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WORKSHOP REPORT: COMPUTATIONAL TOXICOLOGY: FRAMEWORK, PARTNERSHIPS, AND PROGRAM DEVELOPMENT, SEPTEMBER 29-30, 2003, RESEARCH TRIANGLE PARK, NORTH CAROLINA
Citation:
KAVLOCK, R. J., G. T. ANKLEY, T. W. COLLETTE, E. Z. FRANCIS, K. A. HAMMERSTROM, J. R. FOWLE, H. A. TILSON, G. P. TOTH, P. K. SCHMIEDER, G. D. VEITH, E. J. WEBER, D. C. WOLF, AND D. M. YOUNG. WORKSHOP REPORT: COMPUTATIONAL TOXICOLOGY: FRAMEWORK, PARTNERSHIPS, AND PROGRAM DEVELOPMENT, SEPTEMBER 29-30, 2003, RESEARCH TRIANGLE PARK, NORTH CAROLINA . REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, 19(3):265-280, (2005).
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:
Computational toxicology is a new research initiative being developed within the Office of Research and Development (ORD) of the US Environmental Protection Agency (EPA). Operationally, it is defined as the application of mathematical and computer models together with molecular chemistry and biological approaches to improve our understanding of the key toxicological issues faced by the regulatory program offices of EPA. A two-day workshop on the topic was held on September 29-30, 2003, at the EPA's Research Triangle Park campus in North Carolina. The focus of the workshop was on a proposal entitled A Framework for a Computational Toxicology Research Program in ORD (available with ancillary information at www.epa.gov/comptox), which identifies research needs to provide the basis for a focused and integrated research effort utilizing modern computing, chemistry, and molecular biology tools for developing in silico models that can predict the ecological and human health risk of potentially toxic chemicals. Traditional risk assessment of chemicals relies primarily on laboratory testing on a chemical-by-chemical basis to obtain data about adverse effects and the quantitative relationship between doses and likelihood of response. In human health risk assessment, these laboratory data are extrapolated to predict the likelihood of an adverse effect and to estimate risk to humans. The large number of chemicals in commerce for which assessments need to be made and the expense of testing limits our ability to apply standard toxicity testing methods to relatively few of the vast array of chemicals of interest and necessitates new scientific approaches to the problem. The proposal puts forth three strategic objectives of the emerging computational toxicology program in ORD to address this situation: (1) to improve linkages in the source to outcome paradigm used for risk assessment by EPA, (2) develop predictive models for hazard identification, and (3) improve quantitative risk assessment. The first objective is largely technology based, and is intended to develop the tools that will enable advances in the remaining two goals. The objectives are designed to enhance EPAs ability to prioritize and screen chemicals for toxicity for testing, and to develop accurate risk assessments more economically and efficiently. Overall, successful completion of these objectives would allow the EPA to more efficiently screen, test, and evaluate the toxicity of chemicals.