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DESIGN AND PERFORMANCE OF A XENOBIOTIC METABOLISM DATABASE MANAGER FOR METABOLIC SIMULATOR ENHANCEMENT AND CHEMICAL RISK ANALYSIS
MEKENYAN, O., W. J. JONES, R. C. KOLANCZYK, AND P. K. SCHMIEDER. DESIGN AND PERFORMANCE OF A XENOBIOTIC METABOLISM DATABASE MANAGER FOR METABOLIC SIMULATOR ENHANCEMENT AND CHEMICAL RISK ANALYSIS. Presented at SETAC Europe 17th Annual Meeting, Porto, PORTUGAL, May 20 - 24, 2007.
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.
A major uncertainty that has long been recognized in evaluating chemical toxicity is accounting for metabolic activation of chemicals resulting in increased toxicity. In silico approaches to predict chemical metabolism and to subsequently screen and prioritize chemicals for risk assessments has been a goal of regulatory agencies for years. Research is underway to develop the capability for reliably forecasting the metabolism of xenobiotic chemicals (TIssue MEtabolism Simulator [TIMES] software) and to allow prediction of the most likely chemical metabolites to be formed. This information, when interfaced with toxic effect models, allows prediction of parent chemical toxic potential and of chemical metabolites of equal or greater toxicity than the parent chemical. The performance of a metabolic simulator can be enhanced for a particular toxic effect by collecting relevant measured chemical metabolism maps (e.g., from published literature and from specifically designed metabolism experiments). Newly acquired maps for relevant enzymatic transformations used to re-train the metabolism simulator improve simulator reliability. For example, chemicals forecasted to be metabolically transformed to estrogen receptor (ER) active forms are assessed in metabolically-competent liver slices from male fish that produce the metabolites and the subsequent effect, i.e., ER-mediated vitellogenin production. Metabolism data from these studies are used to improve metabolic simulations and to aid in the prioritization of chemicals for testing that have the potential to be bioactivated to ER active species. Metabolism data for training and improvement of the simulator are stored and accessed using the database manager software, MetaPath, capable of ubiquitous sub-structure search functions, depiction and comparison of metabolic maps, and providing access to metabolic maps and associated data. MetaPath software allows automated upload of metabolic maps and metadata from specifically-designed XML-coded Microsoft Word data templates. The metabolism database assembled in MetaPath may also be used as a stand-alone dataset by risk assessors to increase efficiency of metabolism data access allowing the risk assessor to perform searches for specific compounds and toxicophores and identify metabolism commonalities and differences across chemical classes, species, and dose-groups.