The long-term objective of the research described in this application is to develop analytical,computational and database tools that can be used to rapidly identify the chemical structure of compounds in human biofluids. These analytical and computational tools will be useful for: a) understanding disease mechanisms, b) enhancing the speed of disease diagnosis, and, c) enhancing the accuracy of disease prognosis. Our novel approach is to develop algorithms that predict physical/chemical properties of compounds contained in the PubChem database. The physical/chemical properties chosen are those that can be experimentally measured for any unknown compound by HPLC-mass spectrometry. Compounds in the PubChem database whose predicted properties most closely match experimental properties are returned as the most likely candidates for the unknown. We propose to then validate this system using an in vivo model of multiple sclerosis. Our preliminary data describe the validity of this approach using models developed for predicting retention indices, precursor ion survival curves and collision induced dissociation fragmentation spectra. Based on these promising preliminary data, we propose the following specific aims for this application: 1. Develop computational tools that predict physical/chemical properties for compounds in the PubChem (or similar) chemical database, 2. Integrate these computational tools into a software package (MolFind) that will allow rapid structural identification of unknown compounds in complex biofluids, and, 3. Validate the use of MolFind for global metabonomics using an animal model of multiple sclerosis. By facilitating the rapid structural identification of chemical compounds in clinically relevant biofluids, the tools described here will greatly enhance the ability of metabonomics studies to complement and synergize other areas of biomedical research and ultimately improve human health care.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Brazhnik, Paul
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University of Connecticut
Schools of Pharmacy
United States
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Menikarachchi, Lochana C; Dubey, Ritvik; Hill, Dennis W et al. (2016) Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics. Metabolites 6:
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Hamdalla, Mai A; Mandoiu, Ion I; Hill, Dennis W et al. (2013) BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. J Chem Inf Model 53:601-12

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