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Reconstructing Exposures from Biomarkers using Exposure-Pharmacokinetic Modeling - A Case Study with Carbaryl
Citation:
Brown, K., M. Phillips, C. Grulke, M. Yoon, B. Young, R. McDougall, J. Leonard, J. Lu, W. Lefew, AND C. Tan. Reconstructing Exposures from Biomarkers using Exposure-Pharmacokinetic Modeling - A Case Study with Carbaryl. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, 73(3):689-698, (2015).
Impact/Purpose:
The National Exposure Research Laboratory’s (NERL’s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA’s mission to protect human health and the environment. HEASD’s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA’s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Sources of uncertainty involved in exposure reconstruction for a short half-life chemical, carbaryl, were characterized using the Cumulative and Aggregate Risk Evaluation System (CARES), an exposure model, and a human physiologically based pharmacokinetic (PBPK) model. CARES was used to generate time-concentration profiles for 500 virtual individuals orally exposed to carbaryl. These exposure profiles were used as inputs to the PBPK model to predict urinary biomarker concentrations. These matching dietary intake levels and biomarker concentrations allow us to (1) compare three reverse dosimetry approaches based on their ability to predict the central tendency of the intake dose distribution; and (2) identify parameters necessary for a more accurate exposure reconstruction. This study illustrated the trade-offs between using non-iterative reverse dosimetry methods such as Exposure Conversion Factor (fast, less precise) and iterative methods such as Markov Chain Monte Carlo (slow, more precise). This study suggested including urine flow rate and elapsed time between last dose and urine sampling as part of the biomarker sampling collection for better interpretation of urinary biomarker data of short biological half-life chemicals. If critical data gaps can be resolved, exposure reconstruction methods can be utilized to better predict population-level intake doses from large biomonitoring studies.
URLs/Downloads:
REVERSE DOSIMETRY REVISION 10282015.PDF (PDF, NA pp, 320.142 KB, about PDF)Regulatory Toxicology and Pharmacology
