The mechanism and active compounds of semen armeniacae amarum treating coronavirus disease 2019 based on network pharmacology and molecular docking

Network pharmacology integrated molecular docking of fucoidan against oral cancer and in vitro evaluation- A study using GEO datasets

Oral cancer is a widespread health concern in rural India due to a lack of awareness, delayed diagnosis and limited access to affordable treatment options. The current chemotherapy has notable side effects, underscoring the need for new drug candidates with improved bioavailability and specificity. In this current research, fucoidan, a sulphated polysaccharide, was extracted from the brown algae Spatoglossum asperum, and shown to be cytotoxic in vitro against oral cancer cells (KB cell line) at an IC50 of 107.76 µg/ml, suggesting its potential as a drug candidate. This study further aimed to explore the potential therapeutic implications of fucoidan in managing oral cancer using network pharmacology. PharmMapper, Comparative Toxicogenomics Database and SuperPred were initially used to identify fucoidan protein targets. The identified targets were further screened against Gene Expression Omnibus (GSE23558, GSE25099 and GSE146483), OMIM, TCGA and GeneCards datasets to identify oral cancer-specific protein targets. The interactions between the selected proteins were visualised using STRING and Cytoscape. Subsequently, Database for Annotation, Visualization and Integrated Discovery was used for gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of candidate targets. The cancer-related network was assessed using CancerGeneNet, while life expectancy based on the expression of the top 10 CytoHubba ranked hub genes was evaluated using Kaplan-Meier plots. Finally, EGFR, AKT1, HSP90AA1 and SRC were selected for docking and molecular dynamics simulation with fucoidan, using Maestro and GROMACS, respectively.Communicated by Ramaswamy H. Sarma.

Armeniacae semen amarum: a review on its botany, phytochemistry, pharmacology, clinical application, toxicology and pharmacokinetics

Armeniacae semen amarum-seeds of Prunus armeniaca L. (Rosaceae) (ASA), also known as Kuxingren in Chinese, is a traditional Chinese herbal drug commonly used for lung disease and intestinal disorders. It has long been used to treat coughs and asthma, as well as to lubricate the colon and reduce constipation. ASA refers to the dried ripe seed of diverse species of Rosaceae and contains a variety of phytochemical components, including glycosides, organic acids, amino acids, flavonoids, terpenes, phytosterols, phenylpropanoids, and other components. Extensive data shows that ASA exhibits various pharmacological activities, such as anticancer activity, anti-oxidation, antimicrobial activity, anti-inflammation, protection of cardiovascular, neural, respiratory and digestive systems, antidiabetic effects, and protection of the liver and kidney, and other activities. In clinical practice, ASA can be used as a single drug or in combination with other traditional Chinese medicines, forming ASA-containing formulas, to treat various afflictions. However, it is important to consider the potential adverse reactions and pharmacokinetic properties of ASA during its clinical use. Overall, with various bioactive components, diversified pharmacological actions and potent efficacies, ASA is a promising drug that merits in-depth study on its functional mechanisms to facilitate its clinical application.

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.

Identification of active compounds of traditional chinese medicine derived from maxing shigan decoction for COVID-19 treatment: a meta-analysis and in silico study

BACKGROUND

Coronavirus 2019 (COVID-19) poses a serious threat to human health. In China, traditional Chinese medicine (TCM), mainly based on the Maxing Shigan decoction (MXSGD), is used in conjunction with western medicine to treat COVID-19.

RESEARCH DESIGN AND METHODS

We conducted a network meta-analysis to investigate whether MXSGD-related TCM combined with western medicine is more effective in treating COVID-19 compared to western medicine alone. Additionally, using network pharmacology, cross-docking, and molecular dynamics (MD) simulation to explore the potential active compounds and possible targets underlying the therapeutic effects of MXSGD-related TCM.

RESULTS

MXSGD-related TCM combined with western medicine was better for treating COVID-19 compared to western medicine alone. Network pharmacological analysis identified 43 shared ingredients in the MXSGD-related TCM prescriptions and 599 common target genes. Cross-docking of the 43 compounds with 154 proteins that matched these genes led to the identification of 60 proteins. Pathway profiling revealed that the active ingredients participated in multiple signaling pathways that contribute to their efficacy. Molecular docking and MD simulation demonstrated that MOL007214, the most promising molecule, could stably bind to the active site of SARS-CoV-2 3CLpro.

CONCLUSION

This study demonstrates the important role of MXSGD-related TCM in the treatment of COVID-19.

Integrating network pharmacology and transcriptomic validation to investigate the efficacy and mechanism of Mufangji decoction preventing lung cancer

ETHNOPHARMACOLOGICAL RELEVANCE

Mufangji decoction (MFJD), a famous traditional Chinese medicine formula in Synopsis of Golden Chamber (Jingui yaolue), has been utilized to treat cough and asthma and release chest pain over 2000 years in China. Chinese old herbalist doctor use MFJD to treat lung cancer and cancerous pleural fluid, but the preventive effect of MFJD on lung cancer and the underlying mechanism are indefinite.

AIM OF THE STUDY

The goal of this study is to explore the efficacy and mechanism of Mufangji decoction preventing lung cancer referring to the traditional use.

MATERIALS AND METHODS

Tumor allograft experiment and host versus tumor experiment were used to observe the direct anti-tumor effect and indirect anti-tumor immune effect, the mouse lung carcinogenic model was used to evaluate the dose-response and the preventive effect of MFJD on lung cancer. The active ingredients of MFJD were obtained by UPLC-MS/MS. The potential targets of MFJD were screened by network pharmacology and transcriptomics. The therapeutic targets and pathways of MFJD on lung cancer were obtained by protein-protein interaction, molecular docking and David database. The predicted results were verified in vitro and in vivo.

RESULTS

MFJD could significantly prevent tumor growth in host versus tumor experiment but could not in tumor allograft experiment, indicating an anti-tumor immune effect against lung cancer. MFJD could reduce lung nodules with a dose-response in mouse lung carcinogenic model. Myeloperoxidase (MPO) was selected as the core target due to the highest degree value in Protein-Protein interaction network and had potently binding activity to sinomenine and dehydrocostus lactone in molecular docking. In vivo, MPO-expressed neutrophils are negatively correlated with lung cancer progression and MFJD could promote the neutrophil-related immune surveillance. In vitro, sinomenine and dehydrocostus lactone could promote neutrophil phagocytosis, MPO and ROS production in a dose dependent manner. The major compounds from MFJD were identified to regulate 36 targets for lung cancer prevention by UPLC-MS/MS, network pharmacology and transcriptomics. David database exhibited that MFJD plays an important role in immunoregulation by modulating 4 immune-related biological processes and 3 immune-related pathways.

CONCLUSIONS

MFJD prevents lung cancer by mainly promoting MPO expression to maintain neutrophil immune surveillance, its key compounds are sinomenine and dehydrocostus lactone.

Uncovering the Key Targets and Therapeutic Mechanisms of Qizhen Capsule in Gastric Cancer through Network Pharmacology and Bioinformatic Analyses

Objective. This study is aimed at screening out effective active compounds of Qizhen capsule (QZC) and exploring the underlying mechanisms against gastric cancer (GACA) by combining both bioinformatic analysis and experimental approaches. Weighted gene coexpression network analysis (WGCNA), network pharmacology, molecular docking simulation, survival analysis, and data-based differential gene and protein expression analysis were employed to predict QZC’s potential targets and explore the underlying mechanisms. Subsequently, multiple experiments, including cell viability, apoptosis, and protein expression analyses, were conducted to validate the bioinformatics-predicted therapeutic targets. The results indicated that luteolin, rutin, quercetin, and kaempferol were vital active compounds, and TP53, MAPK1, and AKT1 were key targets. Molecular docking simulation showed that the four abovementioned active compounds had high binding affinities to the three main targets. Enrichment analysis showed that vital active compounds exerted therapeutic effects on GACA through regulating the TP53 pathway, MAPK pathway, and PI3K/AKT pathway. Furthermore, data-based gene expression analysis revealed that TP53 and JUN genes were not only differentially expressed between normal and GACA tissues but also correlated with clinical stages. In parallel, in vitro experimental results suggested that QZC exerted therapeutic effects on GACA by decreasing IC50 values, downregulating AKT expression, upregulating TP53 and MAPK expression, and increasing apoptosis of SGC-7901 cells. This study highlights the potential candidate biomarkers, therapeutic targets, and basic mechanisms of QZC in treating GACA, providing a foundation for new drug development, target mining, and related animal studies in GACA.

Clinical Evaluation and Exploration of Mechanisms for Modified Xiebai Powder or Modified Xiebai Powder Combined with Western Medicine in the Treatment of Pneumonia

Objective. To systematically evaluate the clinical efficacy of modified Xiebai Powder or modified Xiebai Powder combined with Western medicine in the treatment of pneumonia and explore its potential mechanism of action. Methods. Meta-analysis was used to screen the eligible literature on randomized controlled trials (RCTs) about Xiebai Powder in the treatment of pneumonia, and Review Manager 5.3 software was used for statistical analysis of the data. Based on the results of the meta-analysis, the active ingredients in Xiebai Powder and their therapeutic targets, disease-related targets, and intersection targets were screened using methods of network pharmacology, and their biological processes and key signaling pathways were analyzed using bioinformatics tools. Molecular docking was carried out to verify and predict the mechanisms for Xiebai Powder combined with Western medicine in the treatment of pneumonia. Results. A total of 16 papers were screened out, with a total of 1,465 patients. The results of the meta-analysis showed that modified Xiebai Powder or modified Xiebai Powder combined with Western medicine were superior to conventional Western medicine in terms of clinical efficacy, shortening the disappearance time of symptoms (body temperature, cough, and pulmonary rales) and reducing the level of C-reactive protein, and the incidence of adverse reactions was significantly reduced. A total of 40 active ingredients in Xiebai Powder and 285 therapeutic targets of Xiebai Powder combined with azithromycin after deduplication were screened out from the database. KEGG enrichment analysis showed that Xiebai Powder combined with azithromycin might play a role in the treatment of pneumonia through the IL-17 signaling pathway, tumor necrosis factor signaling pathway, C-type lectin receptor signaling pathway, Toll-like receptor signaling pathway, and HIF-1 signaling pathway. Conclusions. Modified Xiebai Powder or modified Xiebai Powder combined with azithromycin has better effects in treating pneumonia, and modified Xiebai Powder combined with azithromycin may play a role in treating pneumonia through several pathways such as the IL-17 signaling pathway.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Network Analysis of Herbs Recommended for the Treatment of COVID-19

PURPOSE

In this study, we aimed to identify the pattern and combination of herbs used in the formulae recommended for treating different stages of COVID-19 using a network analysis approach.

METHODS

The herbal formulae recommended by official guidelines for the treatment of COVID-19 are included in the present analysis. To describe the tendency of herbs to form a "herb pair", we computed the mutual information (MI) value and distance-based mutual information model (DMIM) score. We also performed modularity, degree, betweenness, and closeness centrality analysis. Network analyses were performed and visualized for each disease stage.

RESULTS

A total of 142 herbal formulae comprising 416 herbs were analyzed. All possible herbal pairs were examined, and the top frequently used herbal pairs were identified for each disease stage. The herb Glycyrrhizae radix et rhizoma is only identified in one herb pair, even though this herb is identified as one of the herbs with high frequency of use for every disease stage. This suggests that the DMIM score could be used to identify the optimal combination rule of herbal formulae by achieving a balance among the herbs' frequency and relative distance in herbal formulae.

CONCLUSION

Our results presented the prescription patterns and herbal combinations of the herbal formulae recommended for the treatment of COVID-19. This study may provide new insights and ideas for clinical research in the future.