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MODELING A MIXTURE: PBPK/PD APPROACHES FOR PREDICTING CHEMICAL INTERACTIONS.
Citation:
MOSER, V. C. AND K. KRISHNAN. MODELING A MIXTURE: PBPK/PD APPROACHES FOR PREDICTING CHEMICAL INTERACTIONS. Presented at Society of Toxicology, Charlotte, NC, March 25 - 29, 2007.
Description:
Since environmental chemical exposures generally involve multiple chemicals, there are both regulatory and scientific drivers to develop methods to predict outcomes of these exposures. Even using efficient statistical and experimental designs, it is not possible to test in vivo all possible mixtures. Interactions could be based on kinetic (e.g., enzymatic, metabolic interactions) and/or dynamic (e.g., receptor interactions) factors. Recent advances in physiologically based pharmacokinetic/dynamic (PBPK/PD) modeling have confirmed the usefulness of these tools in predicting tissue levels of chemicals and biological responses. Mathematical predictions may be integrated with focused toxicology studies to improve the models, decreasing the amount of animal testing needed. With PBPK/PD modeling, the binary level interactions are interconnected within the mixture model, which can then be used to simulate the kinetics of chemicals in mixtures of higher levels of complexity. The unique ability of PBPK models to facilitate extrapolation of interactions from binary to more complex mixtures arises from the mechanistic basis of the methodology, i.e., linking all mixture components on the basis of interaction mechanisms. The methodology has been shown to be applicable to different mixtures of volatile organic chemicals. Furthermore, the modeling work with organophosphates illustrates how to address interactions in mixtures based on kinetic as well as dynamic factors. Progress in estimating interaction thresholds and employing different approaches for designing complex mixtures (e.g., chemical lumping) have extended the predictability of these models. Thus, the PBPK/PD modeling approach can be used in a range of experimental situations to predict the magnitude of chemical interactions and to assist in determining mechanisms for the interactions. Such information is critical to enhance the scientific basis of extrapolations (species, route, dose) essential for chemical mixture risk assessments.
This is an abstract of a proposed presentation and does not necessarily reflect US EPA policy.