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Biomarker Utility Analysis Using an Exposure-PBPK/PD Model: A Carbaryl Case Study
Citation:
Phillips, M., M. Yoon, B. Young, AND C. Tan. Biomarker Utility Analysis Using an Exposure-PBPK/PD Model: A Carbaryl Case Study. 2015 Annual Meeting of the Society of Toxicology, San Diego, CA, March 22 - 26, 2015.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA 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:
There are two common biomarkers: markers of exposure and markers of health effects. The strength of the correlation between exposure or effect and a biomarker measurement determines the utility of a biomarker for assessing exposures or risks. In the current study, a linked exposure-pharmacokinetic/pharmacodynamic (PBPK/PD) model of carbaryl was used to demonstrate how computational modeling can provide insights into biomarker selection. The Cumulative and Aggregate Risk Evaluation System (CARES) was an exposure model that was used to generate exposure profiles, including magnitude and timing, for inputs to a PBPK/PD model. The PBPK/PD model predicts blood and brain concentrations of the parent chemical and urine concentrations of its principal metabolite, 1-naphthol (1-N), as biomarkers of exposure. In addition, acetylcholinesterase (AChE) inhibition in red blood cells and brain tissue were simulated as biomarkers of effects. The correlation of these simulated biomarker concentrations and intake doses showed that urinary 1-N is the best biomarker of exposure of the candidates considered in this study, and that it is correlated with the dose averaged over the last two days of the simulation. The best biomarker of biological change was red blood cell AChE inhibition. This computational approach is applicable to a wide variety of chemicals and facilitates quantitative analysis of biomarker utility.
