Pharmacology Database Research Articles

Network Pharmacology Studies on the Molecular Mechanism of Hashimoto's Thyroiditis Treated with Shutiao Qiji Decoction.

In recent years, the number of patients with Hashimoto's thyroiditis has been increasing, and traditional Chinese medicine ingredients and combinations have been applied to treat Hashimoto's thyroiditis to increase efficacy and reduce side effects during the treatment process. Shutiao Qiji Decoction is one of the Chinese traditional medicine prescriptions, which is commonly used to treat cancer, tumor, etc. It is also used for thyroid-related diseases in the clinic. Hashimoto's thyroiditis is an autoimmune disease. In this study, the mechanism of Shutiao Qiji Decoction in treating Hashimoto's thyroiditis was studied through network pharmacology and molecular docking verification. Each Chinese medicine ingredient of Shutiao Qiji Decoction was retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The related genes of HT were searched from the UniProt and GeneCards databases. Meanwhile, we used Cytoscape to construct the protein-protein interaction (PPI) visual network analysis, and used the search tool to search the database of Interacting Genes (STRING) to build a PPI network. These key proteins were enriched and analyzed by molecular docking validation, Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Hashimoto's thyroiditis disease model was established in SD rats, and SQD was administered by gavage after the successful establishment of the model. After 6 weeks of continuous administration of the drug by gavage, tissue samples were collected and the thyroid and spleen tissues were visualized by HE staining to verify the therapeutic effect. The results showed that there were 287 TCM active ingredients, 1920 HT-related disease targets, and 176 drug and disease targets in SQD. Through PPI analysis, GP analysis, and KEGG analysis of the common targets of drugs and diseases, we found their pathways of action to be mainly cancer action pathway, PI3K-AKT signaling pathway, and T-cell action pathway. The active ingredients of the drugs in SQD, malvidin, stigmasterol, porin-5-en-3bta-ol, and chrysanthemum stigmasterol, were docked with the related target proteins, MAPK, GSK3β, TSHR, and NOTCH molecules. The best binding energies obtained from docking were mairin with TSHR, stigmasterol with TSHR, poriferast-5-en-3beta-ol with MAPK, and chryseriol with GSK3β, with binding energies of -6.84 kcal/mol, -6.53 kcal/mol, -5.03 kcal/mol, and -5.05 kcal/mol, respectively. HE staining sections of rat thyroid and spleen tissues showed that SQD had a therapeutic effect on Hashimoto's thyroiditis and restored its immune function. It is verified by molecular docking results that Shutiao Qiji Decoction has a potential therapeutic effect on Hashimoto's thyroiditis in the MAPK/TSHR/NOTCH signal pathway, and that the main components, mairin, stigmasterol, poriferast-5-en-3beta-ol, and chryseriol play a role in it. SQD has been shown to have a good therapeutic effect on Hashimoto's thyroiditis.

Mechanism of Polygala-Acorus in Treating Autism Spectrum Disorder Based on Network Pharmacology and Molecular Docking.

Recent epidemic survey data have revealed a globally increasing prevalence of autism spectrum disorders (ASDs). Currently, while Western medicine mostly uses a combination of comprehensive intervention and rehabilitative treatment, patient outcomes remain unsatisfactory. Polygala-Acorus, used as a pair drug, positively affects the brain and kidneys, and can improve intelligence, wisdom, and awareness; however, the underlying mechanism of action is unclear. We performed network pharmacology analysis of the mechanism of Polygala-Acorus in treating ASD and its potential therapeutic effects to provide a scientific basis for the pharmaceutical's clinical application. The chemical compositions and targets corresponding to Polygala-Acorus were obtained using the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform, Chemical Source Website, and PharmMapper database. Disease targets in ASD were screened using the DisGeNET, DrugBank, and GeneCards databases. Gene Ontology functional analysis and metabolic pathway analysis (Kyoto Encyclopedia of Genes and Genomes) were performed using the Metascape database and validated via molecular docking using AutoDock Vina and PyMOL software. Molecular docking analysis showed that the key active components of Polygala- Acorus interacted with the following key targets: EGFR, SRC, MAPK1, and ALB. Thus, the key active components of Polygala-Acorus (sibiricaxanthone A, sibiricaxanthone B tenuifolin, polygalic acid, cycloartenol, and 8-isopentenyl-kaempferol) have been found to bind to EGFR, SRC, MAPK1, and ALB. This study has preliminarily revealed the active ingredients and underlying mechanism of Polygala-Acorus in the treatment of ASD, and our predictions need to be proven by further experimentation.

Network Pharmacology, Molecular Docking and Experimental Verification Revealing the Mechanism of Fule Cream against Childhood Atopic Dermatitis.

The Fule Cream (FLC) is an herbal formula widely used for the treatment of pediatric atopic dermatitis (AD), however, the main active components and functional mechanisms of FLC remain unclear. This study performed an initial exploration of the potential acting mechanisms of FLC in childhood AD treatment through analyses of an AD mouse model using network pharmacology, molecular docking technology, and RNA-seq analysis. The main bioactive ingredients and potential targets of FLC were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and SwissTargetPrediction databases. An herb-compound-target network was built using Cytoscape 3.7.2. The disease targets of pediatric AD were searched in the DisGeNET, Therapeutic Target Database (TTD), OMIM, DrugBank and GeneCards databases. The overlapping targets between the active compounds and the disease were imported into the STRING database for the construction of the protein-protein interaction (PPI) network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersection targets were performed, and molecular docking verification of the core compounds and targets was then performed using AutoDock Vina 1.1.2. The AD mouse model for experimental verification was induced by MC903. The herb-compound-target network included 415 nodes and 1990 edges. Quercetin, luteolin, beta-sitosterol, wogonin, ursolic acid, apigenin, stigmasterol, kaempferol, sitogluside and myricetin were key nodes. The targets with higher degree values were IL-4, IL-10, IL-1α, IL-1β, TNFα, CXCL8, CCL2, CXCL10, CSF2, and IL-6. GO enrichment and KEGG analyses illustrated that important biological functions involved response to extracellular stimulus, regulation of cell adhesion and migration, inflammatory response, cellular response to cytokine stimulus, and cytokine receptor binding. The signaling pathways in the FLC treatment of pediatric AD mainly involve the PI3K-Akt signaling pathway, cytokine‒cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. The binding energy scores of the compounds and targets indicate a good binding activity. Luteolin, quercetin, and kaempferol showed a strong binding activity with TNFα and IL-4. This study illustrates the main bioactive components and potential mechanisms of FLC in the treatment of childhood AD, and provides a basis and reference for subsequent exploration.

Deciphering the Underlying Mechanisms of Sanleng-Ezhu for the Treatment of Idiopathic Pulmonary Fibrosis Based on Network Pharmacology and Single-cell RNA Sequencing Data.

To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown. Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle cells mainly through the viral infection, apoptosis, and p53 signaling pathway.

Inulae Flos is a potential herbal medicine to treat glioma: a study based on gene expression profile analysis, network pharmacology and molecular docking

Inulae Flos is a promising herbal medicine; however, its underlying mechanism in treating glioma remains unclear. This study aimed to elucidate the potential active ingredients and mechanisms of Inulae Flos in glioma treatment. Differentially expressed genes (DEGs) associated with gliomas were identified by analyzing the GSE186057 dataset. The bioactive components of Inulae Flos were searched in the Traditional Chinese Medicine Systems Pharmacology database, and related targets were obtained from the Swiss Target Prediction database. A protein-protein interaction (PPI) network was constructed using the STRING database. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) of these common targets were analyzed using the DAVID database. Finally, AutoDock Vina was used for molecular docking analysis. The tumor-suppressive properties of the crude extract of Inulae Flos (XFH) and britanin were then evaluated with in vitro and in vivo assays. Nineteen active ingredients were obtained from Inulae Flos, and 20 DEGs were identified as the targets of Inulae Flos in glioma treatment. Britanin, luteolin and 1-O-Acetylbritannilactone were identified as the key ingredients. PIK3R1, CDK2, CCNB1, MAPK1, MAPK8, CCNA2, and AR were considered to be the hub target genes of Inulae Flos in glioma treatment. Good binding affinity was observed between the key components and hub targets. In addition, in vitro studies showed that XFH and britanin, a key component of Inulae Flos, could inhibit the growth, migration and invasion glioma cell lines U87 and U138, and repress the tumorigenesis of U138 cells in nude mice. Inulae Flos is promising for glioma treatment, and it exerts its anti-tumor activities via multiple components, targets and pathways.

Causal associations of tea consumption on risk of pancreatic adenocarcinoma and the mediating role of vascular endothelial growth factor D levels.

Tea is one of the most widely consumed beverages in the world. However, the association between tea and risk of pancreatic adenocarcinoma remains controversial. This study aimed to investigate the causal relationship between tea consumption and risk of pancreatic adenocarcinoma and to explore their mediating effects. The two-sample Mendelian randomisation (MR) analysis showed an inverse causal relationship between tea intake and pancreatic adenocarcinoma (OR: 0·111 (0·02, 0·85), P < 0·04). To examine the mediating effects, we explored the potential mechanisms by which tea intake reduces the risk of pancreatic adenocarcinoma. Based on the oral bioavailability and drug-like properties in Traditional Chinese Medicine Systems Pharmacology database, we selected the main active ingredients of tea. We screened out the fifteen representative targeted genes by Pharmmapper database, and the gene ontology enrichment analysis showed that these targeted genes were related to vascular endothelial growth factor (VEGF) pathway. The two-step MR analysis of results showed that only VEGF-D played a mediating role, with a mediation ratio of 0·230 (0·066, 0·394). In conclusion, the findings suggest that VEGF-D mediates the effect of tea intake on the risk of pancreatic adenocarcinoma.

Investigating the Mechanism of Banxia Xiexin Decoction in Treating Gastritis and Diabetes Mellitus through Network Pharmacology and Molecular Docking Analysis

Background: Banxia Xiexin decoration (BXD), a complex prescription in Traditional Chinese Medicine (TCM), clinically acts as a treatment for gastritis and diabetes while its mechanism of treatment remains unknown. Objection: This study aimed to explore the common mechanism of BXD in treating gastritis and diabetes based on network pharmacology and molecular docking technology. Methods: The seven Chinese herbal components and drug targets were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) for gastritis and diabetes using GeneCards, DisGeNET, Comparative Toxicogenomics Database (CTD), and Online Mendelian Inheritance in Man (OMIM) databases. Common drug and disease targets were imported into the STRING data platform for protein-protein interaction (PPI) analysis, and Cytoscape 3.7.2 software for network topology analysis, and core targets were filtered. Results: There were 124 components, 249 targets, 449 targets for gastritis, and 4005 targets for diabetes. After mapping, 83 BXD targets for gastritis and diabetes were obtained, and the targets with high correlation were STAT 3, JUN, TNF, IL-6, etc. More relevant targets were involved in the cancer pathway, AGE-RAGE signaling pathway of diabetic complications, fluid shear stress, and atherosclerosis pathway. Conclusion: This study preliminarily reveals that BXD may play a role in the treatment of gastritis and diabetes mellitus through multi-components, multi-targets, and multi-pathways, and proposes some potential "component-target-pathway" hypotheses in light of previous reports.

Network pharmacology and experimental verification unraveled the mechanism of Bailing Capsule against asthma.

Asthma is a serious public health challenge around the world. Recent studies into traditional Chinese medicine preparations for asthma have yielded promising findings regarding Bailing Capsule's potential in bronchial asthma prevention and treatment. This study aims to initially clarify the potential mechanism of Bailing Capsule in the treatment of asthma using network pharmacology and in vitro experimental approaches. Network pharmacology was adopted to detect the active ingredients of Bailing Capsule via Traditional Chinese Medicine Systems Pharmacology Database, and the key targets and signaling pathways in the treatment of asthma were predicted. Docking and molecular dynamics simulations were conducted to verify the most important interactions formed by these probes within different regions of the binding site. The predicted targets were validated in lipopolysaccharide-induced 16HBE cell experiment. Seven active ingredients were screened from Bailing Capsule, 294 overlapping targets matched with asthma were considered potential therapeutic targets, such as SRC, TP53, STAT3, and E1A binding protein P300. The main functional pathways involving these key targets include phosphatidylinositol 3-kinase/protein kinase B, mitogen-activated protein kinase, renin-angiotensin system and other signaling pathways, which were mainly involved in the inflammatory response, apoptosis, and xenobiotic stimulus. Moreover, molecular docking showed that Cerevisterol have higher affinity for SRC, TP53, STAT3, and E1A binding protein P300 than other main active components, which is close to the docking results of the co-crystallized ligands to proteins. Consequently, Cerevisterol was selected for molecular dynamics simulation and the results show that Cerevisterol can bind most tightly to SRC, TP53, and STAT3. Bailing Capsule can promote the growth of 16HBE cell, reduce the production of IL-4, TNF-α and IL-6, and down-regulate the levels of SRC and STAT3 mRNA. This study preliminarily reveals the potential mechanism of Bailing Capsule against asthma with the aid of network pharmacology and in vitro cell experiment, which provided reference and guidance for in-depth research and clinical application.

Elucidating the Mechanisms of Astragalus Membranaceus in Colorectal Cancer Patients through Bioinformatics Analysis.

Astragalus membranaceus has shown positive clinical efficacy in treating colorectal cancer (CRC). This study aimed to identify the key active components of Astragalus and determine effective targets of these components in CRC patients. We identified active components of Astragalus membranaceus and differentially expressed genes in traditional Chinese medicine systems pharmacology database and The Cancer Genome Atlas. Additionally, the enrichment analysis of differential target genes (DTGs) was performed using the R-package clusterProfiler. Immunocyte correlation analysis and non-coding regulatory network construction were performed for biomarkers using Spearman's method and NetworkAnalyst. Finally, molecular docking of biomarkers and their corresponding molecule drugs was done with Autodock Vina software. We identified 20 active components of Astragalus membranaceus and 1 403 target genes through screening. A total of 2 300 differentially expressed genes, and 3 035 hub genes in CRC were screened. The integration of the target genes with the significantly differentially expressed genes and Hub genes identified resulted in a total of 86 DTGs. Subsequently, the results showed 828 enriched GO biological processes, 184 enriched GO molecular functions, 59 enriched GO cellular components, and 46 enriched KEGG pathways. We also obtained a total of 143 PPI pairs involving 67 nodes. Additionally, we constructed 45 mRNA-TF pairs, 101 miRNA-mRNA pairs, and 200 miRNA- mRNA-TF triplets. Finally, molecular docking was performed for the active component quercetin with F2 and UGT1A1 and formic acid with FGA, AHSG, and KNG1. This study identified the active components of Astragalus membranaceus and their corresponding targets in CRC. These findings provide robust evidence for precision drug therapy in patients with CRC.

A Network Pharmacological Study of Qianjin Sanhuang Decoction in the Treatment of Osteoarthritic Knee Disease

Objective: To predict the action targets of Qianjin Sanhuang decoction in the treatment of knee osteoarthritis through network pharmacology, to explore its potential multi component, multi target, multi, pathway mechanism of action, and to elaborate the relationship between the pharmacological effects of the drug and the disease from the perspective of molecular mechanism. Methods: The Chinese herbal composition of Qianjin Sanhuang decoction was determined by reading ancient texts and reviewing data, and the chemical components of Qianjin Sanhuang decoction were collected through the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP), and the active ingredients of the drug were obtained after oral utilization (OB) and drug-like properties (DL) screening, and the active ingredient target prediction was performed, while the active ingredient targets were retrieved from GeneCards, OMIM, and DisGeNet database to retrieve knee osteoarthritis-related targets. After taking the intersecting targets, a software called cytoscape 3.9.0 was used for mapping, with the goal of constructing a component-target network map, followed by a string database for mapping, with the goal of making a network map of target protein-target protein interactions, and finally, a gene ontology (GO) enrichment analysis and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, this analysis process was performed in the metascape database. Results: A total of 1272 targets related to osteoarthritis of the knee were collected in this study, 89 active ingredients of Qianjin Sanhuang decoction, involving 267 targets, and 121 therapeutic targets involving osteoarthritis of the knee, and the results of GO enrichment analysis showed that it involved the response to inorganic substances, positive regulation of cell migration, response to oxygen content, response to lipopolysaccharide, response to peptides, regulation of cell adhesion, response to extracellular stimuli, negative regulation of cell differentiation, positive regulation of cell death, glandular development, regulation of apoptotic signaling pathways, development of reproductive structures, response to trauma, response to growth factors, regulation of inflammatory response, cellular response to organic recycling compounds, regulation of proteolysis, etc. After KEGG analysis, the results showed that signaling pathways such as AGE-RAGE pathway and HIF-1 pathway were involved. Conclusion: Qianjin Sanhuang decoction can improve the inflammatory response of knee osteoarthritis through potential multi-pathways, multi-targets and multi-pathways, and can have potential therapeutic effects on osteoarthritis.

From complexity to clarity: development of CHM-FIEFP for predicting effective components in Chinese herbal formulas by using big data.

The presence of complex components in Chinese herbal medicine (CHM) hinders identification of the primary active substances and understanding of pharmacological principles. This study was aimed at developing a big-data-based, knowledge-driven in silico algorithm for predicting central components in complex CHM formulas. Network pharmacology (TCMSP) and clinical (GEO) databases were searched to retrieve gene targets corresponding to the formula ingredients, herbal components, and specific disease being treated. Intersections were determined to obtain disease-specific core targets, which underwent further GO and KEGG enrichment analyses to generate non-redundant biological processes and molecular targets for the formula and each component. The ratios of the numbers of biological and molecular events associated with a component were calculated with a formula, and entropy weighting was performed to obtain a fitting score to facilitate ranking and improve identification of the key components. The established method was tested on the traditional CHM formula Danggui Sini Decoction (DSD) for gastric cancer. Finally, the effects of the predicted critical component were experimentally validated in gastric cancer cells. An algorithm called Chinese Herb Medicine-Formula vs. Ingredients Efficacy Fitting & Prediction (CHM-FIEFP) was developed. Ferulic acid was identified as having the highest fitting score among all tested DSD components. The pharmacological effects of ferulic acid alone were similar to those of DSD. CHM-FIEFP is a promising in silico method for identifying pharmacological components of CHM formulas with activity against specific diseases. This approach may also be practical for solving other similarly complex problems. The algorithm is available at http://chm-fiefp.net/.

Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum

Cirsium japonicum, a traditional herb, exhibits significant anti-inflammatory activity. However, the main components and potential mechanisms of C. japonicum remain unclear. The aim of this study is to investigate the anti-inflammatory mechanism of Cirsium japonicum through network pharmacology and cellular experiments. The effective components of and potential targets for the anti-inflammatory activity of C. japonicum were identified using a traditional Chinese medicine systematic pharmacology database, the TCMSP analysis platform, and the GeneCards database. The drug–component–target–disease network diagram was constructed using Cytoscape 3.8.0 software, while the protein interaction network diagram was created using the STRING database and Cytoscape 3.8.0 software. Gene ontology (GO) enrichment and KEGG pathway enrichment analysis were carried out using the DAVID database. Molecular docking between key targets and active components was constructed with AutoDock 4.2.6 software to determine the best binding target. The results revealed that 14 active components of C. japonicum targeted 171 anti-inflammatory proteins. GO function enrichment analysis yielded 173 items, while KEGG pathway enrichment analysis identified 48 signaling pathways related to inflammation regulation. Molecular docking showed a strong affinity of sitosterol, stigmasterol, and other components with key targets such as peroxisome proliferator-activated receptor α recombinant protein (PPARA) and cyclooxygenase-2 (PTGS2). Vanillin, one active ingredient of C. japonicum, inhibited the release of lipopolysaccharide (LPS)-induced inflammatory factors in RAW264.7 cells. These findings suggest that C. japonicum may exert its anti-inflammatory effects by modulating the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signal pathway (PI3K-Akt) and apoptin signal pathway, highlighting the multi-component, multi-target, and multi-channel molecular mechanism underlying its anti-inflammatory properties. Finally, the anti-inflammatory effect of vanillin, an effective component of C. japonicum, was verified by cell experiments. This study provides a new understanding of the pharmacological mechanisms of C. japonicum in the treatment of inflammatory conditions.

Exploring the mechanism of Danggui Sini Decoction in the treatment of myocardial infarction: A systematic review, network pharmacology, and molecular docking.

Myocardial infarction (MI) is one of the leading causes of death worldwide because of its high morbidity and mortality. Traditional Chinese Medicine compounds play a crucial role in preventing cardiovascular diseases. Danggui Sini Decoction (DSD) is widely used clinically for cardiovascular diseases. However, the mechanism, main components, and main targets of DSD in treating MI are still unclear. In this study, we utilized network pharmacology and molecular docking for exploration. MI-related genes were examined using the Genecards database, and the active ingredients of DSD were screened based on System Pharmacology Database and Analysis Platform of Traditional Chinese Medicine by oral bioavailability ≥ 30% and drug-likeness ≥ 0.18. The protein-protein interaction network diagram was generated using the STRING database. The DAVID web platform was used to carry out gene ontology and Kyoto encyclopedia of gene and genome signaling pathway analysis. DSD's screening study revealed 120 primary active ingredients and 561 putative active target genes. The main therapeutic targets were TP53, EGFR, AKT1, IL6, TNF, STAT3, IL1B, CTNNB1, SRC, MYC, JUN, and INS. Gene ontology and Kyoto encyclopedia of gene and genome analyses revealed that DSD treatment of MI mainly involves the positive regulation of the ERK1 and ERK2 cascades, positive regulation of cell proliferation, inflammatory responses, aging, and the MAPK cascade, along with other biological processes. The molecular docking results indicate that DSD drugs may interact with AKT1, EGFR, TP53, and TNF through formononetin, isorhamnetin, β-Sitosterol, and kaempferol, potentially contributing to the treatment of MI. By utilizing a multi-component, multi-pathway, and multi-target mode of action, DSD may have the potential to prevent MI.

Genetic and therapeutic for oral lichen planus and diabetes mellitus: a comprehensive study

BackgroundThis study employed a bidirectional Mendelian Randomization (MR) approach to explore the causal relationships between Oral Lichen Planus (OLP), diabetes mellitus (DM), and glycemic control. It also aims to identify potential pharmacological and herbal treatments that effectively address both OLP and the metabolic dysfunctions associated with DM.MethodsThis study employs a two-way MR approach to investigate the potential causal relationships between diabetes type and glycated hemoglobin (HbA1c) levels, and the risk of OLP. We analyzed differentially expressed genes from the OLP dataset in the Genomics Expression Omnibus (GEO) database, cross-referencing these with HbA1c-related genes for enrichment analysis. Additionally, the Drug-Gene Interaction Database (DGIdb) and Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) were utilized to assess the effectiveness of specific drugs, herbs, and ingredients in treating OLP while managing blood glucose levels.ResultsThe MR analysis revealed a significant association between Type 1 Diabetes mellitus (T1DM) and an increased risk of OLP, unlike Type 2 Diabetes mellitus (T2DM). This finding indicates a unique immunological interaction in T1DM that may predispose individuals to OLP. The drug prediction analysis focused on core targets linked to OLP and HbA1c, evaluating the therapeutic potential of retinoic acid, prednisone, and thalidomide for treating OLP and regulating blood glucose levels. Additionally, herbal medicines such as Ecliptae herbaand Amygdalus communis vas, along with herbal ingredients like quercetin, luteolin, and 17-beta-estradiol, were identified for their anti-inflammatory properties and potential to mitigate metabolic dysfunction in diabetes.ConclusionThe study highlighted a complex interplay between diabetes and OLP, underscoring the efficacy of integrated therapeutic strategies that target both conditions. The findings suggest that both pharmaceutical and herbal treatments can effectively manage the clinical manifestations of OLP and associated metabolic challenges. This holistic approach to treatment could significantly enhance patient outcomes by addressing the interconnected aspects of these chronic conditions.

Exploring the mechanism of Zhengxintai Formula for the treatment of coronary heart disease based on network pharmacology.

Zhengxintai Formula (ZXT) has shown good effects in the clinical treatment of coronary atherosclerotic heart disease (CHD). However, its potential molecular mechanism for treating coronary heart disease is still unknown. The Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform and literature reviews were used to determine the active components and targets of the 6 herbs used in ZXT. Next, we searched disease target databases for targets associated with CHD. Secondly, Cytoscape was used to map the "active compounds-target" network, "protein-protein interaction" network, and "compound-target-disease" network. After that, gene ontology analysis and the pathway analysis by the Kyoto Encyclopedia of Genes and Genomes were performed on the targets. Finally, molecular docking between the compounds and the targets was performed to verify their binding ability. The analysis obtained 116 active compounds of ZXT, corresponding to 611 targets. Thousand three hundred forty-five coronary heart disease targets were collected. Obtained 177 potential ZXT targets for coronary artery disease. Gene ontology analysis yielded 734 biological process entries, 84 cellular component entries, and 122 molecular function entries. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed the key pathways such as "Fluid shear stress and atherosclerosis," "Lipid and atherosclerosis", and "PI3K-Akt signaling pathway." The molecular docking results showed good binding between each screened core target and the core components. ZXT fulfills its role in the treatment of CHD through the core components and core targets that have been screened out, but the exact process still needs to be further investigated.

Multi-Target Mechanisms of Si-Ni-San on Anxious Insomnia: An Example of Network-pharmacology and Molecular Docking Analysis.

Based on comprehensive network-pharmacology and molecular docking analysis, this study was intended to unveil the multiple mechanisms of Si-Ni-San (SNS) in treating anxious insomnia. The compounds of SNS were meticulously analyzed, selected and standardized with references to their pharmacological attributes. The components included chaihu (Bupleurum chinense DC.), baishao (Paeonia lactiflora Pall.), zhishi (Citrus aurantium L.) and gancao (Glycyrrhiza uralensis Fisch. ex DC.). We used the Traditional Chinese Medicine System Pharmacology (TCMSP) Database, Traditional Chinese Medicines Integrated Database (TCMID), GeneCards database, therapeutic target database (TTD) and comparative toxicogenomic database (CTD) to construct the components-compounds-targets networks and used Cytoscape 3.9.1 software to visualize the outcome. Afterwards, the STRING database and Cytoscape 3.9.1 software were utilized to construct and visualize the protein-protein interaction (PPI) network analysis. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also conducted through the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The molecular docking program was carried out using AutoDock 4.2 software to understand interactions between target receptors and compound ligands selected for study. We thoroughly sorted and filtered 31 pharmacologically active compounds from SNS. Subsequently, several potential target genes were predicted, of which there were 59 target genes distinctly associated with anxious insomnia. The PPI analysis indicated that the core target proteins included AKT1, IL6, TNF, SLC6A4, MAOA and GABRA2. The results of our study indicated that SNS potentially remediates anxious insomnia by reducing inflammation, neurodegeneration, and cell apoptosis of neurons. In addition, GO and KEGG enrichment analysis results indicated that SNS could modulate multiple aspects of anxious insomnia through mechanisms related to pathways of neuroactive ligand-receptor interaction. These pathways include various kinds of synaptic transmission pathways, and anti-inflammatory activity associated with response pathways. When we compared the components-compounds-targets networks and the compounds-targets-synaptic pathways networks, the five active compounds, including beta-Sitosterol, Kaempferol, Tetramethoxyluteolin, Isorhamnetin and Shinpterocarpin, were selected to conduct molecular docking experiments. Eleven target proteins, (AKT1, SLC6A4, ADRB2, MAOA, ACHE, ESR1, CYP3A4, CHRNA7, GABRA2, HTR2A and NOS3), which also play significant roles in regulating serotonergic, cholinergic, dopaminergic and GABAergic systems in the PPI network, were selected to act as receptors in molecular docking trials. The results showed that docking pairs isorhamnetin-AKT1, isorhamnetin-SLC6A4, β-sitosterol-MAOA, β- sitosterol-ACHE, isorhamnetin-CHRNA7 and shinpterocarpin-GABRA2 provided the most stable conformations of ligand-receptor binding between key compounds and core target proteins in the SNS. In the study, we offer a computational result, revealing that SNS may alleviate sleep disorders associated with anxiety through a "multi-compounds, multi-targets, and multi-pathways" mechanism. The network-pharmacology and molecular docking outcomes could theoretically confirm the anti-anxiety and anti-insomnia effects of SNS. Although this research is purely statistical and systematic without empirical validation, it serves as a stepping stone and cornerstone for subsequent experimental investigations.

Exploring the mechanisms of Cornus officinalis in the treatment of non-alcoholic steatohepatitis using network pharmacology, molecular docking and molecular dynamics simulations

Non-alcoholic steatohepatitis (NASH) is a predominant metabolic liver disease, typically characterized by hepatic steatosis, oxidative stress, and inflammation. The traditional Chinese medicine Cornus officinalis possesses anti-inflammatory and hepatoprotective pharmacological properties and has shown ameliorative effects on NASH. however, its mechanism of action remains unclear. This study aims to elucidate the mechanisms by which C. officinalis ameliorates NASH. The active components of C. officinalis were analyzed using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), and the corresponding targets were predicted. Subsequently, the DisGeNET, GeneCards, and GEO databases were employed to identify NASH-related targets. Venn diagrams were used to intersect the C. officinalis targets with the NASH targets. Protein–protein interaction (PPI) networks were constructed using the STRING database, and PPI network analysis was performed using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), followed by molecular docking validation. Cornus officinalis was found to contain 20 major active ingredients corresponding to 672 potential targets, 61 of which overlapped with NASH targets. PPI network, GO, and KEGG pathway analyses identified four targets with the highest correlation, and molecular docking results indicated that the active ingredients of C. officinalis exhibited strong binding affinities to NASH targets. The treatment of NASH with C. officinalis is characterized by multiple active ingredients and multiple targets, underscoring the major advantage of traditional Chinese medicine in treating NASH.

Network pharmacology analysis revealed the mechanism and active compounds of jiao tai wan in the treatment of type 2 diabetes mellitus via SRC/PI3K/AKT signaling

Ethnopharmacological relevanceJiao-tai-wan (JTW) is a traditional Chinese herbal prescription, exerts its therapeutic effects on type 2 diabetes mellitus (T2DM). However, its mechanisms and active components remain unclear. Aim of the studyTo investigate the therapeutic mechanisms of JTW in treating type 2 diabetes mellitus (T2DM), focusing on identifying active components, their targets, and validating efficacy through SRC/PI3K/AKT signaling pathway modulation in vitro and in vivo. Materials and methodsActive ingredients were retrieved from the Traditional Chinese Medicine System Pharmacology (TCMSP) and Comprehensive Traditional Chinese Medicine Database (TCMID). Targets for these components were identified using the ChemMapper database based on 3D structural similarity. T2DM-related genes were sourced from the DisGeNET and Gene Expression Omnibus (GEO) databases. Protein-protein interaction (PPI) analysis and functional enrichment analysis were conducted to construct a pathway network of “herbs-active ingredients-candidate targets”, identifying core molecular mechanisms and key active ingredients. SwissDock was used for molecular docking to predict ligands for candidate targets. The diabetic models were established using C57BL/6 mice and human liver HepG2 cell lines. Their Effectiveness and key molecules were verified through biochemical detection and immunoblotting. ResultsTotal 30 active compounds, 597 active ingredient targets, 9631 T2DM-related genes, and 521 overlapping candidate targets were found for JTW on T2DM. Go enrichment indicated the core pathways enriched on insulin and glucose metabolism. The auto-docking demonstrated SRC has potential binds to ingredients of JTW. In vivo, JTW can reduce blood glucose, and blood lipid levels, and HOMA-IR, and increase HOMA-ISI levels in T2DM mice with reduced ALT, AST, MDA levels and increased SOD levels. Meanwhile, decreased phosphorylation of SRC, along with increased levels of phosphorylated PI3K, PI3K, and phosphorylated AKT, were observed. HE staining of liver tissues further confirmed that JTW administration improved liver morphology, reducing inflammation and necrosis. In vitro, JTW significantly ameliorates upstream dysregulation by reducing SRC phosphorylation while enhancing phosphorylated PI3K, PI3K, and AKT phosphorylation levels. ConclusionJTW may alleviate glucose, insulin resistance, and lipid metabolism disorders by the SRC/PI3K/AKT signaling pathway, that provide a novel view of potential active compounds and essential targets in treating T2DM.

Exploring medication rules and mechanism of Chinese medicine for children with cough variant asthma based on data mining, network pharmacology, and molecular docking.

Cough variant asthma (CVA) is a common disease with high incidence among children. Cough is the main clinical symptom and Chinese medicine (CM) has an exact effect on CVA. However, the rules of herb formulation, the pharmacodynamic substances, and the mechanism remained unclear. Therefore, we conducted this article to explore medication rules and molecular mechanism of CM against CVA in children using data mining, network pharmacology, molecular docking, and molecular dynamics simulation. Relevant literatures were collected from China National Knowledge Infrastructure, Chinese Scientific and Technical Journals database, Wanfang database, Pubmed, and Web of science. Excel 2016 was used to extract related data and establish the database for Chinese medical frequency, properties, tastes, and meridian analysis. Association rules were analyzed based on Apriori algorithm using IBM SPSS Modeler 18.0 software and core herb combination was identified. The active ingredients and targets of the core herb combination were acquired form the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. The main targets of CVA were obtained from the GeneCards and Online Mendelian Inheritance in Man database. Core targets were selected by using STRING platform and Cytoscape 3.7.2 software. Metascape platform was utilized to perform gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. The results were verified by molecular docking. SwissADME and pkCSM website were used to analyze the pharmacokinetic profiles and toxicity of key components of the core herb combination. Molecular dynamics simulation was utilized to evaluate the stable of protein-ligand complex. Two hundred seventy-five literatures containing 202 herbs were finally collected. Statistics indicated that these herbs possessed bitter, pungent taste, and warm properties, and belonged to lung meridian. Glycyrrhizae radix et rhizome, Ephedrae herba, and Armeniacae semen amarum were the most frequently used herbs. "Glycyrrhizae radix et rhizoma-ephedrae herba-Armeniacae semen amarum" was the core herb combination with highest support and confidence. Network pharmacology predicted that the main active ingredients, like quercetin, kaempferol, luteolin, etc, might target on RAC-alpha serine/threonine-protein kinase, tumor necrosis factor, interleukin-6, vascular endothelial growth factor A, transcription factor AP-1, interleukin-1 beta, matrix metalloproteinase-9, etc. They played a pivotal role in regulating multiple signaling pathways, such as tumor necrosis factor signaling pathway, IL-17 signaling pathway, and PI3K-Akt signaling pathway. Molecular docking revealed that the key active ingredients were well docked with core targets. The absorption, distribution, metabolism, excretion, and toxicity analysis showed that formononetin, luteolin, naringenin, and quercetin have high gastrointestinal absorption, no AMES toxicity, hepatotoxicity, and skin sensitization. Molecular dynamics simulation revealed that the formononetin-matrix metalloproteinase-9 complex was relatively stable. This article revealed that CM against CVA in children focused on dispelling wind and reducing phlegm, warming lung, and relieving cough. The mechanism of the core herb combination of CM for CVA through muti-components, muti-targets, and muti-pathways.

Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling

This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.