Nuclear magnetic pattern recognition

Metabolomics studies the entire metabolism of an organism. It is possible to consider characterising the complex pattern of cellular proteins and metabolites that are excreted in urine. Pattern recognition techniques of nuclear magnetic resonance spectra have been applied to determine the dose-response using certain classical liver and kidney toxicants (Robertson et al, 2000). This could well provide a signature of the functional state of the kidney, and perturbations in the pattern as a result of exposure to a chemical could be observed. But first it would be necessary to understand how compounds with known effects on the kidney affect these processes. 

ROBERTSON D G, REILY M D, SIGLER R E et al, (2000) Metabonomics Evaluation of nuclear magnetic resonance (NMR) and pattern recognition technology for rapid in vivo screening of liver and kidney toxicants. Toxicol Sci. 57 326-37. 

Gartland KP, Beddell CR, Lindon JC, Nicholson JK. Application of pattern recognition methods to the analysis and classification of toxicological data derived from proton nuclear magnetic resonance spectroscopy of urine. Mol Pharmacol 1991 39 629 642. 

Holmes E, Foxall PJ, Nicholson JK, Neild GH, Brown SM, Beddell CR, Sweatman BC, Rahr E, Lindon JC, Spraul M, Neidig P. Automatic data reduction and pattern recognition methods for analysis of H nuclear magnetic resonance spectra of human urine from normal and pathological states. Anal Biochem 1994 220 284-296. 

T.R. Brunner, R.C. Williams, C.L. Wilkins, P.J. McCombie Hadamard Transformed Carbon-13 Nuclear Magnetic Resonance Spectra - Pattern Recognition Analysis, Anal.Chem., 46, (1974), 1798-1802,... 

Robertson, D.G. Reily, M.D. Sigler, R.E. Wells, D.F. Paterson, D.A. Braden, T.K. Metabonomics Evaluation of Nuclear Magnetic Resonance (NMR) and Pattern Recognition Technology for Rapid in vivo Screening of Liver and Kidney Toxicants, Toxicol. Sci. 57(2), 326-337 (2000). 

Anthony, M.L. Sweatman, B.C. Beddell, C.R. Lindon, J.C. Nicholson, J.K. Pattern Recognition Classification of the Site of Nephrotoxicity Based on Metabolic Data Derived from High Resolution Proton Nuclear Magnetic Resonance Spectra of Urine, Mol. Pharmacol. 46, 199-211 (1994). 

Griffin, J.L. Williams, H.J. Sang, E. Clarke, K. Rae, C. Nicholson, J.K. Metabolic Profiling of Genetic Disorders A Multitissue H-1 Nuclear Magnetic Resonance Spectroscopic and Pattern Recognition Study into Dystrophic Tissue, Anal. Biochem. 293(1), 16-21 (2001). 

C.R. Sweatman, B.C., Rahr, E. Lindon, J.C. Spraul, M. Neidig, P. Automatic Data Reduction and Pattern-Recognition Methods for Analysis Of H-1 Nuclear-Magnetic-Resonance Spectra of Human Urine from Normal and Pathological States. Anal. Biochem. 220(2), 284-296 (1994). 

Anthony ML, Sweatman BC, Beddell CR, Lindoon JC, Nicholson JK Pattern recognition classification of the site of nephrotoxicity based on metabolic data from proton nuclear magnetic resonance spectra of urine. Mol Pharmacol 1994 46 199-211. 

Intensive applications of pattern recognition methods in chemistry were started with pioneering works about spectral interpretations by Isenhour, Jurs, and Kowalski (1969 - 1971). Numerous papers deal with the automatic prediction of molecular structures from mass spectra, infrared spectra and nuclear magnetic resonance spectra (Chapter 13). Predictive abilities of 80 to 95 % are typical of these applications. 

Nuclear magnetic resonance (NMR) spectroscopy is the most informative analytical technique and is widely applied in combinatorial chemistry. However, an automated interpretation of the NMR spectral results is difficult (3,4). Usually the interpretation can be supported by use of spectrum calculation (5-18) and structure generator programs (8,12,18-21). Automated structure validation methods rely on NMR signal comparison using substructure/ subspectra correlated databases or shift prediction methods (8,15,22,23). We have recently introduced a novel NMR method called AutoDROP (Automated Definition and Recognition of Patterns) to rapidly analyze compounds libraries (24-29). The method is based on experimental data obtained from the measured ID or 2D iH,i C correlated (HSQC) spectra. 

Spraul, M., Hofmann, M., Ackermann, M., Nicholls, A. W., Damment, S. J. P., Haselden, N. J., Shockcor, J. P., Nicholson, J. K., and Lindon, J. C. (1997) Flow injection proton nuclear magnetic resonance spectroscopy combined with pattern recognition methods Implications for rapid structural studies and high throughput biochemical screening. Anal. Commun. 34, 339-341. 

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