Based on ultra-high performance liquid chromatography-quadrupole-Exactive Orbitrap mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) technology and network pharmacology, this study explored the pharmacodynamic substances and potential mechanisms of Huazhuo Sanjie Chubi Decoction in the treatment of gouty arthritis(GA). UPLC-Q-Exactive Orbitrap-MS technology was used to identify the components in Huazhuo Sanjie Chubi Decoction, and the qualitative analysis of its active ingredients was carried out, with a total of 184 active ingredients identified. A total of 897 active ingredient targets were screened through the PharmMapper database, and 491 GA-related disease targets were obtained from the OMIM, GeneCards, CTD databases. After Venn analysis, 60 intersecting targets were obtained. The component target-GA target network was constructed through the Cytoscape platform, and the STRING database was used to construct a protein-protein interaction network, with 16 core targets screened. The core targets were subjected to Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses, and the component-target-pathway network was constructed. It was found that the main active ingredients of the formula for the treatment of GA were phenols, flavonoids, alkaloids, and terpenoids, and the key targets were SRC, MMP3, MMP9, REN, ALB, IGF1R, PPARG, MAPK1, HPRT1, and CASP1. Through GO analysis, it was found that the treatment of GA mainly involved biological processes such as lipid response, bacterial response, and biostimulus response. KEGG analysis showed that the pathways related to the treatment of GA included lipids and atherosclerosis, neutrophil extracellular traps(NETs), IL-17, and so on. In summary, phenols, flavonoids, alkaloids, and terpenoids may be the core pharmacodynamic substances of Huazhuo Sanjie Chubi Decoction in the treatment of GA, and the pharmacodynamic mechanism may be related to SRC, MMP3, MMP9, and other targets, as well as lipids and atherosclerosis, NETs, IL-17, and other pathways.
Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology ; Arthritis, Gouty/metabolism* ; Chromatography, High Pressure Liquid/methods* ; Humans ; Mass Spectrometry/methods* ; Protein Interaction Maps/drug effects*

This study identified blood-entering components of Anshen Dropping Pills and explored their anti-insomnia effects and mechanisms. The main blood-entering components of Anshen Dropping Pills were detected and identified by UPLC-Q-TOF-MS/MS. The rationality of the formula was assessed by using enrichment analysis based on the relationship between drugs and symptoms, and core targets of its active components were selected as the the potential anti-insomnia targets of Anshen Dropping Pills through network pharmacology analysis. Furthermore, protein-protein interaction(PPI) network, Gene Ontology(GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were performed on the core targets. An active component-core target network for Anshen Dropping Pills was constructed. Finally, the effects of low-, medium-, and high-dose groups of Anshen Dropping Pills on sleep episodes, sleep duration, and sleep latency in mice were measured by supraliminal and subliminal pentobarbital sodium experiments. Moreover, total scores of the Pittsburgh sleep quality index(PSQI) scale was used to evaluate the changes before and after the treatment with Anshen Dropping Pills in a clinical study. The enrichment analysis based on the relationship between drugs and symptoms verified the rationality of the Anshen Dropping Pills formula, and nine blood-entering components of Anshen Dropping Pills were identified by UPLC-Q-TOF-MS/MS. The network proximity revealed a significant correlation between eight components and insomnia, including magnoflorine, liquiritin, spinosin, quercitrin, jujuboside A, ginsenoside Rb_3, glycyrrhizic acid, and glycyrrhetinic acid. Network pharmacology analysis indicated that the major anti-insomnia pathways of Anshen Dropping Pills involved substance and energy metabolism, neuroprotection, immune system regulation, and endocrine regulation. Seven core genes related to insomnia were identified: APOE, ALB, BDNF, PPARG, INS, TP53, and TNF. In summary, Anshen Dropping Pills could increase sleep episodes, prolong sleep duration, and reduce sleep latency in mice. Clinical study results demonstrated that Anshen Dropping Pills could decrease total scores of PSQI scale. This study reveals the pharmacodynamic basis and potential multi-component, multi-target, and multi-pathway effects of Anshen Dropping Pills, suggesting that its anti-insomnia mechanisms may be associated with the regulation of insomnia-related signaling pathways. These findings offer a theoretical foundation for the clinical application of Anshen Dropping Pills.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Tandem Mass Spectrometry/methods* ; Sleep Initiation and Maintenance Disorders/metabolism* ; Mice ; Network Pharmacology ; Male ; Chromatography, High Pressure Liquid ; Humans ; Protein Interaction Maps/drug effects* ; Sleep/drug effects* ; Female ; Adult

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

Flavonoids are key bioactive components for evaluating the pharmacological activities of Chimonanthus praecox. Exploring the potential flavonoids and pharmacological mechanisms of C. praecox lays a foundation for the rational development and efficient utilization of this plant. This study employed ultra-performance liquid chromatography-tandem mass spectrometry-based widely targeted metabolomics to comprehensively identify the flavonoids in C. praecox. Network pharmacology was employed to explore the bioactive flavonoids and their mechanisms of action. Molecular docking was adopted to validate the predicted results. Finally, the content of bioactive flavonoids in different varieties of C. praecox was measured. The widely targeted metabolomics analysis identified 387 flavonoids in C. praecox, and the flavonoids varied among different varieties. Network pharmacology predicted 96 chemical components including 19 bioactive compounds, 181 corresponding targets and 2 504 disease targets, among which 99 targets were shared by the active components and the disease. Thirty-three core targets were predicted, involving 229 gene ontology terms and 99 pathways (P≤0.05), which indicated that the flavonoids components of C. praecox exhibited pharmacological activities including antioxidant, anti-inflammatory, antimicrobial, and antiviral activities. Topological analysis screened out five core components (salvigenin, laricitrin, isorhamnetin, quercetin, and 6-hydroxyluteolin) and five core targets (SRC, PIK3R1, AKT1, ESR1, and AKR1C3). The predicted bioactive flavonoids from C. praecox stably bound to key targets, which indicated that these flavonoids possessed potential bioactivities in their interactions with the targets. The flavonoids in C. praecox exerted pharmacological activities in a multi-component, multi-target, and multi-pathway manner. The combined application of metabolomics and network pharmacology provides a theoretical basis for in-depth studies on the pharmacological effects and mechanisms of C. praecox.
Flavonoids/metabolism* ; Network Pharmacology ; Metabolomics/methods* ; Molecular Docking Simulation ; Calycanthaceae/chemistry* ; Tandem Mass Spectrometry ; Drugs, Chinese Herbal/chemistry*

Shaoyao Gancao Decoction(SGD) is a classic formula used in the clinical treatment of joint diseases, such as rheumatoid arthritis(RA), though its mechanism of action remains unclear. This study aimed to explore the mechanism of SGD in treating RA through chemical and network pharmacology analyses, combined with cellular experiments. UPLC-Orbitrap-MS~2 was used to qualitatively analyze SGD and drug-containing serum of rats after oral administration of SGD, thereby identifying the chemical composition and plasma components of SGD. Potential targets for the plasma components in treating RA were identified using the SwissTargetPrediction, PharmMapper, GeneCards, and DrugBank databases, and a protein-protein interaction(PPI) network was constructed using the STRING data platform. GO functional enrichment and KEGG pathway enrichment analyses were conducted using the Metascape database. Molecular docking and lipopolysaccharide(LPS)-induced RAW264.7 cell experiments were utilized for in vitro validation. The results identified 95 compounds in SGD, including 15 prototypical absorbed components, i.e., 7 flavonoids, 5 terpenoids, 2 phenolic compounds, and 1 other compound. Network pharmacology analysis revealed that licoisoflavanone, liquiritin apioside, 5-hydroxyferulic acid, albiflorin, hederagenin, and paeoniflorin were the pharmacodynamic components of SGD for treating RA. The core targets of SGD for RA treatment were identified as SRC, MAPK, EGFR, HSP90AA1, and STAT3, with regulation of the NF-κB, PI3K-Akt, and MAPK signaling pathways identified as key mechanisms for anti-RA effects of SGD. Molecular docking results showed that the six core components exhibited high affinity with the key targets SRC, MAPK, and NF-κB. In vitro cellular experiments demonstrated that SGD down-regulated the expression of inflammatory factors, including interleukin-1β(IL-1β), cyclooxygenase-2(COX-2), and tumor necrosis factor-α(TNF-α), in LPS-induced RAW264.7 cells. Western blot analysis revealed that SGD significantly reduced the phosphorylation levels of NF-κB p65 and p38 MAPK proteins. This study provides a scientific basis for further research into the active components and mechanisms of action of SGD in treating RA.
Animals ; Drugs, Chinese Herbal/chemistry* ; Arthritis, Rheumatoid/metabolism* ; Mice ; Network Pharmacology ; RAW 264.7 Cells ; Rats ; Chromatography, High Pressure Liquid/methods* ; Molecular Docking Simulation ; Male ; Rats, Sprague-Dawley ; Humans ; Signal Transduction/drug effects* ; NF-kappa B/genetics* ; Protein Interaction Maps/drug effects*

The Compound Cheqian Tablets are derived from Cheqian Power in Comprehensive Recording of Divine Assistance, and they are made by modern technology with the combination of Plantago asiatica and Coptis chinensis. To investigate the material basis of Compound Cheqian Tablets in the treatment of diabetic nephropathy, in this study, the chemical components of Compound Cheqian Tablets were characterized and analyzed by UPLC-Q-TOF-MS/MS, and a total of 48 chemical components were identified. The identified chemical compounds were analyzed by network pharmacology. By validating with previous literature, six bioactive compounds including acteoside, isoacteoside, coptisine, magnoflorine, palmatine, and berberine were confirmed as the index components for qua-lity evaluation. Furthermore, the content of the six components in the Compound Cheqian Tablets was determined by the "double external standards" quantitative analysis of multi-components by single marker(QAMS), and the relative correction factor of isoacteoside was calculated as 1.118 by using acteoside as the control; the relative correction factors of magnoflorine, palmatine, and berberine were calculated as 0.729, 1.065, and 1.126, respectively, by using coptisine as the control, indicating that the established method had excellent stability under different conditions. The results obtained by the "double external standards" QAMS approximated those obtained by the external standard method. This study qualitatively characterized the chemical components in the Compound Cheqian Tablets by applying UPLC-Q-TOF-MS/MS and screened the pharmacodynamic substance basis for the treatment of diabetic nephropathy via network pharmacology, and primary pharmacodynamic substance groups were quantitatively analyzed by the "double external stan-dards" QAMS method, which provided a scientific basis for clarifying the pharmacodynamic substance basis and quality control of Compound Cheqian Tablets.
Humans ; Tandem Mass Spectrometry ; Berberine/pharmacology* ; Chromatography, High Pressure Liquid/methods* ; Network Pharmacology ; Diabetic Nephropathies ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Tablets

The chemical constituents of Chuanzhi Tongluo Capsules were analyzed and identified using ultra-high performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) to clarify the pharmacological substance basis. In addition, network pharmacology was employed to explore the mechanism of Chuanzhi Tongluo Capsules in the treatment of cerebral infarction. Gradient elution was performed using acetonitrile and 1% acetic acid in water as the mobile phase. Mass spectrometry was performed in positive and negative ion modes. Xcalibur 4.2 software was used for compound analysis, including accurate mass-to-charge ratio and MS/MS fragment information, combined with the comparison of reference standards and literature data. A total of 152 compounds were identified, including 32 organic acids, 35 flavonoids and their glycosides, 33 diterpenes, 13 phthalides, 12 triterpenes and triterpene saponins, 23 nitrogen-containing compounds, and 4 other compounds, and their fragmentation patterns were analyzed. SwissTargetPrediction, GeneCards, DAVID, and other databases were used to predict and analyze the core targets and mechanism of Chuanzhi Tongluo Capsules. Protein-protein interaction(PPI) network topology analysis identified 10 core targets, including TNF, VEGFA, EGFR, IL1B, and CTNNB1. KEGG enrichment analysis showed that Chuanzhi Tongluo Capsules mainly exerted their effects through the regulation of lipid and atherosclerosis, glycoproteins in cancer, MicroRNAs in cancer, fluid shear stress, and atherosclerosis-related pathways. Molecular docking was performed between the key constituents and core targets, and the results demonstrated a strong binding affinity between the key constituents of Chuanzhi Tongluo Capsules and the core targets. This study comprehensively elucidated the chemical constituents of Chuanzhi Tongluo Capsules and explored the core targets and mechanism in the treatment of cerebral infarction based on network pharmacology, providing a scientific reference for the study of the pharmacological substance basis and formulation quality standards of Chuanzhi Tongluo Capsules.
Humans ; Tandem Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Molecular Docking Simulation ; Network Pharmacology ; Drugs, Chinese Herbal/pharmacology* ; Capsules ; Atherosclerosis ; Cerebral Infarction ; Neoplasms

This study is based on ultra-high-performance liquid chromatography(UPLC), gas chromatography-mass spectrometry(GC-MS), and network pharmacology methods to analyze and predict potential quality markers(Q-markers) of Artemisiae Argyi Folium. First, UPLC and GC-MS techniques were used to analyze the content of 12 non-volatile components and 8 volatile components in the leaves of 33 Artemisia argyi germplasm resources as candidate Q-markers. Subsequently, network pharmacology was employed to construct a "component-target-pathway-efficacy" network to screen out core Q-markers, and the biological activity of the markers was validated using molecular docking. Finally, cluster analysis and principal component analysis were performed on the content of Q-markers in the 33 A. argyi germplasm resources. The results showed that 18 candidate components, 60 targets, and 185 relationships were identified, which were associated with 72 pathways related to the treatment of 11 diseases and exhibited 5 other effects. Based on the combination of freedom and component specificity, six components, including eupatilin, cineole, β-caryophyllene, dinatin, jaceosidin, and caryophyllene oxide were selected as potential Q-markers for Artemisiae Argyi Folium. According to the content of these six markers, cluster analysis divided the 33 A. argyi germplasm resources into three groups, and principal component analysis identified S14 as having the highest overall quality. This study provides a reference for exploring Q-markers of Artemisiae Argyi Folium, establishing a quality evaluation system, further studying its pharmacological mechanisms, and breeding new varieties.
Molecular Docking Simulation ; Network Pharmacology ; Plant Breeding ; Chromatography, High Pressure Liquid/methods* ; Gas Chromatography-Mass Spectrometry ; Artemisia/chemistry* ; Drugs, Chinese Herbal/chemistry*

Angelicae Sinensis Radix (AS) is reproted to exert anti-depression effect (ADE) and nourishing blood effect (NBE) in a rat model of depression. The correlation between the two therapeutic effects and its underlying mechanisms deserves further study. The current study is designed to explore the underlying mechanisms of correlation between the ADE and NBE of AS based on hepatic metabonomics, network pharmacology and molecular docking. According to metabolomics analysis, 30 metabolites involved in 11 metabolic pathways were identified as the potential metabolites for depression. Furthermore, principal component analysis and correlation analysis showed that glutathione, sphinganine, and ornithine were related to pharmacodynamics indicators including behavioral indicators and hematological indicators, indicating that metabolic pathways such as sphingolipid metabolism were involved in the ADE and NBE of AS. Then, a target-pathway network of depression and blood deficiency syndrome was constructed by network pharmacology analysis, where a total of 107 pathways were collected. Moreover, 37 active components obtained from Ultra Performance Liquid Chromatography-Triple-Time of Flight Mass Spectrometer (UPLC-Triple-TOF/MS) in AS extract that passed the filtering criteria were used for network pharmacology, where 46 targets were associated with the ADE and NBE of AS. Pathway enrichment analysis further indicated the involvement of sphingolipid metabolism in the ADE and NBE of AS. Molecular docking analysis indciated that E-ligustilide in AS extract exhibited strong binding activity with target proteins (PIK3CA and PIK3CD) in sphingolipid metabolism. Further analysis by Western blot verified that AS regulated the expression of PIK3CA and PIK3CD on sphingolipid metabolism. Our results demonstrated that sphingolipid metabolic pathway was the core mechanism of the correlation between the ADE and NBE of AS.
Rats ; Animals ; Rats, Sprague-Dawley ; Molecular Docking Simulation ; Network Pharmacology ; Drugs, Chinese Herbal/chemistry* ; Metabolomics/methods* ; Mass Spectrometry