Citation:

HILAL, S. H., L. A. CARREIRA, T. WHITESIDE, AND A. N. SARAVANARAJ. CALCULATION OF PHYSICOCHEMICAL PROPERTIES FOR ENVIRONMENTAL MODELING. Presented at American Chemical Society Meeting, San Francisco, CA, September 10 - 14, 2006.

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:

Recent trends in environmental regulatory strategies dictate that EPA will rely heavily on predictive modeling to carry out the increasingly complex array of exposure and risk assessments necessary to develop scientifically defensible regulations. In response to this need, researchers at ERD-Athens and University of Georgia have developed a predictive modeling system SPARC (SPARC Performs Automated Reasoning in Chemistry) that calculates a large number of physical and chemical properties from molecular structure across all classes of organic chemicals. SPARC execution involves the classification of molecular structures and the selection and execution of appropriate mechanistic models, such as induction, resonance, and field effects to quantify reactivity. The basic mechanistic models in SPARC were designed and parameterized to be portable to any type of chemistry or organic chemical structure. This expanded prediction capability allows one to choose, for exhaustive validation, the reaction parameters for which large and reliable data sets exist. Resonance models were developed/calibrated on more than 5000 light absorption spectra, whereas electrostatic interaction models were developed using more than 4500 ionization pKas in water. Solvation models (i.e. dispersion, induction, etc) have been developed using more than 10000 physical property data points on properties such as vapor pressure, boiling point, solubility, activity coefficient, Henry's constant, GC retention times, Kow, etc. At the present time, SPARC predicts chemical properties such as gas phase electron affinity, ionization pKa, ester hydrolysis rate constant, heat of formation, chemical reduction potential and many other physical properties strictly from molecular structure.

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