Optimization of Extraction Process of Hypolipidemic Ingredients in Vine Tea Based on Network Pharmacology and Design Space Method
Based on a quality-derived design concept,the extraction process of vine tea was optimized,and the key process parameters and their possible ranges were determined.The functionality of key components in preventing and treating dyslipidemia was explored utilizing network pharmacology.Dihydromyricetin(DMY),myricitrin(MYT),astragalin(AST),and myricetin(MYE)were selected as index components for a Plackett-Burman experimental design,and initial determinations highlighted extraction time and solvent ratio as the key process parameters.On this basis,a Box-Behnken design was employed,incorporating a two-factor and four-level experimental approach.The multi-index superposition method was adopted to optimize the module Graphical option,facilitating the construction of the design space,which was subsequently validated through testing points both inside and outside the designated area.The optimal extraction parameters were determined as two extraction cycles of 20 minutes each,with a solvent-to-sample ratio of 11.5 and a volume fraction of 50％ethanol.The average concentrations of DMY,MYT,AST,and MYE achieved were 443.5,125.8,1.9,and 2.5 µg·mg-1,respectively,with a mean dry paste yield of 28.1％.Network pharmacology analysis revealed 88 key targets associated with DMY,MYT,AST,and MYE that contribute collectively to dyslipidemia treatment.The protein-protein interaction analysis identified TP53,RELA,and STAT3 as key targets,and KEGG analysis showed that these key targets are involved in lipid regulation and atherosclerosis pathways,as well as in the signaling pathway of advanced glycation end products(AGEs)and their receptors(RAGE),alongside TNF signaling pathways.The extraction process demonstrated stability and feasibility,thereby providing valuable insights for the future development of products related to vine tea.
vine teakey componentsoptimization of extraction processdesign spacenetwork pharmacology
万方数据
维普
