Literature DB >> 20699110

A metabonomic approach for mechanistic exploration of pre-clinical toxicology.

Muireann Coen1.   

Abstract

Metabonomics involves the application of advanced analytical tools to profile the diverse metabolic complement of a given biofluid or tissue. Subsequent statistical modelling of the complex multivariate spectral profiles enables discrimination between phenotypes of interest and identifies panels of discriminatory metabolites that represent candidate biomarkers. This review article presents an overview of recent developments in the field of metabonomics with a focus on application to pre-clinical toxicology studies. Recent research investigations carried out as part of the international COMET 2 consortium project on the hepatotoxic action of the aminosugar, galactosamine (galN) are presented. The application of advanced, high-field NMR spectroscopy is demonstrated, together with complementary application of a targeted mass spectrometry platform coupled with ultra-performance liquid chromatography. Much novel mechanistic information has been gleaned on both the mechanism of galN hepatotoxicity in multiple biofluids and tissues, and on the protection afforded by co-administration of glycine and uridine. The simultaneous identification of both the metabolic fate of galN and its associated endogenous consequences in spectral profiles is demonstrated. Furthermore, metabonomic assessment of inter-animal variability in response to galN presents enhanced mechanistic insight on variable response phentoypes and is relevant to understanding wider aspects of individual variability in drug response. This exemplar highlights the analytical and statistical tools commonly applied in metabonomic studies and notably, the approach is applicable to the study of any toxin/drug or intervention of interest. The metabonomic approach holds considerable promise and potential to significantly advance our understanding of the mechanistic bases for adverse drug reactions.
Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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Year:  2010        PMID: 20699110     DOI: 10.1016/j.tox.2010.07.022

Source DB:  PubMed          Journal:  Toxicology        ISSN: 0300-483X            Impact factor:   4.221


  6 in total

1.  Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Authors:  Ivan Rusyn; Alexander Sedykh; Yen Low; Kathryn Z Guyton; Alexander Tropsha
Journal:  Toxicol Sci       Date:  2012-03-02       Impact factor: 4.849

2.  Serum UPLC-MS/MS metabolic profiling in an experimental model for acute-liver injury reveals potential biomarkers for hepatotoxicity.

Authors:  Esperanza Gonzalez; Sebastiaan van Liempd; Javier Conde-Vancells; Virginia Gutierrez-de Juan; Miriam Perez-Cormenzana; Rebeca Mayo; Agustin Berisa; Cristina Alonso; Cesar A Marquez; Jonathan Barr; Shelly C Lu; Jose M Mato; Juan M Falcon-Perez
Journal:  Metabolomics       Date:  2011-07-14       Impact factor: 4.290

3.  Modeling antibiotic and cytotoxic effects of the dimeric isoquinoline IQ-143 on metabolism and its regulation in Staphylococcus aureus, Staphylococcus epidermidis and human cells.

Authors:  Alexander Cecil; Carina Rikanović; Knut Ohlsen; Chunguang Liang; Jörg Bernhardt; Tobias A Oelschlaeger; Tanja Gulder; Gerhard Bringmann; Ulrike Holzgrabe; Matthias Unger; Thomas Dandekar
Journal:  Genome Biol       Date:  2011-03-21       Impact factor: 13.583

4.  UPLC-Q-TOF/MS based metabolomic profiling of serum and urine of hyperlipidemic rats induced by high fat diet.

Authors:  Qiong Wu; Hai Zhang; Xin Dong; Xiao-Fei Chen; Zhen-Yu Zhu; Zhan-Ying Hong; Yi-Feng Chai
Journal:  J Pharm Anal       Date:  2014-05-10

5.  Effects of low doses of bisphenol A on the metabolome of perinatally exposed CD-1 mice.

Authors:  Nicolas J Cabaton; Cécile Canlet; Perinaaz R Wadia; Marie Tremblay-Franco; Roselyne Gautier; Jérôme Molina; Carlos Sonnenschein; Jean-Pierre Cravedi; Beverly S Rubin; Ana M Soto; Daniel Zalko
Journal:  Environ Health Perspect       Date:  2013-02-21       Impact factor: 9.031

6.  Metabolomic analysis of serum from rats following long-term intake of Chinese sausage.

Authors:  Minxian Rong; Pei Wang; Yuesheng Qiu; Yungang Liu; Yiyuan Wang; Hong Deng
Journal:  Food Nutr Res       Date:  2018-07-09       Impact factor: 3.894

  6 in total

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