Objective  To analyze the potential quality marker (Q-Marker) of Poria cocos based on fingerprint analysis, chemical pattern recognition, network pharmacology, and molecular docking.

Methods  The fingerprint of 20 batches of Poria cocos was established by high performance liquid chromatography (HPLC), and orthogonal partial least squares discriminant analysis (OPLS-DA) was used to screen the main differential components of common peaks between groups. Network pharmacology methods were used to identify the efficacy-related components. The differential components were correlated with the efficacy-related components to predict Q-Marker, which were then validated by molecular docking.

Results  The fingerprints of 20 batches of Poria cocos were established, 21 common peaks were calibrated, and 6 components were identified. OPLS-DA was used to screen 11 differential components, among which 5 were identified. Network pharmacology analysis identified 15 active components, 33 core targets, and 57 KEGG pathways. By integrating the results of fingerprint and network pharmacology, polyporenic acid C, dehydrotumulosic acid, 3-dehydrotrametenolic acid, and 16α-hydroxydehydrotrametenolic acid were selected as Q-Marker.

Conclusion  The potential quality markers of Poria cocos were successfully identified by fingerprinting combined with chemical pattern recognition and network pharmacology methods, providing scientific basis for comprehensive control and evaluation of Poria cocos quality.

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