Exploration of Fuzheng Yugan Mixture on COVID-19 based on network pharmacology and molecular docking

Research in revealing the effects on Cuscuta chinensis to diarrhea type irritable bowel syndrome based on network pharmacology and molecular docking potential mechanism

To explore the potential mechanism in Cuscuta sinensis on diarrhea-type irritable bowel syndrome using network pharmacology and molecular docking techniques. First, the active components and related targets of Cuscuta were found setting oral utilization >30% and drug-like properties greater than or equal to 0.18 as filter information from TCMSP database. The targets of diarrheal irritable bowel syndrome were compiled by searching DrugBank, GeneCards, OMIM, PharmGkb, and TTD databases. The intersections of drugs and targets related to the disease were taken for gene ontology enrichment and Kyoto encyclopedia of genes and genomes enrichment analyses, to elucidate the potential molecular mechanisms and pathway information of Cuscuta sinensis for the treatment of diarrheal irritable bowel syndrome. The protein-protein interaction network was constructed by using the STRING database and visualized with Cytoscape_v3.10.0 software to find the protein-protein interaction network core At last, molecular docking was performed to validate the combination of active compounds with the core target. The target information of Cuscuta and diarrhea-type irritable bowel syndrome was compiled, which can be resulted in 11 active compounds such as quercetin, kaempferol, isorhamnetin, β-sitosterol, and another 17 core targets such as TP53, IL6, AKT1, IL1B, TNF, EGFR, etc, whose Kyoto encyclopedia of genes and genomes was enriched in the pathways of lipids and atherosclerosis, chemical carcinogenesis-receptor activation, PI3K-Akt signaling pathway, and fluid shear stress and atherosclerosis, etc. Docking demonstrated that the core targets and the active compounds were able to be better combined. Cuscuta chinensis may exert preventive effects on diarrhea-type irritable bowel syndrome by reducing intestinal inflammation, protecting intestinal mucosa, and playing an important role in antioxidant response through multi-targets and multi-pathways.

Target prediction and potential application of dihydroartemisinin on hepatocarcinoma treatment

With high incidence of hepatocarcinoma and limited effective treatments, most patients suffer in pain. Antitumor drugs are single-targeted, toxicity, causing adverse side effects and resistance. Dihydroartemisinin (DHA) inhibits tumor through multiple mechanisms effectively. This study explores and evaluates safety and potential mechanism of DHA towards human hepatocarcinoma based on network pharmacology in a comprehensive way. Adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of DHA were evaluated with pkCSM, SwissADME, and ADMETlab. Potential targets of DHA were obtained from SwissTargetPrediction, Drugbank, TargetNET, and PharmMapper. Target gene of hepatocarcinoma was obtained from OMIM, GeneCards, and DisGeNET. Overlapping targets and hub genes were identified and analyzed for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway. Molecular docking was utilized to investigate the interactions sites and hydrogen bonds. Cell counting kit-8 (CCK8), wound healing, invasion, and migration assays on HepG2 and SNU387 cell proved DHA inhibits malignant biological features of hepatocarcinoma cell. DHA is safe and desirable for clinical application. A total of 131 overlapping targets were identified. Biofunction analysis showed targets were involved in kinase activity, protein phosphorylation, intracellular reception, signal transduction, transcriptome dysregulation, PPAR pathway, and JAK-STAT signaling axis. Top 9 hub genes were obtained using MCC (Maximal Clique Centrality) algorithm, namely CDK1, CCNA2, CCNB1, CCNB2, KIF11, CHEK1, TYMS, AURKA, and TOP2A. Molecular docking suggests that all hub genes form a stable interaction with DHA for optimal binding energy were all less than - 5 kcal/mol. Dihydroartemisinin might be a potent and safe anticarcinogen based on its biological safety and effective therapeutic effect.

Mechanism of Sophorae Flavescentis Radix against ovarian cancer via new pharmacology, molecular docking, and experimental verification

The study aims to elucidate the pharmacological mechanisms of Sophorae Flavescentis Radix (SFR, Kushen) against ovarian cancer (OV) by employing an integrated approach that encompasses network pharmacology, molecular docking, and experimental validation. The effective components and potential targets of SFR were identified through screening the Traditional Chinese Medicine Systems Pharmacology (TSMSP) public database using network pharmacology. Core anti-OV targets were pinpointed using protein-protein interaction (PPI) networks. Datasets from The Cancer Genome Atlas (TCGA), the Human Protein Atlas (HPA), and Gene Expression Profiling Interactive Analysis (GEPIA) were used to investigate the mRNA and protein expressions of critical target genes in both normal and cancerous ovarian tissues, alongside their relationship to overall ovarian survival. Functional and pathway enrichment assessments of putative targets were carried out with Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The assessment of stable binding effects was conducted through molecular docking with quercetin, luteolin, and formononetin, and validated by anti-OV cell activity. The investigation identified 22 active SFR components yielding 152 potential targets following the intersection with known OV targets. Analysis of PPI network highlighted 13 crucial target genes, including tumor necrosis factor (TNF) and interleukin-1A (IL-1A). GO enrichment analysis covered 703 biological activities, 72 cellular components, and 144 chemical functions. The KEGG enrichment analysis suggested that anti-cancer effects of SFR are mediated by the TNF, interleukin-17 (IL-17), and AGE-RAGE signaling pathways. Molecular docking demonstrated that TNF and IL-1A were stable and strong binding to quercetin, luteolin, and formononetin, indicating that these stable structures significantly inhibited A2780 OV cell viability. This study demonstrated the ability of TNF and IL-1A combined with quercetin, luteolin, and formononetin to decrease the activity of OV cells, suggesting potential therapeutic effect against OV.

Exploration of Ginkgo biloba leaves on non-small cell lung cancer based on network pharmacology and molecular docking

BACKGROUND

Pharmacological studies have found Ginkgo biloba leaves have the effect of inhibiting neoplasms, it is clinically used in treating various neoplasms. However, the mechanism of Ginkgo biloba leaves in treating non-small cell lung cancer (NSCLC) remains unclear.

METHODS

The active components and corresponding targets of Ginkgo biloba leaves were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) database, and the targets of NSCLC were obtained from the GeneCards, OMIM, TTD, and DrugBank databases. The common targets of NSCLC and Ginkgo biloba leaves were obtained from VENNY 2.1.0. The STRING database was utilized to construct protein-protein intersections, by using the Cytoscape 3.7.1 software, the protein-protein intersection was optimized and the drug-disease network diagram was constructed. The DAVID database was utilized to perform GO and KEGG analysis. Finally, The Autodock Vina software was used to perform molecular docking of core components and targets.

RESULTS

The key components of Ginkgo biloba leaves in treating NSCLC include quercetin, luteolin, and kaempferol, which may act on Tp53, AKT1, and TNF. Bioinformatic annotation analysis results suggest that Ginkgo biloba leaves may implicated in PI3K-AKT and MAPK signaling pathways. The molecular docking results show the firm affinity between key ingredients and targets.

CONCLUSION

The potential mechanism of Ginkgo biloba leaves in treating NSCLC has been discussed in this study, which provides a theoretical basis for the clinical treatment of NSCLC and further experimental validation.

The potential mechanism of Guizhi Fuling Wan effect in the treatment of cervical squamous cell carcinoma: A bioinformatics analysis investigation

As a global malignancy with high mortality rate, targeted drug development for Uterine Cervical Neoplasms is an important direction. The traditional formula Guizhi Fuling Wan (GFW) is widely used in gynecological diseases. However, its potential mechanism of action remains to be discovered. We retrieved GFW and cervical squamous cell carcinoma (CSCC) targets from public databases. The protein-protein interaction network was obtained by string computational analysis and imported Cytoscape_v3.9.0 to obtain the core network and the top 10 Hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for enrichment analysis of the core network, and then molecular docking to verify whether the selected signaling pathway binds well to the core node. Finally, clinical prognostic analysis and expression differences of Hub genes were validated using the Cancer Genome Atlas database and R language. Our search yielded 152 common targets for GFW and CSCC. The interleukin-17 signaling pathway, tumor necrosis factor signaling pathway, and Toll-like signaling pathway were then selected for further molecular docking from the hub genes enrichment analysis results, which showed good binding. Among the Hub genes, JUN, VEGFA, IL1B, and EGF had a poor prognosis for CSCC. In conclusion, this study illustrates that GFW can have adjuvant therapeutic effects on CSCC through multiple targets and multiple pathways, providing a basis for further research.

Network pharmacology-based pharmacological mechanism prediction of Lycii Fructus against postmenopausal osteoporosis

Postmenopausal osteoporosis (PMOP) has become one of most frequent bone diseases worldwide with aging population. Lycii Fructus, a common plant fruit with the property of drug homologous food, has long since been used to treat PMOP. The aim of this study is to explore pharmacological mechanisms of Lycii Fructus against PMOP through using network pharmacology approach. The active ingredients of Lycii Fructus were obtained from Traditional Chinese Medicine System Pharmacology database. Target fishing was performed on these ingredients in UniProt database for identification of the relative targets. Then, we screened the targets related to PMOP using GeneCards database and DisGeNET database. The overlapping genes between PMOP and Lycii Fructus were obtained to perform protein-protein interaction, gene ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis. A total of 35 active ingredients were identified in Lycii Fructus, and fished 158 related targets. Simultaneously, 292 targets associated with PMOP were obtained from GeneCards database and DisGeNET database. By drawing Venn diagram, 41 overlapping genes were obtained, and were considered as therapeutically relevant. Gene ontology enrichment analysis predicted that anti-inflammation and promotion of angiogenesis might be 2 potential mechanism of Lycii Fructus for PMOP treatment. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed several pathways, such as IL-17 pathway, TNF pathway, MAPK pathway, PI3K-Akt signaling pathway and HIF signaling pathway were involved in regulating these 2 biological processes. Through the method of network pharmacology, we systematically investigated the mechanisms of Lycii Fructus against PMOP. The identified multi-targets and multi-pathways provide new insights to further determinate its exact pharmacological mechanisms.

Mechanism of action of Huangbaichen Sanwei formulation in treating T2DM based on network pharmacology and molecular docking

Huangbaichen Sanwei formulation (HBCS) has been reported to have a good hypoglycemic effect, but its pharmacological mechanism of action remains unclear. We used network pharmacology and molecular docking to explore the potential mechanism of action of HBCS against type-2 diabetes mellitus (T2DM). Fifty-five active components from HBCS interfered with T2DM. Twenty-five core targets, such as AKT1, INS, INSR, MAPK1 were identified. Enrichment analyses showed that HBCS was involved mainly including insulin receptor signaling pathway, extracellular region, and insulin-like growth factor receptor binding and other biological processes; common targets had roles in treating T2DM by regulating diabetic cardiomyopathy and insulin resistance. Molecular docking verified that components combined with core targets. HBCS play a part in treating T2DM through multiple components and targets at the molecular level, which lays a theoretical foundation for research using HBCS to treat T2DM. The components, predicted targets, and T2DM targets of HBCS were searched through databases, and common targets were determined. Further screening of the core targets was conducted through the establishment of a protein -protein interaction network. The core targets were analyzed by Gene Ontology (GO) annotation utilizing the DAVID platform. And the enrichment of signaling pathways was explored by employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Cytoscape 3.9.1 was employed to construct a "TCM-components-core target-pathway" network. Autodock Vina was used to dock molecules to compare the binding activity of active molecules with targets.

Deciphering the toxicity-effect relationship and action patterns of traditional Chinese medicines from a smart data perspective: a comprehensive review

In Chinese medicine, the primary considerations revolve around toxicity and effect. The clinical goal is to achieve maximize effect while minimizing toxicity. Nevertheless, both clinical and experimental research has revealed a distinct relationship between these two patterns of action in toxic Traditional Chinese Medicines (TCM). These TCM often exhibit characteristic "double-sided" or "multi-faceted" features under varying pathological conditions, transitioning between effective and toxic roles. This complexity adds a layer of challenge to unraveling the ultimate objectives of Traditional Chinese medicine. To address this complexity, various hypotheses have been proposed to explain the toxicity and effect of Traditional Chinese Medicines. These hypotheses encompass the magic shrapnel theory for effect, the adverse outcome pathway framework, and the indirect toxic theory for toxicity. This review primarily focuses on high-, medium-, and low-toxicity Traditional Chinese Medicines as listed in Chinese Pharmacopoeia. It aims to elucidate the essential intrinsic mechanisms and elements contributing to their toxicity and effectiveness. The critical factors influencing the mechanisms of toxicity and effect are the optimal dosage and duration of TCM administration. However, unraveling the toxic-effect relationships in TCM presents a formidable challenge due to its multi-target and multi-pathway mechanisms of action. We propose the integration of multi-omics technology to comprehensively analyze the fundamental metabolites, mechanisms of action, and toxic effects of TCM. This comprehensive approach can provide valuable insights into the intricate relationship between the effect and toxicity of these TCM.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.