Comprehensive Analysis of Quality Markers in Speranskia tuberculata based on HPLC Fingerprinting, Chemometrics, and Network Pharmacology

Background Speranskia tuberculata is a perennial herbaceous plant that is used in traditional Chinese medicine to dispel wind and dampness, promote blood circulation, and relieve pain. However, quality control markers, mechanisms of action, and systematic screening of quality indicators are still lacking. Objective The high-performance liquid chromatography (HPLC) fingerprint of Speranskia tuberculata was established, and the quality markers (Q-Markers) of Speranskia tuberculata were analyzed by network pharmacology and molecular docking analysis methods. Method In this study, HPLC was used to establish fingerprints and conduct similarity evaluation. Principal component analysis and orthogonal partial least squares discriminant analysis were used to suggest the differences among these fingerprints. The “component–target–pathway” network relationships of characteristic components of Speranskia tuberculata were constructed by network pharmacology. Molecular docking technology was used to analyze the binding affinity between Q-Markers and core targets, and the potential Q-Markers were predicted. Results HPLC characteristic fingerprints identified 19 common peaks, among which four were tentatively identified as chlorogenic acid, kaempferol, diosmetin and amentoflavone. Through network pharmacological and molecular docking analysis, it was predicted that kaempferol and diosmetin could serve as the Q-Markers of Speranskia tuberculata. Conclusions The method established in this study is accurate, reliable, simple, and practical, and can be used as a reference method for Speranskia tuberculata quality detection. Two Q-Markers selected by network pharmacology and molecular docking analysis can provide support and references for Speranskia tuberculata QC. Highlights This study selected Speranskia tuberculata as the research object and initiated the research based on the concept of “medicinal material characteristics—chemical quality—activity prediction” starting from its origin to fill the research gap. The methodologies and strategies presented in this study can be extended to other traditional Chinese medicines, offering novel insights into the quantitative evaluation and identification of Q-Markers.