainty and variability analyses;
Research on numerical and symbolic mathematical/statistical solution methods and computational algorithms/software for deterministic and stochastic systems analysis.
2) Apply ERDEM and other quantitative models to understand pharmacokinetics (PK) and significantly reduce the uncertainty in the dosimetry of specific compounds of regulatory interest.
Examples of the applications are:
exposure of children to pesticides
study design
route-to-route extrapolation
species extrapolation
experimental data analysis
relationship between parametric uncertainty and the distribution of model results
validity of scaling methods within species
validity of scaling methods from one species to another species
reduction of uncertainty factors for risk assessment
Description:
Physiologically based pharmacokinetic (PBPK) modeling is a well-established toxicological tool designed to relate exposure to a target tissue dose. The emergence of federal and state programs for environmental health tracking and the availability of exposure monitoring through biomarkers creates the opportunity to apply PBPK models to estimate exposures to environmental contaminants from urine, blood, and tissue samples. However, reconstructing exposures for large populations is complicated by often having too few biomarker samples, large uncertainties about exposures, and large interindividual variability. In this paper, we use an illustrative case study to identify some of these difficulties and for a process for confronting them by reconstructing population-scale exposures using Bayesian inference. The application consists of interpreting biomarker data from eight adult males with controlled chamber. We successfully reconstructed the exposure scenarios for both subgroups although the reconstruction of one subgroup is different than what is believed to be the true experimental conditions. We were however unable to predict with high certainty the concentration of TCE in air.
This work was supported in part by the US Environmental Protection Agency National Exposure Research Laboratory through Interagency Agreement #DW-988-38190-01-0 and carried out at Lawrence Berkley National Laboratory through the US Department of Energy under contract Grant No. DE-AC03-76SF00098. This work is also supported in part by the Health Tracking Center of Excellence at the University of California Berkley, and funded by interagency agreement No. U50CCUT922409-01 with US Centers for Disease Control.