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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM087714-02
Application #
8064779
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2010-06-01
Project End
2014-05-31
Budget Start
2011-06-01
Budget End
2012-05-31
Support Year
2
Fiscal Year
2011
Total Cost
$296,858
Indirect Cost
Name
University of Connecticut
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
614209054
City
Storrs-Mansfield
State
CT
Country
United States
Zip Code
06269
Menikarachchi, Lochana C; Dubey, Ritvik; Hill, Dennis W et al. (2016) Development of Database Assisted Structure Identification (DASI) Methods for Nontargeted Metabolomics. Metabolites 6:
Hall, L Mark; Hill, Dennis W; Menikarachchi, Lochana C et al. (2015) Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data. Bioanalysis 7:939-55
Paglia, Giuseppe; Angel, Peggi; Williams, Jonathan P et al. (2015) Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification. Anal Chem 87:1137-44
Hamdalla, Mai A; Rajasekaran, Sanguthevar; Grant, David F et al. (2015) Metabolic pathway predictions for metabolomics: a molecular structure matching approach. J Chem Inf Model 55:709-18
Dubey, Ritvik; Hill, Dennis W; Lai, Steven et al. (2015) Correction of precursor and product ion relative abundances in order to standardize CID spectra and improve Ecom50 accuracy for non-targeted metabolomics. Metabolomics 11:753-763
Paglia, Giuseppe; Williams, Jonathan P; Menikarachchi, Lochana et al. (2014) Ion mobility derived collision cross sections to support metabolomics applications. Anal Chem 86:3985-93
Hall, L Mark; Hill, Dennis W; Hall, Lowell H et al. (2014) Development of HPLC Retention Index QSAR Models for Nontargeted Metabolomics. Adv Chromatogr 51:241-79
Hall, Lowell H; Hall, L Mark; Hill, Dennis W et al. (2014) Development of a two-step indirect method for modeling Ecom50. Curr Comput Aided Drug Des 10:374-82
Menikarachchi, Lochana C; Hamdalla, Mai A; Hill, Dennis W et al. (2013) Chemical structure identification in metabolomics: computational modeling of experimental features. Comput Struct Biotechnol J 5:e201302005
Menikarachchi, Lochana C; Hill, Dennis W; Hamdalla, Mai A et al. (2013) In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. J Chem Inf Model 53:2483-92

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