Integrating network pharmacology and in silico analysis deciphers Withaferin-A's anti-breast cancer potential via hedgehog pathway and target network interplay.

This study examines the remarkable effectiveness of Withaferin-A (WA), a withanolide obtained from Withania somnifera (Ashwagandha), in encountering the mortiferous breast malignancy, a global peril. The predominant objective is to investigate WA's intrinsic target proteins and hedgehog (Hh) pathway proteins in breast cancer targeting through the application of in silico computational techniques and network pharmacology predictions. The databases and webtools like Swiss target prediction, GeneCards, DisGeNet and Online Mendelian Inheritance in Man were exploited to identify the common target proteins. The culmination of the WA network and protein-protein interaction network were devised using Stitch and String web tools, through which the drug-target network of 30 common proteins was constructed employing Cytoscape-version 3.9. Enrichment analysis was performed by incorporating Gprofiler, Metascape and Cytoscape plugins. David compounded the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, and enrichment was computed through bioinformatics tools. The 20 pivotal proteins were docked harnessing Glide, Schrodinger Suite 2023-2. The investigation was governed by docking scores and affinity. The shared target proteins underscored the precise Hh and WA network roles with the affirmation enrichment P-value of <0.025. The implications for hedgehog and cancer pathways were profound with enrichment (P < 0.01). Further, the ADMET and drug-likeness assessments assisted the claim. Robust interactions were noticed with docking studies, authenticated through molecular dynamics, molecular mechanics generalized born surface area scores and bonds. The computational investigation emphasized WA's credible anti-breast activity, specifically with Hh proteins, implying stem-cell-level checkpoint restraints. Rigorous testament is imperative through in vitro and in vivo studies.

Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms.

Understanding the mechanisms of candidate drugs play an important role in drug discovery. The activating/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug-target (DT) mechanisms and data scarcity, modelling this problem based on deep learning methods to accurately predict DT activating/inhibiting mechanisms remains a considerable challenge. Here, by considering network pharmacology, we propose a multi-view deep learning model, DrugAI, which combines four modules, i.e. a graph neural network for drugs, a convolutional neural network for targets, a network embedding module for drugs and targets and a deep neural network for predicting activating/inhibiting mechanisms between drugs and targets. Computational experiments show that DrugAI performs better than state-of-the-art methods and has good robustness and generalization. To demonstrate the reliability of the predictive results of DrugAI, bioassay experiments are conducted to validate two drugs (notopterol and alpha-asarone) predicted to activate TRPV1. Moreover, external validation bears out 61 pairs of mechanism relationships between natural products and their targets predicted by DrugAI based on independent literatures and PubChem bioassays. DrugAI, for the first time, provides a powerful multi-view deep learning framework for robust prediction of DT activating/inhibiting mechanisms.

TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms.

Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer's disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.

Machine learning for synergistic network pharmacology: a comprehensive overview.

Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Naringenin inhibits APAP-induced acute liver injury through activating PPARA-dependent signaling pathway.

Acute liver injury (ALI) refers to the damage to the liver cells of patients due to drugs, food, and diseases. In this work, we used a network pharmacology approach to analyze the relevant targets and pathways of the active ingredients in Citri Reticulatae Pericarpium (CRP) for the treatment of ALI and conducted systematic validation through in vivo and in vitro experiments. The network pharmacologic results predicted that naringenin (NIN) was the main active component of CRP in the treatment of ALI. GO functional annotation and KEGG pathway enrichment showed that its mechanism may be related to the regulation of PPARA signaling pathway, PPARG signaling pathway, AKT1 signaling pathway, MAPK3 signaling pathway and other signaling pathways. The results of in vivo experiments showed that (NIN) could reduce the liver lesions, liver adipose lesions, hepatocyte injury and apoptosis in mice with APAP-induced ALI, and reduce the oxidative stress damage of mouse liver cells and the inflammation-related factors to regulate ALI. In vitro experiments showed that NIN could inhibit the proliferation, oxidative stress and inflammation of APAP-induced LO2 cells, promote APAP-induced apoptosis of LO2 cells, and regulate the expression of apoptotic genes in acute liver injury. Further studies showed that NIN inhibited APAP-induced ALI mainly by regulating the PPARA-dependent signaling pathway. In conclusion, this study provides a preliminary theoretical basis for the screening of active compounds in CRP for the prevention and treatment of ALI.

Amentoflavone reverses epithelial-mesenchymal transition in hepatocellular carcinoma cells by targeting p53 signalling pathway axis.

Epithelial-mesenchymal transition (EMT) and its reversal process are important potential mechanisms in the development of HCC. Selaginella doederleinii Hieron is widely used in Traditional Chinese Medicine for the treatment of various tumours and Amentoflavone is its main active ingredient. This study investigates the mechanism of action of Amentoflavone on EMT in hepatocellular carcinoma from the perspective of bioinformatics and network pharmacology. Bioinformatics was used to screen Amentoflavone-regulated EMT genes that are closely related to the prognosis of HCC, and a molecular prediction model was established to assess the prognosis of HCC. The network pharmacology was used to predict the pathway axis regulated by Amentoflavone. Molecular docking of Amentoflavone with corresponding targets was performed. Detection and evaluation of the effects of Amentoflavone on cell proliferation, migration, invasion and apoptosis by CCK-8 kit, wound healing assay, Transwell assay and annexin V-FITC/propidium iodide staining. Eventually three core genes were screened, inculding NR1I2, CDK1 and CHEK1. A total of 590 GO enrichment entries were obtained, and five enrichment results were obtained by KEGG pathway analysis. Genes were mainly enriched in the p53 signalling pathway. The outcomes derived from both the wound healing assay and Transwell assay demonstrated significant inhibition of migration and invasion in HCC cells upon exposure to different concentrations of Amentoflavone. The results of Annexin V-FITC/PI staining assay showed that different concentrations of Amentoflavone induces apoptosis in HCC cells. This study revealed that the mechanism of Amentoflavone reverses EMT in hepatocellular carcinoma, possibly by inhibiting the expression of core genes and blocking the p53 signalling pathway axis to inhibit the migration and invasion of HCC cells.

Exploring the pharmacological mechanism of Glycyrrhiza uralensis against KOA through integrating network pharmacology and experimental assessment.

Knee osteoarthritis (KOA), a major health and economic problem facing older adults worldwide, is a degenerative joint disease. Glycyrrhiza uralensis Fisch. (GC) plays an integral role in many classic Chinese medicine prescriptions for treating knee osteoarthritis. Still, the role of GC in treating KOA is unclear. To explore the pharmacological mechanism of GC against KOA, UPLC-Q-TOF/MS was conducted to detect the main compounds in GC. The therapeutic effect of GC on DMM-induced osteoarthritic mice was assessed by histomorphology, µCT, behavioural tests, and immunohistochemical staining. Network pharmacology and molecular docking were used to predict the potential targets of GC against KOA. The predicted results were verified by immunohistochemical staining Animal experiments showed that GC had a protective effect on DMM-induced KOA, mainly in the improvement of movement disorders, subchondral bone sclerosis and cartilage damage. A variety of flavonoids and triterpenoids were detected in GC via UPLC-Q-TOF/MS, such as Naringenin. Seven core targets (JUN, MAPK3, MAPK1, AKT1, TP53, RELA and STAT3) and three main pathways (IL-17, NF-κB and TNF signalling pathways) were discovered through network pharmacology analysis that closely related to inflammatory response. Interestingly, molecular docking results showed that the active ingredient Naringenin had a good binding effect on anti-inflammatory-related proteins. In the verification experiment, after the intervention of GC, the expression levels of pp65 and F4/80 inflammatory indicators in the knee joint of KOA model mice were significantly downregulated. GC could improve the inflammatory environment in DMM-induced osteoarthritic mice thus alleviating the physiological structure and dysfunction of the knee joint. GC might play an important role in the treatment of knee osteoarthritis.

Mechanism of Astragalus membranaceus (Huangqi, HQ) for treatment of heart failure based on network pharmacology and molecular docking.

Heart failure is a leading cause of death in the elderly. Traditional Chinese medicine, a verified alternative therapeutic regimen, has been used to treat heart failure, which is less expensive and has fewer adverse effects. In this study, a total of 15 active ingredients of Astragalus membranaceus (Huangqi, HQ) were obtained; among them, Isorhamnetin, Quercetin, Calycosin, Formononetin, and Kaempferol were found to be linked to heart failure. Ang II significantly enlarged the cell size of cardiomyocytes, which could be partially reduced by Quercetin, Isorhamnetin, Calycosin, Kaempferol, or Formononetin. Ang II significantly up-regulated ANP, BNP, ß-MHC, and CTGF expressions, whereas Quercetin, Isorhamnetin, Calycosin, Kaempferol or Formononetin treatment partially downregulated ANP, BNP, ß-MHC and CTGF expressions. Five active ingredients of HQ attenuated inflammation in Ang II-induced cardiomyocytes by inhibiting the levels of TNF-α, IL-1ß, IL-18 and IL-6. Molecular docking shows Isorhamnetin, Quercetin, Calycosin, Formononetin and Kaempferol can bind with its target protein ESR1 in a good bond by intermolecular force. Quercetin, Calycosin, Kaempferol or Formononetin treatment promoted the expression levels of ESR1 and phosphorylated ESR1 in Ang II-stimulated cardiomyocytes; however, Isorhamnetin treatment had no effect on ESR1 and phosphorylated ESR1 expression levels. In conclusion, our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of HQ against heart failure. Isorhamnetin, Quercetin, Calycosin, Formononetin and Kaempferol might be the primary active ingredients of HQ, dominating its cardioprotective effects against heart failure through regulating ESR1 expression, which provided a basis for the clinical application of HQ to regulate cardiac hypertrophy and heart failure.

Identification of Key Hypolipidemic Components and Exploration of the Potential Mechanism of Total Flavonoids from Rosa sterilis Based on Network Pharmacology, Molecular Docking, and Zebrafish Experiment.

Hyperlipidemia is a prevalent chronic metabolic disease that severely affects human health. Currently, commonly used clinical therapeutic drugs are prone to drug dependence and toxic side effects. Dietary intervention for treating chronic metabolic diseases has received widespread attention. Rosa sterilis is a characteristic fruit tree in China whose fruits are rich in flavonoids, which have been shown to have a therapeutic effect on hyperlipidemia; however, their exact molecular mechanism of action remains unclear. Therefore, this study aimed to investigate the therapeutic effects of R. sterilis total flavonoid extract (RS) on hyperlipidemia and its possible mechanisms. A hyperlipidemic zebrafish model was established using egg yolk powder and then treated with RS to observe changes in the integral optical density in the tail vessels. Network pharmacology and molecular docking were used to investigate the potential mechanism of action of RS for the treatment of hyperlipidemia. The results showed that RS exhibited favorable hypolipidemic effects on zebrafish in the concentration range of 3.0-30.0 µg/mL in a dose-dependent manner. Topological and molecular docking analyses identified HSP90AA1, PPARA, and MMP9 as key targets for hypolipidemic effects, which were exerted mainly through lipolytic regulation of adipocytes and lipids; pathway analysis revealed enrichment in atherosclerosis, chemical carcinogenic-receptor activation pathways in cancers, and proteoglycans in prostate cancer and other cancers. Mover, chinensinaphthol possessed higher content and better target binding ability, which suggested that chinensinaphthol might be an important component of RS with hypolipidemic active function. These findings provide a direction for further research on RS interventions for the treatment of hyperlipidemia.

Norcantharidin Enhances the Antitumor Effect of 5-Fluorouracil by Inducing Apoptosis of Cervical Cancer Cells: Network Pharmacology, Molecular Docking, and Experimental Validation.

The high recurrence rate of cervical cancer is a leading cause of cancer deaths in women. 5-Fluorouracil (5-FU) is an antitumor drug used to treat many types of cancer, but its diminishing effectiveness and side effects limit its use. Norcantharidin (NCTD), a demethylated derivative of cantharidin, exhibits various biological activities. Here, we investigated whether NCTD could potentiate 5-FU to induce cervical cancer cell death. To assess the cell viability and synergistic effects of the drugs, cell counting kit-8 and colony formation assays were performed using HR-HPV-positive cervical cancer cell lines. Annexin V-FITC/PI staining and TUNEL assays were performed to confirm the induction of apoptosis. The synergistic effect of NCTD on the antitumor activity of 5-FU was analyzed using network pharmacology, molecular docking, and molecular dynamics simulations. Apoptosis-related proteins were examined using immunoblotting. The combination of NCTD and 5-FU was synergistic in cervical cancer cell lines. Network pharmacological analysis identified 10 common targets of NCTD and 5-FU for cervical cancer treatment. Molecular docking showed the strong binding affinity of both compounds with CA12, CASP9, and PTGS1. Molecular dynamics simulations showed that the complex system of both drugs with caspase-9 could be in a stable state. NCTD enhanced 5-FU-mediated cytotoxicity by activating apoptosis-related proteins. NCTD acts synergistically with 5-FU to inhibit cervical cancer cell proliferation. NCTD enhances 5-FU-induced apoptosis in cervical cancer cell lines via the caspase-dependent pathway.

Network Pharmacology Analysis, Molecular Docking Integrated Experimental Verification Reveal the Mechanism of Gynostemma pentaphyllum in the Treatment of Type II Diabetes by Regulating the IRS1/PI3K/Akt Signaling Pathway.

Gynostemma pentaphyllum (Thunb.) Makino (GP), a plant with homology of medicine and food, as a traditional Chinese medicine, possesses promising biological activities in the prevention and treatment of type 2 diabetes mellitus (T2DM). However, the material basis and the mechanism of action of GP in the treatment of T2DM have not been fully elucidated. This study aimed to clarify the active components, potential targets and signaling pathways of GP in treating T2DM. The chemical ingredients of GP were collected by combining UPLC-HRMS analysis and literature research. Network pharmacology revealed that GP had 32 components and 326 potential targets in treating T2DM. The results showed that GP affected T2DM by mediating the insulin resistance signaling pathway, PI3K/Akt signaling pathway and FoxO1 signaling pathway, which had a close relationship with T2DM. Molecular docking results showed that STAT3, PIK3CA, AKT1, EGFR, VEGFA and INSR had high affinity with the active compounds of GP. In vitro, GP extracts obviously increased the glucose uptake and glucose consumption in IR-HepG2 cells. GP extracts increased the levels of PI3K, p-AKT, p-GSK3ß and p-FoxO1 and decreased the expression of p-IRS1, p-GS, PEPCK and G6Pase, which indicated that GP could promote glycogen synthesis and inhibit gluconeogenesis by regulating the IRS1/PI3K/Akt signaling pathway. The results demonstrated that GP could improve insulin resistance by promoting glucose uptake and glycogen synthesis and inhibiting gluconeogenesis through regulating the IRS1/PI3K/Akt signaling pathway, which might be a potential alternative therapy for T2DM.

Network pharmacology-and molecular docking-based investigation of Danggui blood-supplementing decoction in ischaemic stroke.

Danggui blood-supplementing decoction (DBsD) is an herbal preparation treating several diseases including stroke. The present study sought to investigate the potential mechanism of DBsD in ischaemic stroke (IS) using network pharmacology, molecular docking, and cell experiment. Based on the protein-protein (PPI) network analysis, MAPK1 (0.51, 12), KNG1 (0.57, 28), and TNF (0.64, 39) were found with relatively good performance in degree and closeness centrality. The functional enrichment analysis revealed that DBsD contributed to IS-related biological processes, molecule function, and presynaptic/postsynaptic cellular components. Pathway enrichment indicated that DBsD might protect IS by modulating multi-signalling pathways including the sphingolipid signalling pathway. Molecular docking verified the stigmasterol-KNG1, bifendate-TNF, and formononetin-MAPK1 pairs. Cell experiments confirmed the involvement of KNG1 and sphingolipid signalling pathway in hippocampal neuronal cell apoptosis. This study showed that DBsD can protect neuronal cell injury after IS through multiple components, multiple targets, and multiple pathways.

Network pharmacology-based strategy to investigate the mechanisms of artemisinin in treating primary Sjögren's syndrome.

OBJECTIVE: The study aimed to explore the mechanism of artemisinin in treating primary Sjögren's syndrome (pSS) based on network pharmacology and experimental validation. METHODS: Relevant targets of the artemisinin and pSS-related targets were integrated by public databases online. An artemisinin-pSS network was constructed by Cytoscape. The genes of artemisinin regulating pSS were imported into STRING database to construct a protein-protein interaction (PPI) network in order to predict the key targets. The enrichment analyses were performed to predict the crucial mechanism and pathway of artemisinin against pSS. The active component of artemisinin underwent molecular docking with the key proteins. Artemisinin was administered intragastrically to SS-like NOD/Ltj mice to validate the efficacy and critical mechanisms. RESULTS: Network Pharmacology analysis revealed that artemisinin corresponded to 412 targets, and pSS related to 1495 genes. There were 40 intersection genes between artemisinin and pSS. KEGG indicated that therapeutic effects of artemisinin on pSS involves IL-17 signaling pathway, HIF-1 signaling pathway, apoptosis signaling pathway, Th17 cell differentiation, PI3K-Akt signaling pathway, and MAPK signaling pathway. Molecular docking results further showed that the artemisinin molecule had higher binding energy by combining with the key nodes in IL-17 signaling pathway. In vivo experiments suggested artemisinin can restored salivary gland secretory function and improve the level of glandular damage of NOD/Ltj mice. It contributed to the increase of regulatory T cells (Tregs) and the downregulated secretion of IL-17 in NOD/Ltj model. CONCLUSION: The treatment of pSS with artemisinin is closely related to modulating the balance of Tregs and Th17 cells via T cell differentiation.

Icariin attenuates asthmatic airway inflammation via modulating alveolar macrophage activation based on network pharmacology and in vivo experiments.

BACKGROUND: Icariin (ICA) inhibits inflammatory response in various diseases, but the mechanism underlying ICA treating airway inflammation in asthma needs further understood. We aimed to predict and validate the potential targets of ICA against asthma-associated airway inflammation using network pharmacology and experiments. METHODS: The ovalbumin-induced asthma-associated airway inflammation mice model was established. The effects of ICA were evaluated by behavioral, airway hyperresponsiveness, lung pathological changes, inflammatory cell and cytokines counts. Next, the corresponding targets of ICA were mined via the SEA, CTD, HERB, PharmMapper, Symmap database and the literature. Pubmed-Gene and GeneCards databases were used to screen asthma and airway inflammation-related targets. The overlapping targets were used to build an interaction network, analyze gene ontology and enrich pathways. Subsequently, flow cytometry, quantitative real-time PCR and western blotting were employed for validation. RESULTS: ICA alleviated the airway inflammation of asthma; 402 targets of ICA, 5136 targets of asthma and 4531 targets of airway inflammation were screened; 216 overlapping targets were matched and predicted ICA possesses the potential to modulate asthmatic airway inflammation by macrophage activation/polarization. Additionally, ICA decreased M1 but elevated M2. Potential targets that were disrupted by asthma inflammation were restored by ICA treatment. CONCLUSIONS: ICA alleviates airway inflammation in asthma by inhibiting the M1 polarization of alveolar macrophages, which is related to metabolic reprogramming. Jun, Jak2, Syk, Tnf, Aldh2, Aldh9a1, Nos1, Nos2 and Nos3 represent potential targets of therapeutic intervention. The present study enhances understanding of the anti-airway inflammation effects of ICA, especially in asthma.

Network pharmacology prediction and molecular docking analysis reveal the mechanism of modified Bushen Yiqi formulas on chronic obstructive pulmonary disease.

BACKGROUND: The present study aimed to explore the mechanism of the modified Bushen Yiqi formula (MBYF) in the treatment of chronic obstructive pulmonary disease (COPD) based on network pharmacology and molecular docking. METHODS: First, the active ingredients and corresponding targets in MBYF were mined through the Traditional Chinese Medicine Systems Pharmacology database. Subsequently, Online Mendelian Inheritance in Man, DrugBank, and GeneCard were used to screen COPD-related targets. Cytoscape was used to construct a network of candidate components of MBYF in COPD treatment. The overlapping targets of COPD and MBYF were used to treat COPD, and then CytoHubba and CytoNAC plug-ins in Cytoscape were used for topology analysis to build the core network. In addition, core targets were used for Gene Ontology analysis and enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes. Finally, AutoDock Vina software was used to conduct a molecular docking study on the core active ingredients and core targets to verify the above network pharmacological analysis. RESULTS: Seventy-nine active components of MBYF were screened and 261 corresponding targets were found. At the same time, 1307 related targets corresponding to COPD were screened and 111 overlapping targets were matched. By bioinformatics analysis, 10 core targets were identified, and subsequently, enrichment analysis revealed 385 BP, two CC, eight MF and 78 related signaling pathways. The binding of the core active components in MBYF to the core target was further verified by molecular docking, and all showed good binding. CONCLUSIONS: The active components of MBYF, such as quercetin, kaempferol, luteolin, and baicalein, may be the material basis for the treatment of chronic obstructive pulmonary disease. They affect the expression of inflammatory cells and inflammatory factors, protein phosphorylation, and smooth muscle hyperplasia through tumor necrosis factor, interleukin-17, mitogen-activated protein kinase, nuclear factor-kappa B and other signaling pathways.

Exploring the mechanism underlying the therapeutic effects of butein in colorectal cancer using network pharmacology and single-cell RNA sequencing data.

BACKGROUND: Butein has shown substantial potential as a cancer treatment, but its precise mechanism of action in colorectal cancer (CRC) remains unclear. This study aimed to uncover the underlying mechanisms through which butein operates in CRC and to identify potential biomarkers through a comprehensive investigation. METHODS: Target genes associated with butein were sourced from SwissTargetPrediction, CTD, BindingDB and TargetNet. Gene expression data from the GSE38026 dataset and the single-cell dataset (GSE222300) were retrieved from the Gene Expression Omnibus database. The activation of disease-related pathways was assessed using Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and differential gene analysis. Disease-associated genes were identified through differential analysis and weighted gene co-expression network analysis (WGCNA). The protein-protein interaction network was utilized to pinpoint potential drug targets. Molecular complex detection (MCODE) analysis was employed to uncover relevant genes influenced by butein within key subgroup networks. Machine learning techniques were applied for the screening of potential biomarkers, with receiver operating characteristic curves used to evaluate their clinical significance. Single-cell analysis was conducted to assess the pharmacological targets of butein in CRC, with validation performed using the external dataset GSE40967. RESULTS: A total of 232 target genes for butein were identified. Functional enrichment analysis revealed significant enrichment of signaling pathways, including mitogen-activated protein kinase, JAK-STAT and NF-κB, among these genes. Differential analysis, in conjunction with WGCNA, yielded 520 disease-related genes. Subsequently, a disease-drug-gene network consisting of 727 targets was established, and a subnetwork containing 56 crucial genes was extracted. Important pathways such as the FoxO signaling pathway exhibited significant enrichment within these key genes. Machine learning applied to the 56 important genes led to the identification of a potential biomarker, UBE2C. Receiver operating characteristic analysis demonstrated the excellent clinical predictive utility of UBE2C. Single-cell analysis suggested that butein's therapeutic effects might be linked to its influence on epithelial and T cells, with UBE2C expression associated with these cell types. Validation using the external dataset GSE40967 further confirmed the exceptional clinical predictive capability of UBE2C. CONCLUSION: This study combines network pharmacology with single-cell analysis to unravel the mechanisms underlying butein's effects in CRC. Notably, UBE2C emerged as a promising biomarker with superior clinical efficacy. These research findings contribute significantly to our understanding of specific molecular mechanisms, potentially shaping future clinical practices.

Icariside II in NSCLC and COVID-19: Network pharmacology and molecular docking study.

BACKGROUND: Patients with non-small cell lung cancer (NSCLC) are susceptible to coronavirus disease-2019 (COVID-19), but current treatments are limited. Icariside II (IS), a flavonoid compound derived from the plant epimedin, showed anti-cancer,anti-inflammation and immunoregulation effects. The present study aimed to evaluate the possible effect and underlying mechanisms of IS on NSCLC patients with COVID-19 (NSCLC/COVID-19). METHODS: NSCLC/COVID-19 targets were defined as the common targets of NSCLC (collected from The Cancer Genome Atlas database) and COVID-19 targets (collected from disease database of Genecards, OMIM, and NCBI). The correlations of NSCLC/COVID-19 targets and survival rates in patients with NSCLC were analyzed using the survival R package. Prognostic analyses were performed using univariate and multivariate Cox proportional hazards regression models. Furthermore, the targets in IS treatment of NSCLC/COVID-19 were defined as the overlapping targets of IS (predicted from drug database of TMSCP, HERBs, SwissTarget Prediction) and NSCLC/COVID-19 targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of these treatment targets were performed aiming to understand the biological process, cellular component, molecular function and signaling pathway. The hub targets were analyzed by a protein-protein interaction network and the binding capacity with IS was characterized by molecular docking. RESULTS: The hub targets for IS in the treatment of NSCLC/COVID-19 includes F2, SELE, MMP1, MMP2, AGTR1 and AGTR2, and the molecular docking results showed that the above target proteins had a good binding degree to IS. Network pharmacology showed that IS might affect the leucocytes migration, inflammation response and active oxygen species metabolic process, as well as regulate the interleukin-17, tumor necrosus factor and hypoxia-inducible factor-1 signaling pathway in NSCLC/COVID-19. CONCLUSIONS: IS may enhance the therapeutic efficacy of current clinical anti-inflammatory and anti-cancer therapy to benefit patients with NSCLC combined with COVID-19.

Myricetin reduces platelet PANoptosis in sepsis to delay disseminated intravascular coagulation.

Sepsis is a severe inflammatory disease characterized by cytokine storm, often accompanied by disseminated intravascular coagulation (DIC). PANoptosis is a novel form of cell death triggered by cytokine storms, characterized by a cascade reaction of pyroptosis, apoptosis, and necroptosis. It exists in septic platelets and is closely associated with the onset and progression of DIC. However, there remains an unmet need for drugs targeting PANoptosis. The anti-PANoptosis effect of myricetin was predicted using network pharmacology and confirmed through molecular docking. In vitro platelet activation models demonstrated that myricetin significantly attenuated platelet particle release, integrin activation, adhesion, spreading, clot retraction, and aggregation. Moreover, in a sepsis model, myricetin reduced inflammatory infiltration in lung tissue and platelet activation while improving DIC. Additionally, whole blood sequencing samples from sepsis patients and healthy individuals were analyzed to elucidate the up-regulation of the PANoptosis targets. Our findings demonstrate the inhibitory effect of myricetin on septic platelet PANoptosis, indicating its potential as a novel anti-cellular PANoptosis candidate and therapeutic agent for septic DIC. Furthermore, our study establishes a foundation for utilizing network pharmacology in the discovery of new drugs to treat various diseases.