Issue 16, 2020

Identification of blood-activating components from Xueshuan Xinmaining Tablet based on the spectrum–effect relationship and network pharmacology analysis

Abstract

With the aim of identifying the active components of Xueshuan Xinmaining Tablet (XXT) and discussing the potential mechanism involved, the relationship between HPLC fingerprints and its blood-activating effect were established by multivariate statistical analysis, including gray relational analysis (GRA) and partial least squares regression analysis (PLSR). Network pharmacology was used to predict the potential mechanism based on the identified active components. GRA and PLSR analysis showed close correlation between the HPLC fingerprints and blood-activating activity, and peaks P1, P3, P11, P15, P22, P34, P36, P38 and P39 might be potential anti-blood stasis components of XXT. The pharmacological verification showed that salvianic acid A (P1), rutin (P3), ginsenoside Rg1 (P11) and Rb1 (P22), cinobufagin (P36), and tanshinone I (P38) and IIA (P39) had significant blood-activating effects. Based on these seven active compounds, network pharmacology analysis indicated that the anti-blood stasis effect of XXT might be closely related to TNF, PI3K-Akt and NF-κB signaling pathways. The spectrum–effect relationship of XXT was successfully established in this study. The blood-activating components and the anti-blood stasis mechanism were revealed and predicted. These findings could also be beneficial for an exploration of the active components of TCM.

Graphical abstract: Identification of blood-activating components from Xueshuan Xinmaining Tablet based on the spectrum–effect relationship and network pharmacology analysis

Article information

Article type
Paper
Submitted
18 Nov 2019
Accepted
18 Feb 2020
First published
10 Mar 2020
This article is Open Access
Creative Commons BY license

RSC Adv., 2020,10, 9587-9600

Identification of blood-activating components from Xueshuan Xinmaining Tablet based on the spectrum–effect relationship and network pharmacology analysis

J. Tan, J. Liu, H. Wang, Y. Zhang, H. Lin, Z. Wang, H. Si, Y. Zhang, J. Liu, P. Li and K. Sun, RSC Adv., 2020, 10, 9587 DOI: 10.1039/C9RA09623J

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