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UNCERTAINTY ANALYSIS OF TCE USING THE DOSE EXPOSURE ESTIMATING MODEL (DEEM) IN ACSL
Citation:
Tsang, A. M., R N. Brown, F. W. Power, J N. Blancato, AND C. S. Scott. UNCERTAINTY ANALYSIS OF TCE USING THE DOSE EXPOSURE ESTIMATING MODEL (DEEM) IN ACSL. Presented at Annual Meeting of Society of Toxicology, Philadelphia, PA, March 19-23, 2000.
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:
The ACSL-based Dose Exposure Estimating Model(DEEM) under development by EPA is used to perform art uncertainty analysis of a physiologically based pharmacokinetic (PSPK) model of trichloroethylene (TCE). This model involves several circulating metabolites such as trichloroacetic acid (TCA) and trichloroethanol (TCOH) whose exact clearance parameter values are not always well known. A combined sensitivity and Monte Carlo analysis is presented. Some base model parameter values and associated experimental measurements were supplied by Fisher, et al. For imprecisely known parameters, sensitivity analyses use reasonable assumptions about parameter central tendency and natural variation. The natural variation is used in model runs to identify the most sensitive model parameters under various linear and nonlinear model concentration conditions. Using various sets of sensitive parameters, mixed normal and non-normal statistical distributional assumptions are used in Monte Carlo analyses under the assumption of independently varying parameters. Adjustments are made for correlated parameters. Central tendency and upper and lower percentile bounds are provided for time-dependent concentration curves of TCE, TCA, and TCOH, along with similar results for summary dose measures such as steady-state concentrations or area under the concentration curves. Experimental concentration measurements are plotted against predicted model variability.
The U.S. Environmental Protection Agency (EPA), through its Office of Research and Development (ORD), funded this research and approved this abstract as a basis for an oral presentation. The actual presentation has riot been peer reviewed by the EPA. Mention of trade names or commercial products does riot constitute endorsement or recommendation for use.