scholarly journals Research on antithrombotic mechanism of Paeoniae Radix Alba based on data mining, network pharmacology and molecular docking technology

2021 ◽  
Vol 2004 (1) ◽  
pp. 012006
Author(s):  
Jun Wu ◽  
Mengya Guo ◽  
Xingang Shen ◽  
Zhaozhi Qiu ◽  
Yingying Duan ◽  
...  
Keyword(s):  
Data Mining ◽  
Molecular Docking ◽  
Network Pharmacology ◽  
Paeoniae Radix

scholarly journals Integrating Data Mining, Network Pharmacology, and Molecular Docking Verification to Investigate the Molecular Mechanism of Traditional Chinese Medicine Prescriptions for Treating Male Infertility

2021 ◽  
Author(s):  
Xue Bai ◽  
Yibo Tang ◽  
Qiang Li ◽  
Guimin Liu ◽  
Dan Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Male Infertility ◽  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Oxidant Stress ◽  
Estrogen Signaling ◽  
Network Pharmacology

Abstract Background: Male infertility (MI) affects almost 5% adult men worldwide, and 75% of these cases are unexplained idiopathic. There are limitations in the current treatment due to the unclear mechanism of MI, which highlight the urgent need for a more effective strategy or drug. Traditional Chinese Medicine (TCM) prescriptions have been used to treat MI for thousands of years, but their molecular mechanism is not well defined. Methods: Aiming at revealing the molecular mechanism of TCM prescriptions on MI, a comprehensive strategy integrating data mining, network pharmacology, and molecular docking verification was performed. Firstly, we collected 289 TCM prescriptions for treating MI from National Institute of TCM Constitution and Preventive Medicine for 6 years. Then, Core Chinese Materia Medica (CCMM), the crucial combination of TCM prescriptions, was obtained by the TCM Inheritance Support System from China Academy of Chinese Medical Sciences. Next, the components and targets of CCMM in TCM prescriptions and MI-related targets were collected and analyzed through network pharmacology approach.Results: The results showed that the molecular mechanism of TCM prescriptions for treating MI are regulating hormone, inhibiting apoptosis, oxidant stress and inflammatory. Estrogen signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway are the most important signaling pathways. Molecular docking experiments were used to further validate network pharmacology results. Conclusions: This study not only discovers CCMM and the molecular mechanism of TCM prescriptions for treating MI, but may be helpful for the popularization and application of TCM treatment.

scholarly journals Assessing the Anti-inflammatory Mechanism of Reduning Injection by Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Fuda Xie ◽  
Mingxiang Xie ◽  
Yibing Yang ◽  
Miaomiao Zhang ◽  
Xiaojie Xu ◽  
...  
Keyword(s):  
Data Mining ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Complex Disease ◽  
Inflammatory Diseases ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Inflammatory Mechanism ◽  
Reduning Injection ◽  
Anti Inflammatory

Reduning Injection (RDNI) is a traditional Chinese medicine formula indicated for the treatment of inflammatory diseases. However, the molecular mechanism of RDNI is unclear. The information of RDNI ingredients was collected from previous studies. Targets of them were obtained by data mining and molecular docking. The information of targets and related pathways was collected in UniProt and KEGG. Networks were constructed and analyzed by Cytoscape to identify key compounds, targets, and pathways. Data mining and molecular docking identified 11 compounds, 84 targets, and 201 pathways that are related to the anti-inflammatory activity of RDNI. Network analysis identified two key compounds (caffeic acid and ferulic acid), five key targets (Bcl-2, eNOS, PTGS2, PPARA, and MMPs), and four key pathways (estrogen signaling pathway, PI3K-AKT signaling pathway, cGMP-PKG signaling pathway, and calcium signaling pathway) which would play critical roles in the treatment of inflammatory diseases by RDNI. The cross-talks among pathways provided a deeper understanding of anti-inflammatory effect of RDNI. RDNI is capable of regulating multiple biological processes and treating inflammation at a systems level. Network pharmacology is a practical approach to explore the therapeutic mechanism of TCM for complex disease.

scholarly journals The Analysis of The Application and Mechanism of Traditional Chinese Medicine in Osteoarthritis Based on Data Mining and Network Pharmacology

2020 ◽  
Author(s):  
Jie YANG ◽  
Dijin JIAO ◽  
Guoguang Zhang ◽  
Juntong LIU ◽  
Chao QU ◽  
...  
Keyword(s):  
Data Mining ◽  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Cell Metabolism ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
String Database ◽  
The Core ◽  
Drug Molecules

Abstract Background: Using Data Mining to retrieve the core drug of osteoarthritis in clinic, predicting the drug molecular action target through the Network Pharmacology, combining with the related targets of osteoarthritis to identify the key nodes of the interaction, exploring the pharmacological mechanism of Traditional Chinese Medicine against osteoarthritis and other possible mechanisms of actions. Methods: Pubmed, CNKI, VIP, CBM and WanFang Database was used to retrieve the commonly used therapeutic formulations for osteoarthritis patients in clinical, and screen out the core drugs through the Ancient and Modern Medical Case Cloud Platform and software Gephi, filtered out the core drug molecules and targets combined with TCMSP database and the targets of osteoarthritis in Genecard, OMIM database, impoting those datas into R project and Cytoscape to construct the intersection model of Drug molecule-osteoarthritis, carrying out PPI network and GO and KEGG enrichment analysis with String database. Vina molecular docking was implemented to draw molecular docking diagram, and the results were analyzed after comprehensive analysis. Results: The core drug pairs were identified as "Eucommiae Cortex - Achyranthis Bidentatae Radix" through correlation analysis, complex network analysis basing on the coefficient. "Eucommiae Cortex - Achyranthis Bidentatae Radix" can intervene cell behaviors through multiple pathways and regulate cell metabolism, cytokine synthesis, oxidative , cellular immunity as a consequence of topology analysis in String Database. Conclusions: "Eucommia bark - achyranthes" drug molecules can be combined with the target to produce hydrogen bond, hydrophobic function and Pi-Pi directly or indirectly affecting the corresponding targets, to participate in the regulation of osteogenesis and osteoclast proliferation, protect the extracellular matrix, inhibition of cell apoptosis and anti-inflammatory for resistance to osteoarthritis, also, providing the basis for interpretation of its action mechanism.

scholarly journals Mechanism of Paeoniae Radix Alba in the Treatment of Non-alcoholic Fatty Liver Disease Based on Sequential Metabolites Identification Approach, Network Pharmacology, and Binding Affinity Measurement

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhiqiang Luo ◽  
Yang Liu ◽  
Xing Han ◽  
Wenning Yang ◽  
Guopeng Wang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Liver Disease ◽  
Fatty Liver Disease ◽  
Network Pharmacology ◽  
Alcoholic Fatty Liver ◽  
Blood Samples ◽  
Integrated Strategy ◽  
Identification Approach ◽  
Paeoniae Radix ◽  
Alcoholic Fatty Liver Disease

Screening functional food ingredients (FFI) from medicinal and edible plants (MEP) has still remained a great challenge due to the complexity of MEP and its obscure function mechanisms. Herein, an integrated strategy based on sequential metabolites identification approach, network pharmacology, molecular docking, and surface plasmon resonance (SPR) analysis was proposed for quickly identifying the active constituents in MEP. First, the sequential biotransformation process of MEP, including intestinal absorption and metabolism, and hepatic metabolism, was investigated by oral gavage, and intestinal perfusion with venous sampling method. Then the blood samples were analyzed by UPLC-Q Exactive Orbitrap HRMS. Second, the network pharmacology approach was used to explore the potential targets and possible mechanisms of the in vivo metabolites of MEP. Third, molecular docking and SPR approaches were used to verify the specific interactions between protein targets and representative ingredients. The proposed integrated strategy was successfully used to explore the heptoprotective components and the underlying molecular mechanism of Paeoniae Radix Alba (PRA). A total of 44 compounds were identified in blood samples, including 17 porotypes and 27 metabolites. The associated metabolic pathways were oxidation, methylation, sulfation, and glucuronidation. After further screening, 31 bioactive candidates and 377 related targets were obtained. In addition, the bioactive components contained in PRA may have therapeutic potentials for non-alcoholic fatty liver disease (NAFLD). The above results demonstrated the proposed strategy may provide a feasible tool for screening FFI and elaborating the complex function mechanisms of MEP.

scholarly journals Data Mining, Network Pharmacology, and Molecular Docking Explore the Effects of Core Traditional Chinese Medicine Prescriptions in Patients with Rectal Cancer and Qi and Blood Deficiency Syndrome

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shiyu Ma ◽  
Lin Zheng ◽  
Lan Zheng ◽  
Xiaolan Bian
Keyword(s):  
Data Mining ◽  
Rectal Cancer ◽  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Deficiency Syndrome ◽  
Network Pharmacology ◽  
Hub Genes ◽  
The Core ◽  
High Degree

Background. “Zheng” (syndrome) is the basic unit and the basis of traditional Chinese medicine (TCM) treatment. In clinical practice, we have been able to improve the survival time and quality of life for patients with rectal cancer through the treatment of “FuZhengXiaoJi” (strengthening the Qi and reducing accumulation). Purpose. In this study, we elucidated the core prescriptions for patients with rectal cancer and Qi and blood deficiency syndrome, and we explored the potential mechanisms of the prescriptions using an integrated strategy that coupled data mining with network pharmacology. Methods. A Bron–Kerbosch (BK) algorithm was applied to find the core prescriptions. The active ingredients, targets, activated signaling pathways, and biological functions of core prescriptions were analyzed using network pharmacology and directly associated proteins were docked using molecular docking technology to elucidate the multicomponent, multitarget, and inter-related components associated with TCM systematically. Results. Data mining identified 3 core prescriptions, and most of the herbs consisted of “FuZhengXiaoJi” Fang. Network pharmacology identified 15 high-degree active ingredients among the 3 core prescriptions and 16 high-degree hub genes linked with both rectal cancer and the 3 core prescriptions. Additional Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of these 16 targets showed that the most significant pathways were MAPK, interleukin-17, tumor necrosis factor (TNF), and vascular endothelial growth factor (VEGF) pathways. From the 16 genes, TGFB1, IL1B, IL10, IL6, PTGS2, and PPARG closely interacted with the tumor microenvironment, and PPARG, MYC, and ERBB2 were closely linked to survival. In molecular docking, quercetin, kaempferol, and lauric acid showed good binding energy to each target. Conclusion. Data mining, network pharmacology, and molecular docking may help identify core prescriptions, high-degree ingredients, and high-degree hub genes to apply to diseases and treatments. Furthermore, these studies may help discover hub genes that affect the tumor microenvironment and survival. The combination of these tools may help elucidate the relationship between herbs acting on “Zheng” (syndrome) and diseases, thus expanding the understanding of TCM mechanisms.

scholarly journals Prediction of the Active Ingredients, Potential Targets, and Signaling Pathways in ZaoRenDiHuang Capsules for Treatment of Insomnia Based on Network Analysis and Molecular Docking

2020 ◽  
Author(s):  
De Jin ◽  
Jinghua Zhang ◽  
Yuqing Zhang ◽  
Xuedong AN ◽  
Shenghui Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Molecular Docking ◽  
Network Analysis ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Topological Analysis ◽  
Docking Simulation ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Active Ingredients

Abstract Background：Insomnia is a major global public health issue with a high incidence, which presents a significant economic burden. Importantly, insomnia is often accompanied by a myriad of symptoms during the daytime, the most common being insomnia dizziness, headache, malaise, fatigue, anxiety, and even contribute to several diseases. However, the action mode of multi-component and multi-target for Chinese medicine could be a promising therapy for insomnia. According to the previous research, the ZaoRenDiHuang (ZRDH) Capsules showed the noteworthy anti-insomnia effect. Up to now, active ingredients, potential targets, and signaling pathways and mechanism of action are not yet clear. In this study, network pharmacology was employed to elucidate the potential anti-insomnia mechanism of ZRDH.Methods：In this study, an integrated pharmacology approach was implemented, which involved evaluation of absorption, distribution, metabolism and excretion of ZRDH, data mining of the insomnia targets, protein-protein interaction (PPI) network analysis, enrichment analysis, and molecular docking simulation, to predict the bioactive components, potential targets, and molecular mechanism of ZRDH for insomnia.Results：In this work, 44 anti-insomnia components of ZRDH and 65 anti-insomnia targets of insomnia were filtrated through database mining. The Drug-Disease network was constructed andfive key components Jujuboside A, Schizandrin A, Schizandrin C, Schizandrin B, and Spinosin, were further obtained. Sixty-five key targets were identified by topological analysis. Sequential studies turned out, NMURl, CAlCR, GABA, TAER2, ORDS, CYS1TR2, HTR1B, TLR4 were the common key targets. Docking studies indicated that the bioactive compounds could stably bind the pockets of target proteins. The findings of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation suggested that the Neuroactive ligand−receptor interaction, Serotonergic synapse CAMP signaling pathway, HIF−1a signaling pathway, Toll−like receptor signaling pathway, anti-insomnia through data mining and network analysis.Conclusion: In sunmmary, potential mechanisms involved in ZRDH treatment for insomnia involves multiple components and multiple target points as well as multiple pathways. These findings may offer a profile for further investigations of the anti-fibrotic mechanism of ZRDH.

scholarly journals Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Biting Wang ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Molecular Docking ◽  
Liver Fibrosis ◽  
Molecular Mechanisms ◽  
Docking Simulation ◽  
Chinese Herbs ◽  
Network Pharmacology ◽  
Bipartite Network ◽  
Metabolomics Data ◽  
Metabolic Products ◽  
Compound Target

Abstract Background The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein–protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

scholarly journals Exploration in the mechanism of fucosterol for the treatment of non-small cell lung cancer based on network pharmacology and molecular docking

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoling Li ◽  
Baixin Lin ◽  
Zhiping Lin ◽  
Yucui Ma ◽  
Qu Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Molecular Docking ◽  
Small Cell Lung Cancer ◽  
Signaling Pathway ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Erk Signaling ◽  
Network Pharmacology ◽  
Protein Interaction Data ◽  
Small Cell Lung

AbstractFucosterol, a sterol isolated from brown algae, has been demonstrated to have anti-cancer properties. However, the effects and underlying molecular mechanism of fucosterol on non-small cell lung cancer remain to be elucidated. In this study, the corresponding targets of fucosterol were obtained from PharmMapper, and NSCLC related targets were gathered from the GeneCards database, and the candidate targets of fucosterol-treated NSCLC were predicted. The mechanism of fucosterol against NSCLC was identified in DAVID6.8 by enrichment analysis of GO and KEGG, and protein–protein interaction data were collected from STRING database. The hub gene GRB2 was further screened out and verified by molecular docking. Moreover, the relationship of GRB2 expression and immune infiltrates were analyzed by the TIMER database. The results of network pharmacology suggest that fucosterol acts against candidate targets, such as MAPK1, EGFR, GRB2, IGF2, MAPK8, and SRC, which regulate biological processes including negative regulation of the apoptotic process, peptidyl-tyrosine phosphorylation, positive regulation of cell proliferation. The Raf/MEK/ERK signaling pathway initiated by GRB2 showed to be significant in treating NSCLC. In conclusion, our study indicates that fucosterol may suppress NSCLC progression by targeting GRB2 activated the Raf/MEK/ERK signaling pathway, which laying a theoretical foundation for further research and providing scientific support for the development of new drugs.

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