Abstract
Metabolomics allows high-throughput analysis of low-molecular-weight compounds in biofluids that reflect the physiological status and biochemical metabolism of living systems. Hence it has the potential to evaluate toxicity and clarifies the metabolism-related toxic mechanisms. In this study a promising candidate drug parent, triptolide, was given to Sprague–Dawley rats as a model toxicant at a single dose of 0.6, or 2.4 mg/kg, i.g. Both routine biochemical assays and histopathological inspection showed time-dependent hepatic toxicity at the higher dose, but no obvious toxicity at the lower dose. Meanwhile, serum metabolome was profiled using the non-targeted metabolomic tool, gas chromatography time-of-flight mass spectrometry. Based on the acquired metabolomic data, mathematical models were calculated and the metabolic patterns of serum were evaluated using projection to latent structure-discriminant analysis. The relative distance of each treated group from the normal control was calculated to provide a measure of toxicity. Treatment with triptolide at either the higher or lower dose caused deviations in the metabolic pattern and resulted in perturbation of taurine, creatinine, free fatty acids, β-hydroxybutyrate, tricarboxylic acid cycle intermediates, and amino acids. This finding indicates the dysfunction of β-oxidation of free fatty acids and impairment of the mitochondria and confirms the hepatic toxicity of triptolide. The identified toxic markers and the calculated relative distance values quantitatively demonstrated dose- and time-dependent toxicity, whereas the scores plot of the model provided the qualitative information. The metabolomic approach was non-invasive and more sensitive than routine toxic assessment, and the results of both methods correlated well.
References
A, J., Trygg, J., Gullberg, J., et al. (2005). Extraction and GC/MS analysis of the human blood plasma metabolome. Analytical Chemistry, 77, 8086–8094.
Andreadou, I., Papaefthimiou, M., Zira, A., et al. (2009). Metabonomic identification of novel biomarkers in doxorubicin cardiotoxicity and protective effect of the natural antioxidant oleuropein. NMR in Biomedicine, 22, 585–592.
Beger, R. D., Sun, J., & Schnackenberg, L. K. (2010). Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity. Toxicology and Applied Pharmacology, 243, 154–166.
Begley, P., Francis-McIntyre, S., Dunn, W. B., et al. (2009). Development and performance of a gas chromatography-time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum. Analytical Chemistry, 81, 7038–7046.
Boudonck, K. J., Mitchell, M. W., Német, L., et al. (2009). Discovery of metabolomics biomarkers for early detection of nephrotoxicity. Toxicologic Pathology, 37, 280–292.
Chen, B. J., & Chao, N. J. (2002). Immunosuppressive and anti-inflammatory effects of triptolide and its prodrug PG-490–88. Drugs of the Future, 27, 57–60.
Chen, M., Ni, Y., Duan, H., et al. (2008). Mass spectrometry-based metabolic profiling of rat urine associated with general toxicity induced by the multiglycoside of Tripterygium wilfordii Hook f. Chemical Research in Toxicology, 21, 288–294.
Clarke, C. J., & Haselden, J. N. (2008). Metabolic profiling as a tool for understanding mechanisms of toxicity. Toxicologic Pathology, 36, 140–147.
Collings, F. B., & Vaidya, V. S. (2008). Novel technologies for the discovery and quantitation of biomarkers of toxicity. Toxicology, 245, 167–174.
Devlin, T. M. (2006). Textbook of biochemistry with clinical correlations (6th ed.). Hobokan, NJ: Wiley-Liss.
Ebbels, T. M. D., Keun, H. C., Beckonert, O. P., et al. (2007). Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: The consortium on metabolomic toxicology screening approach. Journal of Proteome Research, 6, 4407–4422.
Eriksson, L., Johansson, E., Kettaneh-Wold, N., & Wold, S. (2001). Multi- and megavariate data analysis principles and applications. Umetrics AB, Sweden: Umeatrics Academy.
Goodsaid, F. M., Blank, M., Dieterle, F., et al. (2009). Novel biomarkers of acute kidney toxicity. Clinical Pharmacology and Therapeutics, 86, 490–496.
Griffin, J. L., & Bollard, M. E. (2004). Metabolomics: its potential as a tool in toxicology for safety assessment and data integration. Current Drug Metabolism, 5, 389–398.
Hines, A., Staff, F. J., Widdows, J., et al. (2010). Discovery of metabolic signatures for predicting whole organism toxicology. Toxicological Sciences, 115, 369–378.
Jonsson, P., Johansson, A. I., Gullberg, J., et al. (2005). High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses. Analytical Chemistry, 77, 5635–5642.
Keun, H. C. (2006). Metabolomic modeling of drug toxicity. Pharmacology and Therapeutics, 109, 92–106.
Nicholson, J. K., Connelly, J., Lindon, J. C., & Holmes, E. (2002). Metabolomics: a platform for studying drug toxicity and gene function. Nature Reviews. Drug Discovery, 1, 153–161.
Robertson, D. G. (2005). Metabolomics in toxicology: a review. Toxicological Sciences, 85, 809–822.
Sanins, S. M., Nicholson, J. K., Elcombe, C., & Timbrell, J. A. (1990). Hepatotoxin-induced hypertaurinuria: A proton NMR study. Archives of Toxicology, 64, 407–411.
Schnackenberg, L. K., & Beger, R. D. (2008). The role of metabolic biomarkers in drug toxicity studies. Toxicology Mechanisms and Methods, 18, 301–311.
Trygg, J., Holmes, E., & Lundstedt, T. (2007). Chemometrics in metabolomics. Journal of Proteome Research, 6, 469–479.
Waterfield, C. J., Mesquita, M., Parnham, P., & Timbrell, J. A. (1993). Taurine protects against the cytotoxicity of hydrazine, 1,4-naphthoquinone and carbon tetrachloride in isolated rat hepatocytes. Biochemical Pharmacology, 46, 589–595.
Wold, S. (1978). Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 2, 397–405.
Acknowledgments
This work was supported by the National Key New Drug Creation Special Programs (2009ZX09304-001 and 2009ZX09502-004), the Jiangsu Province Social Development Foundation (BE2008673), the Jiangsu Nature Science Fund (BK2008038) and the National 11th 5-Year Technology Supporting Program of the People’s Republic of China (No. 2006BAI08B04).
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Biochemical assays of Alanine aminotransferase (ALT), aspartate aminotransferase (AST), neutrophils (NE), and white blood cells (WBC) after exposure to triptolide (DOC 84 kb)
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Aa, J., Shao, F., Wang, G. et al. Gas chromatography time-of-flight mass spectrometry based metabolomic approach to evaluating toxicity of triptolide. Metabolomics 7, 217–225 (2011). https://doi.org/10.1007/s11306-010-0241-8
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DOI: https://doi.org/10.1007/s11306-010-0241-8