Plasma metabolic profiling analysis of normal and ANIT-induced cholestasis rats after oral administration of Da-Huang-Xiao-Shi decoction using UHPLC-Q-Orbitrap MS coupled with pattern recognition?

Analytical MethodsANAL METHODS-UK Pub Date: 2018-09-18 DOI: 10.1039/C8AY01945B

Abstract

Da-Huang-Xiao-Shi decoction (DHXSD) is a representative formula for treating jaundice and could have bright prospects owing to its liver protecting effects. DHXSD might synthetically result in a change in the metabolic profiles of cholestasis rats compared with that of normal rats after oral administration. Metabolic profiling analysis has gained significant attention ever since the field of metabolomics began to develop. In this study, a highly sensitive, rapid and high chromatographic resolution ultra-high performance liquid chromatography-hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Orbitrap MS) combined with multivariate statistical analysis method was established to investigate the metabolic profiles of DHXSD in normal and alpha-naphthylisothiocyanate (ANIT)-induced cholestasis rats. In addition, serum biochemical indices and pathological observation analysis were utilized to evaluate the animal model. As a result, a total of 68 prototype compounds and metabolites in normal and cholestasis rats in vivo were screened and tentatively identified according to their exact mass and the relevant literature. Among these, 15 of the compounds were filtered out as potential chemical markers that may be responsible for the pharmacological effects of DHXSD, which offers a basic foundation for quality evaluation, pharmacokinetic research and the clinical safety of drug usage. In conclusion, this study provides an insight into the metabolism of DHXSD in vivo to enable understanding of the metabolic process and therapeutic mechanism of cholestasis.

Graphical abstract: Plasma metabolic profiling analysis of normal and ANIT-induced cholestasis rats after oral administration of Da-Huang-Xiao-Shi decoction using UHPLC-Q-Orbitrap MS coupled with pattern recognition
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