Exploring the Potential Mechanism of Action of Ursolic Acid against Gastric Cancer and COVID-19 using Network Pharmacology and Bioinformatics Analysis.

BACKGROUND: Patients with gastric cancer (GC) are more likely to be infected with 2019 coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the prognosis is worse. It is urgent to find effective treatment methods. OBJECTIVE: This study aimed to explore the potential targets and mechanism of ursolic acid (UA) on GC and COVID-19 by network pharmacology and bioinformatics analysis. METHODS: The online public database and weighted co-expression gene network analysis (WGCNA) were used to screen the clinical related targets of GC. COVID-19-related targets were retrieved from online public databases. Then, a clinicopathological analysis was performed on GC and COVID-19 intersection genes. Following that, the related targets of UA and the intersection targets of UA and GC/COVID-19 were screened. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome Analysis (KEGG) pathway enrichment analyses were performed on the intersection targets. Core targets were screened using a constructed protein-protein interaction network. Finally, molecular docking and molecular dynamics simulation (MDS) of UA and core targets were performed to verify the accuracy of the prediction results. RESULTS: A total of 347 GC/COVID-19-related genes were obtained. The clinical features of GC/COVID-19 patients were revealed using clinicopathological analysis. Three potential biomarkers (TRIM25, CD59, MAPK14) associated with the clinical prognosis of GC/COVID-19 were identified. A total of 32 intersection targets of UA and GC/COVID-19 were obtained. The intersection targets were primarily enriched in FoxO, PI3K/Akt, and ErbB signaling pathways. HSP90AA1, CTNNB1, MTOR, SIRT1, MAPK1, MAPK14, PARP1, MAP2K1, HSPA8, EZH2, PTPN11, and CDK2 were identified as core targets. Molecular docking revealed that UA strongly binds to its core targets. The MDS results revealed that UA stabilizes the protein-ligand complexes of PARP1, MAPK14, and ACE2. CONCLUSION: This study found that in patients with gastric cancer and COVID-19, UA may bind to ACE2, regulate core targets such as PARP1 and MAPK14, and the PI3K/Akt signaling pathway, and participate in antiinflammatory, anti-oxidation, anti-virus, and immune regulation to exert therapeutic effects.

A quantitative systems pharmacology model of the pathophysiology and treatment of COVID-19 predicts optimal timing of pharmacological interventions.

A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment, we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting mAb and antiviral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different time points post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post symptom onset prior to treatment.

Deep learning-based network pharmacology for exploring the mechanism of licorice for the treatment of COVID-19.

Licorice, a traditional Chinese medicine, has been widely used for the treatment of COVID-19, but all active compounds and corresponding targets are still not clear. Therefore, this study proposed a deep learning-based network pharmacology approach to identify more potential active compounds and targets of licorice. 4 compounds (quercetin, naringenin, liquiritigenin, and licoisoflavanone), 2 targets (SYK and JAK2) and the relevant pathways (P53, cAMP, and NF-kB) were predicted, which were confirmed by previous studies to be associated with SARS-CoV-2-infection. In addition, 2 new active compounds (glabrone and vestitol) and 2 new targets (PTEN and MAP3K8) were further validated by molecular docking and molecular dynamics simulations (simultaneous molecular dynamics), as well as the results showed that these active compounds bound well to COVID-19 related targets, including the main protease (Mpro), the spike protein (S-protein) and the angiotensin-converting enzyme 2 (ACE2). Overall, in this study, glabrone and vestitol from licorice were found to inhibit viral replication by inhibiting the activation of Mpro, S-protein and ACE2; related compounds in licorice may reduce the inflammatory response and inhibit apoptosis by acting on PTEN and MAP3K8. Therefore, licorice has been proposed as an effective candidate for the treatment of COVID-19 through PTEN, MAP3K8, Mpro, S-protein and ACE2.

An integrated approach of high-performance liquid chromatography-mass spectrometry-based chemical profiling, network pharmacology, and molecular docking to reveal the potential mechanisms of Qishen Gubiao granules for treating coronavirus disease 2019.

Qishen Gubiao granules, a traditional Chinese medicine preparation composed of nine herbs, have been widely used to prevent and treat coronavirus disease 2019 with good clinical efficacy. In the present study, an integrated strategy based on chemical profiling followed by network pharmacology and molecular docking was employed, to explore the active components and potential molecular mechanisms of Qishen Gubiao granules in the therapy of coronavirus disease 2019. Using the ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry technique, a total of 186 ingredients corresponding to eight structure types in Qishen Gubiao preparation were identified or structurally annotated with the elucidation of the fragmentation pathways in the typical compounds. The network pharmacology analysis screened 28 key compounds including quercetin, apigenin, scutellarein, luteolin and naringenin acting on 31 key targets, which possibly modulated signal pathways associated with immune and inflammatory responses in the treatment of coronavirus disease 2019. The molecular docking results observed that the top 5 core compounds had a high affinity for angiotensin-converting enzyme 2 and 3-chymotrypsin-like protease. This study proposed a reliable and feasible approach for elucidating the multi-components, multi-targets, and multi-pathways intervention mechanism of Qishen Gubiao granules against coronavirus disease 2019, providing a scientific basis for its further quality evaluation and clinical application.

Based on network pharmacology and bioinformatics to analyze the mechanism of action of Astragalus membranaceus in the treatment of vitiligo and COVID-19.

Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. However, the treatment of vitiligo combined with COVID-19 has not been reported. Astragalus membranaceus (AM) has a therapeutic effect on patients with vitiligo and COVID-19. This study aims to discover its possible therapeutic mechanisms and provide potential drug targets. Using the Chinese Medicine System Pharmacological Database (TCMSP), GEO database and Genecards websites and other databases, AM target, vitiligo disease target, and COVID-19 related gene set were established. Then find the crossover genes by taking the intersection. Then use GO, KEGG enrichment analysis, and PPI network to discover its underlying mechanism. Finally, by importing drugs, active ingredients, crossover genes, and enriched signal pathways into Cytoscape software, a "drug-active ingredient-target signal pathway-" network is constructed. TCMSP screened and obtained 33 active ingredients including baicalein (MOL002714), NEOBAICALEIN (MOL002934), Skullcapflavone II (MOL002927), and wogonin (MOL000173), which acted on 448 potential targets. 1166 differentially expressed genes for vitiligo were screened by GEO. CIVID-19 related genes were screened by Genecards. Then by taking the intersection, a total of 10 crossover genes (PTGS2, CDK1, STAT1, BCL2L1, SCARB1, HIF1A, NAE1, PLA2G4A, HSP90AA1, and HSP90B1) were obtained. KEGG analysis found that it was mainly enriched in signaling pathways such as IL-17 signaling pathway, Th17 cell differentiation, Necroptosis, NOD-like receptor signaling pathway. Five core targets (PTGS2, STAT1, BCL2L1, HIF1A, and HSP90AA1) were obtained by analyzing the PPI network. The network of "active ingredients-crossover genes" was constructed by Cytoscape, and the 5 main active ingredients acting on the 5 core crossover genes acacetin, wogonin, baicalein, bis2S)-2-ethylhexyl) benzene-1,2-dicarboxylate and 5,2'-Dihydroxy-6,7,8-trimethoxyflavone. The core crossover genes obtained by PPI and the core crossover genes obtained by the "active ingredient-crossover gene" network are intersected to obtain the three most important core genes (PTGS2, STAT1, HSP90AA1). AM may act on PTGS2, STAT1, HSP90AA1, etc. through active components such as acacetin, wogonin, baicalein, bis2S)-2-ethylhexyl) benzene-1,2-dicarboxylate and 5,2'-Dihydroxy-6,7,8-trimethoxyflavone to activate IL-17 signaling pathway, Th17 cell differentiation, Necroptosis, NOD-like receptor signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and VEGF signaling pathway and other signaling pathways to achieve the effect of treating vitiligo and COVID-19.

Interleukin-10 as Covid-19 biomarker targeting KSK and its analogues: Integrated network pharmacology.

COVID-19 caused by the SARS-CoV-2 virus is widespread in all regions, and it disturbs host immune system functioning leading to extreme inflammatory reaction and hyperactivation of the immune response. Kabasura Kudineer (KSK) is preventive medicine against viral infections and a potent immune booster for inflammation-related diseases. We hypothesize that KSK and KSK similar plant compounds, might prevent or control the COVID-19 infection in the human body. 1,207 KSK and KSK similar compounds were listed and screened via the Swiss ADME tool and PAINS Remover; 303 compounds were filtered including active and similar drug compounds. The targets were retrieved from similar drugs of the active compounds of KSK. Finally, 573 genes were listed after several screening steps. Next, network analysis was performed to finalize the potential target gene: construction of protein-protein interaction of 573 genes using STRING, identifying top hub genes in Cytoscape plug-ins (MCODE and cytoHubba). These ten hub genes play a crucial role in the inflammatory response. Target-miRNA interaction was also constructed using the miRNet tool to interpret miRNAs of the target genes and their functions. Functional annotation was done via DAVID to gain a complete insight into the mechanism of the enriched pathways and other diseases related to the given target genes. In Molecular Docking analysis, IL10 attained top rank in Target-miRNA interaction and also the gene formed prominent exchanges with an excellent binding score (> = -8.0) against 19 compounds. Among them, Guggulsterone has an acute affinity score of -8.8 for IL10 and exhibits anti-inflammatory and immunomodulatory properties. Molecular Dynamics simulation study also performed for IL10 and the interacting ligand compounds using GROMACS. Finally, Guggulsterone will be recommended to enhance immunity against several inflammatory diseases, including COVID19.

Potential Mechanisms of Perillae folium Against COVID-19: A Network Pharmacology Approach.

In China, Perillae folium is widely used to treat colds, especially in the early stages of cold; the effect of taking P. folium is readily noticeable at that time. The active compounds and targets of P. folium were screened from Traditional Chinese Medicine Systems Pharmacology, Chinese Pharmacopoeia, and UniProt. Targets related to the initiation and progression of 2019 Coronavirus Disease (COVID-19) were retrieved from Online Mendelian Inheritance in Man and GeneCards. The potential therapeutic targets of P. folium on COVID-19 were the cross targets between them. Enrichment analysis of Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted by using the Database for Annotation, Visualization and Integrated Discovery website. Molecular docking between key compounds and core targets was performed with AutoDock. The effects of P. folium extract and rosmarinic acid on inflammatory cytokines were tested by a cellular inflammatory model. The "Perillae folium-compound-target-COVID-19" network contained 11 kinds of compounds and 33 matching targets. There were 261 items in the GO functions (P < .05) and 67 items linked to the KEGG signaling pathways (P < .05). Luteolin and rosmarinic acid were key compounds of P. folium. Their docking with the core targets mitogen-activated protein kinase 1 (MAPK1) and chemokine (C-C motif) ligand 2 (CCL2), respectively, showed that they had good affinity with each other. Cell experiments demonstrated that P. folium extract had inhibitory effects on interleukin-6 and tumor necrosis factor (TNF)-α in cells, and was better than rosmarinic acid. Luteolin, rosmarinic acid, and other individual active compounds in P. folium, which may participate in PI3K-Akt, TNF, Jak-STAT, COVID-19, and other multisignaling pathways through multiple targets such as MAPK1 and CCL2, and play a therapeutic role in COVID-19.

Exploring novel targets of sitagliptin for type 2 diabetes mellitus: Network pharmacology, molecular docking, molecular dynamics simulation, and SPR approaches.

Background: Diabetes has become a serious global public health problem. With the increasing prevalence of type 2 diabetes mellitus (T2DM), the incidence of complications of T2DM is also on the rise. Sitagliptin, as a targeted drug of DPP4, has good therapeutic effect for T2DM. It is well known that sitagliptin can specifically inhibit the activity of DPP4 to promote insulin secretion, inhibit islet ß cell apoptosis and reduce blood glucose levels, while other pharmacological mechanisms are still unclear, such as improving insulin resistance, anti-inflammatory, anti-oxidative stress, and anti-fibrosis. The aim of this study was to explore novel targets and potential signaling pathways of sitagliptin for T2DM. Methods: Firstly, network pharmacology was applied to find the novel target most closely related to DPP4. Semi-flexible molecular docking was performed to confirm the binding ability between sitagliptin and the novel target, and molecular dynamics simulation (MD) was carried to verify the stability of the complex formed by sitagliptin and the novel target. Furthermore, surface-plasmon resonance (SPR) was used to explored the affinity and kinetic characteristics of sitagliptin with the novel target. Finally, the molecular mechanism of sitagliptin for T2DM was predicted by the enrichment analysis of GO function and KEGG pathway. Results: In this study, we found the cell surface receptor-angiotensin-converting enzyme 2 (ACE2) most closely related to DPP4. Then, we confirmed that sitagliptin had strong binding ability with ACE2 from a static perspective, and the stability of sitagliptin-ACE2 complex had better stability and longer binding time than BAR708-ACE2 in simulated aqueous solution within 50 ns. Significantly, we have demonstrated a strong affinity between sitagliptin and ACE2 on SPR biosensor, and their kinetic characteristics were "fast binding/fast dissociation". The guiding significance of clinical administration: low dose can reach saturation, but repeated administration was needed. Finally, there was certain relationship between COVID-19 and T2DM, and ACE2/Ang-(1-7)/Mas receptor (MasR) axis may be the important pathway of sitagliptin targeting ACE2 for T2DM. Conclusion: This study used different methods to prove that ACE2 may be another novel target of sitagliptin for T2DM, which extended the application of ACE2 in improving diabetes mellitus.

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

After the World Health Organization declared coronavirus disease 2019 (COVID-19), as a global pandemic, global health workers have been facing an unprecedented and severe challenge. Currently, a mixturetion to inhibit the exacerbation of pulmonary inflammation caused by COVID-19, Fuzheng Yugan Mixture (FZYGM), has been approved for medical institution mixturetion notification. However, the mechanism of FZYGM remains poorly defined. This study aimed to elucidate the molecular and related physiological pathways of FZYGM as a potential therapeutic agent for COVID-19. Active molecules of FZYGM were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), while potential target genes of COVID-19 were identified by DrugBank and GeneCards. Compound-target networks and protein-protein interactions (PPI) were established by Cytoscape_v3.8.2 and String databases, respectively. The gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Finally, a more in-depth study was performed using molecular docking. Our study identified 7 active compounds and 3 corresponding core targets. The main potentially acting signaling pathways include the interleukin (IL)-17 signaling pathway, tumor necrosis factor (TNF) signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation, and coronavirus disease-COVID-19. This study shows that FZYGM can exhibit anti-COVID-19 effects through multiple targets and pathways. Therefore, FZYGM can be considered a drug candidate for the treatment of COVID-19, and it provides good theoretical support for subsequent experiments and clinical applications of COVID-19.

Computer-aided drug design combined network pharmacology to explore anti-SARS-CoV-2 or anti-inflammatory targets and mechanisms of Qingfei Paidu Decoction for COVID-19.

Introduction: Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Severe cases of COVID-19 are characterized by an intense inflammatory process that may ultimately lead to organ failure and patient death. Qingfei Paidu Decoction (QFPD), a traditional Chines e medicine (TCM) formula, is widely used in China as anti-SARS-CoV-2 and anti-inflammatory. However, the potential targets and mechanisms for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects remain unclear. Methods: In this study, Computer-Aided Drug Design was performed to identify the antiviral or anti-inflammatory components in QFPD and their targets using Discovery Studio 2020 software. We then investigated the mechanisms associated with QFPD for treating COVID-19 with the help of multiple network pharmacology approaches. Results and discussion: By overlapping the targets of QFPD and COVID-19, we discovered 8 common targets (RBP4, IL1RN, TTR, FYN, SFTPD, TP53, SRPK1, and AKT1) of 62 active components in QFPD. These may represent potential targets for QFPD to exert anti-SARS-CoV-2 or anti-inflammatory effects. The result showed that QFPD might have therapeutic effects on COVID-19 by regulating viral infection, immune and inflammation-related pathways. Our work will promote the development of new drugs for COVID-19.

Pharmacological Mechanism of NRICM101 for COVID-19 Treatments by Combined Network Pharmacology and Pharmacodynamics.

Symptom treatments for Coronavirus disease 2019 (COVID-19) infection and Long COVID are one of the most critical issues of the pandemic era. In light of the lack of standardized medications for treating COVID-19 symptoms, traditional Chinese medicine (TCM) has emerged as a potentially viable strategy based on numerous studies and clinical manifestations. Taiwan Chingguan Yihau (NRICM101), a TCM designed based on a medicinal formula with a long history of almost 500 years, has demonstrated its antiviral properties through clinical studies, yet the pharmacogenomic knowledge for this formula remains unclear. The molecular mechanism of NRICM101 was systematically analyzed by using exploratory bioinformatics and pharmacodynamics (PD) approaches. Results showed that there were 434 common interactions found between NRICM101 and COVID-19 related genes/proteins. For the network pharmacology of the NRICM101, the 434 common interacting genes/proteins had the highest associations with the interleukin (IL)-17 signaling pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Moreover, the tumor necrosis factor (TNF) was found to have the highest association with the 30 most frequently curated NRICM101 chemicals. Disease analyses also revealed that the most relevant diseases with COVID-19 infections were pathology, followed by cancer, digestive system disease, and cardiovascular disease. The 30 most frequently curated human genes and 2 microRNAs identified in this study could also be used as molecular biomarkers or therapeutic options for COVID-19 treatments. In addition, dose-response profiles of NRICM101 doses and IL-6 or TNF-α expressions in cell cultures of murine alveolar macrophages were constructed to provide pharmacodynamic (PD) information of NRICM101. The prevalent use of NRICM101 for standardized treatments to attenuate common residual syndromes or chronic sequelae of COVID-19 were also revealed for post-pandemic future.

Effect of Cepharanthine on the Stemness of Lung Squamous Cell Carcinoma Based on Network Pharmacology and Bioinformatics.

Background: Lung squamous cell carcinoma (LUSC) has poor survival prognosis and few clinical treatment options. We urgently need to explore new therapeutic drugs in clinical practice. Cepharanthine (CEP) has been shown to have anticancer effects in several tumors, but the mechanism of CEP in treating LUSC has not been reported. Methods: SwissTargetPrediction, PharmMapper, and GeneCards were used to identify targets of CEP and LUSC. Further topological analysis was used to obtain hub genes via Cytoscape. Molecular docking was carried out to verify the combination of CEP with hub targets. Based on bioinformatics, we first analyzed the expression and survival of hub targets in LUSC and further analyzed the correlation between hub targets and cancer stemness, immune cell infiltration, and tumor mutation burden (TMB). Results: A total of 41 targets were identified. Further topological analysis identified 6 hub genes: AURKA, CCNA2, CCNE1, CDK1, CHEK1, and PLK1. Molecular docking analysis showed that CEP had stable binding to all these 6 target proteins. In-depth bioinformatics analysis of these 6 targets showed that high expression of these targets were positively correlated with cancer stemness index and negatively correlated with tumor infiltrating immune cells. In immune subtype analysis, the expressions of these targets were significantly decreased in inflammatory tumors. In addition, we also found that the expressions of these targets were positively correlated with TMB. Conclusion: Based on multidisciplinary analysis, we preliminarily identified potential targets of CEP for LUSC treatment and suggested that CEP may play a role in regulating LUSC stemness.

Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19.

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied. Methods: COVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target. Result: Bioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment. Conclusions: Based on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD.

Dissecting the molecular mechanism of cepharanthine against COVID-19, based on a network pharmacology strategy combined with RNA-sequencing analysis, molecular docking, and molecular dynamics simulation.

OBJECTIVES: Recently, it has been reported that cepharanthine (CEP) is highly likely to be an agent against Coronavirus disease 2019 (COVID-19). In the present study, a network pharmacology-based approach combined with RNA-sequencing (RNA-seq), molecular docking, and molecular dynamics (MD) simulation was performed to determine hub targets and potential pharmacological mechanism of CEP against COVID-19. METHODS: Targets of CEP were retrieved from public databases. COVID-19-related targets were acquired from databases and RNA-seq datasets GSE157103 and GSE155249. The potential targets of CEP and COVID-19 were then validated by GSE158050. Hub targets and signaling pathways were acquired through bioinformatics analysis, including protein-protein interaction (PPI) network analysis and enrichment analysis. Subsequently, molecular docking was carried out to predict the combination of CEP with hub targets. Lastly, MD simulation was conducted to further verify the findings. RESULTS: A total of 700 proteins were identified as CEP-COVID-19-related targets. After the validation by GSE158050, 97 validated targets were retained. Enrichment results indicated that CEP acts on COVID-19 through multiple pathways, multiple targets, and overall cooperation. Specifically, PI3K-Akt signaling pathway is the most important pathway. Based on PPI network analysis, 9 central hub genes were obtained (ACE2, STAT1, SRC, PIK3R1, HIF1A, ESR1, ERBB2, CDC42, and BCL2L1). Molecular docking suggested that the combination between CEP and 9 central hub genes is extremely strong. Noteworthy, ACE2, considered the most important gene in CEP against COVID-19, binds to CEP most stably, which was further validated by MD simulation. CONCLUSION: Our study comprehensively illustrated the potential targets and underlying molecular mechanism of CEP against COVID-19, which further provided the theoretical basis for exploring the potential protective mechanism of CEP against COVID-19.

Application of network composite module analysis and verification to explore the bidirectional immunomodulatory effect of Zukamu granules on Th1 / Th2 cytokines in lung injury.

ETHNOPHARMACOLOGICAL RELEVANCE: Zukamu granules (ZKMG), as the preferred drug for the treatment of colds in Uygur medical theory, has been used for 1500 years. It is also widely used in China and included in the National Essential Drugs List (2018 edition). It has unique anti-inflammatory, antitussive and analgesic effects. AIM OF THE STUDY: Aiming at the research of traditional Chinese medicine (TCM) with the characteristics of overall regulation of body diseases and the immune regulation mechanism with the concept of integrity, this paper put forward the integrated application of network composite module analysis and animal experiment verification to study the immune regulation mechanism of TCM. MATERIALS AND METHODS: The active components and targets of ZKMG were predicted, and network module analysis was performed to explore their potential immunomodulatory mechanisms. Then acute lung injury (ALI) mice and idiopathic pulmonary fibrosis (IPF) rats were used as pathological models to observe the effects of ZKMG on the pathological conditions of infected ALI and IPF rats, determine the contents of Th1, Th2 characteristic cytokines and immunoglobulins, and study the intervention of GATA3/STAT6 signal pathway. RESULTS: The results of network composite module analysis showed that ZKMG contained 173 pharmacodynamic components and 249 potential targets, and four key modules were obtained. The immunomodulatory effects of ZKMG were related to T cell receptor signaling pathway. The validation results of bioeffects that ZKMG could carry out bidirectional immune regulation on Th1/Th2 cytokines in the stage of ALI and IPF, so as to play the role of regulating immune homeostasis and organ protection. CONCLUSIONS: The network composite module analysis and verification method is an exploration to study the immune regulation mechanism of TCM by combining the network module prediction analysis with animal experiments, which provides a reference for subsequent research.

Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review.

Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.

Transcriptomics, Cheminformatics, and Systems Pharmacology Strategies Unveil the Potential Bioactives to Combat COVID-19.

Coronavirus disease (COVID-19) is a viral disease caused by the SARS-CoV-2 virus and is becoming a global threat again because of the higher transmission rate and lack of proper therapeutics as well as the rapid mutations in the genetic pattern of SARS-CoV-2. Despite vaccinations, the prevalence and recurrence of this infection are still on the rise, which urges the identification of potential global therapeutics for a complete cure. Plant-based alternative medicine is becoming popular worldwide because of its higher efficiency and minimal side effects. Yet, identifying the potential medicinal plants and formulating a plant-based medicine is still a bottleneck. Hence, in this study, the systems pharmacology, transcriptomics, and cheminformatics approaches were employed to uncover the multi-targeted mechanisms and to screen the potential phytocompounds from significant medicinal plants to treat COVID-19. These approaches have identified 30 unique COVID-19 human immune genes targeted by the 25 phytocompounds present in four selected ethnobotanical plants. Differential and co-expression profiling and pathway enrichment analyses delineate the molecular signaling and immune functional regulations of the COVID-19 unique genes. In addition, the credibility of these compounds was analyzed by the pharmacological features. The current holistic finding is the first to explore whether the identified potential bioactives could reform into a drug candidate to treat COVID-19. Furthermore, the molecular docking analysis was employed to identify the important bioactive compounds; thus, an ultimately significant medicinal plant was also determined. However, further laboratory evaluation and clinical validation are required to determine the efficiency of a therapeutic formulation against COVID-19.

Anti-Inflammatory Effects and Molecular Mechanisms of Shenmai Injection in Treating Acute Pancreatitis: Network Pharmacology Analysis and Experimental Verification.

Background: Acute pancreatitis (AP) is an inflammatory disorder of the exocrine pancreas without specific treatment. Shenmai injection (SMI) was reported to eliminate the severity of experimental AP. This study aimed to explore the mechanisms underlying the synergistic protective effects of SMI on AP based on network pharmacology and experimental validation. Methods: Network pharmacology analysis and molecular docking based on identified components were performed to construct the potential therapeutic targets and pathways. The principal components of SMI were detected via ultra-high-performance liquid chromatography-coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Effect of SMI and the identified components on cellular injury and IL6/STAT3 signaling was assessed on mouse pancreatic acinar cell line 266-6 cells. Finally, 4% sodium taurocholate (NaT) was used to induce AP model to assess the effects of SMI in treating AP and validate the potential molecular mechanisms. Results: By searching the TCMSP and ETCM databases, 119 candidate components of SMI were obtained. UHPLC-QTOF/MS analysis successfully determined the representative components of SMI: ginsenoside Rb1, ginsenoside Rg1, ginsenoside Re, and ophiopogonin D. Fifteen hub targets and eight related pathways were obtained to establish the main pharmacology network. Subnetwork analysis and molecular docking indicated that the effects of these four main SMI components were mostly related to the interleukin (IL) 6/STAT3 pathway. In vitro, SMI, ginsenoside Rb1, ginsenoside Rg1, ginsenoside Re, and ophiopogonin D increased the cell viability of NaT-stimulated mouse pancreatic acinar 266-6 cells and decreased IL6 and STAT3 expression. In vivo, 10 mL/kg SMI significantly alleviated the pancreatic histopathological changes and the expression of IL6 and STAT3 in the AP mice. Conclusion: This study demonstrated SMI may exert anti-inflammatory effects against AP by suppressing IL6/STAT3 activation, thus providing a basis for its potential use in clinical practice and further study in treating AP.

Exploring the role of Xingren on COVID-19 based on network pharmacology and molecular docking.

Since the outbreak of novel Coronavirus Pneumonia 2019 (COVID-19), the role of Almonds (Xingren) in the protection and treatment of COVID-19 is not clear. Network pharmacology and molecular docking were used to explore the potential mechanism and potential key targets of Xingren on COVID-19. A total of nine common targets between them were obtained, and these targets were involved in multiple related processes of GO and KEGG pathway enrichment analysis. Molecular docking showed that licochalcone B has the best binding energy (-9.33 kJ·mol-1 ) to PTGS2. They are maybe the important ingredient and key potential target. Its possible mechanism is to intervene anxiety disorder in the process of disease development, such as regulation of blood pressure, reactive oxygen species metabolic process, leishmaniasis peroxisome, and IL-17 signaling pathway. PRACTICAL APPLICATIONS: Xingren is a traditional Chinese medicine that has been used and developed in China for many years. It contains a variety of active ingredients and also has the functions of relieving cough, relieving asthma, enhancing human immunity, delaying aging, regulating blood lipids, nourishing brain, and improving intelligence. In this article, the possible mechanisms of action and important targets of Xingren in the prevention and treatment of COVID-19 were discussed through network pharmacology and molecular docking. We also found that active ingredient licochalcone B and the potential target PTGS2 are worthy of further research and analysis. At the same time, the study also provides a theoretical basis and reference for the prevention and treatment of COVID-19 and the development of new drugs.

Analysis of mechanisms of Shenhuang Granule in treating severe COVID-19 based on network pharmacology and molecular docking.

OBJECTIVE: Severe cases of coronavirus disease 2019 (COVID-19) are expected to have a worse prognosis than mild cases. Shenhuang Granule (SHG) has been shown to be a safe and effective treatment for severe COVID-19 in a previous randomized clinical trial, but the active chemical constituents and underlying mechanisms of action remain unknown. The goal of this study is to explore the chemical basis and mechanisms of SHG in the treatment of severe COVID-19, using network pharmacology. METHODS: Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was employed to screen chemical constituents of SHG. Putative therapeutic targets were predicted by searching traditional Chinese medicine system pharmacology database and analysis platform, SwissTargetPrediction, and Gene Expression Omnibus (GEO) databases. The target protein-protein interaction network and enrichment analysis were performed to investigate the hub genes and presumptive mechanisms. Molecular docking and molecular dynamics simulations were used to verify the stability and interaction between the key chemical constituents of SHG and COVID-19 protein targets. RESULTS: Forty-five chemical constituents of SHG were identified along with 131 corresponding therapeutic targets, including hub genes such as HSP90AA1, MMP9, CXCL8, PTGS2, IFNG, DNMT1, TYMS, MDM2, HDAC3 and ABCB1. Functional enrichment analysis indicated that SHG mainly acted on the neuroactive ligand-receptor interaction, calcium signaling pathway and cAMP signaling pathway. Molecular docking showed that the key constituents had a good affinity with the severe acute respiratory syndrome coronavirus 2 protein targets. Molecular dynamics simulations indicated that ginsenoside Rg4 formed a stable protein-ligand complex with helicase. CONCLUSION: Multiple components of SHG regulated multiple targets to inhibit virus invasion and cytokine storm through several signaling pathways; this provides a scientific basis for clinical applications and further experiments.