Salvia miltiorrhiza, Santalum album, and Amomum villosum. It can promote blood circulation and remove blood stasis, and is commonly used in the treatment of gastric and duodenal ulcers, coronary heart disease, angina pectoris, etc. This research is based on network pharmacology and is experimentally verified to explore the potential mechanism of Danshen Decoction in the treatment of ischemic cardiomyopathy (ICM).  Danshen Decoction comprises in vitro hypoxia/reoxygenation model in H9c2 cells was established to verify the predicted active components and therapeutic mechanisms.  The effective components and targets of Danshen Decoction were firstly extracted from Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform, the drug-component-target-disease network was then constructed, the protein-protein interaction (PPI) network was constructed, the Gene Ontology (GO) enrichment analysis was carried out, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was analyzed in order to find the potential active components and therapeutic mechanisms. Finally, the  The results showed that Danshen Decoction has 67 potential active components and 109 therapeutic targets in treating ICM. These targets were rich in a variety of gene functions and different signaling pathways; the main gene targets include TP53, c-Jun, and Akt1. Go enrichment analysis showed that response to drug, membrane raft, and G protein-coupled amine receiver activity rank first in each process, and the main signaling pathways include PI3K-Akt signaling pathway. Through molecular docking and experimental verification of the major active components and core therapeutic targets, the active components of Danshen Decoction demonstrated an ability to reduce the cell damage caused by hypoxia/reoxygenation in H9c2 cells by regulating the core therapeutic target including Akt1, c-Jun, and TP53.  Danshen Decoction has the effect of treating ICM in multiple ways, which is consistent with the results of network pharmacology. This laid a foundation for further study in exploring the active principles and pharmacological mechanism of Danshen Decoction. Ischemic cardiomyopathy (ICM) is diffuse myocardial fibrosis caused by long-term myocardial ischemia. It belongs to the advanced stage of coronary heart disease (CHD) . Its patSalvia miltiorrhiza Bunge, root, and rhizome  (Danshen), Santalum album L., lignum  (Tanxiang) and Amomum villosum Lour, fruit (Amomi Fructus) (Sharen), is commonly used in TCM for the treatment of “different pains in the heart and abdomen” [Shi Fang Ge Kuo.” The ratio of these three herbs is 10 : 1 : 1, each dose of medicine is 44.64 g and decocted with water [qi and relieve pain, and is currently often used in the treatment of gastric and duodenal ulcers, coronary heart disease, angina pectoris, etc. Clinical and basic studies have verified that it can inhibit blood blot, and promote fibrinolysis, anti-thrombosis, and anti-atherosclerosis [Traditional Chinese medicine (TCM) has unique advantages in the treatment of myocardial ischemia. Danshen Decoction, composed of abdomen” . Danshenclerosis , 7, 8. Hclerosis , 9, we sin vitro experiments for further research on the Chinese medicine decoction, and provides guidance for disease treatment. The detailed workflow of this research is shown in Network pharmacology is the use of computers, high-throughput omics data analysis, and other technologies to predict the pharmacological mechanism of Chinese medicine, which provides a new way for the study of action mechanism of Chinese medicine prescriptions, and the steps of our research also refer to the requirements of Network Pharmacology Evaluation Method Guidance –12. ThisSalvia miltiorrhiza, Santalum album, and Amomum villosum, were retrieved through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  [All the effective ingredients of Chinese herbal medicine contained in Danshen Decoction, including dex.php) , 14; anddex.php) .https://www.genecards.org/), OMIM database (https://omim.org/), and DrugBank database (https://www.drugbank.ca/) to collect disease-related genes with PharmGkb database (https://www.pharmgkb.org/), and all collected genes were combined and deduplicated.The keyword “ischemic cardiomyopathy” was entered into Genecards database (https://www.uniprot.org/) was used to search for the human gene name corresponding to the therapeutic targets of the compounds contained in the Danshen Decoction, which were then compared with the protein target gene of ICM screened in the previous step. Then, the common chemical components and common protein target genes of Danshen Decoction-ICM were introduced into Cytoscape 3.8.2 software for visualization. The targets were presented in the form of a network diagram, in which compounds and targets were represented by nodes, and the interaction relationship between nodes were represented by edges to screen core compounds. The STRING data platform (https://string-db.org/) was used to construct the protein-protein interaction (PPI) network of the target protein of Danshen Decoction in the treatment of ICM [The Uniprot database (t of ICM . The obtt of ICM . The TSVt of ICM . After ohttps://www.bioconductor.org/) was used to query the gene IDs of common target genes for drugs-diseases. The R language was used to install the relevant installation package of the Bioconductor platform, P value = 0.05, q value = 0.05 were set for Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively, and the relevant pathway diagrams have been queried [The Bioconductor database ( queried .https://pubchem.ncbi.nlm.nih.gov/), and the ChemOffice software was used to convert it into a 3D structure; protein receptors were searched from PDB database (http://www.rcsb.org/); the Auto Dock Vina docking model was used. The lower the binding energy of the ligand and the receptor, the more stable the binding between the two. The binding energy ≤−5.0 kcal/mol−1 is usually used as the standard to predict the binding between the core active ingredient and the core target [The obtained core gene was used as a protein receptor. The 2D structure of the relevant small molecule ligand was searched in the PubChem database , with a purity of >98%, and the catalog numbers were A0057, A0108, and A0197, respectively. Propranolol hydrochloride was purchased from Shanghai Macklin Biochemical Co., Ltd. , with a purity of >98%, and the catalog number was P854704. TP53, c-Jun, p-c-Jun (Ser73), Akt1, and p-Akt1 (Thr308) antibodies were purchased from Affinity Biosciences Co., Ltd. (USA), the catalog numbers were DF7238, AF6090, AF3095, AF4718, and AF0832, respectively. Lactate dehydrogenase (LDH) and Malondialdehyde (MDA) kits were purchased from Beijing Solarbio Science & Technology Co., Ltd. , and the catalog numbers were BC0685 and CA1410, respectively.Tanshinone IIA, luteolin, and 2) using high-sugar DMEM medium with 100 μg/mL streptomycin, 100 U/mL penicillin, and 10% fatal bovine serum (FBS). The cells were passaged every 2–3 days according to cell status.H9c2 cells were purchased from ATCC  and cultivated under conventional conditions  until the cell confluence reached 80%, and then washed twice with KRB buffer , pre-equilibrate with N2 overnight at 4°C. Then, 100 μL KRB and 0.01% (w/v) bovine serum albumin (BSA) were added to each well and the cells were placed in the hypoxia chamber and replaced the gas with N2 at a flow rate of 30 mL/min for 5 min, then all the connectors of the hypoxia chamber were closed, and the chamber was moved into the 37°C incubator for 2 h. After that, the KRB buffer in the well was replaced with fresh complete medium and incubated in a normal culture environment for 1 h. Dosing intervention should be started 12 h before modeling and continue until the end of modeling.The hypoxia chamber  was used to establish a hypoxia/reoxygenation (H/R) model to create an terature , 21. 1 ×3 cells. After 24 h, the medium was changed and standards were added with concentrations of 1, 10, and 100 μmol/L and incubated for 12 h, repeated 5 times for each group. After incubating for modeling, 20 μL of MTT solution was added and incubated at 37°C for 4 h. The supernatant was aspirated and 150 μL SDS hydrochloric acid was added to dissolve the crystals; microplate reader was used to detect the OD value at a wavelength of 490 nm. Cell proliferation rate = average value of experimental group/average value of control group × 100%. More accurate effective concentration was further explored based on the above experimental results. Optical microscope was used to observe cell morphology observation.The MTT method was used to detect the effects of the selected compounds on the proliferation of H9c2 cells. H9c2 cells in logarithmic growth phase were seeded in 96-well plates at a concentration of 5 × 10H9c2 cells were processed according to the method in the section “Cell models.” They were divided into control group , H/R model group, H/R + compound group (adding different compounds to incubate for 12 h during the establishment of H/R model), and H/R + propranolol group (incubating with positive control drug for 12 h during the establishment of H/R model). The process was repeated three times for each group, the supernatant was collected, and the detection of LDH and MDA were performed in accordance with the kit instructions.Protein extraction was completed on ice after modeling. The concentration of the protein sample was determined by the Bradford method, and the protein loading buffer was added in proportion to the denaturation in a metal bath at 100°C for 10 min. The proteins were separated by SDS-bisacrylamide gel electrophoresis and then transferred onto the PVDF membrane. The TBST solution containing 5% BSA was used for blocking, then TP53, c-Jun, p-c-Jun, Akt1, and p-Akt1 (1 : 1000) antibodies were added and incubated at 4°C overnight. After washing the next day, the secondary antibody (1 : 5000) was added and incubated at room temperature for 2 h. After washing again, the Amersham ImageQuant 800 system  was used to acquire images and analyze the gray values of the bands.The Trizol method was used to extract total RNA from cells. After reverse transcription and amplification, fluorescent quantitative PCR instrument  was used for routine melting curve analysis to determine the Ct value, and GAPDH was used as an internal reference for control. The primers needed for the experiment were synthesized by Shanghai Shenggong Company. The primer sequences used in the present study were listed as follows: Akt1-q-F: CACAGGTCGCTACTATGCCATGAAG, Akt1-q-R: GCAGGACACGGTTCTCAGTAAGC, TP53-q-F: GTACCGTATGAGCCACCTGAG,TP53-q-R: TCCAGCGTGATGATGGTAAG, c-Jun-q-F: GTCCTCCATAAATGCCTGTTCC, c-Jun-q-R: GATGCAACCCACTGACCAGAT, GAPDH-q-F: GGACCTCATGGCCTACATGG, GAPDH-q-R:TAGGGCCT CTC TTGCTCAGT.P < 0.05 was considered as statistically significant.GraphPad Prism  was used to perform statistical analysis on the results. The data were expressed as mean ± standard deviation. One-way ANOVA analysis of variance was used in statistics. The LSD-t test was used for pairwise comparisons between groups, and Salvia miltiorrhiza, Santalum album, and Amomum villosum were 202, 70, and 165, respectively. According to the characteristics of the OB and DL of the compounds, 58, 3, and 10 compounds were screened out; these compounds correspond to 932, 78, and 100 targets, respectively; the full name of targets were converted to Gene Symbol through analysis and conversion in the Uniprot database, the compounds contained in Salvia miltiorrhiza, Santalum album, and Amomum villosum correspond to 824, 89, and 63 genes, respectively. The active compounds of each component herb involved in this study are shown in The compounds contained in There are 2092, 8, 99, and 115 ICM-related gene targets found in the four databases of GeneCards, OMIM, PharmGkb, and DrugBank, respectively, and the targets obtained from the four databases were combined and deduplicated to determine them as targets related to ICM, and a total of 2223 ICM targets were obtained. The genes of these databases were integrated in β-sitosterol involves 24 targets. The top 5 of these compounds were used as candidate compounds to verify their relationship with the targets.The Venny 2.1 online tool was used to find the interconnection of 136 therapeutic targets contained in Danshen Decoction and 2223 ICM-related targets, and a total of 109 overlapping genes were obtained; the Venn diagram is shown in Subsequently, 109 drug-disease overlapping genes were submitted to the STRING database, and 96 PPI network target genes were obtained. The network relationship consisting of 96 nodes and 388 edges is shown in To further clarify the possible effects of the 109 candidate targets, GO enrichment analysis and KEGG biological pathway analysis were carried out on the 109 candidate targets of Danshen Decoction in the treatment of ICM. There are 2005 entries in biological process (BP), 930 entries in cellular component (CC), and 664 entries in molecular function (MF). They rank first in response to drug, membrane raft, and G protein-coupled amine receiver activity. The top 10 entries in each process are shown in KEGG biological pathway analyzed a total of 159 related signal pathways, the top three related signal pathways include PI3K-Akt signaling pathway, human cytomegalovirus infection, and hepatitis B, and the top 30 entries are shown in β-sitosterol. Next, Pymol 2.3.2 software was used for visual analysis of molecular docking. When the three compounds were docked with the corresponding targets, the analysis found that multiple active sites could form hydrogen bonds with different amino acids, and the average affinity of this docking was less than −5.0 kcal/mol−1. However, although tanshinone IIA and β-sitosterol cannot bind to JUN at the hydrogen bonding site, the potential binding of other sites were still shown in the simulated docking. Therefore, they all showed good binding activity to the target, which may be the therapeutic agent for the corresponding target, as shown in The TP53, Akt1, and c-Jun screened out in the PPI network were used as core receptors for molecular docking with corresponding compounds. Among them, TP53 corresponds to tanshinone IIA and luteolin; Akt1 corresponds to luteolin; c-Jun corresponds to tanshinone IIA, luteolin, and β-sitosterol , which reflects the different degrees of protection of these compounds on cells (P < 0.05), and had a significant enhancement effect on the expression of p-Akt1 protein (P < 0.05); however, these compounds did not show a significant effect on TP53 compared with the model group (P > 0.05) (P > 0.05)  . However > 0.05) . In shorNetwork pharmacology is designed to study drug treatments with unclear targets or multiple targets to predict possible therapeutic effects . Danshenβ-sitosterol can also be found in a variety of plants. The current research showed that the content of sitosterol in the circulating blood may have a certain relationship with the cardiovascular disease [β-sitosterol regulates the cell cycle by promoting cellular glutathione production and protects H9c2 cells from apoptosis induced by H/R [Salvia miltiorrhiza. Modern pharmacological studies have shown that tanshinone IIA has anti-inflammatory and antioxidant activities and has a wide range of treatments for cardiovascular diseases [Three effective ingredients were screened out in this study. Luteolin is a widely distributed flavonoid compound that can be found in a variety of plants. A large number of studies have found that luteolin is a potential cardioprotective agent, and it also has many evidences of cardioprotection in epidemiology . β-sitos disease . An in vd by H/R . Tanshindiseases . In thisκB, and Toll-like receptors. Research has shown that the expression and activation of Akt1 are involved in the progression of myocardial fibrosis [κB participates in the response of cells to external stimuli, such as cytokines, radiation, heavy metals, viruses, etc., and plays a key role in the process of cellular inflammation and immune response [β, as a downstream inflammatory factor of inflammation-related signal pathways, has been proved to have extensive regulation on the inflammatory response of blood vessels and myocardium [in vivo. In the human body, ROS acts on lipid to produce peroxidation reaction, the end product of oxidation is MDA, which has cytotoxicity and causes cross-linking polymerization of proteins, nucleic acids, and other life macromolecules. In clinical practice, LDH is often used as an auxiliary diagnostic index of acute myocardial infarction. The determination of three indexes was used to investigate the protective effect of core compounds on the H/R model. The experiment showed that these indexes significantly decreased compared with the model group after using the compounds intervention, indicating that the predicted core compounds to protect heart is likely the active ingredient of Danshen Decoction.GO function analysis showed that the core targets of Danshen Decoction in the treatment of ICM were TP53, Akt1, Jun, etc. TP53 is a human tumor suppressor gene, and it has been confirmed that the expression of this gene is closely related to the suppression of tumor cells . Regulatfibrosis ; Jun canfibrosis , 33. Accfibrosis . NF-κB presponse . IL-1β, ocardium , 36. MAPocardium . VEGFA iocardium . From thocardium –42. ReacThe enrichment of the KEGG pathway of core genes showed that the core targets were involved in multiple pathways related to tumors. Others include PI3K-Akt, fluid shear stress and atherosclerosis, IL-17 signaling pathway, apoptosis, HIF-1, tumor necrosis factor, EGFR, etc. These signaling pathways have been studied in depth in cardiovascular diseases , 41. PI3β-sitosterol in this study, the corresponding therapeutic targets were Akt1 only, and the corresponding targets of tanshinone IIA include TP53 and c-Jun. However, after experimental verification, we found that these compounds have regulatory effects on Akt1, TP53, and c-Jun, which may be bound up with the delay in updating the network pharmacology–related database. Furthermore, the interaction between multiple compounds was not considered in this study, and the most attractive part of traditional Chinese medicine itself is that many compounds play a wide range of regulatory roles, which should be paid attention to in future research. In addition, we did not detect the contents of various predicted compounds in Danshen Decoction, and the data of these substances were obtained from the database. Unfortunately, the proportion of these three compounds verified by our experiments has not been obtained through literature review, which makes it impossible for us to mix these three compounds in proper proportion to observe their improvement on cell damage. Last but not least, verification was carried out at the level of in vitro experiments, and verification in animal models will also be required in future studies with higher levels of evidence. In future research, we will continue to reveal the mechanism of Danshen Decoction in the treatment of related diseases through in vitro and in vivo experiments.Although this study has been partially verified, the following problems still exist due to the limitations of the network pharmacology method: First, the components directly extracted from TCMSP may be inconsistent with the exact compounds absorbed by the patient; second, the compounds included in this study need to meet the OB and DL limits, and there is excessive screening; third, due to the diversity of the corresponding targets of the compound, there may be errors in the enrichment analysis of GO and KEGG; fourth, with the continuous update of the data of each database, the results of future research may be more abundant. However, there must be some errors in the current research. Such problems have been exposed in this study; for example, Our research provides molecular evidence for reference to prove that Danshen Decoction has the effect of treating ischemic cardiomyopathy, and the mechanism of such treatment is the participation of multiple compounds, pathways, and targets. Its core components can regulate cytokine levels and improve hypoxia from multiple aspects, so as to protect the cardiomyocytes of ICM patients. Our network pharmacology research and experimental verification have laid the foundation for further study on the pharmacological mechanism of Danshen Decoction.


Antihistamines have potent efficacy to alleviate COVID-19 (Coronavirus disease 2019) symptoms such as anti-inflammation and as a pain reliever. However, the pharmacological mechanism(s), key target(s), and drug(s) are not documented well against COVID-19. Thus, we investigated to decipher the most significant components and how its research methodology was utilized by network pharmacology. The list of 32 common antihistamines on the market were retrieved via drug browsing databases. The targets associated with the selected antihistamines and the targets that responded to COVID-19 infection were identified by the Similarity Ensemble Approach (SEA), SwissTargetPrediction (STP), and PubChem, respectively. We described bubble charts, the Pathways-Targets-Antihistamines (PTA) network, and the protein–protein interaction (PPI) network on the RPackage via STRING database. Furthermore, we utilized the AutoDock Tools software to perform molecular docking tests (MDT) on the key targets and drugs to evaluate the network pharmacological perspective. The final 15 targets were identified as core targets, indicating that Neuroactive ligand–receptor interaction might be the hub-signaling pathway of antihistamines on COVID-19 via bubble chart. The PTA network was constructed by the RPackage, which identified 7 pathways, 11 targets, and 30 drugs. In addition, GRIN2B, a key target, was identified via topological analysis of the PPI network. Finally, we observed that the GRIN2B-Loratidine complex was the most stable docking score with −7.3 kcal/mol through molecular docking test. Our results showed that Loratadine might exert as an antagonist on GRIN2B via the neuroactive ligand–receptor interaction pathway. To sum up, we elucidated the most potential antihistamine, a key target, and a key pharmacological pathway as alleviating components against COVID-19, supporting scientific evidence for further research.  An initial outburst of inflaming lung was reported firstly at Wuhan in Hubei Province, China, and its severity was alerted to the World Health Organization (WHO) by the Wuhan Municipal Health Commission on 31 December 2019 . Due to https://sea.bkslab.org/) (accessed on 22 December 2021) [http://www.swisstargetprediction.ch/) (accessed on 22 December 2021) [https://pubchem.ncbi.nlm.nih.gov/) (accessed on 22 December 2021). Additionally, the Topological Polar Surface Area (TPSA), extracted by SwissADME (http://www.swissadme.ch/index.php) (accessed on 22 December 2021), is indicated to confirm the antihistamines’ cell membrane permeability, significantly, the threshold of TPSA value is less than 140 Å to adopt as orally administered drugs [Antihistamines prescribed on the market were utilized in this study. The targets of antihistamines were identified via Similarity Ensemble Approach (SEA) (ed drugs . The finhttps://string-db.org/) (accessed on 23 December 2021). There was an observational KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway indicating a key mechanism (lowest rich factor) between antihistamines and COVID-19 related targets.A bubble chart was plotted by the RPackage based on STRING database (https://string-db.org/) (accessed on 26 December 2021). In the PTA network, different colors and shapes (nodes) stand for the signaling pathway (orange square), target (red triangle), and antihistamine (green circle). Gray lines (edges) showed the relationships of PTA. The PTA network was used to construct a size map according to degree of values. In the PTA network, the size of red triangles represents the number of relationships with pathways; the size of green circles stands for the number of relationships with targets. The combined networks were constructed by the RPackage.The Pathways-Targets-Antihistamines (PTA) network was constructed by the RPackage on STRING database (https://string-db.org/) (accessed on 26 December 2021). We performed this to identify crucial targets in the topological analysis from PPI network. Firstly, targets with the greatest Degree of Centrality (DC) values in the upper 30% were sorted for subnetwork assembly using the RPackage. Next, target(s) with the greatest Betweenness Centrality (BC) values in the top 30% from subnetwork were considered as core target(s).The PPI network analysis was performed using the RPackage on STRING database  (accessed on 27 December 2021). The target selected as .pdb format was converted into .pdbqt format via AutoDockTools-1.5.6.A key target of a hub pharmacological pathway, i.e., GRIN 2B (PDB ID: 7EU8), was identified by RCSB PDB (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) (accessed on 27 December 2021). [The ligands were docked with a key target using AutoDock4 by setting up 4 energy ranges and 8 exhaustiveness as default to obtain 10 different poses of ligand molecules . The actr 2021). . We idenA total of 32 antihistamines were selected through drug browsing websites and some literature. The antihistamines’ physicochemical properties and TPSA  are displayed in A total of 794 targets were connected directly with the number of 32 antihistamines, which were identified via two bioinformatics databases (SEA and STP) . A totalp-value < 0.05)  . Based o < 0.05) . STGMAR1, APP, CCR5, and NR1I2 have no association with the pathways against COVID-19. The nodes stood for a total number of pathways, targets, and antihistamines. The edges indicated the relationships of the three elements. Among the 11 targets, NOS3 has the highest degree (4), followed by RPS6KA2 (3), and RPS6KA3 (3). Additionally, among the 30 antihistamines, Mizolastine has the highest degree (16), followed by Bilastine (12), Clemastine (11), Fexofenadine (8), Triprolidine (8), Ebastine (8), and Azelastine (8). The 7 pathways—11 targets—30 antihistamines were constructed with 48 nodes and 125 edges . The conThe antihistamines—COVID-19 target PPI network comprised 13 nodes and 19 edges A Table , and theA total of eight antihistamines  and a hub target (GRIN2B) were selected via network pharmacology, with considerable affinity. A molecular docking test (MDT) was performed to verify the binding stability between eight antihistamines and GRIN 2B at the molecular degree. The AutoDockTools-1.5.6 software was utilized for MDT, the docking score is shown in Additionally, Loratadine, the most significant antihistamine, was the key antihistamine as a COVID-19 alleviator. According to MDT, Loratadine exhibited the best binding scores  to the GRIN2B target, which is displayed in Antihistamines are important agents to inhibit inflammatory responses: allergic rhinitis , asthma In this study, we investigated the pathways-targets-antihistamines (PTA) network and observed that Loratadine, Fexofenadine, Triprolidine, Ebastine, Dimetindene, and Terfenadine had a valid correlation on GRIN2B target on the neuroactive ligand–receptor interaction (KEGG ID: hsa04080), representing significant alleviative effects for COVID-19. A study demonstrated that the induction of GRIN2B is subsequent to neurotoxicity in the brain, due to the neuro-inflammation pathway . It can From our network pharmacology analysis based on rich factor in bubble chart, neuroactive ligand–receptor interaction was the lowest rich factor in seven pharmacological pathways, suggesting that the pathway might be a key mechanism against COVID-19. Furthermore, GRIN2B with the highest degree of value in PPI network, with the highest Betweenness Centrality (BC) in topological analysis, and correlated with neuroactive ligand–receptor interaction was considered as a core target of antihistamines in COVID-19. Then, the best antihistamine on GRIN2B was Loratadine, which can conform stably with valid affinity . Therefore, the hub mechanism of antihistamines against COVID-19 might be to inhibit GRIN2B on the neuroactive ligand–receptor interaction by Loratadine as an antagonist. To sum up, this study demonstrated that the most potential antihistamine mechanism in alleviating COVID-19 is based on network pharmacology. We suggested that the six antihistamines  played a critical role in COVID-19 by affecting the GRIN2B target as well as inhibiting neuroactive ligand–receptor interaction (KEGG ID: hsa04080). Besides, MDT also showed that Loratadine was the best affinity on the GRIN2B target on neuroactive ligand–receptor interaction (KEGG ID: hsa04080), providing an important basis for further clinical trials.


Cerebral atherosclerosis (CA) is a chronic disease caused by multiple infarcts and atrophy causing nerve degenerative syndrome. Ginkgo Folium (GF) and Forsythiae Fructus (FF) have shown positive effects on vascular protection, but their relationship with CA is unclear. This study aimed to identify the potential CA targets and mechanisms of action of GF-FF, using network pharmacology.This study used network pharmacology and molecular docking to examine the potential targets and pharmacological mechanism of GF-FF on CA.Using the traditional Chinese medicine systems pharmacology database and analysis platform, components were screened and corresponding targets were predicted using boundary values and Swiss Target Prediction. Using Cytoscape 3.8.0, a network was established between GF-FF components and CA targets. We extracted disease genes and constructed a network of targets based on the protein-protein interaction networks functional enrichment analysis database. Using Metascape, the Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes of the enriched targets were determined. AutoDock Vina was used to perform molecular docking.Twenty-three active ingredients of GF-FF were confirmed to treat CA, covering 109 targets, of which 48 were CA-related. Luteolin, bicuculline, sesamin, kaempferol, quercetin, and ginkgolide B were the vital active compounds, and EGFR, CYP2E1, CREB1, CYP19A1, PTGS2, PPARG, PPARA, ESR1, MMP9, MAPK14, MAPK8, and PLG were the major targets. The molecular docking showed that these compounds and targets exhibited good intercalation. These 48 protein targets produced effects on CA by modulating pathways such as “apoptosis–multiple species,” “IL-17 signaling pathway,” and “relaxin signaling pathway.”As predicted by network pharmacology, GF-FF exerts anti-tumor effects through multiple components and targets for treatment of CA, providing new clinical ideas for CA treatment. Numerous experiments have confirmed racial differences in CA, and CA is particularly prevalent in Asian, Black, Hispanic, and Indian populations. In the Chinese population, the incidence of CA is up to 46.6% in stroke patients.–4Cerebral atherosclerosis (CA) is a highly prevalent cerebrovascular disease with frequent subclinical and clinical ischemia.,6 Current treatment for CA includes anti-platelet drugs , lipid-regulating drugs ,,9 and surgical treatment .,10 These treatments can treat the symptoms of CA, but accordingly lead to many clinical adverse events.,11 Therefore, traditional Chinese medicine (TCM) is a complementary or alternative approach in the treatment of CA.The development of CA may be associated with a range of risk factors, such as age, hypertension, diabetes, hyperlipidemia, metabolic syndrome, obesity, and smoking. Ginkgo Folium (GF) is sweet, bitter, astringent, and flat, and according to the Chinese Medicine Journal, it can be “astringent to lung qi, stop turbidity, and calm cough and asthma.” Forsythiae Fructus (FF) is bitter and slightly cold and was first described in detail in the classic of Materia medica. GF-FF has a wide range of effects, including anti-inflammatory, antioxidant, hypolipidemic, anti-platelet aggregation, and vascular protection effects. Several studies have shown that sesamin and ginkgolide B, as components of the woody component and terpenoids of GF, respectively, have significant effects on platelet antagonism and vascular protection,,18 their main constituents are listed in Table ,20 and inhibit cancer cell production and apoptosis. However, the mechanism of therapeutic action of GF-FF in the treatment of CA is unclear, and the potential anti-CA mechanism of GF-FF must be elucidated.Chinese medicine has been widely used in the clinical treatment of CA.As part of systems biology, network pharmacology combines pharmacology, bioinformatics, and molecular biology, and has been used to illustrate the multi-targeted effects of herbal medicine in a variety of diseases. The aim of this study was to investigate the mechanism of action of GF-FF for the treatment of CA and to predict the target proteins for drug therapy through network pharmacology and molecular docking. The GF-FF treatment CA process is shown in Figure https://www.cnki.net/), Pubmed (https://pubmed.ncbi.nlm.nih.gov/), Pubchem(https://pubchem.ncbi.nlm.nih.gov/),Medline(https://www.bionity.com/),Wanfang(https://www.wanfangdata.com.cn/), and Web of Science (https://www.webofscience.com/) were searched to identify compounds with a potential effect on CA. The constituents of GF-FF were determined from the (https://old.tcmsp--e.com/tcmsp.php) traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). The oral bioavailability (OB) and drug-likeness (DL) indices recommended by TCMSP were used to verify the drug-forming properties of each compound. In the TCMSP system, OB ≥ 30% and DL ≥ 0.18 were set as the criteria for screening candidate compounds from GF-FF. High OB and DL may result in better absorption rates in clinical settings.Databases such as CNKI (http://www.swissadme.ch/) is a website that provides molecular pharmacokinetic characterization. It can predict the clinical parameters, pharmacokinetic properties, drug-like properties, and medicinal chemistry friendliness of 1 or more small molecules.,24 In this investigation, SwissADME was used to further screen the compound molecules that passed the TCMSP primary screening, with the main screening criteria being that the gastrointestinal absorption was “high.” As a result, a total of 23 GF-FF compounds were identified, in combination with the literature review.Swiss absorption, distribution, metabolism, and excretion (ADME) (http://www.swisstargetprediction.ch/) is a web tool that aims to predict the most probable protein targets of small molecules.,26 We import the compounds that meet the criteria after screening into SwissTargetPrediction system for protein target prediction.SwissTargetPrediction  with the search term “cerebral atherosclerosis.” Subsequently, the protein data was entered into (https://www.uniprot.org/) (Uniprot) to search to the name of the regulated gene. All target searches were based on “Homo sapiens.”Protein targets related to cerebral atherosclerosis were provided by (https://cn.string-db.org/) to analyze the protein-protein interactions. We entered the target genes from the intersection of herbal compounds and diseases into the database, selected the “Homo sapiens” biological series, selected “evidence” in the meaning of network edges column, set the confidence to > 0.4, removed the free proteins, obtained the correlation data between the targets, and imported the acquired data into Cytoscape 3.8.0. By combining all the drug and disease target information, the PPI network was constructed, and 12 core protein targets were selected for prediction through network topology analysis.To further investigate the role of the related targets between GF-FF and CA, we input the related gene information into protein-protein interaction networks functional enrichment analysis (STRING) (https://metascape.org/) database was used to collect information on GO and KEGG analysis data. The GO enrichment analysis was used to screen molecular functions, biological processes, and cellular components. KEGG analysis was used to describe the regulatory pathway corresponding to the expressed gene. Subsequently, GO and KEGG data were uploaded to the Bioinformatics (http://www.bioinformatics.com.cn/) and imageGP (http://www.ehbio.com/ImageGP/) platforms for visualization and analysis. Based on the enrichment values we imported the top 20 KEGG pathways and core targets into Cytoscape 3.8.0 and used its plug-in ClueGo 2.5.8 to visualize the protein-target-gene pathway network.To study the biological functions of potential targets in CA, the Metascape  and 3D structures were downloaded based on the results of the network pharmacological topology analysis. Protein molecules were imported into AutoDock Tools (version 1.5.6 https://autodock.scripps.edu/) software to remove water molecules, hydrogen atoms were added and protein types were set, and finally, the protein data bank, partial charge and atom type files were saved.Core proteins were imported into the protein data bank (https://pubchem.ncbi.nlm.nih.gov/) databases. Energy minimization of compounds was done using Chem3D, saving as mol2 format. Similarly, water molecules were removed, hydrogen atoms were added, drugs were set as ligands in AutoDock Tools, all flexible bonds were made rotatable by default, and finally, the protein data bank, partial charge and atom type file was saved.Drug molecular structures were obtained from TCMSP and PubChem (https://pymol.org/2/).All the treated compounds were used as small molecule ligands and 12 important protein targets were used as receptors. The center position of the grid box was determined according to the interaction between small molecules and targets, and the length, width, and height were set to 50 × 50 × 50. The files were subsequently imported into AutoDock Vina to perform molecular docking, and the results of GF-FF compounds with core protein targets were visualized using python molecule 2.1  and 369 edges, as shown in Figure The relationship between CA and GF-FF was expressed according to the scores of target genes in the above target network construction mapped to the target genes of CA, respectively. In total, 48 target genes were identified to regulate the extent of CA and were regulated by GF-FF drug components, as shown in Table To visualize the gene target regulation network, we performed PPI network construction for composite targets–CA disease targets–other human protein targets. We combined the Genecards database to identify the disease targets regulated by cerebral atherosclerosis, and imported into Cytoscape 3.8.0 to intersect with the traditional chinese medicine targets, using 2 screening processes: the first with the criterion “> or equal to 2 times the degree value” and the second with the criterion “> or equal to degree, betweenness, closeness, LAC, neighborhood connectivity median value.” The final PPI network was constructed with 109 nodes and 1820 edges as shown in Figure We imported the intersected targets into STRING, removed the free protein targets, and obtained 12 core protein targets by topological analysis with the indexes “degree,” “betweenness centrality,” and “closeness centrality” as shown in Table Homo sapiens.” We extracted 10 items in each functional region based on the enrichment values and found that they were related to “monoterpenoid metabolic process,” “regulation of cholesterol storage,” “negative regulation of macrophage derived foam cell differentiation,” “arachidonic acid epoxygenase activity,” “oxidoreductase activity,” and other biological processes. We finally imported the data into the Bioinformatics system for visual analysis as shown in Figure The 12 core protein targets of GF-FF were introduced into the Metascape system for GO and KEGG enrichment analysis, and the species was set to “We performed KEGG enrichment analysis showing the top 20 pathways, including “arachidonic acid metabolism,” “apoptosis–multiple species,” “endocrine resistance,” “insulin resistance,” “regulation of lipolysis in adipocytes,” “IL-17 signaling pathway,” and “relaxin signaling pathway.” The imageGP platform was used for visualization and analysis, as shown in Figure We imported proteins from the STRING database after removing free targets into Cytoscape 3.8.0 software together with the pathways and used its plug-in ClueGo 2.5.8 to visualize the protein-target-gene pathway network. The results showed that “IL-17 signaling pathway,” “relaxin signaling pathway,” “TNF signaling pathway,” “arachidonic acid metabolism,” “type II diabetes mellitus,” “regulation of lipolysis in adipocytes,” “AGE-RAGE signaling pathway in diabetic complications,” “linoleic acid metabolism,” and other pathways played an important mechanism in regulating apoptosis and anti-inflammatory and anti-lipid effects, and providing vascular protection, as shown in Figure  The results showed that the active compounds had good binding and reliable interactions with the core protein targets, as shown in Table Based on the network pharmacology results, we screened 6 important compounds – kaempferol, luteolin, quercetin, sesamin, ginkgolide B, and bicuculline – for molecular docking with the core protein targets to further investigate the interactions. Binding affinities below -5 kcal/mol indicated good interactions.The results showed that sesamin could bind to EGFR, PTGS2, MAPK14, MAPK8, and PLG  is chemotactic to monocytes, upregulating them to produce monocyte colony-stimulating factor and monocyte chemoattractant protein. Activation of monocyte components produces inflammatory factors that induce oxidation of LDL, and oxidative LDL leads to CA formation and exacerbation.,44CA is a chronic cerebrovascular disease characterized by a metabolic syndrome of lipid accumulation, elevated cholesterol, and insulin resistance,A total of 23 active compounds were obtained through TCMSP and SwissADME clinical pharmacological screening for 158 compound targets, by predicting 6 components – sesamin, quercetin, bicuculline, kaempferol, luteolin, and ginkgolide B – as core compounds, among which quercetin, kaempferol, and luteolin are known compounds of GF-FF.,45 Quercetin inhibits cellular oxidation and disrupts inflammatory transmission by stimulating ERK1/2 and P38MAPK factors to improve β-cell function and thus modulate signaling pathways such as the AGE-RAGE/endocrine resistance pathway. GABA(A)R receptors in bicuculline alleviate β-cell dysfunction by promoting the phosphoinositide 3-kinase (PI3K)/Akt signaling pathway downstream to exert insulin resistance and hypotensive and antilipidemic effects. Kaempferol can have a protective effect on brain neurology by reducing PTGS2 and IL-6 levels to inhibit neuroinflammation, reactive oxygen species, and neuronal apoptosis.It has been shown that sesamin alters cellular metabolism and mitigates local oxidative and inflammatory changes through receptor catalysis of PPARα/γ, with down-regulation of the SREBP-1/arachidonic acid metabolism signaling pathway and MAPK/NF-κB/TNFα/ extracellular signal-regulated kinase (ERK) 1/2 signaling pathway.,49 and also alters lipid metabolism and thus protects neurological function by inhibiting IL-1α/β expression. Taken together, it is speculated that the use of GF-FF for the treatment of cerebral atherosclerosis should be a multi-component, multi-target interaction, and the mechanisms of action should be studied more in depth.Luteolin acts on the cyclic adenosine monophosphate signaling pathway by inhibiting CREB1 expression and downregulates mRNA expression to affect insulin expression and inhibit apoptotic effects, Ginkgolide B enhances the expression of ERK1/2/p-EGFR signaling pathway by activating EGFR/Akt trans receptors, Activation of EGFR protein reverse transcription can stimulate the PI3K pathway and ERK regulatory enzyme production, protecting cells from apoptosis. CREB1 protein activation turns on the transcription of downstream genes as a pathway of cellular metabolic activity, accelerating glucose uptake and maintaining the stability of the body’s internal environment.In this study, EGFR, CREB1, PTGS2, PPARG, PPARA, MMP9, MAPK8, and MAPK14 were identified as core protein targets for the treatment of CA. Reducing the expression of PPARα, MMP9, and IL-1β proteins, elevate MAPK levels, inhibit reactive oxygen specie production, and enhance nitric oxide bioactivity in blood vessels, thereby ameliorate endothelial disorders and hypertension, reducing the inflammatory response, and altering the progression of cerebral atherosclerosis and thrombosis. Regarding the process of arachidonic acid and peroxidase regulation, the ∆ 5 desaturated acid output profile affects n-6 unsaturated fatty acids, which in turn regulates arachidonic acid production; sesamin increases peroxidase mRNA levels, thereby activating PPARα and PTGS2 pathway regulation for the treatment of hypertension and lipid oxidation.,53 Therefore, we speculate that GF-FF regulation of CA may be related to the above biological processes.GO analysis showed that GF-FF is involved in biological processes such as cholesterol storage regulation, negative regulation of macrophage-derived foam cell differentiation, arachidonic acid cyclooxygenase activity, and oxidase activity. Macrophage differentiation into lipid-rich foam cells, a hallmark of the early stages of cerebral atherosclerosis, inhibits oxLDL-mediated phagocytic foam cell formation and reduces the size of atherosclerotic lesions by downregulating LOX-1 and mRNA protein expression through the AMPK and TNF-α regulatory pathways.,55 The IL-17 signaling pathway plays a major role in cellular metabolism and anti-inflammation in the body, and has a significant role in fighting inflammation by inducing the expression of cytokines and chemokines targeting NF-κB and MAPK through the IL receptor family. In summary, GF-FF herbal compounds modulate numerous pathways such as AGE-RAGE, TNF, and IL-17, and therefore are considered to treat CA. We speculate that GF-FF could play an important role in the treatment of CA.KEGG pathway analysis showed that GF-FF treats CA pathways, including “arachidonic acid metabolism,” “apoptosis–multiple species,” “endocrine resistance,” “insulin resistance,” “regulation of lipolysis in adipocytes,” “IL-17 signaling pathway,” “AGE-RAGE signaling pathway in diabetic complications,” “TNF signaling pathway,” and “relaxin signaling pathway.” The TNF signaling pathway has anti-inflammatory effects and regulates apoptosis and other protective neurological functions by activating the TNF-RI protein pathway through TNF-α, leading to the expression of the transcription factor NF-κB, producing the cytokines IL-1α/β and resisting cellular inflammation and apoptotic processes.The present study still has some limitations, we used only modern bioinformatics methods to explore the effects of GF-FF in the treatment of CA by using network pharmacology and molecular docking. Firstly, data from online databases are based on reviewed and predicted data, and unconfirmed and unrecorded data were not included in our study. Secondly, for the 23 compounds as well as the 6 core compounds identified here, quantitative studies are incomplete, and future studies should be done on the content of the components. Thirdly, although sesamin, quercetin, bicuculline, kaempferol, luteolin, and ginkgolide B were identified as important compounds, this does not fully represent GF-FF, and therefore pharmacodynamic and molecular biology experiments need to be considered to further investigate our results. Therefore, the potential mechanism of action of GF-FF for the treatment of CA has not yet been explained and confirmed, and we believe that this topic has great research potential and application value.In this study, sesamin, quercetin, bicuculline, kaempferol, luteolin, and ginkgolide B were identified as important herbal compounds, and EGFR, CREB1, PTGS2, PPARG, PPARA, MMP9, MAPK8, and MAPK14 were identified as major protein targets for the treatment of CA. Molecular docking showed that these compounds and their targets have good binding effects. GF-FF may exert therapeutic effects through the AGE-RAGE, TNF, IL-17, and MAPK signaling pathways. In addition, our study approach may further elucidate the mechanism of GF-FF for the treatment of CA.Data curation: Jinfei Zhang.Formal analysis: Jinfei Zhang, Jiqin Tang.Methodology: Jinfei Zhang, Jiqin Tang, Chuntao Yang, Guoxiu Zu.Investigation: Chuntao Yang, Guoxiu Zu.Funding acquisition: Jiqin Tang.Writing – original draft: Jinfei Zhang, Jiqin Tang.Writing – review & editing: Jinfei Zhang, Jialin Gai, Hengqin Ma.


Background: Knee osteoarthritis (KOA) is a degenerative disease that develops over time. Icariin (ICA) has a positive effect on KOA, although the mechanism is unknown. To investigate drug-disease connections and processes, network pharmacology is commonly used. The molecular mechanisms of ICA for the treatment of KOA were investigated using network pharmacology, molecular docking and literature research approaches in this study.Methods: We gathered KOA-related genes using the DisGeNET database, the OMIM database, and GEO microarray data. TCMSP database, Pubchem database, TTD database, SwissTargetPrediction database, and Pharmmapper database were used to gather ICA-related data. Following that, a protein-protein interaction (PPI) network was created. Using the Metascape database, we performed GO and KEGG enrichment analyses. After that, we built a targets-pathways network. Furthermore, molecular docking confirms the prediction. Finally, we looked back over the last 5 years of literature on icariin for knee osteoarthritis to see if the findings of this study were accurate.Results: core targets relevant to KOA treatment include TNF, IGF1, MMP9, PTGS2, ESR1, MMP2 and so on. The main biological process involved regulation of inflammatory response, collagen catabolic process, extracellular matrix disassembly and so on. The most likely pathways involved were the IL-17 signaling pathway, TNF signaling pathway, Estrogen signaling pathway.Conclusion: ICA may alleviate KOA by inhibiting inflammation, cartilage breakdown and extracellular matrix degradation. Our study reveals the molecular mechanism of ICA for the treatment of KOA, demonstrating its potential value for further research and as a new drug. Knee osteoarthritis (KOA) is a chronic progressive disease with pain, swelling, and even deformity of the knee joint as the main clinical manifestation . OA is tin vivo and in vitro experiments (Icariin (ICA) is one of the main components of the Chinese medicine Epimedium. Epimedium was first recorded in the Yellow Emperor’s Inner Canon in 100 B.C. Traditional Chinese medicine believed that Epimedium could strengthen muscles and bones, and it was widely used in clinical practice. Due to the development of modern medicine, its active ingredients have been further studied. Modern research has discovered that ICA, one of its active ingredients, has strong biological activity on various systems, such as the nervous system , reprodueriments . The moseriments . Howevereriments . Since tNetwork pharmacology analysis is one of the most commonly used methods to study the pharmacology of traditional Chinese medicine (TCM). By searching the databases, constructing the networks, enrichment analysis and other steps, we can get the components, potential targets, correlation with diseases and other information of TCM . This lehttps://www.ncbi.nlm.nih.gov/geo/) (2 (fold change) > 1 or log2 (fold change) < -1 as the standard, which was considered to have significant differences in the expression. The KOA related genes were collected by integrating databases through searches with the key words “knee osteoarthritis” and “Homo sapiens,” followed by taking the intersection. Two databases include: DisGeNET database (https://www.disgenet.org/web/DisGeNET/menu/home) (https://omim.org/) (www.graphpad.com) and TBtools (https://github.com/CJ-Chen/TBtools)  , Series nu/home)  and OMIMim.org/) . Next, aTBtools) .https://tcmspw.com/tcmsp.php) (https://pubchem.ncbi.nlm.nih.gov/)  and drug likeness (DL) were obtained from the TCMSP database (msp.php) . Studiesmsp.php) . Then thih.gov/) .http://swisstargetprediction.ch/) (http://db.idrblab.net/ttd/) (http://www.lilab-ecust.cn/pharmmapper/) (https://www.uniprot.org/)  , Therapeet/ttd/)  and Pharmapper/) , followeot.org/) .https://bioinfogp.cnb.csic.es/tools/venny/) (https://string-db.org/) (http://www.cytoscape.org) and the degree and edge betweenness of the nodes were calculated by the NetworkAnalyzer tool.The potential targets of ICA were matched with KOA-related genes to obtain the core targets. The venn diagram was drawn through venny 2.1.0 (/venny/) . Next, tdb.org/) . After thttp://metascape.org/gp/index.html) (http://bioinformatics.com.cn/).Based on the above data, GO and KEGG enrichment analysis was performed in the Metascape database (ex.html) . The GenTo investigate the interactions between key genes and pathways of ICA and KOA, the ICA-KOA-targets-pathways network was constructed using the results of metascape database analysis. The degree and edge betweenness were analyzed by topological analysis tools in the cytoscape 3.7.2 software.https://www.rcsb.org/)  (http://autodock.scripps.edu/) (http://vina.scripps.edu/)   with themol.org)  for modips.edu/)  to add cps.edu/) . Moleculps.edu/) . We chosin vivo studies, in vitro studies and reviews. The search terms we used were “icariin”, “osteoarthritis”, “knee osteoarthritis”, “cartilage”, “chondrocytes”, and “extracellular matrix”. To ensure the relevance of the literature to this study, the results were collated by two independent researchers.To validate the predictions, we also searched for relevant studies over the last 5 years, including A total of 1105 differentially expressed genes were identified by analysis of gene microarray Series GSE169077, which was used to draw a volcano may and heat map . The resMolecular formula, molecular weight, 2D structure, 3D structures, and ADME information of ICA were collected from the TCMSP database and the Pubchem database . The OB The obtained 21 common genes were uploaded to the String database to produce the PPI network. After that, the results were imported into cytoscape 3.7.2 software and the topological parameters of the network was analyzed. The result showed that the network had a total of 18 nodes and 78 edges . Accordip-value order and displayed in a visual bubble diagram, and all results were shown if less than 15 , while the KEGG entries were 112 . The top 15 most significant entries were filtered in ascending  than 15 . The res than 15 ; Table 3 than 15 .Based on the data above, the 21 core target genes were individually imported into the Autodock Vina 1.1.2 software to dock with the ICA. Affinity was the score used by the software to calculate the binding ability. It is generally believed that affinity < −7 kcal/mol indicated a stronger binding activity . After cThrough our study of the literature, we screened twenty-eight articles from the last 5 years relevant to this study. Of these, 8 papers mentioned that icariin inhibited inflammation, 6 papers indicated that icariin reduced chondrocyte apoptosis and 4 papers indicated that icariin inhibited extracellular matrix degradation. In addition, 9 papers mentioned that icariin promotes chondrocyte proliferation, differentiation, cellular autophagy or cartilage repair . In the Despite the significant developments in the diagnosis and treatment of KOA, single drug development for a single target is still slow . This is2) and so on  and matrix degrading enzymes . In addition, it can inhibit the synthesis of type II collagen . TNF is a powerful pro-inflammatory factor that binds to two types of receptors, including TNFR1 and TNFR2, which are expressed on the surface of both chondrocytes and synovial cells  Figure . TNF bincollagen . A studycollagen . In addiIcariin may contribute to the treatment of KOA by promoting cartilage repair and attenuating cartilage extracellular matrix degradation.In KOA, chondrocyte apoptosis and extracellular matrix degradation are severe pathological changes . The extOur research has found substantial evidence that icariin can promote cartilage repair in KOA over the last 5 years . Other sIn addition to the above findings, we found that insulin-like growth factor 1 (IGF-1) is also one of the key targets of icariin. It is closely related to the proliferation, differentiation and maintenance of the phenotype of cartilage . One stuWe also found that icariin also binds to the estrogen receptors and activates estrogen signaling pathway . The estNetwork pharmacology as a new technology has a wide range of applications in exploring the molecular mechanisms of Chinese medicine. In this study, we predicted the molecular mechanism of ICA for the treatment of KOA and obtained 21 relevant targets. Further analysis obtained the core targets as TNF, MMP9, IGF1, PTGS2, ESR1 and so on. After GO and KEGG analysis, we found that the key pathway for ICA treatment of KOA is the IL-17 signaling pathway, TNF signaling pathway and Estrogen signaling pathway with a regulatory effect on the extracellular matrix degradation and inflammation. Molecular docking verified the affinity of key targets. Finally, we reviewed the literature related to icariin for treatment of KOA in the last 5 years, and the results are generally consistent with the prediction.Taken together, ICA is able to inhibit KOA inflammation, chondrocyte apoptosis, matrix degradation, and promote cartilage repair through multiple pathways at multiple targets. Through network pharmacology and molecular docking technology, we obtained the interaction relationship between genes, protein molecules and revealed the molecular mechanism of ICA for the treatment of KOA, making it possible as a new drug.


Cough variant asthma (CVA) has no definitive diagnosis or pathogenic causes, and there is currently no effective and safe treatment.  The network pharmacology was employed to investigate possible targets of Zhisou San (ZSS) in CVA treatment. The main chemical constituents of seven herbs in ZSS were collected based on the TCMSP. To explain the main mechanism, we sequentially screened the targets of each active ingredient and constructed the network of “herb-ingredient-target-disease.” The core targets of ZSS were further confirmed by the molecular docking analysis. Furthermore, pulmonary function, histopathology, and biochemical assays in mice were used to investigate the effect of ZSS on the treatment of CVA. β-sitosterol predicted by molecular docking. Furthermore, experiments in vivo were conducted for further validation that ZSS had essential effects on lung function and histopathology as well as the inflammatory state in CVA mice, which was significantly related to regulating the Th17/Treg immune balance to reduce inflammation as the important pharmacological mechanism.  A total of 137 active ingredients and 86 potential targets for the ZSS in the treatment of CVA were screened, which were connected with the regulation of inflammatory response and immune balance, such as IL-17 signaling pathway, Th17 cell differentiation, TNF signaling pathway, Toll-like receptor signaling pathway, MAPK signaling pathway, T-cell receptor signaling pathway, Th1 and Th2 cell differentiation, and other signaling pathways closely related to the pathogenesis of CVA. Thereinto, 29 core targets contained 8 of the highest scores and could evidently bind to components such as stigmasterol, quercetin, stemoninine B, luteolin, and  This study revealed that ZSS has multicomponent and multipathway characteristics of ZSS in the treatment of CVA, which was primarily associated with inflammation and Th17/Treg immune balance. This study provides a scientific foundation for systematically elaborating the pharmacological activities and mechanism of ZSS, as well as explaining the reliability of the TCM compatibility theory. Cough variant asthma (CVA) is a type of asthma in which the chronic and recurrent cough is the main or sole clinical manifestation . It is pPlatycodon grandiflorum (Jacq.) A. DC. (JG), Nepeta cataria L. (JJ), Aster tataricusL.f. (ZW), Stemonasessilifolia (Miq.) Miq. (BB), Cynanchumstauntonii (Decne.) Schltr. ex H.Lév. (BQ), Glycyrrhiza uralensis Fisch. (GC), and Citrus reticulata Blanco (CP) has been used in relieving cough, eliminating phlegm, and diffusing the lung for hundreds of years [TCM formula mainly refers to two or more herbs used in combination, as the primary form of clinical application in TCM, which meets the demand for treatment of multifactor diseases requiring interactions with multitarget in modern medicine. Zhisou San (ZSS), a famous prescription of Dr. Cheng Guopeng in the Qing dynasty of China, composed of seven herbs including of years . This foof years , 12. NevRecently, network pharmacology, whose theme is “network target,” has provided efficient means and new ideas for the systematic study of the pharmacodynamics mechanism of TCM with special features such as multitarget and multichannel , 14. NetIn the present study, a network pharmacology approach, which included target prediction, network analysis, and pathway enrichment, was employed to predict bioactive compounds and therapeutic targets of ZSS on CVA. Then, molecular docking was performed to validate major targets by computer modeling technology for component-target binding affinities. Finally, using the CVA mouse model, we conducted in vivo validation by pulmonary functional, histopathology, and biochemical assays. This study presented a systematical approach for the exploration of potential molecular mechanisms in the CVA treatment by ZSS, which was also expected to guide the experimental study and the clinical application of ZSS, as well as to provide a reference for the network pharmacology research of other TCM prescription.https://old.tcmsp-e.com/tcmsp.php), and they were screened based on two key parameters of absorption, distribution, metabolism, and excretion (ADME) such as oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 for preliminary screening to obtain the active compounds and their targets. In addition, targets of several unpredicted active compounds were added based on available literature. After the screening, compound-related targets were verified using the Uniprot Protein Database (https://www.uniprot.org) and converted to the corresponding gene name in order to standardize the protein target information.All chemical compounds of 7 herbs from Zhisou San (ZSS) were searched through the Traditional Chinese Medicine Systems Pharmacology database and analysis platform , OMIM (http://www.omim.org), DRUGBANK (https://www. drugbank.ca), and DisGeNET (https://www.disgenet.org). In the GeneCards database, a higher score indicates that the gene is closely associated with developmental disorders. If there were too many targets associated with disease, targets with scores greater than the median would be set as potential targets that were then merged and removed duplicates to construct the CVA-related targets set.Taking “cough variant asthma” as a keyword, the potential targets of CVA were explored in the databases including GeneCards (https://string-db.org) to construct a (protein-protein interaction) PPI network, in which the species were set as “Homo sapiens,” minimum interaction threshold was set as “highest confidence” (>0.9), and the rest of the parameters were on their default settings. The PPI network is classed as an undirected graph since the interactions between proteins are reciprocal. Some modules with high density in the PPI complex network are considered the biologically significant collections, which can be obtained through further analysis of the PPI network by the MCOD plug-in of Cytoscape 3.7.2. Core targets were selected from the module with the highest score to establish the relationship network of ZSS in the treatment of CVA.In order to clarify the interaction of relevant targets of ZSS in the treatment of CVA, the intersection between compound-related targets of ZSS and CVA-related targets was firstly taken by R language to draw the Venn diagram. Then, intersection targets were imported into the STRING 11.0 database (https://metascape.org/gp/index.html) has comprehensive annotation capabilities and monthly updates the data of gene annotation. Targets of ZSS in the CVA treatment were input into the Metascape platform under the restricted species as humans to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. GO enrichment was mainly reflected in three aspects: molecular function (MF), biological process (BP), and cellular components (CC). The result set was sorted by the value of -log(P), and the top biological processes or pathways were screened out for further analysis.Metascape platform (https://www1.rcsb.org/). Next, these crystal structures were modified with the software of PyMol to remove the original ligand and water molecules and saved in the PDBQT format. TCMSP database (https://old.tcmsp-e.com/tcmsp.php) was used to collect and download the two-dimensional structures of related compounds as small-molecule ligands and save them in the MOL2 format. Finally, 3D crystal structures of protein targets and the corresponding compounds ligand were imported into AutoDock 4.0 software for molecular docking.Molecular docking is a simulation method that studies the interactions between molecules and predicts binding mode and affinity, which has become an important skill in the field of computer-assisted drug research. AutoDock 4.0 software was used for molecular docking. The top core targets of ZSS in the treatment of CVA were selected according to the value of the MCOLD score, and their crystal structures were downloaded from the protein database (Platycodon grandiflorum (Jacq.) A. DC.) (JG), 10 g Schizonepetae Herba (Nepeta cataria L.) (JJ), 15 g Asteris Radix et Rhizoma (Aster tataricusL.f.) (ZW), 10 g Stemonae Radix (Stemona sessilifolia (Miq.) Miq.) (BB), 15 g Cynanchi Stauntonii Rhizoma et Radix (Cynanchumstauntonii (Decne.) Schltr. ex H.Lév.) (BQ), 10 g Glycyrrhizae Radix et Rhizoma  (GC), and 15 g Citri Reticulatae Pericarpium (Citrus reticulata Blanco) (CP), which were added to boiling water about 10 times the volume of herbs, extracted twice and 30 mins each time. Then, the extract was filtrated and evaporated by a water bath and lyophilized for containing 6.25 g of raw herbs per gram, kept in 4°C cold storage. The dry powder was dissolved in the saline used in this experiment.Zhisou San (ZSS) is composed of seven herbs including 10 g Platycodonis Radix (n = 6 per group): control group (Ctrl), model group (Mod), low dose of ZSS group (LZSS), and high dose of ZSS group (HZSS). On days 1, 7, and 14, mice in the control group were intraperitoneally injected with 200 μl normal saline, and mice in other groups were intraperitoneally injected with 200 μl sensitization solution . One week after the last injection, all groups except the Ctrl were given 0.5% ovalbumin for ultrasonic atomization 30 min a day; then, all mice were, respectively, treated with saline, ZSS extracts at the dose of 0.75 g/kg and 1.5 g/kg once per day for 14 consecutive days. All experiments were repeated three times at least and approved by the Animal Care and Ethics Committee at the Fifth Medical Centre, Chinese PLA (People's Liberation Army).Male BALB/c mice at 2-3 months of age were purchased from Beijing SiPeiFu Biotechnology Co., Ltd.  and kept on a 12 h light-dark cycle with free access to food and water. After one week of acclimatization, mice were first randomly divided into 4 groups .The airway reactivity of mice was measured by a noninvasive pulmonary function instrument . Mice were placed in a flow-type global plethysmography box, and one mouse was monitored in each round. According to the set procedure, the basal value, the value of normal saline, and the value of excitation by 3.12 mg/ml. 6.25 mg/ml, 12.5 mg/ml, 25 mg/ml, and 50 mg/ml methacholine (Mch) were measured successively to calculate the enhanced pause (Penh) value of each Mch concentration through the system. The volume of each Mch dose was 0.1 ml, and the atomizing time was 1 min. The left lung was fixed with a 10% formalin neutral buffer solution, embedded in paraffin, sectioned to a thickness of 5 β, IL-6, TNF-a, IL-17, and IL-10 in mice were measured with ELISA kits according to the manufacturer's instructions. All determinations were performed in triplicate.Serum levels of IL-1μm cell strainers  in PBS to obtain single-cell suspensions. Spleen cells were stained with CD3-FITC (BD) and CD4-percp-cy5.5 (BD), as well as CD4-FITC (BD) and CD25-PE (BD). After the primary staining, spleen cells were intracellularly stained with IL-17A-PE (eBioscience) and Foxp3-PE (eBioscience) in Perm/Wash for 40 min at room temperature avoiding light. Cells were then washed with Perm/Wash, resuspended in PBS containing 1 μg/ml DAPI. Spleen immunostaining was recorded by LSRII/Fortessa (BD Biosciences), and data were analyzed using FlowJo software V10.The spleen was isolated and meshed through 45 P < 0.05 was considered to be significant.All results were presented as means ± standard deviations (SD). Statistical analyses were analyzed using the SPSS 13.0 package  by analysis of variance (ANOVA). Neuman–Keuls or Tukey's multiple-comparisons test was conducted as ANOVA justified post hoc comparisons between group means. After screening from the TCMSP database with two key parameters of (OB) ≥30% and drug-likeness (DL) ≥0.18, we basically captured the information that 4 compounds and 92 targets were from JG, 10 compounds and 307 targets were from JJ, 13 compounds and 381 targets were from ZW, 28 compounds and 288 targets were from BB, 4 compounds and 47 targets were from BQ, 86 compounds and 1752 targets were from GC, and 5 compounds and 95 targets were from CP. Removing duplicates, a total of 137 compounds and 276 related targets were collected from the prescription of ZSS .1052 CVA-related targets were obtained from the GeneCards database, and other relevant targets were supplemented combined with the OMIM, Disgenet, and DRUGBANK databases. Removing duplicates after merging, a total of 1571 CVA-related targets were finally collected. We then obtained the 165 intersected targets between the compounds of ZSS and CVA as the potential therapeutic targets .β2-adrenergic receptor (β2-ADR), delta-type opioid receptor (OPRD1), C-X-C motif chemokine 10 (CXCL10), muscarinic acetylcholine receptor M2 (CHRM2), D(1) dopamine receptor (DRD1), prostaglandin E2 receptor EP3 subtype (PTGER3), mu-type opioid receptor (OPRM1), and intercellular adhesion molecule 1 (ICAM1) are listed in These 165 relevant targets were imported into the STRING data platform to obtain the PPI network in the CVA treatment by ZSS . 29 coreP values that represent the degree of enrichment. GO analysis included three parts, namely, BP, CC, and MF. As shown in In order to further systematically elucidate the mechanism of ZSS in the CVA treatment, the Metascape data platform was used to analyze the signaling pathways by enriching the relevant targets. The top 20 GO items and top 30 KEGG pathways were selected based on β2-ADR, and the compound with the least binding energy was stigmasterol ; 2 compounds showed strong affinity with OPRD1, and the compound with the least binding energy was shinpterocarpin ; compound quercetin showed strong affinity with CXCL10 with the binding energy −6.62 kcal/mol; 11 compounds showed strong affinity with CHRM2, and the compound with the least binding energy was stemoninine B ; compound bisdehydroneotuberostemonine showed strong affinity with DRD1 with the binding energy −9.38 kcal/mol; 2 compounds showed strong affinity with PTGER3, and the compound with the least binding energy was luteolin ; 10 compounds showed strong affinity with OPRM1, and two compounds with the least binding energy were β-sitosterol  and bisdehydroneotuberostemonine ; 3 compounds showed strong affinity with ICAM1, and the compound with the least binding energy was luteolin .8 high-ranking core targets according to MCODE score further be predicted as the binding ability of compound to each protein using molecular docking . 2 compoTo investigate the effects of ZSS on CVA in vivo, sensitization liquid was injected into the mouse tail vein and the OVA was atomized to stimulate mice for the CVA animal model shown in β, IL-6, TNF-α, IL-17, and IL-10 in the serum of mice. As shown in β, IL-6, TNF-α, and IL-17 significantly increased in the CVA model group compared with the control group, while ZSS could effectively lower the level of these inflammatory factors  is a chronic airway inflammatory disease involving multiple inflammatory mediators and inflammatory cells. To evaluate the anti-inflammatory effect of ZSS, we detected the level of cytokines IL-1 Figures . Convers Figures . The lev+ CD4+IL-17A+ cells from the spleens of the CVA group was higher than that of the control group; additionally, administration of ZSS markedly reduced the percentage of Th17 cells in the spleens . . Howeverγ levels, and regulating Th1/Th2 immunological imbalance [Traditional Chinese medicine (TCM) has a long history, solid theoretical foundation, and clinical experience in the understanding of CVA, and many clinical studies have proved the definite effect on the prevention and treatment of CVA , 18. Accmbalance . Howeverβ2-ADR, NOS2, CHRM2, DRD1, OPRM1, PTGER3, and CXCL10 in the formula were related to relaxing the broncho-gastrointestinal smooth muscle, alleviating the inflammatory response of bronchial epithelial cells and the differentiation of Th17 cells, etc., which reflected the effects of reducing phlegm and inflammation of ZW, BQ, and BB. Targets such as ICAM1, AKT1, MAPK, FOS, and BCL2 were found to serve a protective role in the immune system, demonstrating the importance of GC in enhancing immunity and harmonizing the herbs. Therefore, through the analysis of network pharmacology, the modern medical connotation of ZZS can be further explored, and the essence of the TCM alliance theory can also be further analyzed.Combining the theoretical analysis of the compatibility of TCM prescriptions with the network pharmacology method to construct the drug-disease-target network, it was discovered that the mechanism of ZSS for the CVA treatment was not solely targeting a certain pathway or target but might work through the joint intervention of multiple targets and complex pathways, confirming the theory of the synergistic effect of herbs compatibility. Analysis results showed that 137 main chemical components of ZSS acting on 165 targets including 29 core targets played a significant role in the treatment of CVA, which were concerned with the regulation of inflammatory response and T-cell differentiation, such as IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, MAPK signaling pathway, T-cell receptor signaling pathway, Th17 cell differentiation, and Th1 and Th2 cell differentiation. Further analysis of the ZSS composition, we found that the targets such as + T-cell subpopulation Th1/Th2 [γ secreted by Th1 inhibits Th2 function [+ helper T-cell subpopulation Th17 is recently identified, which produces the cytokine IL-17 that plays an important role in the development of asthma pathogenesis [α, PGE2, IL-1, IL-6, and IL-8, which are not only involved in airway remodeling but also promote further exacerbation of airway inflammation [The multiple core targets and multiple signaling pathways predicted by the network pharmacological analysis of ZSS in the CVA treatment were essential in the pathogenesis of asthma. Asthma is a chronic inflammatory disease of the airways that involves various inflammatory cells such as T cells, eosinophils, mast cells, and neutrophils, which leads to the development of airway hyper-responsiveness . Studies Th1/Th2 . Among tfunction . A new Cogenesis , 24. IL-ogenesis . Activatammation . This suβ2-ADR agonists are bronchial antispasmodics with wide clinical applications, which can effectively relieve the acute symptoms of asthma by selectively stimulating β2-ADR to relax airway smooth muscle for achieving bronchodilator effects [α is a pleiotropic proinflammatory cytokine, which promotes airway hyper-responsiveness, mucus production, various inflammatory cell activation, and phenotypes of asthma, and some studies showed that TNF-α as a target for asthma treatment has significant efficacy in clinical trials [ effects . PGE2 ca effects , 28. In  effects , 30. MAP effects . MAPK si effects . TNF-α il trials .β, IL-6, IL-17, and TNF-α in the serum also increased significantly. However, ZSS could effectively relieve these changes to improve the CVA symptoms. Relevant studies have proved that the onset of CVA is not only related to the classical Th1/Th2 balance but also regulated by Th17/Treg immune balance from multiple perspectives [To further explore pharmacological effects and potential mechanisms in vivo, the mice model of CVA was established by intraperitoneal injection of ovalbumin and aluminum hydroxide as well as inhalation of ovalbumin. Sneezing, nasal rubbing, shortness of breath, abdominal muscle twitching, and other behaviors were observed in mice, which simulated well the clinical characteristics of CVA. Meanwhile, the histopathology showed that there were a large number of inflammatory cells infiltrating the trachea, bronchus, and lung tissues, and they appeared to goblet cell proliferation and smooth muscle volume increase. In addition, levels of inflammatory cytokines such as IL-1pectives , 35. BasκB signaling [β-sitosterol might suppress asthma by inhibiting cellular response and subsequent Th2 cytokine release [Studies have shown that some active ingredients of ZSS were effective in the treatment of asthma. For instance, quercetin could improve asthma response by inhibiting airway inflammation , 37; kaeignaling , 39; β-s release . Our furIn summary, based on the combination of network pharmacology and experimental validation, our study proved the great clinical value of ZSS in the treatment of CVA. The findings revealed that a high dose of ZSS (1.5 g/kg/day) had critical impacts on improvements in pulmonary function, histopathology, and the inflammatory state in CVA mice, which was significantly related to regulating the Th17/Treg immune balance as the important pharmacological mechanism. However, considering the fact that ZSS contains a number of chemicals that act on multiple targets, it has certain limitations to define the active components and action targets of ZSS from the perspective of network pharmacology through the search of relevant databases, and its conclusions need to be confirmed by the clinical studies, so as to more systematically and scientifically elaborate the efficacy of ZSS in the treatment of CVA.


Colorectal cancer (CRC) is a common digestive tract malignancy with rising incidence and morbidity worldwide during recent years. Yi-Yi-Fu-Zi-Bai-Jiang-San (YYFZBJS), a traditional Chinese medicine formula, has showed positive effects against cancers. However, the mechanisms underlying its anticancer effects requires investigation.in vitro and in vivo.Information on bioactive compounds, potential YYFZBJS targets, and CRC-associated genes, was obtained from public databases. The key targets and ingredients as well their corresponding signaling pathways were identified using bioinformatic approaches, including Kyoto encyclopedia of genes and genomes (KEGG) analyses, gene ontology (GO), and protein–protein interaction (PPI). Subsequently, molecular docking was used to verify the main compounds-targets. Potential YYFZBJS therapeutic effects against CRC were validated In vivo analysis found that YYFZBJS inhibited tumor growth and PI3K/AKT signaling in a mouse model of CRC.Using pharmacological network analysis, 40 YYFZBJS active compounds and 21 potential anti-CRC targets were identified. YYFZBJS was an important regulator of CRC through various targets and signaling pathways, particularly the cell cycle and PI3K/AKT pathway. Additionally, YYFZBJS suppressed the proliferation of CRC cells. Flow cytometry showed that YYFZBJS induced apoptosis and cell cycle arrest in the G2/M phase. Western blotting analysis indicated that YYFZBJS reduced the protein levels of CDK1, p-AKT, and p-PI3K, without altering total PI3K and AKT protein levels. As predicted by network pharmacology and validated by the experimental results, YYFZBJS inhibited proliferation, induced apoptosis and arrested cell cycle progression in CRC by modulating the CDK1/PI3K/Akt signaling pathway. Among the symptoms of CRC patients, the most common are abdominal pain, anemia, and rectal bleeding  is used as an adjunctive therapeutic option for preventing CRC metastasis, enhancing chemotherapeutic effects while minimizing toxicity, and increasing the patients’ quality of life . Furtherrophages . Notablyrophages , 7. Herbrophages . Like TCNetwork pharmacology combines systems biology, polypharmacology, and molecular networks to comprehensively describe the relationship between drugs, their targets, and diseases in terms of a visual active ingredients–targets–diseases network . MoleculHere, the molecular mechanisms mediating the effects of YYFZBJS on CRC were elucidated Figure 1https://tcmsp-e.com) (https://www.uniprot.org), respectively.Active YYFZBJS compounds were identified on the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (p-e.com) . In thisp-e.com) . Additiohttps://www.ncbi.nlm.nih.gov/gds), Online Mendelian Inheritance in Man Database  and GeneCards (https://www.genecards.org). The term “Colorectal Cancer” was searched in OMIM and GeneCards to obtain all CRC targets, as well as in GEO to obtain dataset GSE22598 (Patient data) and GPL570 platform files. The R (version 3.6.0) package, LIMMA, was used to identify differentially expressed genes (DEGs) using p≥0.05 and log2 fold change (FC) ≥1 as cutoff thresholds. Finally, we summarized the differentially expressed genes by taking the intersection of the DEGs identified from the GSE22598 and the CRC targets acquired from GeneCards and OMIM.Information on disease targets was obtained using datasets from GEO (Using R (version 3.6.0), the drug-disease common targets were identified and visualized on a Venn diagram. Using Cytoscape (version 3.8.2), the protein–protein interaction (PPI) network between putative YYFZBJS targets was combined with that of the CRC-related targets. Next, the Cytoscape plugin, CytoNCA, was used to identify nodes with topological importance in the interaction network based on betweenness centrality (BC) and degree centrality (DC). These variables reflect the topological significance and their applications in systems pharmacology and network pharmacology have been previously described .http://www.bioconductor.org/) and used histograms and bubble charts to present the top 20 processes (p <0.05). Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis was done using the R packages “clusterProfiler” and “pathview” and the top 20 KEGG pathways visualized on heatmaps. The network of functional pathways was developed using Cytoscape.Gene ontology (GO) biological process enrichment analysis was conducted for the common drug targets on R’s bioconductor environment (http://www.rcsb.org/) were imputed it into MOE to construct mating pockets of molecular docking after deleting water molecules, preparing protein structure, and minimizing energy. The structures of the compounds used for mating pocket construction and molecular docking analysis were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/)  v2015.10 in order to validate compound–target interactions. Protein 3D structures from the protein data bank  .in vitro assays, the YYFZBJS lyophilized powder was dissolved in culture media by ultra-sonication for 20 min, followed by centrifugation at 13,000 rpm for 5 min. The obtained supernatant was filtered with a 0.22 μm filter membrane as the extract solution. Before using, the extract solution was diluted to the desired concentrations. Culture media without YYFZBJS was used as negative control. For in vivo experiments, mice were intragastrically administered a YYFZBJS decoction.The composition of YYFZBJS was 60g Yi-yi-ren (Semen Coicis), 12g Fu-Zi (monkshood), and 30g Bai-jiang-cao (Herba Patriniae) were purchased from Union hospital of Tongji Medical College, Huazhong University of Science and Technology under the guidance of Chinese Pharmacopeia 2020 edition. The extracts of the above variously prepared YYFZBJS were obtained by the following process. Firstly, all herbs were soaked for 30 min and decocted twice independently with the 8-fold volume of water, 1 h each time. Secondly, the decoction was collected and filtrated through filter paper. Thirdly, an 8-fold volume of 95% ethanol was added to the filtrate remains and reflux twice, 1 h each time. Next, these two parts of filtrates were combined, condensed to a density of 1.0 g/mL by rotary evaporation at 55°C and frozen at -80°C. Finally, it was converted into lyophilized powder by a freeze-drying machine . For Fifty male C57BL/6J (6–8 weeks) mice were obtained from the SPF (Beijing) biotechnology Co., Ltd. The animals were housed in pathogen-free conditions. CRC was induced using azoxymethane  and dextran sulfate sodium  as described before . BrieflyThe human CRC cell lines, HCT116 and SW480, were purchased from the American Type Culture Collection  and confirmed to be mycoplasma-free using a LookOut mycoplasma PCR detection kit . SW480 cells were cultured in DMEM/high glucose media . HCT116 cells were cultured in McCoy’s 5A media  supplemented with 10% FBS  at 37 °C, 5% CO2.®2000  according to manufacturer instructions. After 6 h, the transfection mixture was replaced with fresh growth media.HCT116 and SW480 cells (4×105 per well) were seeded in six-well plates and then transfected with a CDK1 overexpression plasmid  using LipofectamineMice were sacrificed and whole intestines collected and washed with ice-cold phosphate-buffered saline (PBS). To assess inflammation, the length of the whole colon was measured before and after being opened longitudinally . Adenomahttps://www.xiantao.love/products.Cell proliferation was assessed using the cell counting kit-8 (CCK-8) assay . HCT116 and SW480 were seeded at 3 × 103 cells/well in 96 well plates and cultured to 80% confluence. They were then treated with YYFZBJS at 0, 30, 60 and 90 μg/mL for 24 h. Next, 10µL of CCK-8 solution was added into each well and the cells incubated for 90 min followed by absorbance (450 nm) reading on a microplate reader . The data are representative of three independent experiments in triplicate. IC50 values were calculated from the percent of viability reported to untreated cells using online tools HCT116 and SW480 cells were treated with 30, 60 and 90 μg/mL of YYFZBJS or PBS as a control for 24 h. The cells (3 × 103 per dish) were then cultured in 10 cm dish, and the medium was changed every 5 days for 14 days. Next, the colonies were fixed with 2% paraformaldehyde  for 30min and stained with 0.1% crystal violet  for 15min. They were then washed thrice, dried, and the number of positively stained colonies counted and imaged under a microscope .HCT116 and SW480 cells were treated with YYFZBJS  or PBS (negative control) for 24 h. The cells (1×106) were then collected, fixed in cold ethanol, and stored at 4°C overnight. The next day, they were washed twice with cold PBS and stained with 200μL PI/RNase staining buffer  at 4 °C for 30 min in the dark. Measurements were taken using a flow cytometer  and the data analyzed using FlowJo 8.1 software.Apoptotic cells were detected using an Annexin V-APC/7-ADD apoptosis detection kit . Briefly, cells were detached using EDTA-free trypsin, centrifuged, and the supernatant discarded. They were then rinsed with ice-cold PBS, centrifuged, and the cell pellet resuspended in 200 μl of 1 × binding buffer. The cells were then transferred into a FACS tube and stained with Annexin V-APC and 7-AAD (5µL each) in the dark for 15 mins, at room temperature. Next, flow cytometry measurements were taken, and the data analyzed on FlowJo 8.1.Total protein was extracted from the YYFZBJS-treated CRC cells or mouse CRC tissues using RIPA lysis buffer supplemented with a phosphatase and protease inhibitor cocktail. Protein concentration was then measured using a BCA assay kit according to manufacturer instructions. Next, 40μg of each sampled were resolved using 10% SDS-PAGE and transferred onto 0.45μm PVDF membranes . Membranes were then blocked using 5% skimmed milk in TBST for 1 h at room temperature, and then washed with TBST. The membranes were then incubated overnight (at 4°C) with antibodies against CDK1, p-PI3K , PI3K , p-AKT , AKT  and β-actin . The membranes were then washed and incubated with HRP-conjugated goat anti-mouse or anti-rabbit (1:1000) secondary antibodies for 2 h, at room temperature. Signal was then developed using an ECL kit.Statistical analysis was done using SPSS (version 26.0) and data plotted for visualization using GraphPad Prism (version 8). Data were expressed as mean ± SD. For normally distributed data, independent sample t test and one way ANOVA were used to statistically compare two groups and multiple groups, respectively. Non-normally distributed data were compared using nonparametric rank sum test. p<0.05 indicated statistically significant differences.A total of 40 active YYFZBJS components were retrieved  from TCMSP (http://bioinformatics.psb.ugent.be/webtools/Venn/) , identified 1308 DEGs, which were visualized on heatmaps and volcano plots , molecular function (MF), and cell composition (CC) of the 21 hub target proteins. The top 20 BP, CC, and MF terms  are shown in via multiple factors, including EGFR, BCL2, CCND1, CDK1, and CDK4. Next, a gene–pathway network was constructed using the significantly enriched pathways. Genes that influence these pathways are shown in We used KEGG pathway analysis to identify potential pathways underlying the effects of YYFZBJS in CRC treatment. KEGG annotation revealed that 149 of the 163 target genes were enriched for 97 pathways (p<0.001). The top 20 significantly enriched pathways are shown. Furthermore, KEGG enrichment analysis showed that several target genes were closely associated with the cell cycle, cellular senescence, microRNAs in cancer, PI3K/Akt signaling, and p53 signaling pathway  and ten potential target genes  were selected through network analysis, with high node scores and confidence. Molecular docking studies were conducted to identify the interactions between the active YYFZBJS ingredients and CRC-related potential target genes at the molecular level. The 3D structures of the active ingredients and potential target genes were obtained from TCMSP and RCSB PDB, respectively. After dehydration and hydrogenation, we performed docking analysis using AutoDock tools and the AutoDock Vina software. The absolute binding energy value correlated positively with the connective stability of the ingredient molecule and target protein. The most stable connective patterns and binding energies are shown on in vivo analysisAccording to the scoring results, quercetin-CDK1, kaempferol-CDK1, quercetin-CCND1, luteolin-CCND1, quercetin-PLAU, luteolin-MET had a strong correlation. Based on network analysis and molecular docking simulation, quercetin, which targets multiple CRC-related targets, with strong binding to the cell cycle factor, CDK1, was selected as candidate drug for https://tcga-data.nci.nih.gov/tcga/) were analyzed. The publicly available data indicated that among the core target proteins, the levels of CDKN1A, CCND1, CDK1, MYC, PLAU, FOS, MET, MCL1, HMOX1, and MMP3 were significantly different in CRC tissues when compared with normal colorectal tissues  collected. We then measured colon length and counted the number of tumors. Colon length shortening is regarded as a hallmark of inflammation upon DSS treatment . Here, wvia CDK1 and PI3K/AKT signaling. Thus, we examined protein levels of CDK1, PI3K, AKT, and p-AKT in colorectal tissues using western blotting. This analysis revealed that the expression of CDK1 was reduced upon treatment with YYFZBJS at 3.825, 7.65, and 15.3 mg/kg  and PI3K in the control group indicated PI3K/Akt signaling activation. Compared with the control group, p-Akt and PI3K were significantly suppressed in the YYFZBJS group while total Akt levels were unaffected, and these effects were YYFZBJS dose-dependent. These findings indicate that YYFZBJS’s anti-CRC effects are mediated by PI3K/Akt signaling. The PI3K/Akt signaling pathway, which is dysregulated in many cancers, including CRC, may influence CRC invasion, migration, proliferation, and autophagy . It is aThere are several limitations to our study. Because the public databases utilized in this study are continuously updated, some bioactive ingredients and target genes may not have been included in our analysis. Additionally, other signaling pathways  may be involved in the anti-tumor effects of YYFZBJS. Further research is needed to examine the potential involvement of such pathways. Moreover, YYFZBJS contains active components such as quercetin, luteolin, kaempferol, stigmasterol, and sitosterol by network pharmacology, however these are also widely distributed in many herbs/plants/fruits/vegetables and are not very specific. The aim of this paper is to investigate the effect of compound prescription on CRC. Later, we will find the unique active components of single herb and YYFZBJS in combination with high performance liquid chromatography, and further study its mechanism of action. Nonetheless, our findings provide important preliminary information on the role of YYFZBJS in CRC treatment and indicate that YYFZBJS may be a promising CRC therapeutic candidate.Our results indicate that in CRC, YYFZBJS inhibited proliferation and induced apoptosis by modulating the CDK1/PI3K/Akt signaling pathway and highlight YYFZBJS as a promising adjuvant in CRC treatment.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The animal study was reviewed and approved by Tongji Medical College, Huazhong University of Science and Technology.SY and JL contributed to design of the study. JL, NL and FZ carried out the network pharmacology analyses. JL, LS and YL performed the experiments. JL and FZ wrote and the manuscript. LS, NL, MZ and SW contributed to manuscript revision. All authors contributed to the article and approved the submitted version.This work was supported by the Natural Science Foundation of China (No: 81874397), and the Project of Key R&D Program of Hubei Province (No: 2020BCA065).We thank all the individuals who contributed to study. We also would like to acknowledge the reviewers for their valuable comments and suggestions.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.