Integration of fingerprint-activity relationship and chemometric analysis to accurately screen Q-markers for the quality control of traditional Chinese medicine compound preparation: Jinlian Qingre granules as an example

Min He; Shan Mao; Qingyu Du; Xin Gao; Jie Shi; Xin Zhou; Fang Zhang; Youyuan Lu; Hanqing Wang; Yongjie Yu; Lei Sun; Xia Zhang

Arabian Journal of Chemistry (Feb 2024)

Integration of fingerprint-activity relationship and chemometric analysis to accurately screen Q-markers for the quality control of traditional Chinese medicine compound preparation: Jinlian Qingre granules as an example

Min He,
Shan Mao,
Qingyu Du,
Xin Gao,
Jie Shi,
Xin Zhou,
Fang Zhang,
Youyuan Lu,
Hanqing Wang,
Yongjie Yu,
Lei Sun,
Xia Zhang

Affiliations

Min He: School of Pharmacy, Ningxia Medical University, Yinchuan, China
Shan Mao: School of Pharmacy, Ningxia Medical University, Yinchuan, China
Qingyu Du: School of Pharmacy, Ningxia Medical University, Yinchuan, China; Qinghai Provincial Drug Inspection and Testing Institute, Xining, China
Xin Gao: School of Pharmacy, Ningxia Medical University, Yinchuan, China
Jie Shi: School of Pharmacy, Ningxia Medical University, Yinchuan, China
Xin Zhou: School of Pharmacy, Ningxia Medical University, Yinchuan, China
Fang Zhang: Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, China
Youyuan Lu: School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China
Hanqing Wang: School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China
Yongjie Yu: School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China
Lei Sun: School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China; Corresponding authors at: School of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia 750000, China.
Xia Zhang: School of Pharmacy, Ningxia Medical University, Yinchuan, China; Key Laboratory of Ningxia Minority Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan, China; Corresponding authors at: School of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia 750000, China.

Journal volume & issue: Vol. 17, no. 2
p. 105481

Abstract

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This study aims to develop a comprehensive strategy that accurately screens and quantifies quality markers (Q-markers) by combining LC-MS metabolomics with fingerprint-effect relationships, chemometrics, and QAMS methods for the quality control in traditional Chinese medicine compound preparations (CMP). First, the chemical compositions in Jinlian Qingre granules (JQG) were analyzed and identified using ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Next, the HPLC fingerprint of JQG was established, and chemometrics were employed to evaluate JQG quality. Then, potential anti-inflammatory bioactive constituents were screened using a fingerprinting-effect relationship model utilizing partial least squares regression (PLSR) and a back propagation neural network optimized using a genetic algorithm (GA-BPNN). On this basis, Q-markers of JQG were determined and analyzed using quantitative analysis of multi-components by the single marker (QAMS) employing three components as reference substances (RS). 24 chemical constituents in JQG were characterized using UHPLC-HRMS. Twenty-four common peaks were assigned to the fingerprint. Six compounds, mangiferin, 2′'-O-beta-L-galactopyranosylorientin, orientin, veratric acid, vitexin, and harpagoside, identified from the common peaks associated with anti-inflammatory efficacy, could be used as Q-markers of JQG. The content determination results displayed no significant difference in the content of Q-markers in 20 batches of JQG samples measured using the external standard method and QAMS. Q-markers could be efficiently screened and determined by integrating multidisciplinary technologies. This comprehensive strategy could be beneficial for the thorough evaluation of CMP.

Published in Arabian Journal of Chemistry

ISSN: 1878-5352 (Print)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Science: Chemistry
Website: http://www.journals.elsevier.com/arabian-journal-of-chemistry/

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