Novel systems biology experimental pipeline reveals matairesinol's antimetastatic potential in prostate cancer: an integrated approach of network pharmacology, bioinformatics, and experimental validation.

Matairesinol (MAT), a plant lignan renowned for its anticancer properties in hormone-sensitive cancers like breast and prostate cancers, presents a promising yet underexplored avenue in the treatment of metastatic prostate cancer (mPC). To elucidate its specific therapeutic targets and mechanisms, our study adopted an integrative approach, amalgamating network pharmacology (NP), bioinformatics, GeneMANIA-based functional association (GMFA), and experimental validation. By mining online databases, we identified 27 common targets of mPC and MAT, constructing a MAT-mPC protein-protein interaction network via STRING and pinpointing 11 hub targets such as EGFR, AKT1, ERBB2, MET, IGF1, CASP3, HSP90AA1, HIF1A, MMP2, HGF, and MMP9 with CytoHuba. Utilizing DAVID, Gene Ontology (GO) analysis highlighted metastasis-related processes such as epithelial-mesenchymal transition, positive regulation of cell migration, and key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including cancer, prostate cancer, PI3K-Akt, and MAPK signaling, while the web resources such as UALCAN and GEPIA2 affirmed the clinical significance of the top 11 hub targets in mPC patient survival analysis and gene expression patterns. Our innovative GMFA enrichment method further enriched network pharmacology findings. Molecular docking analyses demonstrated substantial interactions between MAT and 11 hub targets. Simulation studies confirmed the stable interactions of MAT with selected targets. Experimental validation in PC3 cells, employing quantitative real-time reverse-transcription PCR and various cell-based assays, corroborated MAT's antimetastatic effects on mPC. Thus, this exhaustive NP analysis, complemented by GMFA, molecular docking, molecular dynamics simulations, and experimental validations, underscores MAT's multifaceted role in targeting mPC through diverse therapeutic avenues. Nevertheless, comprehensive in vitro validation is imperative to solidify these findings.

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.

Crebanine mitigates glucocorticoid-induced osteonecrosis of the femoral head by restoring bone remodelling homeostasis via attenuating oxidative stress.

The onset of osteonecrosis of the femoral head (ONFH) is intimately associated with the extensive administration of glucocorticoids (GCs). Long-term stimulation of GCs can induce oxidative stress in both osteoclasts (OCs) and osteoblasts (OBs), resulting in the disturbance of bone remodelling. An alkaloid named crebanine (CN) demonstrates pharmacological properties including anti-inflammation and reactive oxygen species (ROS) modulation. Our objective is to assess the therapeutic potential of CN in treating ONFH and elucidate the associated underlying mechanisms. The network pharmacology analysis uncovered that CN played a role in regulating ROS metabolism. In vitro, CN demonstrated its ability to reduce the dexamethasone (DEX)-stimulated generation of OCs and suppress their resorptive function by downregulating the level of osteoclast marker genes. Concurrently, CN also mitigated DEX-induced damage to OBs, facilitating the restoration of osteoblast marker gene expression, cellular differentiation and function. These effects were achieved by CN augmenting the antioxidant system to reduce intracellular ROS levels. Furthermore, in vitro results were corroborated by micro-CT and histological data, which also showed that CN attenuated MPS-induced ONFH in mice. This study highlights the therapeutic potential of CN in counteracting GCs-induced ONFH.

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.

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.

Network Pharmacology, Molecular Docking, and Molecular Dynamics Study to Explore the Effect of Resveratrol on Type 2 Diabetes.

This network pharmacology study represents a significant step in understanding the potential of Resveratrol as an antidiabetic agent and its molecular targets. Targets for Type 2 diabetes were obtained from the MalaCards and DisGeNET databases, while targets for Resveratrol were sourced from the STP and CTD databases. Subsequently, we performed matching to identify common disease-compound targets. The identified genes were analyzed using the ShinGO-0.76.3 database for functional enrichment analysis and KEGG pathway mapping. A protein-protein interaction network was then constructed using Cytoscape software, and hub genes were identified. These hub genes were subjected to molecular docking and dynamic simulations using AutoDock Vina and Gromacs software. According to functional enrichment and KEGG pathway analysis, Resveratrol influences insulin receptors, endoplasmic reticulum functions, and oxidoreductase activity and is involved in the estrogen and HIF-1 pathways. Ten hub genes were identified, including ESR1, PTGS2, SRC, NOS3, MMP9, IGF1R, CYP19A1, MTOR, MMP2, and PIK3CA. The proteins associated with these genes exhibited high interaction with Resveratrol in the molecular docking analysis, and molecular dynamics showed a stable interaction of Resveratrol with ESR1, MMP9, PIK3CA, and PTGS2. In conclusion, our work enhances the understanding of the antidiabetic activity of Resveratrol, which future studies should experimentally corroborate.

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.

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.

Effects of Curcumin on Radiation/Chemotherapy-Induced Oral Mucositis: Combined Meta-Analysis, Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

The study aims to investigate the effects of curcumin on radiation/chemotherapy-induced oral mucositis (R/CIOM) and preliminarily explore its mechanism. Randomized controlled trials were identified from the PubMed, Embase, Web of Science, Cochrane Library, Medline, and Google Scholar databases. RevMan 5.4 was used for statistical analysis to calculate the combined risk ratios (RRs). The mechanism was analyzed through network pharmacology, molecular docking, and a molecular dynamics simulation. The targets of curcumin were collected in HERB, PharmMapper, Targetnet, Swiss Target Prediction, and SuperPred. OMIM, GeneCards, and Disgenet were used to collect relevant targets for R/CIOM. Cytoscape software 3.8.0 was used to construct the component-target-pathway network. Protein-Protein Interaction (PPI) networks were constructed using the STRING database. GO and KEGG enrichment analyses were performed by Metascape. AutoDock Vina 4.2 software was used for molecular docking. The molecular dynamics simulation was performed by Gromacs v2022.03. It is found that 12 studies involving 565 patients were included. Meta-analyses showed that curcumin reduced the incidence of severe R/CIOM (RR 0.42 [0.24, 0.75]) and the mean severity of R/CIOM (MD -0.93 [-1.34, -0.52]). Eleven core target genes were identified in the treatment of R/CIOM with curcumin. The results of molecular docking and the molecular dynamics simulation showed that curcumin had strong binding energy and stability with target proteins including MAPK3, SRC, and TNF. Overall, these findings suggest curcumin can effectively improve severe R/CIOM, perhaps by affecting MAPK3, SRC, and TNF.

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.

Integrating network pharmacology, molecular docking and experimental verification to explore the therapeutic effect and potential mechanism of nomilin against triple-negative breast cancer.

BACKGROUND: Nomilin is a limonoid compound known for its multiple biological activities, but its role in triple negative breast cancer (TNBC) remains unclear. This study aims to uncover the potential therapeutic effect of nomilin on TNBC and elucidate the specific mechanism of its action. METHODS: We employed weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the GeneCards database to identify potential targets for TNBC. Simultaneously, we utilized the Swiss Target Prediction, ChEMBL, and STITCH databases to identify potential targets of nomilin. The core targets and mechanisms of nomilin against TNBC were predicted through protein-protein interaction (PPI) network analysis, molecular docking, and enrichment analysis. The results of the network pharmacology were corroborated by conducting experiments. RESULTS: A total of 17,204 TNBC targets were screened, and 301 potential targets of nomilin were identified. Through the PPI network, eight core targets of nomilin against TNBC were pinpointed, namely BCL2, Caspase3, CyclinD1, EGFR, HSP90AA1, KRAS, PARP1, and TNF. Molecular docking, molecular dynamics simulation and proteome microarray revealed that nomilin exhibits strong binding activity to these core proteins. Enrichment analysis results indicated that the anti-TNBC effect of nomilin is associated with PI3K/Akt pathway. In vitro and in vivo experiments have demonstrated that nomilin inhibits TNBC cell proliferation and migration while promoting cell apoptosis through the PI3K/Akt pathway. CONCLUSION: For the first time, the research effectively discovered the objectives and mechanisms of nomilin in combating TNBC using network pharmacology, molecular docking, molecular dynamics simulation, proteome microarray and experimental confirmation, presenting a hopeful approach for treating TNBC.