Objective: To carry out the quality control of classical prescription Qianghuo Shengshi decoction (QSD) and predictive analysis of related substances in the treatment of rheumatoid arthritis by this decoction based on fingerprint and network pharmacology research, and to provide a basis for its quality control and clinical application. Methods: Waters Xbridge column(250 mm×4.6 mm, 5 μm) was adopted, and the mobile phase was acetonitrile -0.2% formic acid aqueous solution with gradient elution. The flow rate was 1.0 mL·min-1, the injection volume was 20 μL,the column temperature was 35 ℃ and the detection wavelengths were 254 nm (0-19 min, 35-70 min) and 320 nm (19-35 min, 70-80 min). The fingerprint was established and the common peaks were determined. By comparing with the relative retention time and UV spectra of the reference substances, the corresponding compounds of the chromatographic peak were identified. Then the common peaks were identified by single decoction and negative decoction. Through network pharmacology analysis, the "QSD-Active Ingredients-Target-Disease Network Diagram" was established and pathway prediction and analysis was performed. Results: The HPLC fingerprint analysis method of the QSD substance standard corresponding sample was established, and 16 common peaks could be demarcated by determining corresponding samples in 15 batches of QSD substance standards, including all 7 decoction pieces. Nine ingredients were identified, including prim-O-glucosylcimifugin, ferulic acid, liquiritin, 5-O-methylvisammioside, glycyrrhizic acid, notopterol, ligustilide, osthole and columbianadin. The similarity of each batch of substance standard corresponding sample was higher than 0.9. Through network pharmacology, the mechanism between the 9 pharamcodynamic components identified in the fingerprint and rheumatoid arthritis were predicted and analyzed. The top ingredients in order were glycyrrhizic acid, osthole, columbianadin, ferulic acid and ligustilide. Protein-protein interaction was significantly enriched in inflammation related targets such as signal transducer and activator of transcription 3, as well as interleukin 6 and tumor necrosis factor. Among the enriched pathways, rheumatoid arthritis ranked the highest. In addition, it enriched many inflammation related pathways such as IL-17 signaling pathway and Th17 cell differentiation. Conclusion: The established QSD fingerprint analysis method is feasible and stable, and the chromatographic peaks are clearly assigned. The network pharmacology method is used to analyze the correlation between the 9 components of QSD and rheumatoid arthritis, and to provide support for it as anefficacy-related substance.
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