NMR-based metabonomic toxicity classification: hierarchical cluster analysis and k-nearest-neighbour approaches. Beckonert, O., Bollard, M. E., Ebbels, T. M. D., Keun, H. C., Antti, H., Holmes, E., Lindon, J. C., & Nicholson, J. K. Analytica Chimica Acta, 490(1-2):3–15, August, 2003. Place: Amsterdam Publisher: Elsevier Science Bv WOS:000185047900002doi abstract bibtex The COnsortium for MEtabonomic Toxicology (COMET) project is constructing databases and metabolic models of drug toxicity using ca. 100,000 H-1 NMR spectra of biofluids from animals treated with model toxins. Mathematical models characterising the effects of toxins on endogenous metabolite profiles will enable rapid toxicological screening of potential drug candidates and discovery of novel mechanisms and biomarkers of specific types of toxicity. The metabolic effects and toxicity of 19 model compounds administered to rats in separate studies at toxic (high) and sub-toxic (low) doses were investigated. Urine samples were collected from control and dosed rats at 10 time points over 8 days and were subsequently analysed by 600 MHz H-1 NMR spectroscopy. In order to classify toxicity and to reveal similarities in the response of animals to different toxins, principal component analysis (PCA), hierarchical cluster analysis (HCA) and k-nearest-neighbour (kNN) classification were applied to the data from the high-dose studies to reveal dose and time-related effects. Both PCA and HCA provided valuable overviews of the data, highlighting characteristic metabolic perturbations in the urine spectra between the four groups: controls (C), liver (L) toxins, kidney (K) toxins and other (O) treatments, and revealed further differences between subgroups of liver toxins. kNN analysis of the multivariate data using both leave-one-out (LOO) cross-validation and training and test-set (50:50) classification successfully predicted all the different toxin classes. The four treatment groups (control, liver, kidney and other) were predicted with 86, 85, 91 and 88% success rate (training/test). In a study-by-study comparison, 81% of the samples were predicted into the correct toxin study (training/test). This work illustrates the high power and reliability of metabonomic data analysis using H-1 NMR spectroscopy together with chemometric techniques for the exploration and prediction of toxic effects in the rat. (C) 2003 Elsevier Science B.V. All rights reserved.
@article{beckonert_nmr-based_2003,
title = {{NMR}-based metabonomic toxicity classification: hierarchical cluster analysis and k-nearest-neighbour approaches},
volume = {490},
issn = {0003-2670},
shorttitle = {{NMR}-based metabonomic toxicity classification},
doi = {10/bq63jx},
abstract = {The COnsortium for MEtabonomic Toxicology (COMET) project is constructing databases and metabolic models of drug toxicity using ca. 100,000 H-1 NMR spectra of biofluids from animals treated with model toxins. Mathematical models characterising the effects of toxins on endogenous metabolite profiles will enable rapid toxicological screening of potential drug candidates and discovery of novel mechanisms and biomarkers of specific types of toxicity. The metabolic effects and toxicity of 19 model compounds administered to rats in separate studies at toxic (high) and sub-toxic (low) doses were investigated. Urine samples were collected from control and dosed rats at 10 time points over 8 days and were subsequently analysed by 600 MHz H-1 NMR spectroscopy. In order to classify toxicity and to reveal similarities in the response of animals to different toxins, principal component analysis (PCA), hierarchical cluster analysis (HCA) and k-nearest-neighbour (kNN) classification were applied to the data from the high-dose studies to reveal dose and time-related effects. Both PCA and HCA provided valuable overviews of the data, highlighting characteristic metabolic perturbations in the urine spectra between the four groups: controls (C), liver (L) toxins, kidney (K) toxins and other (O) treatments, and revealed further differences between subgroups of liver toxins. kNN analysis of the multivariate data using both leave-one-out (LOO) cross-validation and training and test-set (50:50) classification successfully predicted all the different toxin classes. The four treatment groups (control, liver, kidney and other) were predicted with 86, 85, 91 and 88\% success rate (training/test). In a study-by-study comparison, 81\% of the samples were predicted into the correct toxin study (training/test). This work illustrates the high power and reliability of metabonomic data analysis using H-1 NMR spectroscopy together with chemometric techniques for the exploration and prediction of toxic effects in the rat. (C) 2003 Elsevier Science B.V. All rights reserved.},
language = {English},
number = {1-2},
journal = {Analytica Chimica Acta},
author = {Beckonert, O. and Bollard, M. E. and Ebbels, T. M. D. and Keun, H. C. and Antti, H. and Holmes, E. and Lindon, J. C. and Nicholson, J. K.},
month = aug,
year = {2003},
note = {Place: Amsterdam
Publisher: Elsevier Science Bv
WOS:000185047900002},
keywords = {biofluid, h-1-nmr spectroscopy, hierarchical cluster analysis, k-nearest-neighbour, kidney, metabonomics, mouse, nmr, partial-least, pattern-recognition, principal component analysis, squares discriminant analysis, toxicity prediction, urine},
pages = {3--15},
}
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Mathematical models characterising the effects of toxins on endogenous metabolite profiles will enable rapid toxicological screening of potential drug candidates and discovery of novel mechanisms and biomarkers of specific types of toxicity. The metabolic effects and toxicity of 19 model compounds administered to rats in separate studies at toxic (high) and sub-toxic (low) doses were investigated. Urine samples were collected from control and dosed rats at 10 time points over 8 days and were subsequently analysed by 600 MHz H-1 NMR spectroscopy. In order to classify toxicity and to reveal similarities in the response of animals to different toxins, principal component analysis (PCA), hierarchical cluster analysis (HCA) and k-nearest-neighbour (kNN) classification were applied to the data from the high-dose studies to reveal dose and time-related effects. Both PCA and HCA provided valuable overviews of the data, highlighting characteristic metabolic perturbations in the urine spectra between the four groups: controls (C), liver (L) toxins, kidney (K) toxins and other (O) treatments, and revealed further differences between subgroups of liver toxins. kNN analysis of the multivariate data using both leave-one-out (LOO) cross-validation and training and test-set (50:50) classification successfully predicted all the different toxin classes. The four treatment groups (control, liver, kidney and other) were predicted with 86, 85, 91 and 88% success rate (training/test). In a study-by-study comparison, 81% of the samples were predicted into the correct toxin study (training/test). This work illustrates the high power and reliability of metabonomic data analysis using H-1 NMR spectroscopy together with chemometric techniques for the exploration and prediction of toxic effects in the rat. (C) 2003 Elsevier Science B.V. 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