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An Integrated Computational Framework for the Interpretation of Organophosphorus Pesticide BiomarkersEPA Grant Number: R833451
Title: An Integrated Computational Framework for the Interpretation of Organophosphorus Pesticide Biomarkers
Investigators: Reisfeld, Brad , Chambers, Janice E. , Lyons, Michael A. , Mayeno, Arthur N. , Yang, Raymond S.H.
Institution: Colorado State University , Mississippi State University - Main Campus
EPA Project Officer: Pascual, Pasky
Project Period: October 1, 2007 through September 30, 2010
Project Amount: $748,582
RFA: Interpretation of Biomarkers Using Physiologically Based Pharmacokinetic Modeling (2006) RFA Text | Recipients Lists
Research Category: Health , Health Effects
Although various biomarkers have been used to assess exposure to and poisoning from organophosphorus (OP) pesticides/insecticides, the complexity of OP absorption, distribution, metabolism, and elimination, especially for mixtures of these chemicals, warrants integration of computational modeling tools with the biomarker data for more accurate quantitation and assessment of actual whole body exposures and target tissue dosimetry. The objective of this project is to create a computer-assisted framework to aid in the identification, characterization, and understanding of biomarkers resulting from human exposure to mixtures of OP insecticides, using chlorpyrifos and diazinon as the initial test compounds. The framework will use existing human biomarker data, along with information about population and exposure variability and uncertainty, to reconstruct absorbed dose and exposure scenarios, as well as to predict levels of biomarkers resulting from known exposures to one or multiple OP insecticides.
We shall utilize several pre-existing and proven software tools and models to construct the framework, combined with the innovative application of Bayesian statistical analysis. Specifically, for the simulation core, we shall employ MCSim: a software package for designing and performing Monte Carlo stochastic simulations and Bayesian inference through Markov Chain Monte Carlo algorithms. For physiologically-based pharmacokinetic models of chlorpyrifos and diazinon, we shall build upon existing validated models, enhancing the metabolism description to include pharmacokinetic and pharmacodynamic interactions among the OP insecticides and their metabolites. For tool development and validation, we shall exploit the large body of available data (e.g., NHANES database), and perform targeted experimentation in animals. This strategy allows the integration of computational and data elements into a coherent and unified framework, and will facilitate the application of the tool in novel and useful ways.
We anticipate that the software tool developed, and targeted data acquired, will be useful in the interpretation of biomarkers indicative of exposure to OP insecticide mixtures, including the effects of population and dose variability and uncertainty. Therefore, we expect that the outcome of this effort will be a more accurate approach to the assessment of exposure from these mixtures in the cumulative risk assessment process. As a result, the overall benefit of this project will be an improvement in the ability of the U.S. Environmental Protection Agency to protect public health.