Chaonan Su,Caihong Li,Kang Sun,Wenjing Li,Rongxia Liu
Walnut leaves are rich in phenolic components with antibiotic and antioxidative effects. However, few studies have reported the quantitative analysis of active components in walnut leaf. In this study, a novel method for quantifying the active components in walnut leaves was developed by combining ultra-high performance liquid chromatography-hybrid quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) with quantitative analysis of multi-components by a single marker (QAMS). In total, 13 bioactive components were analyzed by a single marker, quercetin. To evaluate the accuracy of this method, an auxiliary quantification method with 13 reference standards was established and validated. The standard method differences (SMDs) of the quantification results between QAMS and the auxiliary method were lower than 20%, indicating that the QAMS method can accurately determine the active components in walnut leaves. This method can provide a reference to address the absence of reference standards for analyzing other foods and herbs.
Rongxia Liu,Chaonan Su,Yumeng Xu,Kangle Shang,Kang Sun,Caihong Li,Jing Lu
Ethnopharmacological relevance Walnut leaf (WL) is a hypoglycemic herbal medication with blood glucose-lowering activity that can affect diabetes mellitus (DM). However, the active components of WL and the mechanisms by which these compounds affect DM are unclear. Aim of study This study aimed to determine these effective ingredients and elucidate the potential mechanisms by which they affect DM via ultra-high performance liquid chromatography-hybrid quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) coupled with network pharmacology analysis. Materials and methods First, UHPLC-Q-Orbitrap HRMS was utilized to identify components of WL. Second, the putative targets of the components were identified and predicted based on chemical similarity and online databases. Third, the key candidate targets and potential active components were identified through topological analysis of a component-disease target interaction network. Finally, interactions between active components and therapeutic targets were confirmed by molecular docking analysis. Results One hundred and thirty components were identified in WL, among which 38 were considered potentially bioactive, as they showed hypoglycemic effects. Among these 38, 8 key active components possessed high similarities and shared 4 targets with approved drugs. These findings were confirmed by molecular docking analysis. Conclusion The approach combining UHPLC-Q-Orbitrap HRMS with network pharmacology analysis is a rapid and effective tool to identify potentially bioactive constituents in medicinal plants and prescriptions.
危险品化学品经营许可证（带存储）