Prediction and quality evaluation of quality markers of

Introduction: Methods: The aim of this study was to identify the Q-markers of GSB that define its therapeutic efficacy against liver injury. This was achieved through establishing HPLC fingerprints and conducting "small molecule-protein" interaction screening, followed by verification using Results: First, the HPLC fingerprints of 44 batches of GSB from differentproducing areas were established, revealing 25 common peaks. Principal component analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) of the 25 peak areas resulted in the identification of five components based on Variable Importance in Projection (VIP) values >1. Subsequently, these small-molecule components of GSB were screened for interaction with liver proteins, leading to the identification of three components with strong efficacy, namely, swertiamarin, gentiopicroside, and sweroside, which were designated as Q-markers of GSB associated with liver injury treatment. Furthermore, the therapeutic efficacy of these Q-markers was validated using a hydrogen peroxide-induced NCTC 1469 cell injury model. Finally, the Q-markers were quantified, and the quality of GSB samples from different origins was evaluated through PCA, entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Hierarchical Cluster Analysis (HCA) based on the quantification results. Discussion: The potential Q-markers of GSB were predicted through the integration of HPLC fingerprinting, pattern recognition, and small molecule-protein interaction analysis. Cell experiment results showed that swertiamarin, gentiopicroside, and sweroside significantly alleviated liver cell injury. Quantitative analysis of the Q-markers identified significant differences in their contents across GSB samples from different producing areas. This methodology provides a novel and comprehensive framework for defining Q-markers and evaluating GSB quality.