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MODELING CHEMICAL FATE AND METABOLISM FOR COMPUTATIONAL TOXICOLOGY
Citation:
Weber, E J., T W. Collette, W J. Jones, J F. Kenneke, C S. Mazur, L A. Suarez, C T. Stevens, J W. Washington, K Wolfe, N L. Wolfe, G W. Bailey, AND R S. Parmar. MODELING CHEMICAL FATE AND METABOLISM FOR COMPUTATIONAL TOXICOLOGY. Presented at Science Forum 2003, Washington, DC, May 5-7, 2003.
Impact/Purpose:
This task is divided into four major research areas: (1) Development of computational tools and databases for screening-level modeling of the environmental fate of organic chemicals; (2) Metabolism of xenobiotics: Enhancing the development of a metabolic simulator; (3) Metabonomics: The use of advanced analytical tools to identify toxicity pathways; and (4) Software infrastructure to support development and application of transformation/metabolic simulators.
For many chemicals, multiple transformation/metabolic pathways can exist. Consequently, transformation/metabolic simulators must utilize transformation rate data for prioritization of competing pathways. The prioritization process thus requires the integration of reliable rate data. When this data is absent, it is necessary to generate a database with metabolic and transformation rate constants based on: (1) experimentally measured values, including those requiring the use of advanced analytical techniques for measuring metabolic rate constants in vivo and in vitro; (2) rate constants derived from SPARC and mechanistic-based QSAR models; and (3) data mined from the literature and Program Office CBI. A long-term goal of this project is to build this database. This information will be used to enhance the predictive capabilities of the transformation/metabolic simulators. As indicated previously, exposure genomics, which provide early signs of chemical exposure based on changes in gene expression, will be used to guide chemical fate and metabolism studies. The incorporation of exposure genomics into fate studies will provide information concerning (1) the minimal concentrations at which biological events occur; and (2) the identification of biologically relevant chemicals(s) in mixtures.
The capability of categorizing chemicals and their metabolites based on toxicity pathway is imperative to the success of the CompTox Research Program. Metabonomics, which is the multi-parametric measurement of metabolites in living systems due to physiological stimuli and/or genetic modification, provides such a capability. The application of metabonomics to toxicity testing involves the elucidation of changes in metabolic patterns associated with chemical toxicity based on the measurement of component profiles in biofluids, and enables the generation of spectral profiles for a wide range of endogenous metabolites. Metabolic profiles can provide a measure of the real outcome of potential changes as the result of xenobiotic exposure.
Description:
The goal of ORD's Computational Toxicology initiative is to develop the science for EPA to prioritize toxicity-testing requirements for chemicals subject to regulation. Many toxic effects, however, result from metabolism of parent chemicals to form metabolites that are much more toxic than the parent. Consequently, an accurate computerized simulator of metabolism is essential for meeting the objectives of the Computational Toxicology initiative. Because the liver is the primary organ for chemical metabolism, initial efforts will focus on the development of a metabolic simulator describing liver metabolism (i.e., a virtual liver). The primary goal of this research is to develop a computational system that will predict and prioritize metabolic pathways for the liver metabolism of organic chemicals.
The metabolic simulator must allow for prioritization of many competing metabolic pathways for parent chemicals. The prioritization process requires the integration of reliable rate data. When this data is absent, it is necessary to populate a database with metabolic rate constants based on: 1) experimentally measured values, 2) rate constants derived from mechanistic-based SPARC or QSAR models, and 3) advanced spectroscopic techniques (e.g., NMR) for measuring metabolic rate constants and identifying metabolites in vivo and in vitro. An initial challenge of this research is the selection of representative chemicals for study. To ensure focus on the highest priority chemicals, a workgroup specifically on metabolism will be formed. Such a workgroup will allow partners outside of ORD (e.g., OPPT, OSW and OW) an opportunity to have their priority questions answered in addition to the ORD research agenda.