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

Abstract

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.