scholarly journals Identify the Key Active Ingredients and Pharmacological Mechanisms of Compound XiongShao Capsule in Treating Diabetic Peripheral Neuropathy by Network Pharmacology Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Meixiang Yu ◽  
Xin Song ◽  
Wanhua Yang ◽  
Ziwei Li ◽  
Xiaoqin Ma ◽  
...  
Keyword(s):  
Peripheral Neuropathy ◽  
Signaling Pathway ◽  
Clinical Efficacy ◽  
Diabetic Peripheral Neuropathy ◽  
Kinase Activity ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Molecular Function ◽  
Active Ingredients ◽  
Xiongshao Capsule

Compound XiongShao Capsule (CXSC), a traditional herb mixture, has shown significant clinical efficacy against diabetic peripheral neuropathy (DPN). However, its multicomponent and multitarget features cause difficulty in deciphering its molecular mechanisms. Our study aimed to identify the key active ingredients and potential pharmacological mechanisms of CXSC in treating DPN by network pharmacology and provide scientific evidence of its clinical efficacy. CXSC active ingredients were identified from both the Traditional Chinese Medicine Systems Pharmacology database, with parameters of oral bioavailability ≥ 30% and drug-likeness ≥ 0.18, and the Herbal Ingredients’ Targets (HIT) database. The targets of those active ingredients were identified using ChemMapper based on 3D-structure similarity and using HIT database. DPN-related genes were acquired from microarray dataset GSE95849 and five widely used databases (TTD, Drugbank, KEGG, DisGeNET, and OMIM). Next, we obtained candidate targets with therapeutic effects against DPN by mapping active ingredient targets and DPN-related genes and identifying the proteins interacting with those candidate targets using STITCH 5.0. We constructed an “active ingredients-candidate targets-proteins” network using Cytoscape 3.61 and identified key active ingredients and key targets in the network. We identified 172 active ingredients in CXSC, 898 targets of the active ingredients, 110 DPN-related genes, and 38 candidate targets with therapeutic effects against DPN. Three key active ingredients, namely, quercetin, kaempferol, and baicalein, and 25 key targets were identified. Next, we input all key targets into ClueGO plugin for KEGG enrichment and molecular function analyses. The AGE-RAGE signaling pathway in diabetic complications and MAP kinase activity were determined as the main KEGG pathway and molecular function involved, respectively. We determined quercetin, kaempferol, and baicalein as the key active ingredients of CXSC and the AGE-RAGE signaling pathway and MAP kinase activity as the main pharmacological mechanisms of CXSC against DPN, proving the clinical efficacy of CXSC against DPN.

scholarly journals Network Pharmacology and Molecular Docking Study on the Potential Mechanism of Yi-Qi-Huo-Xue-Tong-Luo Formula in Treating Diabetic Peripheral Neuropathy

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yixuan Lin ◽  
Chuqiao Shen ◽  
Fanjing Wang ◽  
Zhaohui Fang ◽  
Guoming Shen
Keyword(s):  
Molecular Docking ◽  
Peripheral Neuropathy ◽  
Signaling Pathway ◽  
Diabetic Peripheral Neuropathy ◽  
Kegg Pathway ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Ppi Network ◽  
Active Ingredients ◽  
Kegg Pathway Analysis

Objective. To investigate the potential mechanism of action of Yi-Qi-Huo-Xue-Tong-Luo formula (YQHXTLF) in the treatment of diabetic peripheral neuropathy (DPN). Methods. Network pharmacology and molecular docking techniques were used in this study. Firstly, the active ingredients and the corresponding targets of YQHXTLF were retrieved using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform; subsequently, the targets related to DPN were retrieved using GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmgkb, Therapeutic Target Database (TTD) and Drugbank databases; the common targets of YQHXTLF and DPN were obtained by Venn diagram; afterwards, the “YQHXTLF Pharmacodynamic Component-DPN Target” regulatory network was visualized using Cytoscape 3.6.1 software, and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the potential targets using R 3.6.3 software. Finally, molecular docking of the main chemical components in the PPI network with the core targets was verified by Autodock Vina software. Results. A total of 86 active ingredients and 229 targets in YQHXTLF were screened, and 81 active ingredients and 110 targets were identified to be closely related to diabetic peripheral neuropathy disease. PPI network mapping identified TP53, MAPK1, JUN, and STAT3 as possible core targets. KEGG pathway analysis showed that these targets are mostly involved in AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, and MAPK signaling pathway. The molecular docking results showed that the main chemical components of YQHXTLF have a stable binding activity to the core pivotal targets. Conclusion. YQHXTLF may act on TP53, MAPK1, JUN, and STAT3 to regulate inflammatory response, apoptosis, or proliferation as a molecular mechanism for the treatment of diabetic peripheral neuropathy, reflecting its multitarget and multipathway action, and providing new ideas to further uncover its pharmacological basis and mechanism of action.

scholarly journals Potential mechanisms of Guizhi decoction against hypertension based on network pharmacology and Dahl salt-sensitive rat model

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jiye Chen ◽  
Yongjian Zhang ◽  
Yongcheng Wang ◽  
Ping Jiang ◽  
Guofeng Zhou ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Rat Model ◽  
Experimental Studies ◽  
Matrix Metalloproteinase 2 ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Active Compounds ◽  
Guizhi Decoction

Abstract Background Guizhi decoction (GZD), a classical Chinese herbal formula, has been widely used to treat hypertension, but its underlying mechanisms remain elusive. The present study aimed to explore the potential mechanisms and therapeutic effects of GZD on hypertension by integrating network pharmacology and experimental validation. Methods The active ingredients and corresponding targets were collected from the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP). The targets related to hypertension were identified from the CTD, GeneCards, OMIM and Drugbank databases. Multiple networks were constructed to identify the key compounds, hub targets, and main biological processes and pathways of GZD against hypertension. The Surflex-Dock software was used to validate the binding affinity between key targets and their corresponding active compounds. The Dahl salt-sensitive rat model was used to evaluate the therapeutic effects of GZD against hypertension. Results A total of 112 active ingredients, 222 targets of GZD and 341 hypertension-related targets were obtained. Furthermore, 56 overlapping targets were identified, five of which were determined as the hub targets for experimental verification, including interleukin 6 (IL-6), C–C motif chemokine 2 (CCL2), IL-1β, matrix metalloproteinase 2 (MMP-2), and MMP-9. Pathway enrichment analysis results indicated that 56 overlapping targets were mainly enriched in several inflammation pathways such as the tumor necrosis factor (TNF) signaling pathway, Toll-like receptor (TLR) signaling pathway and nuclear factor kappa-B (NF-κB) signaling pathway. Molecular docking confirmed that most active compounds of GZD could bind tightly to the key targets. Experimental studies revealed that the administration of GZD improved blood pressure, reduced the area of cardiac fibrosis, and inhibited the expression of IL-6, CCL2, IL-1β, MMP-2 and MMP-9 in rats. Conclusion The potential mechanisms and therapeutic effects of GZD on hypertension may be attributed to the regulation of cardiac inflammation and fibrosis.

scholarly journals Elucidation of the active compounds and molecular mechanisms of Smilacis Chinae Rhizoma for treatment of pelvic inflammatory disease based on network pharmacology and MMGBSA-docking

2020 ◽  
Author(s):  
Yunsen Zhang ◽  
Zikuang Zhao ◽  
Wenxiang Wang ◽  
Qi Li ◽  
Huimin Chen ◽  
...  
Keyword(s):  
Pelvic Inflammatory Disease ◽  
Signaling Pathway ◽  
Inflammatory Disease ◽  
Molecular Mechanisms ◽  
Binding Free Energy ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Active Compounds ◽  
Bidirectional Regulation

Abstract Background Smilacis Chinae Rhizoma (SCR) is widely used in the treatment of pelvic inflammatory disease (PID). However, its active ingredients and the mechanisms against PID remain elusive. This study aimed to clarify the active ingredients and explore their molecular mechanisms on PID. Method Network pharmacology and MMGBSA-docking exploited the active compounds and mechanisms against PID, as well as validating the binding mode of candidate targets.Results Network pharmacology revealed 32 active compounds and 718 compound-related targets mapped to 91 pathways which were clustered 7 genres (e.g., immunoregulation). C-T-P network and PPI analysis illustrated 17 PID-related targets, indicating that SCR may decrease inflammation, ameliorate fibrosis, and inhibit microorganisms via bidirectionally regulating IL-17 signaling pathway. Furthermore, active compounds were uncovered that bound to prostaglandin-endoperoxide synthase 2, matrix metalloprotein-9, lipocalin, signal transducer and activator of transcription 3, myeloperoxidase, and tumor necrosis factor. 19 active compounds (e.g., rutin (-66.43 kcal/mol), moracin M (-37.01 kcal/mol) and oxyresveratrol (-38.84 kcal/mol)) were found to show excellent binding free energy, demonstrating that H-bond, Pi electron cloud and electrostatic potential as the main binding ability to these targets. Conclusion Approach of network pharmacology and MMGBSA-docking revealed the active ingredients, such as rutin, moracin M, and oxyresveratrol, in SCR and dissected it exhibits the therapeutic effects (e.g., decrease inflammation, ameliorate fibrosis, and inhibit microorganisms) of PID by the bidirectional regulation of IL-17 signaling pathway.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology.

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Cellular Response ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanism ◽  
Effector Mechanisms

Abstract Objective: To examine the potential effector mechanisms of Xuebijing (XBJ) on coronavirus disease 2019 (COVID-19) based on network pharmacology.Methods: We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound-potential-effector target” network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets. Results: We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets—glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), and MAPK14—had degree values >40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin–angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)–protein kinase B (Akt)–vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “ingredient-target-pathway” effector network. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

scholarly journals Network pharmacology evaluation of the active ingredients and potential targets of XiaoLuoWan for application to uterine fibroids

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Yonghui Yu ◽  
Fang Yang ◽  
Hong Liu
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Calcium Signaling ◽  
Signaling Pathway ◽  
Uterine Fibroids ◽  
Therapeutic Targets ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Calcium Signaling Pathway

Abstract XiaoLuoWan (XLW) is a classical formula in traditional Chinese medicine (TCM) that has satisfactory therapeutic effects for uterine fibroids (UFs). However, its underlying mechanisms remain unclear. To elucidate the pharmacological actions of XLW in treating UFs, an ingredient–target–disease framework was proposed based on network pharmacology strategies. The active ingredients in XLW and their putative targets were obtained from the TCM systems pharmacology database and analysis platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) platforms. The known therapeutic targets of UFs were acquired from the DigSee and DrugBank databases. Then, the links between putative XLW targets and therapeutic UF targets were identified to establish interaction networks by Cytoscape. Finally, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of overlapping gene targets were performed in the STRING database and visualized in R software. In total, 9 active compounds were obtained from 74 ingredients, with 71 curative targets predicted in XLW. Moreover, 321 known therapeutic targets were closely related to UFs, with 29 targets overlapping with XLW and considered interacting genes. Pathway enrichment revealed that the calcium signaling pathway was significantly enriched and the mitogen-activated protein kinase (MAPK) signaling pathway, cAMP signaling pathway, cancer and vascular smooth muscle contraction pathways, cGMP-PKG signaling pathway, and AGE-RAGE signaling pathway were closely associated with XLW intervention for UFs. In conclusion, the network pharmacology detection identified 9 available chemicals as the active ingredients in XLW that may relieve UFs by regulating 29 target genes involved in the calcium signaling pathway, MAPK pathway and cAMP pathway. Network pharmacology analyses may provide more convincing evidence for the investigation of classical TCM prescriptions, such as XLW.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Cellular Response ◽  
Interleukin 2 ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanisms

Abstract Objective To examine the potential effector mechanisms of Xuebijing (XBJ) on coronavirus disease 2019 (COVID-19) based on network pharmacology.Methods We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound–potential-effector target” network and protein–protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) Biological Process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets.Results We obtained 147 potential COVID-19 effector targets of XBJ. Fourteen of these targets—glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), Estrogen Receptor 1 (ESR1), and MAPK14—had degree values > 40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide (LPS), and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin–angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)–protein kinase B (Akt)–vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “ingredient–target–pathway” effector network. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. The results of this study validated current pharmacological mechanistic studies of XBJ in the treatment of sepsis and severe pneumonia and could better explain XBJ’s effector mechanisms in the clinical treatment of COVID-19.

scholarly journals Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xinmiao Wang ◽  
Haoyu Yang ◽  
Lili Zhang ◽  
Lin Han ◽  
Sha Di ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Signaling Pathway ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Go Analysis

Background. Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods. The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the “SZF-key targets-DKD” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results. We obtained 182 potential targets and 30 key targets. Furthermore, a “SZF-key targets-DKD” network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion. This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanism ◽  
Effector Mechanisms

Abstract Background: Chinese medicine Xuebijing (XBJ) has proven to be effective in the treatment of mild coronavirus disease 2019 (COVID-19) cases. But the bioactive compounds and potential mechanisms of XBJ for COVID-19 prevention and treatment are unclear. This study aimed to examine the potential effector mechanisms of XBJ on COVID-19 based on network pharmacology.Methods: We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound-potential-effector target” network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets. We used AutoDock vina and PyMOL software for molecular docking. Results: We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets-glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), and MAPK14 had degree values >40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin-angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)-protein kinase B (Akt)-vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “drug-ingredient-target-pathway” effector network. The molecular docking results showed that the core 18 effective ingredients had a docking score of less than -4.0 with those top 10 targets. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that key ingredients and their targets have potential binding activity, the existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

scholarly journals Potential Mechanism of Guizhi Decoction in Hypertension Treatment Based on Network Pharmacology and Dahl Salt-Sensitive Rat Model

2020 ◽  
Author(s):  
Ji-ye Chen ◽  
Yong-jian Zhang ◽  
Yong-cheng Wang ◽  
Guo-feng Zhou ◽  
Jin-long Yang ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Rat Model ◽  
Cardiac Fibrosis ◽  
Matrix Metalloproteinase 2 ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Potential Mechanism ◽  
Active Ingredients ◽  
Active Compounds ◽  
Guizhi Decoction

Abstract Introduction: Guizhi decoction (GZD), a classical Chinese herbal formula, has been widely used to treat hypertension, but its underlying mechanisms remain elusive. The present study aimed to explore its therapeutic effects and potential mechanisms in the treatment of hypertension using network pharmacology and experimental validation.Methods: The active ingredients and corresponding targets were collected from Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP). The targets related to hypertension were identified from multiple databases, and multiple networks were constructed to identify key compounds, hub targets, and main biological processes and pathways of GZD against hypertension. The Surflex-Dock software was used to validate the binding affinity between key targets and their corresponding active compounds. The Dahl salt-sensitive rat model was used to evaluate the therapeutic effects of GZD on hypertension. Results: A total of 112 active ingredients, 222 targets of GZD and 341 hypertension- related targets were obtained. Furthermore, 56 overlapping targets were identified, five of which were determined as the hub targets to perform experimental verification, including interleukin 6 (IL-6), C-C motif chemokine 2 (CCL2), IL-1β, matrix metalloproteinase 2(MMP-2), and MMP9. Pathway enrichment results indicated that 56 overlapping targets mainly enriched in several inflammation pathways such as the tumor necrosis factor (TNF) signaling pathway, toll-like receptor (TLR) signaling pathway and nuclear factor kappa-B (NF-κB) signaling pathway. Molecular docking confirmed that most active compounds of GZD showed tight binding ability with the key targets. Experimental results demonstrated that compared with the group fed a high-salt diet in this study, the GZD improved blood pressure, reduced the area of cardiac fibrosis, and inhibited the expression of IL6, CCL2, IL1β, MMP2 and MMP9 in rats.Conclusions: The potential mechanism of the therapeutic effect of GZD on hypertension may be attributed to the regulation of cardiac inflammation and fibrosis.

scholarly journals Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology

2020 ◽  
Author(s):  
Wenjiang Zheng ◽  
Qian Yan ◽  
Yongshi Ni ◽  
Shaofeng Zhan ◽  
Liuliu Yang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Growth Factor ◽  
Protein Kinase ◽  
Signaling Pathway ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Active Ingredients ◽  
Effector Mechanism ◽  
Effector Mechanisms

Abstract Background: Chinese medicine Xuebijing (XBJ) has proven to be effective in the treatment of mild coronavirus disease 2019 (COVID-19) cases. But the bioactive compounds and potential mechanisms of XBJ for COVID-19 prevention and treatment are unclear. This study aimed to examine the potential effector mechanisms of XBJ onCOVID-19 based on network pharmacology.Methods: We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound-potential-effector target” network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets. We used AutoDock vina and PyMOL software for molecular docking. Results: We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets-glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), and MAPK14 had degree values >40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin-angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)-protein kinase B (Akt)-vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “drug-ingredient-target-pathway” effector network. The molecular docking results showed that the core 18 effective ingredients had a docking score of less than -4.0 with those top 10 targets. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that key ingredients and their targets have potential binding activity, the existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

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