scholarly journals Analysis of the Active Components and Mechanism of Three Prescriptions in the Treatment of COVID-19 Via Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (9) ◽  
pp. 1934578X2110477
Author(s):  
Fei Wang ◽  
Jia-Hui Chen ◽  
Bo Liu ◽  
Ting Zhang
Keyword(s):  
Molecular Docking ◽  
Binding Energies ◽  
Network Pharmacology ◽  
Active Components ◽  
Chinese Medicines ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Infection And Inflammation ◽  
Analysis Platform

Purpose: Prescriptions of Han-Shi-Yu-Fei (HSYF), Han-Shi-Zu-Fei (HSZF), and Yi-Du-Bi-Fei (YDBF) were effective in treating COVID-19. Based on network pharmacology and molecular docking, overlapping Traditional Chinese medicines (TCMs), their active components, and core targets were explored in this study. Methods: First, the overlapping TCMs and their active components were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) by evaluating Oral Bioactivity (OB) and Drug Likeness (DL). The overlapping targets of potential components and COVID-19 were collected by SwissTargetPrediction, Gene Cards, and Venn 2.1.0 databases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed via DAVID6.8.1 database. Through comprehensive analysis of the “prescriptions-TCMs-components” (P-T-C), “components-targets-pathways” (C-T-P) and “protein–protein interaction” (PPI) networks constructed by Cytoscape 3.7.1 software, the active components and core targets were obtained. Finally, the binding energies of these components with ACE2 and SARS-CoV-2 3CL were analyzed by AutDockTools-1.5.6 and PyMOL software. Results: In all, five overlapping TCMs, 40 potential active components, and 47 candidate targets were obtained and analyzed in these prescriptions. There were 288 GO entries ( P < 0.05), including 211 biological process (BP), 40 cell composition (CC), and 37 molecular function (MF) entries. Most of the 105 KEGG pathways ( P < 0.05) were involved with viral infection and inflammation. Through “PPI” and “C-T-P” networks, the core targets (EGFR, PTGS2, CDK2, GSK3B, PIK3R1, and MAPK3) and active components (Q27134551, acanthoside B, neohesperidin, and irisolidone) with high degrees were obtained. Molecular docking results showed that the above-mentioned four components could inhibit the binding of ACE2 and SARS-COV-2 3CL to protect against COVID-19. Conclusion: In this study, the active components and core targets of three prescriptions in the treatment of COVID-19 were elaborated by network pharmacology and molecular docking, providing a reference for their applications.

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

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.

scholarly journals Pharmacological Mechanisms Underlying the Anti-asthmatic Effects of Modified Guomin Decoction Determined by Network Pharmacology and Molecular Docking

2021 ◽  
Vol 8 ◽  
Author(s):  
Guishu Wang ◽  
Bo Zhou ◽  
Zheyi Wang ◽  
Yufeng Meng ◽  
Yaqian Liu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Molecular Mechanism ◽  
Airway Remodeling ◽  
Enrichment Analysis ◽  
Chronic Inflammatory Disease ◽  
Network Pharmacology ◽  
Active Components ◽  
Active Compounds ◽  
Protein Protein Interaction ◽  
Pathway Annotation

BackgroundAsthma is a chronic inflammatory disease characterized by Th2-predominant inflammation and airway remodeling. Modified Guo Min decoction (MGMD) has been an extensive practical strategy for allergic disorders in China. Although its potential anti-asthmatic activity has been reported, the exact mechanism of action of MGMD in asthma remains unexplored.MethodsNetwork pharmacology approach was employed to predict the active components, potential targets, and molecular mechanism of MGMD for asthma treatment, including drug-likeness evaluation, oral bioavailability prediction, protein–protein interaction (PPI) network construction and analysis, Gene Ontology (GO) terms, and Reactome pathway annotation. Molecular docking was carried out to investigate interactions between active compounds and potential targets.ResultsA total of 92 active compounds and 72 anti-asthma targets of MGMD were selected for analysis. The GO enrichment analysis results indicated that the anti-asthmatic targets of MGMD mainly participate in inflammatory and in airway remolding processes. The Reactome pathway analysis showed that MGMD prevents asthma mainly through regulation of the IL-4 and IL-13 signaling and the specialized pro-resolving mediators (SPMs) biosynthesis. Molecular docking results suggest that each bioactive compounds (quercetin, wogonin, luteolin, naringenin, and kaempferol) is capable to bind with STAT3, PTGS2, JUN, VEGFA, EGFR, and ALOX5.ConclusionThis study revealed the active ingredients and potential molecular mechanism by which MGMD treatment is effective against airway inflammation and remodeling in asthma through regulating IL-4 and IL-13 signaling and SPMs biosynthesis.

A Study on the Mechanism of Lavender in the Treatment of Insomnia Based on Network Pharmacology

2020 ◽  
Vol 23 (5) ◽  
pp. 419-432
Author(s):  
Yao Wang ◽  
Junbo Zou ◽  
Yanzhuo Jia ◽  
Yulin Liang ◽  
Xiaofei Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Target Protein ◽  
Ammonium Ion ◽  
Network Pharmacology ◽  
Common Disease ◽  
Active Components ◽  
Target Proteins ◽  
Analysis Platform

Aim and Objective: The common disease of insomnia has complex and diverse clinical manifestations. Lavender represents an effective treatment of insomnia, but the molecular mechanism underlying the effectiveness of this treatment is not clear. The purpose of this study is to investigate the active components, target proteins and molecular pathways of lavender in the treatment of insomnia, thus explaining its possible mechanism. Materials and Methods: Firstly, 54 active components of lavender were identified by gas chromatography-mass spectrometry (GC-MS). The target protein of lavender was predicted by the Traditional Chinese Medicine System Pharmacological Database and Analysis Platform and the SwissTargetPredicating tool, and the target protein of insomnia was predicted by the DisGeNET and DrugBank databases. Then, the "component-target-disease" network diagram was constructed using the Cytoscape 3.7.1 software. KEGG and GO enrichments were analyzed using the R statistical language. Finally, the key target proteins were verified by collecting and verifying the target protein GEO data using the Discovery Studio 3.5 molecular docking verification software. Results: 906 target proteins of lavender were predicted by the Traditional Chinese Medicine System Pharmacological Database and Analysis Platform and the SwissTargetPredicating tool, and 182 insomnia target proteins were predicted by the DisGeNET and DrugBank databases. The results of GO enrichment analysis showed that it included the reaction process of ammonium ion, the regulation of the membrane potential and the secretion of catecholamine, while the results of KEGG enrichment included the calcium signaling pathway, serotonin synapse, morphine addiction and many more. Finally, using the Discovery Studio3.5 molecular docking verification software, it was verified that the key target proteins are ADRB1 and HLA-DRB1. Conclusion: The components in the lavender essential oil include the Ethyl 2-(5-methyl-5-vinyltetrahydrofuran- 2-yl)propan-2-ylcarbonate (0.774); 5-Oxatricyclo[8.2.0.04,6]dodecane, 4,12,12-trimethyl- 9-methylene-, (1R,4R,6R,10S)-(0.147); P-Cymen-7-ol (0.063); .alpha-Humulenem (0.317); Acetic acid, hexyl ester (1.374); etc. The role lavender plays in the treatment of insomnia might be accomplished through the regulation of the key targets ADRB1 and HLA-DRB1.

scholarly journals Network Pharmacology-Based and Molecular Docking-Based Analysis of Suanzaoren Decoction for the Treatment of Parkinson’s Disease with Sleep Disorder

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yan-yun Liu ◽  
Li-hua Yu ◽  
Juan Zhang ◽  
Dao-jun Xie ◽  
Xin-xiang Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Molecular Docking ◽  
Sleep Disorder ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Active Components ◽  
Protein Protein Interaction ◽  
Receptor Signaling Pathway

This study is aimed at exploring the possible mechanism of action of the Suanzaoren decoction (SZRD) in the treatment of Parkinson’s disease with sleep disorder (PDSD) based on network pharmacology and molecular docking. Traditional Chinese Medicine Systems Pharmacology (TCMSP) was used to screen the bioactive components and targets of SZRD, and their targets were standardized using the UniProt platform. The disease targets of “Parkinson’s disease (PD)” and “Sleep disorder (SD)” were collected by OMIM, GeneCards, and DisGeNET databases. Thereafter, the protein-protein interaction (PPI) network was constructed using the STRING platform and visualized by Cytoscape (3.7.2) software. Then, the DAVID platform was used to analyze the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Cytoscape (3.7.2) software was also used to construct the network of the “herb-component-target-pathway.” The core active ingredients and core action targets of the drug were verified by molecular docking using AutoDock software. A total of 135 Chinese herbal components and 41 corresponding targets were predicted for the treatment of PDSD using SZRD. Fifteen important signaling pathways were screened, such as the cancer pathway, TNF signaling pathway, PI3K-AKT signaling pathway, HIF-1 signaling pathway, and Toll-like receptor signaling pathway. The results of molecular docking showed that the main active compounds could bind to the representative targets and exhibit good affinity. This study revealed that SZRD has the characteristics and advantages of “multicomponent, multitarget, and multipathway” in the treatment of PDSD; among these, the combination of the main active components of quercetin and kaempferol with the key targets of AKT1, IL6, MAPK1, TP53, and VEGFA may be one of the important mechanisms. This study provides a theoretical basis for further study of the material basis and molecular mechanism of SZRD in the treatment of PDSD.

scholarly journals Exploring the Mechanism of Laminaria for the Treatment of Alzheimer's Disease based on Network Pharmacology

2021 ◽  
Author(s):  
Le Yu ◽  
Shuchen Pei ◽  
Kangyao Yuan ◽  
Jian Zhang ◽  
Jingya Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Laminaria Japonica ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Active Components ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Response To Stress ◽  
Go Terms

Abstract Background：Laminaria japonica has also been reported to have a therapeutic effect on AD, but the mechanism is not entirely clear. To explore the mechanism of Laminaria for the treatment of Alzheimer's disease (AD), the “active components-targets” network and the protein-protein interaction (PPI) network were constructed for analyzing targets’ functions and pathways. Methods：The main active components of Laminaria were extracted using the TCMSP database and were predicted and screened by GeneCards. Cytoscape was used to construct the “drug-components-targets-disease” network. STRING and Cytoscape were applied to map the PPI network. The Gene Ontology (GO) terms and KEGG pathways of targets were analyzed by Metascape. Results: Seven active components involving 23 active targets were obtained. The network analysis elucidated that Laminaria was mainly involved in cell process, metabolic process, response to stress and other biological processes. CASP3, PPARG, RELA, CCND1 and CASP9 played a key role in treating AD by regulating two small cell lung cancer and Toxoplasmosis. Conclusion: This study demonstrated that Laminaria could prevent and treat AD with advantages of multi-components, multi-targets and multi-pathways, which explored a new way for further research on the mechanism of Laminaria in the treatment of AD.

scholarly journals Study on the Potential Mechanism of Fructus Tribuli in the Treatment of Hypertensive Vascular Remodeling Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shuyue Wang ◽  
Fei Guo ◽  
Xiaochen Sun ◽  
Xiao Song ◽  
Yaohui Yuan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Signaling Pathway ◽  
Vascular Remodeling ◽  
Network Pharmacology ◽  
Research Approach ◽  
Active Components ◽  
Active Compounds ◽  
Ppi Networks

Background. Hypertensive vascular remodeling (HVR) is the pathophysiological basis of hypertension, which is also an important cause of vascular disease and target organ damage. Treatment with Fructus Tribuli (FT), a traditional Chinese medicine, has a positive effect on HVR. However, the pharmacological mechanisms of FT are still unclear. Therefore, this study aimed to reveal the potential mechanisms involved in the effects of FT on HVR based on network pharmacology and molecular docking. Materials and Methods. We selected the active compounds and targets of FT according to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Swiss Target Prediction database, and the targets of HVR were collected from the Online Mendelian Inheritance in Man (OMIM), GeneCards, and DrugBank databases. The protein-protein interaction network (PPI) was established using the STRING database. Moreover, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and network analysis were performed to further explore the potential mechanisms. Finally, molecular docking methods were used to evaluate the affinity between the active compounds and the main target. Results. Seventeen active compounds of FT  and 164 potential targets for the treatment of HVR were identified. Component-target and PPI networks were constructed, and 12 main active components and 33 main targets were identified by analyzing the topological parameters. Additionally, GO analysis indicated that the potential targets were enriched in 483 biological processes, 52 cellular components, and 110 molecular functions. KEGG analysis revealed that the potential targets were correlated with 122 pathways, such as the HIF-1 signaling pathway, ErbB signaling pathway, and VEGF signaling pathway. Finally, molecular docking showed that the 12 main active components had a good affinity for the top five main targets. Conclusion. This study demonstrated the multiple compounds, targets, and pathway characteristics of FT in the treatment of HVR. The network pharmacology method provided a novel research approach to analyze potential mechanisms.

scholarly journals Uncovering Quercetin’s Effects against Influenza A Virus Using Network Pharmacology and Molecular Docking

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1627
Author(s):  
Minjee Kim ◽  
Young Bong Kim
Keyword(s):  
Molecular Docking ◽  
Influenza A ◽  
Target Genes ◽  
Network Pharmacology ◽  
Binding Affinities ◽  
Anticancer Activities ◽  
Biological Targets ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Docking Method

(1) Background: Re-emerging influenza threats continue to challenge medical and public health systems. Quercetin is a ubiquitous flavonoid found in food and is recognized to possess antioxidant, anti-inflammatory, antiviral, and anticancer activities. (2) Methods: To elucidate the targets and mechanisms underlying the action of quercetin as a therapeutic agent for influenza, network pharmacology and molecular docking were employed. Biological targets of quercetin and target genes associated with influenza were retrieved from public databases. Compound–disease target (C-D) networks were constructed, and targets were further analyzed using KEGG pathway analysis. Potent target genes were retrieved from the compound–disease–pathway (C-D-P) and protein–protein interaction (PPI) networks. The binding affinities between quercetin and the targets were identified using molecular docking. (3) Results: The pathway study revealed that quercetin-associated influenza targets were mainly involved in viral diseases, inflammation-associated pathways, and cancer. Four targets, MAPK1, NFKB1, RELA, and TP53, were identified to be involved in the inhibitory effects of quercetin on influenza. Using the molecular docking method, we evaluated the binding affinity of each ligand (quercetin)–target and discovered that quercetin and MAPK1 showed the strongest calculated binding energy among the four ligand–target complexes. (4) Conclusion: These findings identified potential targets of quercetin and suggest quercetin as a potential drug for influenza treatment.

scholarly journals Potential Mechanism of Dingji Fumai Decoction Against Atrial Fibrillation Based on Network Pharmacology, Molecular Docking, and Experimental Verification Integration Strategy

2021 ◽  
Vol 8 ◽  
Author(s):  
Yi Liang ◽  
Bo Liang ◽  
Wen Chen ◽  
Xin-Rui Wu ◽  
Wu-Sha Liu-Huo ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Molecular Docking ◽  
Protein Complexes ◽  
Network Pharmacology ◽  
Potential Mechanism ◽  
Active Components ◽  
Functional Protein ◽  
Active Compounds ◽  
Systematic Analysis ◽  
Ppi Networks

Background: Dingji Fumai Decoction (DFD), a traditional herbal mixture, has been widely used to treat arrhythmia in clinical practice in China. However, the exploration of the active components and underlying mechanism of DFD in treating atrial fibrillation (AF) is still scarce.Methods: Compounds of DFD were collected from TCMSP, ETCM, and literature. The targets of active compounds were explored using SwissTargetPrediction. Meanwhile, targets of AF were collected from DrugBank, TTD, MalaCards, TCMSP, DisGeNET, and OMIM. Then, the H-C-T-D and PPI networks were constructed using STRING and analyzed using CytoNCA. Meanwhile, VarElect was utilized to detect the correlation between targets and diseases. Next, Metascape was employed for systematic analysis of the mechanism of potential targets and protein complexes in treating AF. AutoDock Vina, Pymol, and Discovery Studio were applied for molecular docking. Finally, the main findings were validated through molecular biology experiments.Results: A total of 168 active compounds and 1,093 targets of DFD were collected, and there were 89 shared targets between DFD and AF. H-C-T-D network showed the relationships among DFD, active compounds, targets, and AF. Three functional protein complexes of DFD were extracted from the PPI network. Further systematic analysis revealed that the regulation of cardiac oxidative stress, cardiac inflammation, and cardiac ion channels were the potential mechanism of DFD in treating AF. Addtionally, molecular docking verified the interactions between active compounds and targets. Finally, we found that DFD significantly increased the level of SIRT1 and reduced the levels of ACE, VCAM-1, and IL-6.Conclusions: DFD could be utilized in treating AF through a complicated mechanism, including interactions between related active compounds and targets, promoting the explanation and understanding of the molecular biological mechanism of DFD in the treatment of AF.

scholarly journals Study on the Antianxiety Mechanism of Suanzaoren Decoction Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Xiaocong Xu ◽  
Bingbing Gao ◽  
Xiongying Li ◽  
Shanshan Lei
Keyword(s):  
Molecular Docking ◽  
Binding Energy ◽  
Interaction Analysis ◽  
Modern Medicine ◽  
Network Pharmacology ◽  
Active Components ◽  
Material Basis ◽  
Go Enrichment ◽  
Antianxiety Drug ◽  
Analysis Platform

Objective. Suanzaoren Decoction (SZRT) is a classic decoction to calm the nerves in traditional Chinese medicine (TCM). It has been extensively treated as an antianxiety drug in modern times, but the material basis and pharmacological mechanisms are still unclear. To explore the material basis and corresponding potential targets, as well as to elucidate the mechanism of SZRT, network pharmacology and molecular docking methods were utilized. Methods. The main chemical compounds and potential targets of SZRT were collected from the pharmacological database analysis platform (TCMSP). Anxiety targets were obtained from the GeneCards database. Then, a target compound network was established using overlapping genes and the corresponding potential compounds. Protein interaction analysis, GO enrichment, and KEGG pathway enrichment were performed using the STRING database, DAVID database, and KOBAS database. Finally, molecular docking was conducted between MAOB and its corresponding active compound in SZRT to further verify the results. Results. A total of 137 active components in SZRT were screened from the TCMSP database, and 210 corresponding targets were predicted. A total of 5434 anxiety-related targets were obtained from the disease target database, and finally 22 potential targets of SZRT on antianxiety were obtained. The constructed C-T network showed that the average degree of active components was 5.4, and four of them interacted with six or more targets. PPI analysis shows that key genes such as MAOA, MAOB, IL1B, TNF, NR3CI, and HTR3A were identified as potential therapeutic targets. A pathway analysis showed that SZRT may participate in neurotransmitter regulation and immunoregulation in a synergistic way to treat anxiety. The binding energy between the active compounds and MAOB was low, indicating good binding. The results of molecular docking showed that all the 10 active ingredients were able to successfully dock with MAOB, and the binding energy of coumaroyltyramine with MAOB was the lowest, that is, −9.6 kcal/mol, and the binding method was hydrogen bonding. Conclusions. SZRT produces antianxiety effects mainly by affecting the neurotransmitter release, transmission, and immunoregulation. This study provides a new approach to elucidating the molecular mechanism and material basis of SZRT in the treatment of anxiety, and it will also benefit the application of TCM in modern medicine.

scholarly journals Molecular Mechanism of the Effect of Huanglian Jiedu Decoction on Type 2 Diabetes Mellitus Based on Network Pharmacology and Molecular Docking

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Bei Yin ◽  
Yi-Ming Bi ◽  
Guan-Jie Fan ◽  
Ya-Qing Xia
Keyword(s):  
Molecular Docking ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Binding Capacity ◽  
Topological Analysis ◽  
Herbal Medicines ◽  
Differentially Expressed ◽  
Network Pharmacology ◽  
Active Components ◽  
Ppi Networks

Background. Huanglian Jiedu Decoction (HLJDD) is a Traditional Chinese Medicine (TCM) formula comprising four herbal medicines. This decoction has long been used in China for clinically treating T2DM. However, the molecular mechanism of HLJDD treat for T2DM is still not fully known. Hence, this study was designed to reveal the synergistic mechanism of HLJDD formula in the treatment of T2DM by using network pharmacology method and molecular docking. Methods. Retrieving and screening of active components of different herbs in HLJDD and corresponding T2DM-related target genes across multiple databases. Subsequently, STRING and Cytoscape were applied to analysis and construct PPI network. In addition, cluster and topological analysis were employed for the analysis of PPI networks. Then, the GO and KEGG enrichment analysis were performed by using ClueGO tool. Finally, the differentially expressed analysis was used to verify whether the expression of key target genes in T2DM and non-T2DM samples was statistically significant, and the binding capacity between active components and key targets was validated by molecular docking using AutoDock. Results. There are 65 active components involved in 197 T2DM-related targets that are identified in HLJDD formula. What is more, 39 key targets (AKT1, IL-6, FOS, VEGFA, CASP3, etc.) and 3 clusters were obtained after topological and cluster analysis. Further, GO and KEGG analysis showed that HLJDD may play an important role in treating T2DM and its complications by synergistically regulating many biological processes and pathways which participated in signaling transduction, inflammatory response, apoptotic process, and vascular processes. Differentially expressed analysis showed that AKT1, IL-6, and FOS were upregulated in T2DM samples and a significant between sample differential expression. These results were validated by molecular docking, which identified 5 high-affinity active components in HLJDD, including quercetin, wogonin, baicalein, kaempferol, and oroxylin A. Conclusion. Our research firstly revealed the basic pharmacological effects and relevant mechanisms of the HLJDD in the treatment of T2DM and its complications. The prediction results might facilitate the development of HLJDD or its active compounds as alternative therapy for T2DM. However, more pharmacological experiments should be performed for verification.

Sign in / Sign up

Export Citation Format

Share Document