Metabolomic Profiling of Leptadenia reticulata : Unveiling Therapeutic Potential for Inflammatory Diseases through Network Pharmacology and Docking Studies.
Yashaswini Mallepura AdinarayanaswamyDeepthi PadmanabhanPurushothaman NatarajanSenthilkumar PalanisamyPublished in: Pharmaceuticals (Basel, Switzerland) (2024)
Medicinal plants have been utilized since ancient times for their therapeutic properties, offering potential solutions for various ailments, including epidemics. Among these, Leptadenia reticulata , a member of the Asclepiadaceae family, has been traditionally employed to address numerous conditions such as diarrhea, cancer, and fever. In this study, employing HR-LCMS/MS(Q-TOF) analysis, we identified 113 compounds from the methanolic extract of L. reticulata . Utilizing Lipinski's rule of five, we evaluated the drug-likeness of these compounds using SwissADME and ProTox II. SwissTarget Prediction facilitated the identification of potential inflammatory targets, and these targets were discerned through the Genecard, TTD, and CTD databases. A network pharmacology analysis unveiled hub proteins including CCR2, ICAM1, KIT, MPO, NOS2, and STAT3. Molecular docking studies identified various constituents of L. reticulata , exhibiting high binding affinity scores. Further investigations involving in vivo testing and genomic analyses of metabolite-encoding genes will be pivotal in developing efficacious natural-source drugs. Additionally, the potential of molecular dynamics simulations warrants exploration, offering insights into the dynamic behavior of protein-compound interactions and guiding the design of novel therapeutics.
Keyphrases
- molecular dynamics simulations
- molecular docking
- mass spectrometry
- oxidative stress
- ms ms
- bioinformatics analysis
- human health
- small molecule
- protein protein
- network analysis
- dendritic cells
- transcription factor
- case control
- single cell
- immune response
- risk assessment
- molecular dynamics
- drug induced
- irritable bowel syndrome
- adverse drug
- climate change
- infectious diseases
- big data