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Published on: June 24, 2019
A quality marker study on salvianolic acids for injection
Wei Li1, Navaneethakrishnan Polachi2, Xiangyang Wang3
1National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing - 100700, China; Center for Post-doctoral Research, China Academy of Chinese Medical Sciences, Beijing - 100700, China; State Key Laboratory of Core Technology in Innovative Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin - 300410, China; Tasly Academy, Tasly Holding Group Co., Ltd., Tianjin - 300410, China.
Background:
The quality of Chinese medicine (CM) has being an active and challenging research area for CM. Prof. Chang-Xiao Liu et al first proposed the concept of quality marker (Q-Marker) for the quality evaluation and control on CM. This article describe the exploratory studies of Q-Marker in salvianolic acids for injection (SAI) based on this new concept.
Purpose:
This study was designed to screen Q-Marker of SAI and establish its quality control method based on the concept of CM Q-Marker.
Methods:
Based on the concept of CM Q-Marker, the SAI was investigated for the identification of chemical components and their sources. The pharmacological effects on cerebral ischemia and reperfusion induced injury in rats were also investigated. Furthermore, the target cell extracts and pharmacokinetic studies were conducted to screen Q-Markers. Finally, the fingerprints and determination based on Q-Markers were established to assess the quality of SAI more effectively.
Results:
Overall, 20 constituents in SAI were identified. It was found that salvianolic acid B (SA-B), rosmarinic acid (RA), lithospermic acid (LA), salvianolic acid D (SA-D) and salvianolic acid Y (SA-Y) are major chemical components of SAI. Based on chemical components identifications, analysis of their sources, target cell extracts and pharmacokinetic studies, four phenolic acids, namely SA-B, RA, LA and SA-D, were screened and determined as effective Q-Markers of SAI.
Conclusion:
This study demonstrated that the described method is a powerful approach for detecting Q-Markers, which can be used as control index for the quality assessment of CM.
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