NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition
Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug...
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| Published in | NMR in biomedicine Vol. 18; no. 3; pp. 143 - 162 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.05.2005
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug‐induced response, it is necessary to characterize the normal degree of physiological variation in the absence of stimuli. Here, 1H NMR spectroscopic‐based analyses of the metabolic composition of urine in experimental animals under various normal physiological conditions are reviewed. In particular, the effects of inter‐animal and diurnal variation, gender, age, diet, species, strain, hormonal status and stress on the biochemical composition of urine are explored. Pattern recognition methods facilitate the comparison of urine NMR spectra over a given time‐course, enabling the establishment of changes in profile and highlighting the dynamic metabolic status of an organism. Thus metabonomic approaches based on information‐rich spectroscopic data sets can be used to evaluate normal physiological variation and for investigation of drug safety issues. Copyright © 2004 John Wiley & Sons, Ltd. |
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| AbstractList | Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug-induced response, it is necessary to characterize the normal degree of physiological variation in the absence of stimuli. Here, 1H NMR spectroscopic-based analyses of the metabolic composition of urine in experimental animals under various normal physiological conditions are reviewed. In particular, the effects of inter-animal and diurnal variation, gender, age, diet, species, strain, hormonal status and stress on the biochemical composition of urine are explored. Pattern recognition methods facilitate the comparison of urine NMR spectra over a given time-course, enabling the establishment of changes in profile and highlighting the dynamic metabolic status of an organism. Thus metabonomic approaches based on information-rich spectroscopic data sets can be used to evaluate normal physiological variation and for investigation of drug safety issues. Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug‐induced response, it is necessary to characterize the normal degree of physiological variation in the absence of stimuli. Here, 1H NMR spectroscopic‐based analyses of the metabolic composition of urine in experimental animals under various normal physiological conditions are reviewed. In particular, the effects of inter‐animal and diurnal variation, gender, age, diet, species, strain, hormonal status and stress on the biochemical composition of urine are explored. Pattern recognition methods facilitate the comparison of urine NMR spectra over a given time‐course, enabling the establishment of changes in profile and highlighting the dynamic metabolic status of an organism. Thus metabonomic approaches based on information‐rich spectroscopic data sets can be used to evaluate normal physiological variation and for investigation of drug safety issues. Copyright © 2004 John Wiley & Sons, Ltd. Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug-induced response, it is necessary to characterize the normal degree of physiological variation in the absence of stimuli. Here, super(1)H NMR spectroscopic-based analyses of the metabolic composition of urine in experimental animals under various normal physiological conditions are reviewed. In particular, the effects of inter-animal and diurnal variation, gender, age, diet, species, strain, hormonal status and stress on the biochemical composition of urine are explored. Pattern recognition methods facilitate the comparison of urine NMR spectra over a given time-course, enabling the establishment of changes in profile and highlighting the dynamic metabolic status of an organism. Thus metabonomic approaches based on information-rich spectroscopic data sets can be used to evaluate normal physiological variation and for investigation of drug safety issues. |
| Author | Stanley, Elizabeth G. Lindon, John C. Holmes, Elaine Bollard, Mary E. Nicholson, Jeremy K. |
| Author_xml | – sequence: 1 givenname: Mary E. surname: Bollard fullname: Bollard, Mary E. email: mary.bollard@imperial.ac.uk organization: Biological Chemistry, Biomedical Sciences Division, Sir Alexander Fleming Building, Imperial College, South Kensington, London SW7 2AZ, UK – sequence: 2 givenname: Elizabeth G. surname: Stanley fullname: Stanley, Elizabeth G. organization: Biological Chemistry, Biomedical Sciences Division, Sir Alexander Fleming Building, Imperial College, South Kensington, London SW7 2AZ, UK – sequence: 3 givenname: John C. surname: Lindon fullname: Lindon, John C. organization: Biological Chemistry, Biomedical Sciences Division, Sir Alexander Fleming Building, Imperial College, South Kensington, London SW7 2AZ, UK – sequence: 4 givenname: Jeremy K. surname: Nicholson fullname: Nicholson, Jeremy K. organization: Biological Chemistry, Biomedical Sciences Division, Sir Alexander Fleming Building, Imperial College, South Kensington, London SW7 2AZ, UK – sequence: 5 givenname: Elaine surname: Holmes fullname: Holmes, Elaine organization: Biological Chemistry, Biomedical Sciences Division, Sir Alexander Fleming Building, Imperial College, South Kensington, London SW7 2AZ, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/15627238$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Animals Body Fluids - metabolism Gene Expression Profiling - methods Humans laboratory animals Magnetic Resonance Spectroscopy - methods metabonomics NMR physiological variation Proteome - metabolism Proteomics - methods Urinalysis - methods Urine - chemistry |
| Title | NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition |
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