4.5 Article

Identification and evaluation of the chemical similarity of Yindan xinnaotong samples by ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry fingerprinting

Journal

JOURNAL OF SEPARATION SCIENCE
Volume 39, Issue 3, Pages 611-622

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/jssc.201500836

Keywords

Fingerprint analysis; Similarity evaluation; Traditional Chinese medicine; Yindan xinnaotong

Funding

  1. Chinese drug discovery initiative project [2012ZX09201201]

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Yindan xinnaotong, a compound preparation used in traditional Chinese medicine, is composed of eight herbs: Ginkgo biloba leaf (yinxingye), Salvia miltiorrhizae (danshen), Herba gynostemmatis (jiaogulan), Erigerontis herba (dengzhanxixin), Allii sativi bulbus (dasuan), Notoginseng radixe rhizoma (sanqi), Crataegi fructus (shanzha), and Borneolum (tianranbingpian). Yindan xinnaotong is primarily used to treat cardiovascular and cerebrovascular diseases. However, to date, no scientific methods have been established to assess the quality of Yindan xinnaotong. Therefore, a combinatorial method was developed based on chemical constituent identification and fingerprint analysis to assess the consistency of Yindan xinnaotong quality. In this study, ultra high performance liquid chromatography coupled with time-of-flight mass spectrometry was used to identify the chemical components of Yindan xinnaotong soft capsules. Approximately 74 components were detected, of which 70, including flavonoids, ginkgolide, phenolic acid, diterpenoid tanshinones, and ginsenoside, were tentatively identified. A fingerprint analysis was also conducted to evaluate the uniformity of the quality of Yindan xinnaotong soft capsules. Ten batches of Yindan xinnaotong soft capsules were analyzed. All of the resulting chromatograms were imported into the Similarity Evaluation System for Chromatographic Fingerprints of TCM (Chinese Pharmacopoeia Commission, version 2004A). The similarity scores of common peaks from these samples ranged from 0.903-1.000, indicating that samples from different batches were highly correlated.

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