scholarly journals Predicting the Molecular Mechanism of Sini Jia Renshen Decoction in Treating Severe COVID-19 Patients Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (12) ◽  
pp. 1934578X2110592
Author(s):  
Yi Wen Liu ◽  
Ai Xia Yang ◽  
Li Lu ◽  
Tie Hua Huang
Keyword(s):  
Molecular Docking ◽  
Protein Interactions ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Active Ingredients ◽  
Active Compounds ◽  
Pathway Network ◽  
Function Enrichment Analysis

Objective: To explore the potential mechanism of Sini jia Renshen Decoction (SJRD) in the treatment of COVID-19 based on network pharmacology and molecular docking. Methods: The active compounds and potential therapeutic targets of SJRD were collected through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Then a string database was used to build a protein–protein interactions (PPI) network between proteins, and use the David database to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct an active ingredients-core target-signaling pathway network, and finally the active ingredients of SJRD were molecularly docked with the core targets to predict the mechanism of SJRD in the treatment of COVID-19. Results: A total of 136 active compounds, 51 core targets and 93 signaling pathways were selected. Molecular docking results revealed that quercetin, 3,22-dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid, 18α-hydroxyglycyrrhetic acid, gomisin B and ignavine had considerable binding ability with ADRB2, PRKACA, DPP4, PIK3CG and IL6. Conclusions: This study preliminarily explored the mechanism of multiple components，multiple targets，and multiple pathways of SJRD in the treatment of COVID-19 by network pharmacology．

scholarly journals Predicting the Molecular Mechanism of Shenling Baizhu San in Treating Convalescent Patients With COVID-19 Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (10) ◽  
pp. 1934578X2110460
Author(s):  
Ying Zhang ◽  
Li Lu ◽  
YiWen Liu ◽  
AiXia Yang ◽  
Yanfang Yang
Keyword(s):  
Molecular Docking ◽  
Protein Interactions ◽  
Enrichment Analysis ◽  
Binding Energies ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interactions ◽  
Active Ingredients ◽  
Active Components ◽  
Pathway Network

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein–protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than −5 kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.

scholarly journals Exploration of the mechanism of Ziyin Tongluo Formula on preventing and treating postmenopausal osteoporosis based on the network pharmacology and molecular docking

2020 ◽  
Author(s):  
Rong-Bin Chen ◽  
Ying-Dong Yang ◽  
Kai Sun ◽  
Shan Liu ◽  
Wei Guo ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Postmenopausal Osteoporosis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Active Compounds ◽  
Core Proteins ◽  
Key Genes

Abstract Background: Postmenopausal osteoporosis (PMOP) is a global chronic and metabolic bone disease, which poses huge challenges to individuals and society. Ziyin Tongluo Formula (ZYTLF) has been proved effective in the treatment of PMOP. However, the material basis and mechanism of ZYLTF against PMOP have not been thoroughly elucidated.Methods: Online databases were used to identify the active ingredients of ZYTLF and corresponding putative targets. Genes associated with PMOP were mined, and then mapped with the putative targets to obtain overlapping genes. Multiple networks were constructed and analyzed, from which the key genes were selected. The key genes were imported to the DAVID database to performs GO and KEGG pathway enrichment analysis. Finally, AutoDock Tools and other software were used for molecular docking of core compounds and key proteins. Results: Ninety-two active compounds of ZYTLF corresponded to 243 targets, with 129 target genes interacting with PMOP, and 50 key genes were selected. Network analysis showed the top 5 active ingredients including quercetin, kaempferol, luteolin, scutellarein, and formononetin., and the top 50 key genes such as VEGFA, MAPK8, AKT1, TNF, ESR1. Enrichment analysis uncovered two significant types of KEGG pathways in PMOP, hormone-related signaling pathways (estrogen , prolactin, and thyroid hormone signaling pathway) and inflammation-related pathways (TNF, PI3K-Akt, and MAPK signaling pathway). Moreover, molecular docking analysis verified that the main active compounds were tightly bound to the core proteins, further confirming the anti-PMOP effects. Conclusions: Based on network pharmacology and molecular docking technology, this study initially revealed the mechanisms of ZYTLF on PMOP, which involves multiple targets and multiple pathways.

Systematic evaluation of the mechanisms of Mulberry leaf (Morus alba Linne) acting on diabetes based on network pharmacology and molecular docking

2020 ◽  
Vol 23 ◽  
Author(s):  
Qiguo Wu ◽  
Yeqing Hu
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Compounds ◽  
Pathway Network ◽  
Pathway Enrichment ◽  
Mulberry Leaves ◽  
Mulberry Leaf

Background: Diabetes mellitus is one of the most common endocrine metabolic disorder diseases. The application of herbal medicine to control glucose levels and improve insulin action might be a useful approach in the treatment of diabetes. Mulberry leaves (ML) has been reported to exert important activities of anti-diabetic. Objective: In this work, we aimed to explore the multi-targets and multi-pathways regulatory molecular mechanism of Mulberry leaves (ML, Morus alba Linne) acting on diabetes. Methods: Identification of active compounds of Mulberry leaves using Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Bioactive components were screened by FAF-Drugs4 website (Free ADME-Tox Filtering Tool). The targets of bioactive components were predicted from SwissTargetPrediction website, and the diabetes related targets were screened from GeneCards database. The common targets of ML and diabetes are used for Gene Ontology (GO) and pathway enrichment analysis. The visualization networks were constructed by Cytoscape 3.7.1 software. The construction of biological networks were performed to analyze the mechanisms as follows: (1) Compound-Target network; (2) Common target-Compound network; (3) Common targets protein interaction network; (4) Compound-Diabetes protein-protein interactions (PPI) network; (5) Target-Pathway network; (6) Compound-Target-Pathway network. At last, the prediction results of network pharmacology were verified by molecular docking method. Results: 17 active components were obtained by TCMSP database and FAF-Drugs4 website. 51 potential targets (11 common targets and 40 associated indirect targets) were obtained and used to build the PPI network by String database. Furthermore, the potential targets were used to GO and pathway enrichment analysis. 8 key active compounds (quercetin, Iristectorigenin A, 4-Prenylresveratrol, Moracin H, Moracin C, Isoramanone, Moracin E and Moracin D) and 8 key targets (AKT1, IGF1R, EIF2AK3, PPARG, AGTR1, PPARA, PTPN1 and PIK3R1) were obtained to play major roles in Mulberry leaf acting on diabetes. And the signal pathways involved in the mechanisms mainly include AMPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, insulin signaling pathway and insulin resistance. The molecular docking results show that the 8 key active compounds have good affinity with the key target of AKT1, and the 5 key targets (IGF1R, EIF2AK3, PPARG, PPARA and PTPN1) have better affinity than AKT1 with the key compound of quercetin. Conclusion: Based on network pharmacology and molecular docking of this work provided an important systematic and visualized basis for further understanding the synergy mechanism of ML acting on diabetes.

scholarly journals A Combined Network Pharmacology and Molecular Docking Approach to Investigate Candidate Active Components and Multitarget Mechanisms of Hemerocallis Flowers on Antidepressant Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tiancheng Ma ◽  
Yu Sun ◽  
Chang Jiang ◽  
Weilin Xiong ◽  
Tingxu Yan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Protein Interactions ◽  
Kegg Pathway ◽  
Endocrine System ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Antidepressant Effect ◽  
Pathway Enrichment Analysis ◽  
Active Components ◽  
Pathway Network

Objective. The purpose of our research is to systematically explore the multiple mechanisms of Hemerocallis fulva Flowers (HF) on depressive disorder (DD). Methods. The components of HF were searched from the literature. The targets of components were obtained from PharmMapper. After that, Cytoscape software was used to build a component-target network. The targets of DD were collected from DisGeNET, PharmGKB, TTD, and OMIM. Protein-protein interactions (PPIs) among the DD targets were executed to screen the key targets. Afterward, the GO and KEGG pathway enrichment analysis were performed by the KOBAS database. A compound-target-KEGG pathway network was built to analyze the key compounds and targets. Finally, the potential active substances and targets were validated by molecular docking. Results. A total of 55 active compounds in HF, 646 compound-related targets, and 527 DD-related targets were identified from public databases. After treated with PPI, 219 key targets of DD were acquired. The gene enrichment analysis suggested that HF probably benefits DD patients by modulating pathways related to the nervous system, endocrine system, amino acid metabolism, and signal transduction. The network analysis showed the critical components and targets of HF on DD. Results of molecular docking increased the reliability of this study. Conclusions. It predicted and verified the pharmacological and molecular mechanism of HF against DD from a holistic perspective, which will also lay a foundation for further experimental research and rational clinical application of DD.

scholarly journals Exploring the Pharmacological Mechanism of Duhuo Jisheng Decoction in Treating Osteoporosis Based on Network Pharmacology

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Zhencheng Xiong ◽  
Can Zheng ◽  
Yanan Chang ◽  
Kuankuan Liu ◽  
Li Shu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Active Compounds ◽  
Treatment Of Osteoporosis ◽  
Target Proteins

Objective. The purpose of this work is to study the mechanism of action of Duhuo Jisheng Decoction (DHJSD) in the treatment of osteoporosis based on the methods of bioinformatics and network pharmacology. Methods. In this study, the active compounds of each medicinal ingredient of DHJSD and their corresponding targets were obtained from TCMSP database. Osteoporosis was treated as search query in GeneCards, MalaCards, DisGeNET, Therapeutic Target Database (TTD), Comparative Toxicogenomics Database (CTD), and OMIM databases to obtain disease-related genes. The overlapping targets of DHJSD and osteoporosis were identified, and then GO and KEGG enrichment analysis were performed. Cytoscape was employed to construct DHJSD-compounds-target genes-osteoporosis network and protein-protein interaction (PPI) network. CytoHubba was utilized to select the hub genes. The activities of binding of hub genes and key components were confirmed by molecular docking. Results. 174 active compounds and their 205 related potential targets were identified in DHJSD for the treatment of osteoporosis, including 10 hub genes (AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF). Pathway enrichment analysis of target proteins indicated that osteoclast differentiation, AGE-RAGE signaling pathway in diabetic complications, Wnt signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, calcium signaling pathway, and TNF signaling pathway were the specifically major pathways regulated by DHJSD against osteoporosis. Further verification based on molecular docking results showed that the small molecule compounds (Quercetin, Kaempferol, Beta-sitosterol, Beta-carotene, and Formononetin) contained in DHJSD generally have excellent binding affinity to the macromolecular target proteins encoded by the top 10 genes. Conclusion. This study reveals the characteristics of multi-component, multi-target, and multi-pathway of DHJSD against osteoporosis and provides novel insights for verifying the mechanism of DHJSD in the treatment of osteoporosis.

scholarly journals Network pharmacology-based active compounds and pharmacological mechanisms of ginseng for depression in post-COVID-19

2020 ◽  
Author(s):  
Na Wang ◽  
Xianlei Wang ◽  
Mengjiao He ◽  
Wenxiu Zheng ◽  
Xiaoqing Cai ◽  
...  
Keyword(s):  
Immune Regulation ◽  
Enrichment Analysis ◽  
Sudden Onset ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Active Compounds ◽  
Life Threatening Illness ◽  
Novel Coronavirus

Abstract Introduction: The novel coronavirus disease 2019 (COVID-19) is in the midst of worldwide panic. Sudden onset of an immediate life-threatening illness, quarantine and unemployment caused by epidemic are all contributors to depression. Ginseng has been reported to be an effective and safe clinical treatment on both immune-regulation and anti-depression. However, the mechanism of its anti-depression effect has not been fully characterized. In order to provide theoretical guidance for further clinical application in post-pandemic, we investigated active compounds and pharmacological mechanisms of ginseng to exert anti-depressant activity using network pharmacology, and discussed the active ingredients with immune-regulation and anti-depression.Methods: Information on compounds in ginseng was obtained from public databases, and genes related to depression were gathered using the GeneCards database. Networks of ginseng-associated targets and depression-related genes were constructed through STRING database. Potential targets and pathway enrichment analysis related to the therapeutic efficacy of ginseng for depression were identified using Cytoscape and Database for Annotation, Visualization and Integrated Discovery (DAVID). Results: Network pharmacological analysis of ginseng in treatment of depression identified 16 active ingredients, 47 potential targets, 32 GO terms, and 8 target gene-regulated major pathways. Among them, kaempferol, beta-sitosterol, stigmasterol, fumarine and frutinone A are bioactive compounds and key chemicals. Core genes in PPI network were AKT1, CASP3, NOS3, TNF, and PPARG. Enrichment results revealed that ginseng could regulate multiple aspects of depression through neuroactive ligand-receptor interaction, HIF-1 signaling pathway, and Serotonergic synapse. More importantly, we found that frutinone A and kaempferol are key ingredients in ginseng with dual activities of immune-regulation and anti-depression. Conclusions: We discovered that the therapeutic activities of ginseng for depression mainly involve neurotransmitters, neurotrophic factors, neurogenesis, HPA axis and inflammatory response. Pharmacological network analysis can help to explain the potential effects of ginseng for treating depression, indicating that ginseng is a preferable herb clinically for immune-regulation and anti-depression in post-pandemic.

scholarly journals Identifying Synergistic Mechanisms of Multiple Ingredients in Shuangbai Tablets against Proteinuria by Virtual Screening and a Network Pharmacology Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaoqin Ma ◽  
Meixiang Yu ◽  
Chenxia Hao ◽  
Wanhua Yang
Keyword(s):  
Molecular Docking ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Human Phenotype ◽  
Herbal Mixture ◽  
Relationship Of

Shuangbai Tablets (SBT), a traditional herbal mixture, has shown substantial clinical efficacy. However, a systematic mechanism of its active ingredients and pharmacological mechanisms of action against proteinuria continues being lacking. A network pharmacology approach was effectual in discovering the relationship of multiple ingredients and targets of the herbal mixture. This study aimed to identify key targets, major active ingredients, and pathways of SBT against proteinuria by network pharmacology approach combined with thin layer chromatography (TLC). Human phenotype (HP) disease analysis, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and molecular docking were used in this study. To this end, a total of 48 candidate targets of 118 active ingredients of SBT were identified. Network analysis showed PTGS2, ESR1, and NOS2 to be the three key targets, and beta-sitosterol, quercetin, and berberine were the three major active ingredients; among them one of the major active ingredients, quercetin, was discriminated by TLC. These results of the functional enrichment analysis indicated that the most relevant disease including these 48 candidate proteins is proteinuria, SBT treated proteinuria by sympathetically regulating multiple biological pathways, such as the HIF-1, RAS, AGE-RAGE, and VEGF signaling pathways. Additionally, molecular docking validation suggested that major active ingredients of SBT were capable of binding to HIF-1A and VEGFA of the main pathways. Consequently, key targets, major active ingredients, and pathways based on data analysis of SBT against proteinuria were systematically identified confirming its utility and providing a new drug against proteinuria.

scholarly journals Network Pharmacology Study and Molecular Docking on the Mechanism of Treatment of Colorectal Cancer by Xiao-Chai-Hu-Tang

2021 ◽  
Author(s):  
Jingyun Jin ◽  
Bin Chen ◽  
Xiangyang Zhan ◽  
Zhiyi Zhou ◽  
Hui Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Gene Ontology ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Signal Pathways ◽  
Active Ingredients ◽  
Pathway Gene

Abstract Background and objective: To predict the targets and signal pathways of Xiao-Chai-Hu-Tang (XCHT) in the treatment of colorectal cancer (CRC) based on network pharmacology, to further analyze its anti-CRC material basis and mechanism of action.Methods: TCMSP and TCMID databases were adopted to screen the active ingredients and potential targets of XCHT. CRC-related targets were retrieved by analyzing published microarray data (accession number GSE110224) from the Gene Expression Omnibus (GEO) database. The above common targets were used to construct the “herb-active ingredients-target” network by Cytoscape 3.8.0 software. And then, the protein-to-protein interaction（PPI）was constructed and analyzed with BisoGenet and CytoNCA plug-in in Cytoscape. Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis for target genes were then performed using the R package of cluster Profiler. Further, AutoDock Vina software was used to conduct molecular docking studies on the active ingredients and key targets to verify the network pharmacological analysis results.Results: A total of 71 active ingredients of XCHT and 20 potential targets for anti-CRC were identified. The network analysis revealed that quercetin, stigmasterol, kaempferol, baicalein, acacetin may be the key compounds. And PTGS2, NR3C2, CA2, MMP1 may be the key targets. The active ingredients of XCHT interacted with most disease targets of CRC. It fully showed that XCHT exerted its therapeutic effect through the synergistic action of the multi-compound, multi-target, and multi-pathway. Gene ontology enrichment analysis showed 46 GO entries, including 20 biological processes, 6 cellular components, and 20 molecular functions. 11 KEGG signaling pathways had been identified, including IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, and NF-kappa B signaling pathway. It showed that XCHT played a role in the treatment of CRC by regulating different signal pathways. Molecular docking confirmed the correlation between five core compounds (including quercetin, stigmasterol, kaempferol, baicalein, and acacetin) and PTGS2.Conclusion: The potential active ingredients, possible targets, and key biological pathways for the efficacy of XCHT in the treatment of CRC were preliminarily described, which provided a theoretical basis for further experimental verification research.

scholarly journals Study on Mechanism of Jiawei Chaiqin Wendan Decoction in Treatment of Vestibular Migraine Based on Network Pharmacology and Molecular Docking Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Menglin Liu ◽  
Genhao Fan ◽  
Daopei Zhang ◽  
Mingjun Zhu ◽  
Huailiang Zhang
Keyword(s):  
Molecular Docking ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Signal Pathway ◽  
Vestibular Migraine ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Drug Compound

Objective. To predict the main active ingredients, potential targets, and key pathways of Jiawei Chaiqin Wendan decoction treatment in vestibular migraine and explore possible mechanisms by network pharmacology and molecular docking technology. Methods. The active ingredients and related targets of Jiawei Chaiqin Wendan decoction were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The corresponding genes of the target were queried by UniProt database, and the “drug-compound-target-disease” network was constructed by Cytoscape 3.7.2 software. GO functional enrichment analysis and KEGG pathway enrichment analysis were carried out by R software and Bioconductor, and column chart and bubble chart were drawn by Prism software and OmicShare database for visualization. Finally, the mechanism and potential targets of Jiawei Chaiqin Wendan decoction in the treatment of vestibular migraine were predicted. Results. The “drug-compound-target-disease” network contains 154 active ingredients and 85 intersection targets. The key targets include AKT1, IL6, MAPK3, VEGFA, EGFR, CASP3, EGF, MAPK1, PTGS2, and ESR1. A total of 1939 items were obtained by GO functional enrichment analysis ( P  < 0.05). KEGG pathway enrichment analysis screened 156 signal pathways ( P  < 0.05), involving PI3K-Akt signal pathway, AGE-RAGE signal pathway in diabetes complications, MAPK signal pathway, HIF-1 signal pathway, IL-17 signal pathway, etc. Molecular docking results showed that quercetin, luteolin, kaempferol, tanshinone IIa, wogonin, naringenin, nobiletin, dihydrotanshinlactone, beta-sitosterol, and salviolone have good affinity with core target proteins IL6, PTGS2, MAPK1, MAPK3, and CGRP1. Conclusion. The active ingredients in Jiawei Chaiqin Wendan decoction may regulate the levels of inflammatory factors and neurotransmitters by acting on multiple targets such as IL6, MAPK3, MAPK1, and PTGS2, so as to play a therapeutic role in vestibular migraine.

scholarly journals Deciphering Pharmacological Mechanism of Buyang Huanwu Decoction for Spinal Cord Injury by Network Pharmacology Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zhencheng Xiong ◽  
Feng Yang ◽  
Wenhao Li ◽  
Xiangsheng Tang ◽  
Haoni Ma ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Molecular Docking ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Buyang Huanwu Decoction ◽  
Active Compounds ◽  
Cord Injury

Objective. The purpose of this study was to investigate the mechanism of action of the Chinese herbal formula Buyang Huanwu Decoction (BYHWD), which is commonly used to treat nerve injuries, in the treatment of spinal cord injury (SCI) using a network pharmacology method. Methods. BYHWD-related targets were obtained by mining the TCMSP and BATMAN-TCM databases, and SCI-related targets were obtained by mining the DisGeNET, TTD, CTD, GeneCards, and MalaCards databases. The overlapping targets of the abovementioned targets may be potential therapeutic targets for BYHWD anti-SCI. Subsequently, we performed protein-protein interaction (PPI) analysis, screened the hub genes using Cytoscape software, performed Gene Ontology (GO) annotation and KEGG pathway enrichment analysis, and finally achieved molecular docking between the hub proteins and key active compounds. Results. The 189 potential therapeutic targets for BYHWD anti-SCI were overlapping targets of 744 BYHWD-related targets and 923 SCI-related targets. The top 10 genes obtained subsequently included AKT1, IL6, MAPK1, TNF, TP53, VEGFA, CASP3, ALB, MAPK8, and JUN. Fifteen signaling pathways were also screened out after enrichment analysis and literature search. The results of molecular docking of key active compounds and hub target proteins showed a good binding affinity for both. Conclusion. This study shows that BYHWD anti-SCI is characterized by a multicomponent, multitarget, and multipathway synergy and provides new insights to explore the specific mechanisms of BYHWD against SCI.

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