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

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

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

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

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

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

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

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

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

scholarly journals A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Paeonia lactiflora Pall. On the Treatment of Alzheimer’s Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Qiang Zeng ◽  
Longfei Li ◽  
Yu Jin ◽  
Zongzheng Chen ◽  
Lihong Duan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Inflammatory Response ◽  
Signaling Pathway ◽  
Cell Apoptosis ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Paeonia Lactiflora ◽  
Active Compounds ◽  
Target Proteins ◽  
Underlying Mechanisms

Objective. To investigate the potential active compounds and underlying mechanisms of Paeonia lactiflora Pall. (PLP) on the treatment of Alzheimer’s disease (AD) based on network pharmacology. Methods. The active components of PLP were collected from Traditional Chinese Medicine System Pharmacology (TCMSP) database, and their possible target proteins were predicted using TCMSP, SwissTargetPrediction, and STITCH databases. The putative AD-related target proteins were identified from Therapeutic Target Database (TTD), GeneCards, and MalaCards database. The compound-target-disease network interactions were established to obtain the key targets about PLP acting on AD by network topology analysis. Then, the function annotation and signaling pathways of key targets were performed by GO and KEGG enrichment analysis using DAVID tools. Finally, the binding capacity between active ingredients and key targets was validated by molecular docking using SystemsDock tools. Results. There were 7 active compounds involving in 151 predicted targets identified in PLP. Besides, a total of 160 AD-related targets were identified. Among these targets, 30 shared targets of PLP and AD were acquired. After topological analysis of the PLP potential target-AD target network, 33 key targets that were highly responsible for the therapeutic effects of PLP on AD were obtained. Further GO and KEGG enrichment analysis showed that these key targets were significantly involved in multiple biological processes and pathways which participated in cell apoptosis and inflammatory response and maintained the function of neurons to accomplish the anti-AD activity. The molecular docking analysis verified that the 7 active compounds had definite affinity with the key targets. Conclusions. The ameliorative effects of PLP on AD were predicted to be associated with regulating neural cell apoptosis, inflammatory response, and neurotrophy via various pathways such as PI3K-Akt signaling pathway, MAPK signaling pathway, and neurotrophin signaling pathway.

scholarly journals Integrating Network Pharmacology with Molecular Docking to Unravel the Active Compounds and Potential Mechanism of Simiao Pill Treating Rheumatoid Arthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Mengshi Tang ◽  
Xi Xie ◽  
Pengji Yi ◽  
Jin Kang ◽  
Jiafen Liao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Binding Activity ◽  
New Combination ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Potential Mechanism

Objective. To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking. Methods. Related compounds were obtained from TCMSP and BATMAN-TCM database. Oral bioavailability and drug-likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM-RA, compounds-targets, and pathways-targets-compounds were visualized through Cytoscape 3.7.1. The protein-protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE). Results. A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes (PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA) were core target genes in the network. Enrichment analysis indicated that PI3K-Akt, TNF, and IL-17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets. Conclusion. The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA.

scholarly journals Uncovering the Mechanisms and Molecular Targets of Weibing Formula 1 against Gastritis: Coupling Network Pharmacology with GEO Database

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ke Chen ◽  
Luojian Zhang ◽  
Zhen Qu ◽  
Feng Wan ◽  
Jia Li ◽  
...  
Keyword(s):  
Real Time ◽  
Signaling Pathway ◽  
Quantitative Pcr ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Gene Expression Omnibus ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Real Time Quantitative Pcr

Weibing Formula 1, a classic traditional formula, has been widely used clinically to treat gastritis in recent years. However, the potential pharmacological mechanism of Weibing Formula 1 is still unclear to date. A network pharmacology-based strategy was performed to uncover the underlying mechanisms of Weibing Formula 1 against gastritis. Furthermore, we structured the drug-active ingredients-genes–disease network and PPI network of shared targets, and function enrichment analysis of these targets was carried out. Ultimately, Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR were used to verify the related genes. We found 251 potential targets corresponding to 135 bioactive components of Weibing Formula 1. Then, 327 gastritis-related targets were known gastritis-related targets. Among which, 60 common targets were shared between potential targets of Weibing Formula 1 and known gastritis-related targets. The results of pathway enrichment analysis displayed that 60 common targets mostly participated in various pathways related to Toll-like receptor signaling pathway, MAPK signaling pathway, cytokine-cytokine receptor interaction pathway, chemokine signaling pathway, and apoptosis. Based on the GSE60427 dataset, 15 common genes were shared between differentially expressed genes and 60 candidate targets. The verification results of the GSE5081 dataset showed that except for DUOX2 and VCAM1, the other 13 genes were significantly upregulated in gastritis, which was consistent with the results in the GSE60427 dataset. More importantly, real-time quantitative PCR results showed that the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly upregulated and NOS2, EGFR, and IL-10 were downregulated in gastritis patients, while the expressions of PTGS2, MMP9, CXCL2, and CXCL8 were significantly downregulated and NOS2, EGFR, and IL-10 were upregulated after the treatment of Weibing Formula 1. PTGS2, NOS2, EGFR, MMP9, CXCL2, CXCL8, and IL-10 may be the important direct targets of Weibing Formula 1 in gastritis treatment. Our study revealed the mechanism of Weibing Formula 1 in gastritis from an overall and systematic perspective, providing a theoretical basis for further knowing and application of this formula in the future.

scholarly journals Exploring the Mechanisms Underlying the Therapeutic Effect of Xixin Decoction on Alzheimer's Disease Based on Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Zhuo Zhang ◽  
Jiang-lin Xu ◽  
Ming-qing Wei ◽  
Ting Li ◽  
Jing Shi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Therapeutic Effect ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Active Compounds

Abstract Background and objective: Alzheimer’s disease (AD) has been a worldwide problem, not only the treatment but also the prevention. As a commonly used Chinese Herbal Formula, Xixin Decoction (XXD) has significant therapeutic effect on AD but without clear mechanism. This study was aimed to predict the main active compounds and targets of XXD in the treatment of AD and to explore the potential mechanism by using network pharmacology and molecular docking. Methods: The compounds of XXD were searched in the TCMSP and the TCMID database, and the active compounds were screened based on the ADME model and SwissADME platform. SwissTargetPrediction platform was used to search for the primary candidate targets of XXD. The common targets related to AD obtained by two databases (GeneCards and DisGeNET) were determined as candidate proteins involved in AD. To acquire the related targets of XXD in the treatment of AD, the target proteins related to AD were intersected with the predicted targets of XXD. Then these overlapping targets were imported into the STRING database to build PPI network including hub targets; Cytoscape 3.7.2 software was used to construct the topology analysis for the herb-compound-target network diagram while one of it’s plug-in called CytoNCA was used to calculate degree value to screen the main active compounds of XXD. GO and KEGG pathway enrichment analyses were conducted to explore the core mechanism of action and biological pathways associated with the decoction via Metascape platform. We used AutoDock Vina and PyMOL 2.4.0 softwares for molecular docking of hub targets and main compounds.Results: We determined 114 active compounds which meet the conditions of ADME screening, 973 drug targets, and 973 disease targets. However, intersection analysis screened out 208 shared targets. PPI network identified 9 hub targets, including TP53, PIK3CA, MAPK1, MAPK3, STAT3, AKT1, etc. The 10 main active compounds play a major role in treatment of AD by XXD. Hub targets were found to be enriched in 10 KEGG pathways, involving the Pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, Alzheimer's disease, Neuroactive ligand-receptor interaction, Dopaminergic synapse, Serotonergic synapse and MAPK signaling pathway. The docking results indicated that the 8 hub targets exhibit good binding activity with the 9 main active compounds of XXD.Conclusions: We found the advantages of multi-compounds-multi-targets-multi-pathways regulation to reveal the mechanism of XXD for treating AD based on network pharmacology and molecular docking. Our study provided a theorical basis for further clinical application and experimental research of XXD for anti-AD in the future.

scholarly journals Study on the Multitarget Mechanism of Sanmiao Pill on Gouty Arthritis Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Huiqin Qian ◽  
Qianqian Jin ◽  
Yichen Liu ◽  
Ning Wang ◽  
Yuru Chu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Gouty Arthritis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Receptor Signaling ◽  
Active Compounds ◽  
Receptor Signaling Pathway ◽  
Compound Target

Sanmiao pill (SMP), a Chinese traditional formula, had been used to treat gouty arthritis (GA). However, the active compounds and underlying mechanism remained unclear. Hence, network pharmacology and molecular docking were utilized to explore bioactive compounds and potential mechanism of action of SMP in treating GA. In the study, the compounds of SMP, corresponding targets, and GA-related targets were mined from various pharmacological databases. Then, herb-compound-target, compound-target, PPI, and target-pathway networks were constructed. Ultimately, molecular docking was carried out to verify the predicted results. The results indicated that 47 active compounds, 338 targets, and 144 disease targets were collected. Network analysis implied that Phellodendron chinense Schneid. played a vital role in the whole formula. Moreover, 7 compounds (quercetin, kaempferol, wogonin, rutaecarpine, baicalein, beta-sitosterol, and stigmasterol) and 4 targets (NFKB1, RELA, MAPK1, and TNF) might be the kernel compounds and targets of SMP against GA. According to GOBP and KEGG pathway enrichment analysis and target-pathway network, SMP might exert a therapeutic role in GA by regulating numerous biological processes and pathways, including lipopolysaccharide-mediated signaling pathway, positive regulation of transcription, Toll-like receptor signaling pathway, JAK-STAT signaling pathway, NOD-like receptor signaling pathway, and MAPK signaling pathway. The results of molecular docking showcased that 11 pairs of compound with targets had tight binding strength. Thereinto, 4 compounds of MAPK1 and 5 compounds of NFKB1 possessed a better combination, suggesting that MAPK1 and NFKB1 might be considered as therapeutic targets in treatment of GA. This study verified that SMP had synergistic effect on GA by multicomponents, multitargets, and multipathways.

scholarly journals Integrating Network Pharmacology and Experimental Validation Deciphers the Mechanism of Guizhi Fuling Wan against Adenomyosis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haoxian Wang ◽  
Jihong Zhang ◽  
Qinqin Zhu ◽  
Xianyun Fu ◽  
Chenjie Li
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Animal Experiments ◽  
Enrichment Analysis ◽  
Estrogen Signaling ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Verification Methods

Aim. This study aimed to predict the key targets and endocrine mechanisms of Guizhi Fuling Wan (GZFLW) in treating adenomyosis (AM) through network pharmacology, molecular docking, and animal experiment verification. Methods. The related ingredients and targets of GZFLW in treating AM were screened out using TCMSP, BATMAN-TCM, SwissTargetPrediction, and PubChem Database. Then, the protein-protein interaction (PPI) analysis and the network of compound-hub targets were constructed. At the same time, the key targets were uploaded to the Metascape Database for KEGG pathway enrichment analysis. After that, the molecular docking technology of the main active components and hub targets was performed. Furthermore, animal experiments were used to verify the results of network pharmacology analysis. Results. A total of 55 active ingredients of GZFLW and 44 overlapping targets of GZFLW in treating AM were obtained. After screening, 25 hub targets were collected, including ESR1, EGF, and EGFR. Then, the KEGG pathway enrichment analysis results indicated that the endocrine therapeutic mechanism of GZFLW against AM is mainly associated with the estrogen signaling pathway, endocrine resistance, and an EGFR tyrosine kinase signaling pathway. Then, molecular docking showed that the significant compounds of GZFLW had a strong binding ability with ERα and EGFR. More importantly, the animal experiments confirmed that the GZFLW could downregulate the abnormal infiltration of the endometrial epithelium into the myometrium and had no interference with the normal sexual cycle. This effect may be directly related to intervening the local estrogen signaling pathway of the endometrial myometrial interface (EMI). It may also be associated with the myometrium cells’ estrogen resistance via GPER/EGFR signaling pathway. Conclusion. The endocrine mechanism of GZFLW in treating AM was explored based on network pharmacology, molecular docking, and animal experiments, which provided a theoretical basis for the clinical application of GZFLW.

scholarly journals Exploring Molecular Mechanism of Traditional Chinese Medicine Euphorbiae Semen on Reversing of Multidrug Resistance in Leukemia Based on Network Pharmacology Strategy and Molecular Docking Technology

2020 ◽  
Author(s):  
Xiao Song ◽  
Fei Guo ◽  
Xiao-Chen Sun ◽  
Shu-Yue Wang ◽  
Yao-Hui Yuan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Biological Functions ◽  
Active Compounds ◽  
The Core ◽  
The World ◽  
Target Network ◽  
Compound Target

Abstract Background: Leukemia was listed by the World Health Organization as one of the five most intractable diseases in the world. The multi-drug resistance (MDR) of leukemia cells limits the efficacy of anti-tumor drugs and is the major reason for the chemotherapy failure and recurrence of leukemia chemotherapy. Some studies have shown that Euphorbiae semen (ES) possesses the characteristics of new therapeutic drugs for MDR. However, the molecular mechanisms and active compounds have not yet been fully clarified. Therefore, there is a need for explore its active compounds and demonstrate its mechanisms through network pharmacology and molecular docking technology.Method: First, the TCMSP database was searched and screened the active compounds of the ES, supplemented with compounds verified by literature, so as to further identify the core compounds in the active ingredient. Simultaneously, the TCMSP and Swiss database were searched to the targets of active compounds, and the targets of reverses leukemia multidrug resistance (RL-MDR) were screened in the relevant databases, such as GeneCards and DrugBank. Then, the targets of active compounds were intersected with RL-MDR targets to obtain potential targets of ES acting on MDR. The compound–target network was constructed by Cytoscape. The target protein–protein interaction network was built using STRING and Cytoscape database. Second, the R language and DAVID database were used to analyse Gene Ontology (GO) biological functions analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways enrichment. Finally, molecular docking method was utilized to investigate the binding activity between the core targets and the active compounds of ES.Results: Compound–target network mainly contained 22 compounds and 81 corresponding targets. Finally, seven components in ES were selected and 10 core targets were identified; Key targets contained JUN, CASP3, MAOA, AR, PPARG, DRD2, ADRA2A, CHRM2, PTGS2 and MAPK14. GO enrichment analysis indicated the main biological functions of potential genes of ES in the treatment of MDR. KEGG pathway enrichment analysis showed the main pathways, mainly including apoptosis, pathways in cancer, p53 signaling pathway, VEGF signaling pathway, TNF signaling pathway and PI3K–Akt signaling pathway. Finally, we chose the top 10 common targets for molecular docking with the 7 active compounds of ES. The results of molecular docking indicated that the compounds of ES, which had good affinity with targets. Conclusion: The molecular mechanism of ES in the treatment of MDR showed the synergistic reaction of multi-compound, multi-target, and multi-pathway of traditional Chinese medicine, which provided ideas for further clinical research.

scholarly journals Chemical Characteristics of Platycodon grandiflorum and its Mechanism in Lung Cancer Treatment

2021 ◽  
Vol 11 ◽  
Author(s):  
Yaling Deng ◽  
Xianwen Ye ◽  
Yufan Chen ◽  
Hongmin Ren ◽  
Lanting Xia ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Topological Analysis ◽  
Mapk Signaling ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Triterpenoid Saponins ◽  
Platycodon Grandiflorum ◽  
Tof Ms

Objective: The technology, network pharmacology and molecular docking technology of the ultra performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS) were used to explore the potential molecular mechanism of Platycodon grandiflorum (PG) in the treatment of lung cancer (LC).Methods: UPLC-Q-TOF-MS/MS technology was used to analyze the ingredients of PG and the potential LC targets were obtained from the Traditional Chinese Medicine Systems Pharmacology database, and the Analysis Platform (TCMSP), GeneCards and other databases. The interaction network of the drug-disease targets was constructed with the additional use of STRING 11.0. The pathway enrichment analysis was carried out using Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) in Metascape, and then the “Drug-Ingredients-Targets-Pathways-Disease” (D-I-T-P-D) network was constructed using Cytoscape v3.7.1. Finally, the Discovery Studio 2016 (DS) software was used to evaluate the molecular docking.Results: Forty-seven compounds in PG, including triterpenoid saponins, steroidal saponins and flavonoids, were identified and nine main bioactive components including platycodin D were screened. According to the method of data mining, 545 potential drug targets and 2,664 disease-related targets were collected. The results of topological analysis revealed 20 core targets including caspase 3 (CASP3) and prostaglandin-endoperoxide synthase 2 (PTGS2) suggesting that the potential signaling pathway potentially involved in the treatment of LC included MAPK signaling pathway and P13K-AKT signaling pathway. The results of molecular docking proved that the bound of the ingredients with potential key targets was excellent.Conclusion: The results in this study provided a novel insight in the exploration of the mechanism of action of PG against LC.

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