Traditional Chinese medicine is an important part of complementary alternative medicine. Jiedu Qingjin formula (JDQJF) is an effective national invention patent for the treatment of non-small cell lung cancer (NSCLC). We investigated the molecular biological mechanisms based on network pharmacology, molecular docking, and molecular dynamics simulations. Compounds of JDQJF were screened through the TCMSP, ETCM, and literature. Targets were searched by DrugBank and predicted by SwissTargetPrediction. GEO database was applied for screening differentially expressed genes between cancerous tissues and healthy tissues of NSCLC. Subsequently, the protein-protein interaction between JDQJF and NSCLC were obtained by Cytoscape. Visual analyses were carried out to extract candidate genes, then subjected to Metascape for enrichment analyses. Finally, molecular docking was performed by AutoDock, and the best complexes were subjected to molecular dynamics simulation and binding energy calculations by MMPBSA. A total of 273 compounds, 390 targets, 3146 GO terms, and 174 KEGG pathways were obtained. Five potential compounds (quercetin, adenosine, apigenin, heptadecanoic acid, and luteolin) were notably modulated by key targets AKT1, MAPK3, and RAF1. Enrichment results included cell cycle process, growth transduction factor, immune response-activating transduction, and involved PI3K/AKT, MAPK, NF-κB and VEGF pathway. RAF1-quercetin showed the highest binding affinity (-9.1 kcal/mol), revealed stable interactions during the simulation, and the highest estimated relative binding energy of the RAF1-Heptadecanoic was -184.277 kcal/mol. This study suggested that EMT-related, inflammation-related, immune-related, and angiogenesis-related pathways may be associated with JDQJF, and involved in the advancement of NSCLC, which points out the research direction for subsequent utility mechanism validation.Communicated by Ramaswamy H. Sarma.
Keywords: Jiedu Qingjin formula; biological mechanisms; gene differential expression; molecular docking; molecular dynamics simulations; network pharmacology; non-small cell lung cancer.