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PHARMACOKINETIC MODELS IN THE DESIGN OF BIOMONITORING PROGRAMS
Citation:
Rigas, M L., M S. Okino, AND J J. Quackenboss. PHARMACOKINETIC MODELS IN THE DESIGN OF BIOMONITORING PROGRAMS. Presented at International Society of Exposure Analysis 2002 Conference, Vancouver, Canada, August 11-15, 2002.
Impact/Purpose:
The objectives for this task are to
Compile results and important findings from NERL-sponsored children's exposure studies;
Determine which pathways produce the greatest contribution to aggregate exposure among children for specific classes of pesticides;
Identify and quantify the factors that influence pesticide exposures among children;
Develop input parameters (e.g. multimedia pesticide distributions, exposure factor data) for exposure and dose models for assessing aggregate exposures and cumulative risks;
Evaluate exposure and dose models, including algorithms for estimating route-specific exposures, against real world data; and
Identify additional data gaps for modeling aggregate exposure and dose.
Description:
Measurements of chemicals in tissues, blood, or urine can be related to health effects because they are an integrated measure of absorbed dose following exposure. However, the direct relationship between biomonitoring data and pathway-specific exposures is more tenuous. In chemical exposure studies biomonitoring is used as a surrogate for total exposure, but there is frequently debate regarding how often samples must be collected to adequately characterize exposure. Pharmacokinetic (PK) models are mathematical representations of, at minimum, absorption and clearance and can be used to estimate internal dose based on multi-route exposure. The reverse calculation to extract a unique exposure profile from biomonitoring data is not possible. However, pharmacokinetic modeling can be used to aid in the design of appropriate biomonitoring strategies for field studies. We used data from a previously published study of worker exposure to 2,4-dichlorophenoxyacetic acid (2,4-D). In this study, researchers collected 7 total urine samples per worker for several days following an occupational exposure. By evaluating the properties of a PK model we determine the optimal times to collect urine during the post-exposure period. By mathematically minimizing the uncertainty in the model output and setting urine sampling at the times of minimum uncertainty, we find that it is possible to estimate exposure sufficiently utilizing as few as 3 total urine collections. As a result, cost and participant burden can be reduced.
This work has been funded by the U.S. Environmental Protection Agency through efforts of its Office of Research and Development. It has been subjected to Agency review and approved for publication but does not necessarily reflect policy or view of the Agency.