scholarly journals Exploring the Potential Mechanism of Shennao Fuyuan Tang for Ischemic Stroke Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jia Min Li ◽  
Zhen Ni Mu ◽  
Tian Tian Zhang ◽  
Xin Li ◽  
Yan Shang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Molecular Docking ◽  
Signal Pathway ◽  
Biologically Active ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
The Core ◽  
Target Network ◽  
Core Components

Background and Objective. Shennao Fuyuan Tang (SNFYT) is an effective herbal formula for ischemic stroke (IS). It has been in China for more than 20 years, but its effective biologically active components and underlying mechanisms remain to be elucidated. This study aimed to investigate the mechanism of action of SNFYT for the treatment of IS from both network pharmacology and molecular docking aspects. Methods. Screen the biologically active components and potential targets of SNFYT through Traditional Chinese Medicine Systems Pharmacology (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID), and related literature. In addition, DrugBank, OMIM, DisGeNET, and the Therapeutic Target Database were searched to explore the therapeutic targets of IS. The cross-targets of SNFYT potential targets and IS treatment targets were taken as candidate gene targets, and GO and KEGG enrichment analyses were performed on the candidate targets. On this basis, the SNFYT-component-target network and protein-protein interaction (PPI) network were constructed using Cytoscape 3.7.2. Finally, AutoDock was used to verify the molecular docking of core components and core targets. Results. We screened out 95 potentially active components and 143 candidate targets. SNFYT-component-target network, PPI network, and Cytoscape analysis identified four core active ingredients and 14 core targets. GO enrichment analyzed 2333 biological processes, 79 cell components, and 149 molecular functions. There are 170 KEGG-related signal pathways P < 0.05 , including the IL-17 signal pathway, TNF signal pathway, and HIF-1 signal pathway. The molecular docking results of the core components and the core targets showed good binding power. Conclusions. SNFYT may achieve the effect of treating ischemic stroke through its anti-inflammatory effect through a signal pathway with core targets as the core.

scholarly journals Network Pharmacology and Molecular Docking Analysis on Molecular Targets and Mechanisms of Buyang Huanwu Decoction in the Treatment of Ischemic Stroke

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiang Gao ◽  
Danfeng Tian ◽  
Zhenyun Han ◽  
Jingfeng Lin ◽  
Ze Chang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Molecular Docking ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
Nf Kappa B ◽  
Buyang Huanwu Decoction ◽  
Pathway Network ◽  
Target Network

Background and Objective. With the exact clinical efficacy, Buyang Huanwu decoction (BHD) is a classical prescription for the treatment of ischemic stroke (IS). Here, we aimed to investigate the pharmacological mechanisms of BHD in treating IS using systems biology approaches. Methods. The bioactive components and potential targets of BHD were screened by TCMSP, BATMAN-TCM, ETCM, and SymMap databases. Besides, compounds that failed to find the targets from the above databases were predicted through STITCH, SwissTargetPrediction, and SEA. Moreover, six databases were searched to mine targets of IS. The intersection targets were obtained and analyzed by GO and KEGG enrichment. Furthermore, BHD-IS PPI network, compound-target network, and herb-target-pathway network were constructed by Cytoscape 3.6.0. Finally, AutoDock was used for molecular docking verification. Results. A total of 235 putative targets were obtained from 59 active compounds in BHD. Among them, 62 targets were related to IS. PPI network showed that the top ten key targets were IL6, TNF, VEGFA, AKT1, etc. The enrichment analysis demonstrated candidate BHD targets were more frequently involved in TNF, PI3K-Akt, and NF-kappa B signaling pathway. Network topology analysis showed that Radix Astragali was the main herb in BHD, and the key components were quercetin, beta-sitosterol, kaempferol, stigmasterol, etc. The results of molecular docking showed the active components in BHD had a good binding ability with the key targets. Conclusions. Our study demonstrated that BHD exerted the effect of treating IS by regulating multitargets and multichannels with multicomponents through the method of network pharmacology and molecular docking.

scholarly journals Research on the Mechanism of Guizhi to Treat Nephrotic Syndrome Based on Network Pharmacology and Molecular Docking Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dan He ◽  
Qiang Li ◽  
Guangli Du ◽  
Jijia Sun ◽  
Guofeng Meng ◽  
...  
Keyword(s):  
Nephrotic Syndrome ◽  
Molecular Docking ◽  
Active Component ◽  
Network Pharmacology ◽  
Protective Mechanism ◽  
Ppi Network ◽  
Active Components ◽  
Active Compounds ◽  
The Core ◽  
Target Network

Objective. Nephrotic syndrome (NS) is a common glomerular disease caused by a variety of causes and is the second most common kidney disease. Guizhi is the key drug of Wulingsan in the treatment of NS. However, the action mechanism remains unclear. In this study, network pharmacology and molecular docking were used to explore the underlying molecular mechanism of Guizhi in treating NS. Methods. The active components and targets of Guizhi were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Hitpick, SEA, and Swiss Target Prediction database. The targets related to NS were obtained from the DisGeNET, GeneCards, and OMIM database, and the intersected targets were obtained by Venny2.1.0. Then, active component-target network was constructed using Cytoscape software. And the protein-protein interaction (PPI) network was drawn through the String database and Cytoscape software. Next, Gene Ontology (GO) and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. And overall network was constructed through Cytoscape. Finally, molecular docking was conducted using Autodock Vina. Results. According to the screening criteria, a total of 8 active compounds and 317 potential targets of Guizhi were chosen. Through the online database, 2125 NS-related targets were identified, and 93 overlapping targets were obtained. In active component-target network, beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were the important active components. In PPI network, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 were the core targets. GO and KEGG analyses showed that the main pathways of Guizhi in treating NS involved VEGF, Toll-like receptor, and MAPK signaling pathway. In molecular docking, the active compounds of Guizhi had good affinity with the core targets. Conclusions. In this study, we preliminarily predicted the main active components, targets, and signaling pathways of Guizhi to treat NS, which could provide new ideas for further research on the protective mechanism and clinical application of Guizhi against NS.

Molecular Mechanism of The Effect of Huanglian Jiedu Decoction On Ulcerative Colitis Based On Network Pharmacology And Molecular Docking

2021 ◽  
Author(s):  
Jing Yang ◽  
Chao-Tao Tang ◽  
Ruiri Jin ◽  
Bixia Liu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Molecular Docking ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Signal Pathway ◽  
Intestinal Barrier Function ◽  
Network Pharmacology ◽  
Bioactive Components ◽  
Ppi Network ◽  
Active Components

Abstract Huanglian jiedu decoction (HLJDD) is a heat-clearing and detoxifying agent composed of four kinds of Chinese herbal medicine. Previous studies have shown that HLJDD can improve the inflammatory response of ulcerative colitis (UC) and maintain intestinal barrier function. However, its molecular mechanism is not completely clear. In this study, we verified the bioactive components (BCI) and potential targets of HLJDD in the treatment of UC by means of network pharmacology and molecular docking, and constructed the pharmacological network and PPI network. Then the core genes were enriched by GO and KEGG. Finally, the bioactive components were docked with the key targets to verify the binding ability between them. A total of 54 active components related to UC were identified. Ten genes are considered to be very important to PPI network. Functional analysis showed that these target genes were mainly involved in the regulation of cell response to different stimuli, IL-17 signal pathway and TNF signal pathway. The results of molecular docking showed that the active components of HLJDD had good affinity with Hub gene. This study systematically elucidates the "multi-component, multi-target, multi-pathway" mechanism of anti-UC with HLJDD for the first time, suggesting that HLJDD or its active components may be candidate drugs for the treatment of ulcerative colitis.

scholarly journals Based on network pharmacology and molecular docking to explore the Traditional Chinese Medicine anti-influenza mechanisms:Chinese protocol for diagnosis and treatment of influenza.

2020 ◽  
Author(s):  
yifei Chen
Keyword(s):  
Molecular Docking ◽  
Influenza A ◽  
Influenza Infection ◽  
Diagnosis And Treatment ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
Pharmacological Mechanism ◽  
Severe Influenza ◽  
Target Network

Background Explore the possible mechanism of anti-influenza virus, based on the study of the active components-drug-target network, Protein-Protein Interaction (PPI) network and molecular docking verification, we explored the potential action mechanism of TCM in Chinese protocol for diagnosis and treatment of influenza 2019. Methods Screening the active components and potential targets of 12 drugs in the scheme by using TCMSP database, and Obtaining the target of influenza by GeneCard, Durgbank, OMIM, TTD and PharmGkb databases. Then, constructed the “component-durg-target” network and PPI network were by Cytoscape3.8.0 software. Morethan, analyzed and the biological function and pathway, verified the molecular docking by AutoDock Vina software. Results The 12 drugs in the recommended scheme (XBCQ) for severe influenza contain 192 active components and involve 31 key antiviral targets, which may play an antiviral role through biological processes such as lipopolysaccharide, pathogen molecular reaction and regulate signaling pathway via the IL-17, influenza A, TNF, Toll-like receptors. Conclusion TCM play critical therapeutic roles through “multi-components, multi-targets and multi-pathways” mechanisms in influenza infection. The antiviral pharmacological mechanism of Xuanbai Chengqi decoction, which was analyzed by network pharmacology and molecular docking, provide a new idea for further exploring the diagnosis and treatment of severe influenza.

scholarly journals Systematic Understanding of Mechanism of Danggui Shaoyao San against Ischemic Stroke Using a Network Pharmacology Approach

2022 ◽  
Vol 2022 ◽  
pp. 1-20
Author(s):  
Sijie Li ◽  
Yong Yang ◽  
Wei Zhang ◽  
Haiyan Li ◽  
Wantong Yu ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Molecular Docking ◽  
Western Blot ◽  
Signaling Pathway ◽  
Cellular Response ◽  
Network Pharmacology ◽  
Active Compounds ◽  
The Core ◽  
Target Network ◽  
Compound Target

Purpose. Danggui Shaoyao San (DSS) was developed to treat the ischemic stroke (IS) in patients and animal models. The purpose of this study was to explore its active compounds and demonstrate its mechanism against IS through network pharmacology, molecular docking, and animal experiment. Methods. All the components of DSS were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using OMIM, CTD database, and TTD database. The herb-compound-target network was constructed by Cytoscape software. The target protein-protein interaction network was built using the STRING database. The core targets of DSS were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we achieved molecular docking between the hub proteins and the key active compounds. Finally, animal experiments were performed to verify the core targets. Triphenyltetrazolium chloride (TTC) staining was used to calculate the infarct size in mice. The protein expression was determined using the Western blot. Results. Compound-target network mainly contained 51 compounds and 315 corresponding targets. Key targets contained MAPK1, SRC, PIK3R1, HRAS, AKT1, RHOA, RAC1, HSP90AA1, and RXRA FN1. There were 417 GO items in GO enrichment analysis ( p < 0.05 ) and 119 signaling pathways ( p < 0.05 ) in KEGG, mainly including negative regulation of apoptosis, steroid hormone-mediated signaling pathway, neutrophil activation, cellular response to oxidative stress, and VEGF signaling pathway. MAPK1, SRC, and PIK3R1 docked with small molecule compounds. According to the Western blot, the expression of p-MAPK 1, p-AKT, and p-SRC was regulated by DSS. Conclusions. This study showed that DSS can treat IS through multiple targets and routes and provided new insights to explore the mechanisms of DSS against IS.

scholarly journals Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Buyang Huanwu Decoction in the treatment of Ischemic Stroke

2020 ◽  
Author(s):  
Qiang Gao ◽  
Danfeng Tian ◽  
Zhenyun Han ◽  
Jingfeng Lin ◽  
Ze Chang ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Molecular Docking ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
Buyang Huanwu Decoction ◽  
Pathway Network ◽  
Drug Compound ◽  
Compound Target

Abstract Background and objective: With the exact clinical efficacy, Buyang Huanwu decoction (BHD) is a classical prescription for the treatment of ischemic stroke (IS). Here, we aimed to investigate the pharmacological mechanisms of BHD in treating IS using systems biology approaches.Methods: The bioactive components and potential targets of BHD were screened by TCMSP, BATMAN-TCM, ETCM, and SymMap databases. Besides, compounds that failed to find the targets from the above databases were predicted through STITCH, SWISS, and SEA. Moreover, six databases were searched to mine targets of IS. The intersection targets were obtained, and analyzed by GO and KEGG enrichment. Furthermore, BHD-IS PPI network, compound-compound target-IS network and pathway of drug-compound target-IS network were constructed by Cytoscape 3.6.0. Finally, AutoDock was used for molecular docking verification.Results：A total of 253 putative targets were obtained from 60 active compounds in BHD. Among them, 62 targets were related to IS. PPI network showed that the top ten key targets were IL6, TNF, VEGFA, and AKT1, etc. The enrichment analysis demonstrated candidate BHD targets were more frequently involved TNF, PI3K-Akt, and NF-kappa B signaling pathway. Network topology analysis showed that Radix Astragali was the main herb in BHD, and the key components were quercetin, beta-Sitosterol, kaempferol, and stigmasterol, etc. The results of molecular docking showed the active components in BHD had a good binding ability with the key targets.Conclusions: This study firstly adopted the methods of network pharmacology and molecular docking to reveal the relationships among herbs in BHD, the putative targets and IS-related pathways.

scholarly journals Mechanism of Compound Houttuynia Mixture as an Anti-COVID-19 Drug Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (5) ◽  
pp. 1934578X2110167
Author(s):  
Xing-Pan Wu ◽  
Tian-Shun Wang ◽  
Zi-Xin Yuan ◽  
Yan-Fang Yang ◽  
He-Zhen Wu
Keyword(s):  
Molecular Docking ◽  
Target Prediction ◽  
Interaction Network ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Active Components ◽  
Discovery Studio ◽  
The Core ◽  
Pathway Annotation

Objective To explore the anti-COVID-19 active components and mechanism of Compound Houttuynia mixture by using network pharmacology and molecular docking. Methods First, the main chemical components of Compound Houttuynia mixture were obtained by using the TCMSP database and referring to relevant chemical composition literature. The components were screened for OB ≥30% and DL ≥0.18 as the threshold values. Then Swiss Target Prediction database was used to predict the target of the active components and map the targets of COVID-19 obtained through GeneCards database to obtain the gene pool of the potential target of COVID-19 resistance of the active components of Compound Houttuynia mixture. Next, DAVID database was used for GO enrichment and KEGG pathway annotation of targets function. Cytoscape 3.8.0 software was used to construct a “components-targets-pathways” network. Then String database was used to construct a “protein-protein interaction” network. Finally, the core targets, SARS-COV-2 3 Cl, ACE2 and the core active components of Compound Houttuyna Mixture were imported into the Discovery Studio 2016 Client database for molecular docking verification. Results Eighty-two active compounds, including Xylostosidine, Arctiin, ZINC12153652 and ZINC338038, were screened from Compound Houttuyniae mixture. The key targets involved 128 targets, including MAPK1, MAPK3, MAPK8, MAPK14, TP53, TNF, and IL6. The HIF-1 signaling, VEGF signaling, TNF signaling and another 127 signaling pathways associated with COVID-19 were affected ( P < 0.05). From the results of molecular docking, the binding ability between the selected active components and the core targets was strong. Conclusion Through the combination of network pharmacology and molecular docking technology, this study revealed that the therapeutic effect of Compound Houttuynia mixture on COVID-19 was realized through multiple components, multiple targets and multiple pathways, which provided a certain scientific basis of the clinical application of Compound Houttuynia mixture.

The Mechanism of Compound Danshen Dripping Pills in the Treatment of Diabetic Retinopathy Based on Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Xuedong An ◽  
LiYun Duan ◽  
YueHong Zhang ◽  
De Jin ◽  
Shenghui Zhao ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Cell Proliferation ◽  
Diabetic Retinopathy ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Active Ingredient ◽  
Network Pharmacology ◽  
Asiatic Acid ◽  
Active Components ◽  
The Core

Abstract BackgroundOur previous randomized, double-blind, placebo-controlled, multi-center clinical study showed that Compound Danshen Dripping Pills (CDDP) had a significant and safe effect in the treatment of diabetic retinopathy (DR), but its mechanism is still unclear, which we would explain based on network pharmacology and molecular docking.MethodThe active ingredients of CDDP (composed of Panax notoginseng, Salvia miltiorrhiza Bge., and Borneol) were searched in the TCMSP database. The validated target and Smiles number of the active ingredient are queried through the PubChem database, and the predicted target of the active ingredient is obtained through the Swisstarget Prediction database. The Drugbank, TTD, and DisGeNET databases were retrieved to obtain the related targets of DR. The core targets were obtained by the cluster analysis function of Cytoscape, and then the Protein-Protein Interaction was performed. The GO and KEGG signal pathways were enriched and clustered in David database. The potential active components and targets were docking with Autodock Vina, and the results were visualized by PyMOL.Result51 active components and 922 validation and prediction targets of CDDP, 715 targets of DR and 154 co-targets were obtained. Cluster analysis showed that there were two clusters, a total of 64 targets. Go and KEGG signal pathway enrichment analysis showed that the top 20 mainly included TNF and HIF-1 signaling pathway. In GO analysis, BP mainly includes positive regulation of smooth muscle cell proliferation and response to hypoxia, CC mainly includes extracellular space and extracellular domain, MF mainly includes protein binding and protein binding recognition. In KEGG database, the key genes in the TNF signaling pathway were TNF, NFkB and VEGF, in HIF-1 signaling pathway were the IL-6, STAT3, HIF1A and VEGF. Molecular docking results showed that all components of CDDP had a certain docking ability with TNF, NFkB, VEGF, IL-6, STAT3 and HIF1A, which of Asiatic acid and Salvianolic acid j was the strongest.Conclusion Based on the network pharmacology and molecular docking, the core active components of CDDP, mainly including Asiatic acid and Salvianolic acid j, which may play a role in regulating cell proliferation and response to inflammation and hypoxia by regulating the binding and recognition of intracellular and extracellular proteins, that is, mainly through TNF signaling pathway and HIF-1 signaling pathway.

Discussing the Mechanism of Dahuang Huanglian Xiexin Decoction in the Treatment of Type 2 Diabetes Mellitus via Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Xi Cen ◽  
Yan Wang ◽  
LeiLei Zhang ◽  
XiaoXiao Xue ◽  
Yan Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Potential Mechanism ◽  
Active Components ◽  
The Core ◽  
Core Genes

Abstract BackgroundType 2 diabetes mellitus (T2DM) is regarded as Pi Dan disease in traditional Chinese medicine (TCM). Dahuang Huanglian Xiexin Decoction (DHXD), a classical TCM formula, has been used for treating Pi Dan disease in clinic, its pharmacological mechanism has not been elucidated. MethodsThis study used network pharmacological analysis and molecular docking approach to explore the mechanism of DHXD on T2DM. Firstly, the compounds in DHXD were obtained from TCMSP and TCMID databases, the potential targets were determined based on TCMSP and UniProt databases. Next, Genecards, Digenet and UniProt databases were used to identify the targets of T2DM. Then, the protein-protein interaction (PPI) network was established with overlapping genes of T2DM and compounds, and the core targets in the network were identified and analyzed. Then, the David database was used for GO and KEGG enrichment analysis. Finally, the target genes were selected and the molecular docking was completed by Autodock software to observe the binding level of active components with target genes.ResultsA total of 397 related components and 128 overlapping genes were identified. After enrichment analysis, it was found that HIF-1, TNF, IL-17 and other signaling pathways, as well as DNA transcription, gene expression, apoptosis and other cellular biological processes had the strongest correlation with the treatment of T2DM by DHXD, and most of them occurred in the extracellular space, plasma membrane and other places, which were related to enzyme binding and protein binding. In addition, 42 core genes of DHXD, such as VEGFA, TP53 and MAPK1, were considered as potential therapeutic targets, indicating the potential mechanism of DHXD on T2DM. Finally, the results of molecular docking showed that HIF-1 pathway had strong correlation with the target genes INSR and GLUT4, quercetin and berberine had the strongest binding power with them respectively.ConclusionThis study summarized the main components of DHXD in the treatment of T2DM, identified the core genes and pathways, and systematically analyzed the interaction of related targets, trying to lay the foundation for clarifying the potential mechanism of DHXD on T2DM, so as to carry out further research in the future.

scholarly journals Mechanism of Action of Bu-Fei-Yi-Shen Formula in Treating Chronic Obstructive Pulmonary Disease Based on Network Pharmacology Analysis and Molecular Docking Validation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Longchuan Wu ◽  
Yu Chen ◽  
Jiao Yi ◽  
Yi Zhuang ◽  
Lei Cui ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Mechanism Of Action ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Chronic Obstructive ◽  
Biological Functions ◽  
Active Ingredients ◽  
Obstructive Pulmonary Disease ◽  
The Core ◽  
Target Network

Objective. To explore the mechanism of action of Bu-Fei-Yi-Shen formula (BFYSF) in treating chronic obstructive pulmonary disease (COPD) based on network pharmacology analysis and molecular docking validation. Methods. First of all, the pharmacologically active ingredients and corresponding targets in BFYSF were mined by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the analysis platform, and literature review. Subsequently, the COPD-related targets (including the pathogenic targets and known therapeutic targets) were identified through the TTD, CTD, DisGeNet, and GeneCards databases. Thereafter, Cytoscape was employed to construct the candidate component-target network of BFYSF in the treatment of COPD. Moreover, the cytoHubba plug-in was utilized to calculate the topological parameters of nodes in the network; then, the core components and core targets of BFYSF in the treatment of COPD were extracted according to the degree value (greater than or equal to the median degree values for all nodes in the network) to construct the core network. Further, the Autodock vina software was adopted for molecular docking study on the core active ingredients and core targets, so as to verify the above-mentioned network pharmacology analysis results. Finally, the Omicshare database was applied in enrichment analysis of the biological functions of core targets and the involved signaling pathways. Results. In the core component-target network of BFYSF in treating COPD, there were 30 active ingredients and 37 core targets. Enrichment analysis suggested that these 37 core targets were mainly involved in the regulation of biological functions, such as response to biological and chemical stimuli, multiple cellular life processes, immunity, and metabolism. Besides, multiple pathways, including IL-17, Toll-like receptor (TLR), TNF, and HIF-1, played certain roles in the effect of BFYSF on treating COPD. Conclusion. BFYSF can treat COPD through the multicomponent, multitarget, and multipathway synergistic network, which provides basic data for intensively exploring the mechanism of action of BFYSF in treating COPD.

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