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Journal of Pharmaceutical and Biomedical Analysis
Volume 46, Issue 4, 13 March 2008, Pages 757-762
 
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doi:10.1016/j.jpba.2007.11.042    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2007 Elsevier B.V. All rights reserved.

Aristolochic acid induced changes in the metabolic profile of rat urine

Wan Chana and Zongwei CaiCorresponding Author Contact Information, a, E-mail The Corresponding Author

aDepartment of Chemistry, Hong Kong Baptist University, Kowloon, Hong Kong, SAR, China

Received 30 May 2007; 
revised 27 November 2007; 
accepted 27 November 2007. 
Available online 3 December 2007.

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Abstract

Prolonged exposure to aristolochic acid (AA) has shown to pose rapid progressive renal fibrosis in Belgium women in a slimming regimen in the early 90 s. We hypothesize that changes in metabolic profile could have occurred before symptoms were observed, which may allow early treatment. In this study, metabonomics was used for toxicology study of AA in rats. Liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (Qq-TOF) was used for the analysis of endogenous metabolites in rat urine samples. The difference in metabolic profiles between the control and the dosed rats was well observed by the principal component analysis (PCA) of the MS data. Significant changes of two metabolite markers, kynurenic acid and hippuric acid, were detected in the rat urine samples. The identification of potential biomarkers was performed by high-resolution mass measurement and MS–MS analyses on a Qq-TOF. We believe that metabolic profiling may act as a preclinical protocol for AA exposure before symptoms are observed.

Keywords: Metabonomics; Nephrotoxin; Aristolochic acid; Biomarker; LC-MS

Article Outline

1. Introduction
2. Experimental
2.1. Chemicals
2.2. Rat in vivo experiment and urine sample preparation
2.3. LC-ESI-MS analysis
2.3.1. HPLC system
2.3.2. MS system
2.4. Data analysis of MS spectra
3. Results and discussion
3.1. Laboratory parameters from animal experiment
3.2. Sample preparation and LC-MS analysis
3.3. Principal component analysis
3.4. Biomarker identification
4. Conclusion
Acknowledgements
References








 
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