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A systematic data screening strategy for comprehensive characterization of chemical components in Suan-Zao-Ren decoction and their metabolic profiles in rat plasma and brain using ultra high performance liquid chromatography quadrupole time-of-flight mass spectrometry

Abstract

Suan-Zao-Ren decoction was a classic traditional Chinese medicinal prescription which has been used in clinical treatment on insomnia and anxiety for thousands of years. In order to explore the pharmacological material basis, the various components and their metabolic pathways in rat serum and brain based on ultra high performance liquid chromatography quadrupole time-of-flight mass spectrometry coupled with data mining were firstly investigated in this research. Overall, a total of 110 components were identified or tentatively identified from Suan-Zao-Ren decoction. 35 prototype components and 36 metabolites were detected in rat plasma, whereas 18 prototype components and 4 metabolites were detected in rat brain. The absorbed components which could across the blood-brain-barrier to the brain were firstly observed after oral administration of Suan-Zao-Ren decoction. The metabolic pathways of potential bioactive components mainly involved hydrolysis, sulfate conjugation, glucuronide conjugation and other metabolic reactions. The results of our work demonstrated a well-developed method for compositional characterization both in vitro and in vivo, which could provide a detailed chemical basis for sequential pharmacological mechanism research.

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Publication details

The article was received on 01 Aug 2019, accepted on 16 Sep 2019 and first published on 07 Oct 2019


Article type: Paper
DOI: 10.1039/C9AY01652J
Anal. Methods, 2019, Accepted Manuscript

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    A systematic data screening strategy for comprehensive characterization of chemical components in Suan-Zao-Ren decoction and their metabolic profiles in rat plasma and brain using ultra high performance liquid chromatography quadrupole time-of-flight mass spectrometry

    Y. Du, B. Wu, F. Xiao, T. Yan, Y. Jia, K. Bi and B. He, Anal. Methods, 2019, Accepted Manuscript , DOI: 10.1039/C9AY01652J

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