The Mechanism of Compound Anshen Essential Oil in the Treatment of Insomnia Was Examined by Network Pharmacology

The active component-target network and protein-protein interaction network of Compound Anshen essential oil were constructed. The target functions and related pathways were analyzed to explore the mechanism of Compound Anshen essential oil in the treatment of insomnia. GC-MS was used to detect the chemical composition of Compound Anshen essential oil, and the TCMSP, STITCH, TTD, and DrugBank databases were searched to predict and screen the targets of Compound Anshen essential oil in the treatment of insomnia. Cytoscape software was used to construct the network diagrams of the active component-action target and protein-protein interaction networks, ClueGO software was used to analyze the GO enrichment and KEGG pathway of the target, and the systemsDock website database was used for molecular docking. The analysis of the network results showed that the activity of Compound Anshen essential oil mainly involves biological processes such as the phospholipase C-activating G protein-coupled receptor signaling pathway, response to ammonium ions, calcium ion transport into the cytosol, and chloride transport. The results of molecular docking showed that linalool, caryophyllene, dibutyl phthalate, (-)-4-terpineol, and (-)-α-terpineol have good binding activity with ADRB2, DRD2, ESR1, KCNH2, NR1H4, NR1I2, NR1I3, and TRPV1 targets. This study demonstrates the multicomponent, multitarget, and multichannel characteristics of Compound Anshen essential oil and provides a new therapeutic idea and method for further research on the mechanism of Compound Anshen essential oil in the treatment of insomnia.

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An Analysis of the Anti-Neuropathic Effects of Qi She Pill Based on Network Pharmacology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/7193832 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Yong-jia Song ◽

Jia-min Bao ◽

Long-yun Zhou ◽

Gan Li ◽

Kim Sia Sng ◽

...

Keyword(s):

Mechanism Of Action ◽

Protein Interaction ◽

Protein Interaction Network ◽

Biological Process ◽

Interaction Network ◽

Network Pharmacology ◽

Protein Protein Interaction ◽

Target Network ◽

Protein Protein Interaction Network ◽

Compound Target

Background. Qi She Pill (QSP) is a traditional prescription for the treatment of neuropathic pain (NP) that is widely used in China. However, no network pharmacology studies of QSP in the treatment of NP have been conducted to date. Objective. To verify the potential pharmacological effects of QSP on NP, its components were analyzed via target docking and network analysis, and network pharmacology methods were used to study the interactions of its components. Materials and Methods. Information on pharmaceutically active compounds in QSP and gene information related to NP were obtained from public databases, and a compound-target network and protein-protein interaction network were constructed to study the mechanism of action of QSP in the treatment of NP. The mechanism of action of QSP in the treatment of NP was analyzed via Gene Ontology (GO) biological process annotation and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway enrichment, and the drug-like component-target-pathway network was constructed. Results. The compound-target network contained 60 compounds and 444 corresponding targets. The key active compounds included quercetin and beta-sitosterol. Key targets included PTGS2 and PTGS1. The protein-protein interaction network of the active ingredients of QSP in the treatment of NP featured 48 proteins, including DRD2, CHRM, β2-adrenergic receptor, HTR2A, and calcitonin gene-related peptide. In total, 53 GO entries, including 35 biological process items, 7 molecular function items, and 11 cell related items, were identified. In addition, eight relevant (KEGG) pathways were identified, including calcium, neuroactive ligand-receptor interaction, and cAMP signaling pathways. Conclusion. Network pharmacology can help clarify the role and mechanism of QSP in the treatment of NP and provide a foundation for further research.

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Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

Natural Product Communications ◽

10.1177/1934578x20982138 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1934578X2098213

Author(s):

Xiaodong Deng ◽

Yuhua Liang ◽

Jianmei Hu ◽

Yuhui Yang

Keyword(s):

Diabetes Mellitus ◽

Molecular Docking ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Network Pharmacology ◽

Hub Genes ◽

Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

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Elucidating the Mechanisms of Action of Lianhua Qingwen decoction for the Treatment of COVID-19 via Systems Pharmacology Approaches

10.21203/rs.3.rs-21103/v2 ◽

2021 ◽

Author(s):

Xiting Wang ◽

Tao Lu

Keyword(s):

Molecular Docking ◽

Blood Circulation ◽

Interaction Network ◽

Enrichment Analysis ◽

Detection Algorithm ◽

Network Pharmacology ◽

Systems Pharmacology ◽

Protein Protein Interaction ◽

Further Development ◽

Protein Protein Interaction Network

Abstract Due to the severity of the COVID-19 epidemic, to identify a proper treatment for COVID-19 is of great significance. Traditional Chinese Medicine (TCM) has shown its great potential in the prevention and treatment of COVID-19. One of TCM decoction, Lianhua Qingwen decoction displayed promising treating efficacy. Nevertheless, the underlying molecular mechanism has not been explored for further development and treatment. Through systems pharmacology and network pharmacology approaches, we explored the potential mechanisms of Lianhua Qingwen treating COVID-19 and acting ingredients of Lianhua Qingwen decoction for COVID-19 treatment. Through this way, we generated an ingredients-targets database. We also used molecular docking to screen possible active ingredients. Also, we applied the protein-protein interaction network and detection algorithm to identify relevant protein groupings of Lianhua Qingwen. Totally, 605 ingredients and 1,089 targets were obtained. Molecular Docking analyses revealed that 35 components may be the promising acting ingredients, 7 of which were underlined according to the comprehensive analysis. Our enrichment analysis of the 7 highlighted ingredients showed relevant significant pathways that could be highly related to their potential mechanisms, e.g. oxidative stress response, inflammation, and blood circulation. In summary, this study suggests the promising mechanism of the Lianhua Qingwen decoction for COVID-19 treatment. Further experimental and clinical verifications are still needed.

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Network Pharmacology and Molecular Docking Suggest the Mechanism for Biological Activity of Rosmarinic Acid

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5190808 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Minglong Guan ◽

Lan Guo ◽

Hengli Ma ◽

Huimei Wu ◽

Xiaoyun Fan

Keyword(s):

Biological Activity ◽

Molecular Docking ◽

Protein Interaction ◽

Protein Interaction Network ◽

Rosmarinic Acid ◽

Target Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis

Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit (WebGestalt). At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.

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Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/8872593 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Ping Yang ◽

Haifeng He ◽

Shangfu Xu ◽

Ping Liu ◽

Xinyu Bai

Keyword(s):

Molecular Docking ◽

Signaling Pathways ◽

Target Genes ◽

Target Prediction ◽

Interaction Network ◽

Network Pharmacology ◽

Core Network ◽

Active Ingredients ◽

Protein Protein Interaction ◽

Highly Correlated

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

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Network Pharmacology-Based Study on the Mechanism of Aloe Vera for Treating Cancer

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/6077698 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Jing Xie ◽

Jun Wu ◽

Sihui Yang ◽

Huaijun Zhou

Keyword(s):

Molecular Docking ◽

Drug Targets ◽

Aloe Vera ◽

Beta Carotene ◽

Network Pharmacology ◽

Potential Mechanism ◽

Active Ingredients ◽

Protein Protein Interaction ◽

Target Network ◽

Molecule Docking

Background. Aloe vera has long been considered an anticancer herb in different parts of the world. Objective. To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. Methods. The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. Results. By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest. Conclusion. This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research.

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Using Network Pharmacology to Explore the Mechanism of Peach Kernel-Safflower in the Treatment of Diabetic Nephropathy

BioMed Research International ◽

10.1155/2021/6642584 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Jingxue Han ◽

Xinwei Wang ◽

Jingyi Hou ◽

Yu Liu ◽

Peng Liu ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Fluid Shear Stress ◽

Interaction Network ◽

Network Pharmacology ◽

Screening Process ◽

Protein Protein Interaction ◽

Target Network ◽

The Common ◽

Relationship Of ◽

Protein Protein Interaction Network

Objective. The mechanism of peach kernel-safflower in treating diabetic nephropathy (DN) was investigated using network pharmacology. Methods. Network pharmacology methodology was applied to screen the effective compounds of peach kernel-safflower in the SymMap and TCMSP databases. Potential targets were then screened in the ETCM, SEA, and SymMap databases to construct a compound-target network. This was followed by screening of DN targets in OMIM, Gene, and GeneCards databases. The common targets of drugs and diseases were selected for analysis in the STRING database, and the results were imported into Cytoscape 3.8.0 to construct a protein-protein interaction network. Next, GO and KEGG enrichment analyses were performed. Finally, Schrödinger molecular docking verified the reliability of the results. Results. A total of 23 effective compounds and 794 potential targets resulted from our screening process. Quercetin and luteolin were identified as the main effective ingredients in peach kernel-safflower. Furthermore, five key targets (VEGFA, IL6, TNF, AKT1, and TP53), AGE-RAGE, fluid shear stress and atherosclerosis, IL-17, and HIF-1 signaling pathways may be involved in the treatment of DN using peach kernel-safflower. Conclusions. This study embodies the complex network relationship of multicomponents, multitargets, and multipathways of peach kernel-safflower to treat DN and provides a basis for further research on its mechanism.

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Predicting the Mechanism of the Analgesic Property of Yanhusuo Based on Network Pharmacology

Natural Product Communications ◽

10.1177/1934578x19883071 ◽

2019 ◽

Vol 14 (10) ◽

pp. 1934578X1988307

Author(s):

Wen-Ping Xiao ◽

Yan-Fang Yang ◽

He-Zhen Wu ◽

Yi-yi Xiong

Keyword(s):

Molecular Docking ◽

Metabolic Pathways ◽

Interaction Network ◽

Binding Activity ◽

Network Pharmacology ◽

Active Components ◽

Network Diagram ◽

Pathway Interaction ◽

Pathway Interaction Network ◽

Pathway Analyses

Yanhusuo (Corydalis Rhizoma) extracts are widely used for the treatment of pain and inflammation. The effects of Yanhusuo in pain assays were assessed in a few studies. However, there are few studies on its analgesic mechanism. In this paper, network pharmacology was used to explore the analgesic components of Yanhusuo and its analgesic mechanism. The active components of Yanhusuo were screened by TCMSP database, combined with literature data. PharmMapper and GeneCards databases were used for screening the analgesic targets of the components. The protein interaction network diagram was drawn by String database and Cytoscape software, the gene ontology and KEGG pathway analyses of the target were performed by DAVID database, and the component–target–pathway interaction network diagram was further drawn by Cytoscape3.6.1 software. System Dock Web Site verified the molecular docking among components and targets. Finally, an interaction network of the component–target–pathway of Yanhusuo was constructed, and the functions and pathways were analyzed for preliminarily investigating the mechanism of Yanhusuo in analgesia. The results showed that the active components of analgesic in Yanhusuo were Corynoline, 13-methylpalmatrubine, dehydrocorydaline, saulatine, 2,3,9,10-tetramethoxy-13-methyl-5,6-dihydroisoquinolino[2,1-b]isoquinolin-8-on-e, and Capaurine. The mechanisms were involved in metabolic pathways, PI3k-Akt signaling pathway, pathways in cancer, and so on. The top 3 targets were NOS3, glucose-6-phosphate dehydrogenase, and glucose-6-phosphate isomerase in components-target-pathways network, and they were all enriched in metabolic pathways. Meanwhile the molecular docking showed that there was a high binding activity between the 6 components and the important target proteins, as a further certification for the subsequent network analysis. This study reveals the relationship of the components, targets, and pathways of active components in Yanhusuo, and provides new ideas and methods for further research on the analgesic mechanism of Yanhusuo.

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Virtual Screening of Potential Anti-fatigue Mechanism of Polygonati Rhizoma Based on Network Pharmacology

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666191106110615 ◽

2020 ◽

Vol 22 (9) ◽

pp. 612-624 ◽

Cited By ~ 1

Author(s):

Ze-Feng Wang ◽

Ye-Qing Hu ◽

Qi-Guo Wu ◽

Rui Zhang

Keyword(s):

Virtual Screening ◽

Interaction Network ◽

Enrichment Analysis ◽

Vital Role ◽

Network Pharmacology ◽

Pathway Network ◽

Protein Protein Interaction ◽

Target Network ◽

Protein Protein Interaction Network ◽

Fatigue Mechanism

Background and Objective: A large number of people are facing the danger of fatigue due to the fast-paced lifestyle. Fatigue is common in some diseases, such as cancer. The mechanism of fatigue is not definite. Traditional Chinese medicine is often used for fatigue, but the potential mechanism of Polygonati Rhizoma (PR) is still not clear. This study attempts to explore the potential anti-fatigue mechanism of Polygonati Rhizoma through virtual screening based on network pharmacology. Methods: The candidate compounds of PR and the known targets of fatigue are obtained from multiple professional databases. PharmMapper Server is designed to identify potential targets for the candidate compounds. We developed a Herbal medicine-Compound-Disease-Target network and analyzed the interactions. Protein-protein interaction network is developed through the Cytoscape software and analyzed by topological methods. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment are carried out by DAVID Database. Finally, we develop Compound-Target-Pathway network to illustrate the anti-fatigue mechanism of PR. Results: This approach identified 12 active compounds and 156 candidate targets of PR. The top 10 annotation terms for GO and KEGG were obtained by enrichment analysis with 35 key targets. The interaction between E2F1 and PI3K-AKT plays a vital role in the anti-fatigue effect of PR due to this study. Conclusions: This study demonstrates that PR has multi-component, multi-target and multipathway effects.

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Predicting the Molecular Mechanism of “Angong Niuhuang Pills” in the Treatment of COVID-19 Based on Network Pharmacology

Natural Product Communications ◽

10.1177/1934578x211024032 ◽

2021 ◽

Vol 16 (6) ◽

pp. 1934578X2110240

Author(s):

Peng-yu Chen ◽

Chen Wang ◽

Ying Zhang ◽

Chong Yuan ◽

Bing Yu ◽

...

Keyword(s):

Molecular Docking ◽

Treatment Plan ◽

Interaction Network ◽

Network Pharmacology ◽

Signal Pathways ◽

Active Components ◽

Protein Protein Interaction ◽

Pathway Annotation ◽

Chinese Patent ◽

8Th Edition

Introduction Angong Niuhuang Pills (AGNH), a Chinese patent medicine recommended in the “Diagnosis and Treatment Plan for COVID-19 (8th Edition),” may be clinically effective in treating COVID-19. The active components and signal pathways of AGNH through network pharmacology have been examined, and its potential mechanisms determined. Methods We screened the components in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) via Drug-like properties (DL) and Oral bioavailability (OB); PharmMapper and GeneCards databases were used to collect components and COVID-19 related targets; KEGG pathway annotation and GO bioinformatics analysis were based on KOBAS3.0 database; “herb-components-targets-pathways” (H-C-T-P) network and protein-protein interaction network (PPI) were constructed by Cytoscape 3.6.1 software and STRING 10.5 database; we utilized virtual molecular docking to predict the binding ability of the active components and key proteins. Results A total of 87 components and 40 targets were screened in AGNH. The molecular docking results showed that the docking scores of the top 3 active components and the targets were all greater than 90. Conclusion Through network pharmacology research, we found that moslosooflavone, oroxylin A, and salvigenin in AGNH can combine with ACE2 and 3CL, and then are involved in the MAPK and JAK-STAT signaling pathways. Finally, it is suggested that AGNH may have a role in the treatment of COVID-19.

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