Published online Jul 6, 2023. doi: 10.12998/wjcc.v11.i19.4579
Peer-review started: May 4, 2023
First decision: May 12, 2023
Revised: May 15, 2023
Accepted: May 25, 2023
Article in press: May 25, 2023
Published online: July 6, 2023
The background of this study is focused on Polygoni Cuspidati Rhizoma et Radix (PCRR), a traditional Chinese medicine (TCM) known for its anti-inflammatory effects in various human diseases. However, the anti-inflammatory effects and mechanisms of action of PCRR on acute lung injury (ALI) remain unclear. This study aimed to identify the active ingredients of PCRR for the treatment of ALI using multiple databases to obtain potential targets. In this study, a combination of network pharmacology, bioinformatics, and molecular docking techniques were used to investigate the mechanism of action of PCRR in ALI. The identification of these active ingredients and potential targets for treatment could lead to further research to evaluate the efficacy of PCRR as a therapeutic option for ALI.
The research motivation for this study is to explore the anti-inflammatory effects and underlying mechanisms of PCRR on ALI. Despite the known benefits of PCRR in the treatment of various inflammatory diseases, its effectiveness and mechanism of action in ALI are not fully understood. The objective of this study was to use network pharmacology and molecular docking techniques to identify active compounds and potential targets of PCRR for the treatment of ALI. The ultimate goal of this study was to propose a strategy to elucidate the mechanisms of TCM at the network pharmacology level and to provide insights into the development of novel therapeutic options for ALI.
The active compounds in PCRR are involved in the treatment of ALI. Multiple databases were used to identify potential targets for fishing and target genes closely associated with ALI. Network pharmacology and bioinformatics resources were utilized to predict the molecular mechanisms of PCRR in ALI. Verification of the combination of major active ingredients and core targets using molecular docking techniques. In this study, we present a proposed strategy to better understand the mechanisms of TCM through network pharmacology. Additionally, we emphasize the potential therapeutic benefits of PCRR in treating ALI.
The active compounds of PCRR were identified through data collected from various databases including Traditional Chinese Medicine Systems Pharmacology, STITCH, and PubMed. Target ALI databases were built using the Therapeutic Target, DrugBank, DisGeNET, Online Mendelian Inheritance in Man, and Genetic Association databases. Network pharmacology analysis Network construction, target prediction, topological feature analysis, and enrichment analysis were conducted to determine the potential targets and molecular mechanisms of PCRR in ALI. Bioinformatics analysis: Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes network pathway enrichment analyses were performed using bioinformatics resources from the Database for Annotation, Visualization, and Integrated Discovery. The combination of major active ingredients and core targets was verified using molecular docking techniques.
Thirteen bioactive compounds corresponding to the 433 PCRR targets were identified. A total of 128 genes closely associated with ALI were identified, out of which 60 genes overlapped with potential therapeutic targets identified by PCRR. These 60 genes were considered to be relevant for developing therapeutic interventions for ALI. According to functional enrichment analysis, PCRR was found to have pharmacological effects on ALI by regulating various pathways such as the cell cycle, apoptosis, drug metabolism, inflammation, and immune modulation. Molecular docking results revealed a strong associative relationship between the active ingredient and core target. This study suggests a method for understanding how TCM works at the network pharmacology level. Additionally, it emphasizes the potential of PCRR as a treatment for ALI. Overall, these results provide important insights into the potential therapeutic effects and mechanisms of action of PCRR in ALI, which could lead to further research evaluating the efficacy of PCRR as a therapeutic option for ALI.
This study identified 13 bioactive compounds in PCRR that are related to the treatment of ALI. Using network pharmacology and molecular docking techniques, we identified potential targets of PCRR in ALI and predicted the molecular mechanisms underlying its therapeutic effects. The findings of this study indicate that PCRR may have a beneficial impact on reducing inflammation in patients with ALI by regulating various pathways such as the cell cycle, cell apoptosis, drug metabolism, inflammation, and immune modulation. Overall, This study presents a network pharmacology-based approach to understand the mechanisms of TCM and emphasizes the therapeutic benefits of PCRR in treating ALI.
However, further experimental validation is required to confirm the predicted molecular mechanisms and therapeutic effects of PCRR in ALI. Future studies should explore the optimal dosage and administration methods for PCRR in treating ALI, as well as the potential adverse effects. Novel active ingredients and targets related to PCRR for the treatment of ALI can be identified through database mining and experimental verification. The network pharmacology approach used in this study can be applied to investigate the mechanisms of action of other TCMs and their potential therapeutic effects in various diseases. The integration of multiple omics data and machine learning techniques could enhance the accuracy and efficiency of predicting drug-target interactions and uncover the molecular mechanisms of TCM.