Wuai Zhou, Huan Zhang, Xin Wang, Jun Kang, Wuyan Guo, Lihua Zhou, Huiyun Liu, Menglei Wang, Ruikang Jia, Xinjun Du, Weihua Wang, Bo Zhang, Shao Li
Journal: Phytomedicine : international journal of phytotherapy and phytopharmacology 2022;95():153837
PMID: 34883416
BACKGROUND
Moluodan (MLD) is a traditional Chinese patent medicine for the treatment of chronic atrophic gastritis (CAG). However, the mechanism of action (MoA) of MLD for treating CAG still remain unclear.
PURPOSE
Elucidate the MoA of MLD for treating CAG based on network pharmacology.
STUDY DESIGN
Integrate computational prediction and experimental validation based on network pharmacology.
METHODS
Computationally, compounds of MLD were scanned by LC-MS/MS and the target profiles of compounds were identified based on network-based target prediction method. Compounds in MLD were compared with western drugs used for gastritis by hierarchical clustering of target profile. Key biological functional modules of MLD were analyzed, and herb-biological functional module network was constructed to elucidate combinatorial rules of MLD herbs for CAG. Experimentally, MLD's effect on different biological functional modules were validated from both phenotypic level and molecular level in 1- Methyl-3-nitro-1-nitrosoguanidine (MNNG)-induced GES-1 cells.
RESULTS
Computational results show that the target profiles of compounds in MLD can cover most of the biomolecules reported in literature. The MoA of MLD can cover most types of MoA of western drugs for CAG. The treatment of CAG by MLD involved the regulation of various biological functional modules, e.g., inflammation/immune, cell proliferation, cell apoptosis, cell differentiation, digestion and metabolism. Experimental results show that MLD can inhibit cell proliferation, promote cell apoptosis and differentiation, reduce the inflammation level and promote lipid droplet accumulation in MNNG-induced GES-1 cells.
CONCLUSION
The network pharmacology framework integrating computational prediction and experimental validation provides a novel way for exploring the MoA of MLD.
Copyright © 2021. Published by Elsevier GmbH.
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