Use of a drug and food active ingredient citreorosein in the preparation of a drug for preventing and treating myocardial injury

Abstract

The present application relates to the technical field of biological medicine, in particular to application of a medicinal and edible active ingredient Citreorosein in preparation of a drug for preventing and treating myocardial injury. In view of the problem that the application of the medicinal and edible active ingredient Citreorosein in prevention and treatment of myocardial injury is not studied in the prior art, the present application provides application of the medicinal and edible active ingredient Citreorosein in preparation of a drug for preventing and treating myocardial injury. Through transcriptome data, bioinformatics screening, drug property evaluation and cell experiment verification, the protective effect of Citreorosein on myocardial injury is found and confirmed, thereby providing a new candidate active ingredient and application scheme for preventing and treating myocardial injury.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, specifically to the application of the active ingredient citreorosein in the preparation of drugs for the prevention and treatment of myocardial injury. Background Technology

[0002] Myocardial injury is a crucial pathological basis for the occurrence and development of various cardiovascular diseases, and can be seen in various pathological states such as myocardial ischemia, hypoxia, metabolic stress, inflammatory stimulation, and heart failure. After myocardial cells are damaged, they typically exhibit decreased cell viability, impaired cell membrane integrity, enhanced oxidative stress and inflammatory responses, and elevated levels of myocardial injury-related indicators such as lactate dehydrogenase, creatine kinase isoenzymes, and cardiac troponin. Persistent or recurrent myocardial injury can further induce myocardial cell death, extracellular matrix deposition, myocardial remodeling, and decreased cardiac function, and in severe cases, can promote the occurrence and progression of heart failure.

[0003] Heart failure (HF) is a clinical syndrome resulting from the progression of various cardiovascular diseases to their end-stage. Its main pathophysiological feature is impaired cardiac pumping and / or filling function, leading to insufficient cardiac output to meet the body's metabolic needs. The development of HF is not driven by a single factor but involves multiple mechanisms, including cardiomyocyte damage, abnormal inflammatory responses, extracellular matrix remodeling, dysregulation of cytokines, energy metabolism disorders, and an imbalance between cardiomyocyte survival and death. Therefore, screening for cardioprotective interventions from abnormal genes, molecular networks, and key pathways related to HF is crucial for discovering candidate active substances for preventing and treating myocardial injury.

[0004] Currently, clinical treatments for myocardial injury and related cardiovascular diseases mainly include measures such as improving hemodynamics, regulating the neuroendocrine system, antiplatelet therapy, anticoagulation, lipid-lowering, and improving myocardial energy metabolism. While these treatments can improve symptoms and slow disease progression to some extent, their direct protective effects on myocardial cell damage remain limited, especially in reducing myocardial cell damage under hypoxic and hypoglycemic stress conditions, improving the survival rate of damaged myocardial cells, and reducing the release of myocardial injury markers. It is still necessary to find active ingredients with good safety profiles, clearly defined targets, and potential translational value.

[0005] "Medicine and food sharing the same origin" is an important concept in traditional Chinese medicine, referring to substances that can be used as food and also possess certain medicinal value. These substances typically contain multiple bioactive components, exhibiting multi-component, multi-target, and multi-pathway regulatory characteristics, and show a high degree of compatibility with the multi-factor, multi-mechanism-related disease characteristics of myocardial injury and heart failure. In recent years, based on methods such as network pharmacology, transcriptomics, and molecular simulation, numerous studies have been conducted on the potential applications of these substances and their active ingredients in cardiovascular diseases, suggesting that these active ingredients have potential in anti-inflammatory, anti-apoptotic, and myocardial damage mitigation effects.

[0006] However, existing research on the prevention and treatment of myocardial injury using active pharmaceutical ingredients (APIs) largely focuses on commonly reported components such as flavonoids and polyphenols. For some less studied but potentially valuable API monomers, systematic screening, target prediction, drugability evaluation, and functional validation remain insufficient. In particular, there are currently no reports on the application of the API citreorosein in preventing and treating myocardial injury, especially hypoxia and / or hypoglycemia-induced cardiomyocyte damage. Summary of the Invention

[0007] To address the lack of existing research on the application of the medicinal and edible active ingredient Citreorosein in the prevention and treatment of myocardial injury, this application provides an application of Citreorosein in the preparation of drugs for the prevention and treatment of myocardial injury. Through publicly available transcriptomic data, bioinformatics screening, drug-likeness evaluation, and cell experiments, the protective effect of Citreorosein on myocardial injury has been discovered and confirmed, thus providing a new candidate active ingredient and application scheme for the prevention and treatment of myocardial injury.

[0008] In a first aspect, the present invention provides the application of the medicinal and edible active ingredient Citreorosein in the preparation of drugs for the prevention and treatment of myocardial injury.

[0009] Optionally, myocardial injury refers to myocardial injury that accompanies the development and progression of heart failure.

[0010] Optionally, myocardial injury is hypoxia and / or glucose-induced myocardial cell injury.

[0011] Secondly, the present invention also provides a pharmaceutical composition for preventing and treating myocardial injury, comprising the edible and medicinal active ingredient Citreorosein and pharmaceutically acceptable excipients.

[0012] Optionally, pharmaceutically acceptable excipients include one or more of fillers, binders, disintegrants, solubilizers, lubricants, stabilizers, or diluents.

[0013] Thirdly, the present invention also provides a method for screening citreorosein, a medicinal and edible active ingredient for preventing and treating myocardial damage related to heart failure, comprising the following steps:

[0014] Gene expression profile data of heart failure disease samples and control samples were obtained, and differential expression analysis was performed on the gene expression profile data to obtain a set of differentially expressed genes related to heart failure.

[0015] Construct a database of active ingredients that are both medicinal and edible, which includes the names of substances that are both medicinal and edible, the names of active ingredients, and the corresponding molecular structure information of the active ingredients.

[0016] By predicting the molecular-protein interactions between the active ingredients in the database of active ingredients from the same source as food and medicine and the differentially expressed genes related to heart failure, we can obtain gene-active ingredient-molecular structure information relationship pairs.

[0017] Toxicological and drug-likeness evaluations were conducted on the active ingredients in the gene-active ingredient-molecular structure information relationship pairs to screen and obtain candidate key active ingredients.

[0018] Molecular docking verification was performed on the candidate key active ingredients and their corresponding gene-encoded proteins. Based on the toxicological evaluation results, drug property evaluation results, and molecular docking verification results, the medicinal and edible active ingredient Citreorosein was identified from the candidate key active ingredients.

[0019] Optionally, gene expression profile data were obtained from the GSE57338 dataset. Differential expression analysis was performed using the limma method, with the selection criteria being |log2FC|>0.5 and P.value<0.05, to obtain a set of differentially expressed genes related to heart failure.

[0020] Optionally, the construction of the database of active ingredients that are both food and medicine includes: using traditional Chinese medicine that is both food and medicine as the search object, screening active ingredients with oral bioavailability ≥30% and drug-likeness index ≥0.18, and obtaining the SMILES molecular structure information corresponding to the active ingredients.

[0021] Optionally, the molecular-protein interaction prediction is performed using the GraphBAN model, and the intersection of the prediction results of BindingDB, BioSNAP and KIBA models is taken, and gene-active ingredient-molecular structure information relationship pairs with an interaction probability greater than 0.5 are selected as candidate relationship pairs.

[0022] Optionally, the toxicological assessment is judged as having no obvious toxicity or low toxicity if all five indicators are negative: hERG channel blockade, human hepatotoxicity, ocular corrosivity, respiratory toxicity, and acute oral toxicity in rats.

[0023] Compared with existing technologies, the application of the medicinal and edible active ingredient Citreorosein of the present invention in the preparation of drugs for preventing and treating myocardial injury related to heart failure brings the following significant effects:

[0024] 1. Based on the abnormal transcriptome genes and molecular networks related to myocardial injury, this invention combines screening of active medicinal and edible ingredients, drug-likeness evaluation and structural verification to screen Citreorosein from a variety of active medicinal and edible ingredients. The screening path is clear and the technical basis is sufficient, avoiding the limitations of simply relying on experience to select materials, and improving the specificity and reliability of candidate active ingredient discovery.

[0025] 2. This invention validated the protective effect of Citreorosein using an OGD-induced H9C2 cardiomyocyte injury model. The results showed that Citreorosein, within a relatively safe concentration range, can improve the survival rate of damaged cardiomyocytes and reduce the levels of LDH, NT-proBNP, CK-MB, and cTnI, indicating that it can effectively alleviate cardiomyocyte damage and has clear application value.

[0026] 3. This invention is the first to propose the application of citreorosein, a medicinal and edible active ingredient, in the preparation of drugs for the prevention and treatment of myocardial injury related to heart failure, providing new candidate molecules and application directions for the development of medicinal and edible active ingredients in the intervention of myocardial injury. This approach combines screening criteria, structural support, and experimental verification, and can provide a foundation for subsequent drug composition development and related formulation research. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 In the diagram, A represents a volcano plot of differentially expressed genes; B represents a heatmap of differentially expressed genes.

[0029] Figure 2In the diagram, A is a circular plot of GO enrichment analysis. The outermost layer is red for BP, blue for CC, and green for MF. The numbers above the blue bars in the second layer indicate the number of enriched genes. The third layer is pink for upregulated genes and green for downregulated genes. The taller the innermost bars, the greater the enrichment score. B is a GO enrichment function description infographic, showing the top 5 functions with the most enriched genes in biological process (BP), cellular component (CC), and molecular function (MF).

[0030] Figure 3 The KEGG analysis results for candidate genes are shown below. The outermost red layer represents the enriched pathways; the numbers above the blue layer in the second layer indicate the number of enriched genes; the third layer shows pink for upregulated genes and green for downregulated genes; the taller the innermost bar, the greater the enrichment score.

[0031] Figure 4 The PPI network for candidate genes is plotted based on Degree values. The color of Degree represents the number of gene interactions. The redder the color, the more genes interacting with it; the bluer the color, the fewer genes interacting with it. The color intensity of the lines varies with the strength of the interaction. The stronger the interaction, the redder the line and the wider the chord.

[0032] Figure 5 The Venn plot shows the prediction results of the three algorithms. Light pink represents the prediction results of BindingDB, light green represents the prediction results of BioSNAP, and purple represents the prediction results of KIBA. The overlapping part represents the results common to the three algorithms.

[0033] Figure 6 This is a network diagram of "medicine and food - key active ingredients - biomarkers". Red circles represent biomarkers, blue squares represent key active substances corresponding to biomarkers, and yellow diamonds represent traditional Chinese medicines containing key active substances.

[0034] Figure 7 This is a molecular docking model of CTSK and Citreorosein; the upper part shows the three-dimensional structure of the protein corresponding to the biomarker, the lower part shows the three-dimensional structure of the active ingredient of the targeted drug, the red-gray ball-and-stick results in the middle represent the molecular structure of the drug, the red dashed lines represent hydrogen bonds, and the blue dashed lines represent hydrophobic interactions; the surrounding area shows the specific amino acids of the key target, and the thick blue boxes represent docking sites.

[0035] Figure 8 This is a CTD analysis chart, where blue represents the Inference Score and orange represents the number of References.

[0036] Figure 9 The graph shows the changes in RMSD from molecular dynamics simulations. The horizontal axis represents time, and the vertical axis represents RMSD. Smaller fluctuations indicate more stable binding between the protein and the drug.

[0037] Figure 10 The graph shows the changes in RMSF from molecular dynamics simulations. The horizontal axis represents the amino acid sequence, and the vertical axis represents RMSF. The greater the fluctuation, the greater the flexibility of the protein's amino acids.

[0038] Figure 11 The graph shows the energy changes in a molecular dynamics simulation. The horizontal axis represents time, and the vertical axis represents the total energy. The lower the energy and the smaller the fluctuation, the more stable the system.

[0039] Figure 12 The graph shows the changes in hydrogen bonds as simulated by molecular dynamics. The horizontal axis represents time, and the vertical axis represents the number of hydrogen bonds. The more hydrogen bonds there are, the more stable the bond.

[0040] Figure 13 A bar chart showing cell proliferation;

[0041] Figure 14 A bar chart showing cell proliferation;

[0042] Figure 15 Bar chart showing LDH activity in cell supernatants of each group;

[0043] Figure 16 Bar chart showing the content of each indicator in the cell supernatant of each group. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various modifications or alterations to the present invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0045] Example 1: Screening of Citreorosein, a medicinal and edible active ingredient

[0046] 1. Data sources and differentially expressed gene screening

[0047] To obtain the differential values ​​(DEGs) between disease tissue samples and control tissue samples, the R package "limma" (v 3.58.1) [PMCID: PMC4402510] was used to perform differential analysis on HF samples and control samples (HF vs control) in the training set GSE57338 to obtain the DEGs. The thresholds were set as |log2FC|>0.5 and P.value<0.05.

[0048] Statistical analysis revealed 419 differentially expressed genes (DEGs) between the HF group and the control group. Among these, 225 genes were upregulated and 194 genes were downregulated in the disease samples. Based on the results, the R packages "ggplot2" (v3.5.1) [PMID: 35751589] and "pheatmap" were used.

[0049] (v1.0.12)[Kolde R (2019)._pheatmap:Pretty Heatmaps_.R packageversion1.0.12,<https: / / CRAN.R-project.org / package=pheatmap> Draw volcano maps and heat maps. Figure 1 A is the volcano plot for differential analysis. The plot only shows the top 5 upregulated and downregulated genes with the largest changes in |log2FoldChange|. Red dots in the plot represent upregulated DEGs, and blue dots represent downregulated DEGs. Figure 1 B is a heatmap of differential analysis, which also shows the top 5 gene names with the largest changes in |log2FoldChange| for both upregulation and downregulation.

[0050] 2. GO and KEGG enrichment analysis

[0051] 2.1 GO analysis

[0052] To investigate the biological functions of candidate genes, the R package "clusterProfiler" (v 4.7.1.3) [PMID: 22455463] was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on differentially expressed genes. GO enrichment analysis included biological processes, cellular components, and molecular functions. GO and KEGG enrichment analyses were performed using the R package "clusterProfiler" (v 4.7.1.3) [PMID: 22455463] with a threshold of P-value < 0.05, resulting in GO and KEGG enrichment plots.

[0053] GO enrichment analysis used a P-value < 0.05 as the selection criterion. A total of 847 BP entries, 52 CC entries, and 131 MF entries were co-enriched from 419 differentially expressed genes. The results are as follows: Figure 2 A and Figure 2As shown in B, the BP, CC, and MF sections respectively display the top 5 entries with the most enriched genes.

[0054] In terms of biological processes (BP): the candidate genes were significantly enriched in functions such as extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, positive regulation of cytokine production, and regulation of inflammatory response.

[0055] Regarding cellular components (CC): the candidate genes were significantly enriched in functions such as collagen-containing extracellular matrix, external side of plasma membrane, secretory granule membrane, secretory granule lumen, and cytoplasmic vesicle lumen.

[0056] In terms of molecular function (MF): the candidate genes were significantly enriched in functions such as extracellular matrix structural constituent, glycosaminoglycan binding, sulfur compound binding, receptor ligand activity, and G protein-coupled receptor binding.

[0057] 2.2 KEGG Analysis

[0058] KEGG enrichment analysis was performed using the R package "clusterProfiler" (v 4.7.1.3) [PMID: 22455463] with a P-value < 0.05. 419 differentially expressed genes were enriched into 75 pathways. The results are as follows: Figure 3The candidate genes were significantly enriched in the following pathways: PI3K-Akt signaling pathway, phagosome, Coronavirus disease - COVID-19, Cytokine-cytokine receptor interaction, Cytoskeleton in muscle cells, AGE-RAGE signaling pathway in diabetic complications, Tuberculosis, Proteoglycans in cancer, Lipid and atherosclerosis, and Human T-cell leukemia virus 1 infection (showing the top 10 functions with the most enriched genes).

[0059] 3. Construction of PPI protein interaction network

[0060] To explore the interactions between proteins encoded by candidate genes, a PPI network was constructed for 419 differentially expressed genes using the STRING database (http: / / string-db.org) (accessed: August 6, 2025). With an interaction score > 0.9, 294 isolated gene nodes without protein-protein interactions with other genes were removed, and the network diagram was plotted using the R package "circlize" (v 0.4.16) [PMID: 24930139]. Figure 4 The graph contains 125 nodes and 151 edges. These 419 differentially expressed genes were then selected for further analysis.

[0061] 4. Construction of a library of active ingredients in food and medicine

[0062] To obtain information on active ingredients and their targets derived from both food and medicine, a search was conducted based on 106 traditional Chinese medicines (TCMs) published by the National Health Commission. Information was extracted from the TCMSP (https: / / old.tcmsp-e.com / tcmsp.php) database and analysis platform (accessed August 1, 2025). TCMSP contains multi-dimensional data on the pharmacokinetic parameters, potential target predictions, and pharmacological effects of TCM components, and is commonly used in research on the molecular mechanisms and efficacy of TCMs. During the search, the names of the medicinal and food ingredients were used as keywords, and oral bioavailability (OB) ≥30% and drug-likeness index (DL) ≥0.18 were set as screening criteria to extract active monomers that met the conditions. For drugs not found on the TCMSP platform, a supplementary search was conducted on the bioinformatics analysis platform for molecular mechanisms of traditional Chinese medicine (BATMAN-TCM, http: / / bionet.ncpsb.org.cn / batman-tcm / # / search) (accessed August 1, 2025). Subsequently, the corresponding SMILES structures for each component were searched in the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / ) (accessed August 1, 2025). PubChem is an open chemical substance database provided by the National Center for Biotechnology Information (NCBI) and is widely used for structure standardization, molecular description, and medicinal chemistry analysis. Information on 106 medicinal and edible active ingredients was obtained from the TCMSP website for 88 of them, and information on the remaining 18 medicinal ingredients was found in the BATMAN-TCM database. The resulting all_herb_smile.csv data table records the names of medicinal and edible herbs, the names of their active ingredients, and their corresponding SMILES molecular structures. It contains 1185 pairs of "medicinal / edible—active ingredient—SMILES structure" relationships, serving as the foundation for subsequent active ingredient screening, interaction prediction, and drug development evaluation.

[0063] The all_herb_smile.csv data table includes three columns: CID, HERB, and SMILES. CID represents the compound number of the active ingredient in the PubChem database, HERB represents the source of the medicinal and edible Chinese medicine corresponding to the active ingredient, and SMILES represents the molecular structure information of the SMILES corresponding to the active ingredient. Due to space limitations, this embodiment only lists some representative CID-HERB-SMILES relationship pairs, including the record corresponding to Citreorosein. The results are shown in Table 1.

[0064] Table 1. Relationships between CID, HERB, and SMILES for some food-medicine homologous active ingredients.

[0065]

[0066] In addition, according to the above screening criteria, eligible medicinal and edible active ingredients were obtained, and their molecular structure information was further acquired. To facilitate the explanation of the basic information of the screened active ingredients, Table 2 lists the names, molecular structures, oral bioavailability (OB), and drug-likeness index (DL) of some representative active ingredients.

[0067] Table 2 Information on Active Substances

[0068]

[0069] Note: Number is the active substance code, Compound is the active substance name, Molecule structure is the active substance structure, OB is the oral bioavailability, and DL is the drug-likeness index.

[0070] 5. GraphBAN predicts candidate key active ingredients

[0071] To predict potential interactions between food and medicinal active ingredients (DEGs) and candidate gene-encoded proteins, we introduced GraphBAN, a graph neural network-based molecular-protein interaction prediction platform, to predict potential interactions between active ingredients and candidate gene-encoded proteins. Based on the prediction scores, we used...

[0072] BindingDB(https: / / www.bindingdb.org / rwd / bind / index.jsp);

[0073] BioSNAP(https: / / snap.stanford.edu / biodata / datasets / 10002 / 10002-ChG-Miner.html);

[0074] KIBA (https: / / researchportal.helsinki.fi / en / datasets / kiba-a-benchmark-dataset-for-drug-target-prediction) (accessed August 4, 2025)

[0075] The intersection of the three models predicting interaction probabilities > 0.5 yields the Gene-CID-SMILES relationship pair. Figure 5 ).

[0076] A total of 728 Gene-CID-SMILES (gene-active ingredient-active ingredient SMILES) pairs were obtained by screening for high-affinity candidate key active ingredients. After removing duplicates, 26 active ingredients remained, which were selected as candidate key active ingredients for further analysis. Due to space limitations, Table 3 only lists the Gene-CID-SMILES pairs related to Citreorosein, the core active ingredient of this invention.

[0077] Table 3 Gene-CID-SMILES Relationships

[0078]

[0079] 6. Toxicological and drug-grade property assessment

[0080] To comprehensively evaluate the pharmacokinetic characteristics, toxicological risks, and safety and druggability of candidate key active ingredients as potential interventional molecules, the ADMETlab 2.0 platform (https: / / admetmesh.scbdd.com / ) (accessed: August 5, 2025) was used for systematic analysis. This platform integrates multidimensional prediction modules for small molecule compounds in terms of absorption, distribution, metabolism, excretion, and toxicity, and is widely used for early screening and toxicological assessment of drug lead compounds. Five indicators—hERG channel blockade, human hepatotoxicity, ocular corrosivity, respiratory toxicity, and acute oral toxicity in rats—were used as criteria for toxicity assessment. If all five indicators were negative, the compound was considered to have no significant toxicity or low toxicity, thus initially excluding components with high safety risks. The results are shown in Table 4, and 13 medicinal and edible active ingredients with no significant toxicity or low toxicity were obtained through screening.

[0081] Table 4 Toxicological Assessment

[0082]

[0083] Note: hERG indicates hERG channel blockade, H-HT indicates human hepatotoxicity, ROA indicates acute oral toxicity in rats, EC indicates ocular corrosivity, and Respiratory indicates respiratory toxicity.

[0084] Based on the toxicological assessment, the druggability of the compounds was further evaluated using the SwissADME platform (http: / / www.swissadme.ch / ) (accessed: August 5, 2025) (Table 5). The SMILES structural information of the active pharmaceutical ingredients was input into the platform, and their molecular weight (MW), number of hydrogen bond acceptors (HBA), number of hydrogen bond donors (HBD), and LogP value (Logarithm of the Partition Coefficient) were calculated. These parameters were used to assess the degree of compliance of the compounds with Lipinski's Rule of Five, and to determine their oral accessibility based on Lipinski's Rule of Five. This rule states that when MW < 500 g / mol, HBA < 10, HBD ≤ 5, and LogP ≤ 4.15, the compound generally has good oral bioavailability, and the fewer violations (maximum one violation), the greater the druggability potential. In addition to basic physicochemical properties, the topological polar surface area (TPSA) was also evaluated simultaneously. TPSA < 140 Ų was considered beneficial for transmembrane permeation and gastrointestinal absorption. Based on the above comprehensive indicators, key active ingredients with good pharmacokinetic performance and low toxicity risk were screened, as shown in Table 5. A total of three key active ingredients were obtained: Citreorosein (361512), TAXIFOLIN (439533), and 6,6a(2)-Dimethoxygossypol (375713). The genes corresponding to the key active ingredients were used as biomarkers, and a total of seven biomarkers were obtained (CTSK, CFAP61, SLC11A1, ADDRL3, COL14A1, NAMPT, and CPAMD8).

[0085] Table 5. Evaluation of Drug Properties

[0086]

[0087] Note: MW represents molecular weight, HBA represents hydrogen bond acceptor, HBD represents hydrogen bond donor, Log P represents the logarithm of the segmentation coefficient, Lipinski violations represents the degree to which a compound conforms to Lipinski's Rule of Five, and TPSA represents topological polar surface area.

[0088] 7. Construction of the "Medicine and Food - Key Active Ingredients - Biomarkers" Network

[0089] To further elucidate which genes might be involved in the occurrence and development of diseases through which substances that are homologous to food and medicine, genes interacting with the key active ingredients from the previous step were defined as biomarkers, and a three-layer interaction network of "food and medicine - key active ingredients - biomarkers" was constructed. Specifically, using the names of the food and medicine, the monomeric compounds of the key active substances, and the biomarkers as nodes, a hierarchical three-level node interaction network was constructed, with nodes connected by edges representing functional or structural associations. This resulted in the construction of the "food and medicine - key active ingredients - biomarkers" network using 3 key active substances and 7 biomarkers. Figure 6 The graph contains 11 nodes and 11 edges.

[0090] 8. Molecular docking analysis

[0091] To further validate the binding affinity between key active substances and biomarkers and improve the accuracy of the interaction network between targeted drugs and key genes, molecular docking was performed on each biomarker and key active substance. The 3D structures of the biomarker proteins (key targets) were downloaded from the UniProt database (https: / / www.uniprot.org / ) (accessed August 8, 2025), and the 3D structures of the key active substances were retrieved from the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / ) (accessed August 8, 2025). Proteins and ligands were uploaded to the CB-Dock online website (https: / / cadd.labshare.cn / cb-dock2 / php / index.php) for molecular docking. Figure 7 (Accessed: August 8, 2025), and the binding free energy was calculated. It is generally believed that when the conformation of the ligand-receptor complex is stable, a higher total score indicates a higher affinity between the receptor and the ligand. As shown in Table 6, the binding energy of the seven biomarkers to their corresponding key active substances is greater than 5 kcal / mol, indicating that they have good binding activity.

[0092] Table 6. List of Key Targets - Targeted Drug Total Score

[0093]

[0094] Note: Biomarker (3D Structural ID) is the gene name and corresponding protein code, targeteddrug is the name of the active substance, and Total Score is the binding energy release.

[0095] 9. Disease Prediction Analysis

[0096] To further explore the potential role of biomarkers in disease development, we used the Comparative Toxicogenomics Database (CTD, http: / / ctdbase.org / ) (accessed August 8, 2025) to predict their association with related diseases. Furthermore, we used Excel to create radar charts showing the expression differences of biomarkers across different disease types (sorted by inference score related to heart failure, with the top 8 diseases displayed), visually illustrating their potential biological significance in various disease states. Figure 8 As shown, seven genes are significantly associated with Heart Diseases, Heart Defects, Congenital Heart Disease, Heart Septal Defects, Ventricular Heart Septal Defects, Atrial Heart Septal Defects, Heart Failure, and Heart Injuries.

[0097] 10. Molecular Dynamics Simulation

[0098] To further verify the binding stability and interaction behavior between the key active ingredient and the biomarker-encoded protein, molecular dynamics simulations were performed on the complex structure at a dynamic level to capture its conformational changes under near-physiological conditions. Molecular dynamics simulations were conducted using GROMACS 2024.4 software, adhering to the AMBER99SB-ILDN force field rules throughout the process. The TIP3P (TIP3-point) water model was used, and the box shape was set to a cube. A 1-nanometer distance was maintained between the box edge and the protein edge, and ions were added to maintain the electroneutrality of the entire system. Energy minimization was performed using the steepest descent method. Subsequently, the system underwent a thermal bath (NVT, involving particle number, volume, and temperature) and a pressure bath (NPT, involving particle number, pressure, and temperature). During NVT and NPT, temperature coupling was achieved using the V-rescale method, with a reference temperature of 300 K, a step size of 2 femtoseconds, and a total process duration of 100 picoseconds. The molecular dynamics simulation duration was set to 20 nanoseconds. Calculate the RMSD, RMSF, total energy, and number of hydrogen bonds of the protein-drug complex. Figure 9-12 ).

[0099] like Figure 9 As shown, smaller fluctuations in RMSD indicate more stable binding between small molecules and proteins. Figure 10 RMSF represents the fluctuation of each amino acid during the simulation process. The results show that the protein amino acids are flexible and stable in binding with small molecule drug ligands during the simulation period. Figure 11 Throughout all simulations, the total energy remained at a low level with a stable trend and minimal fluctuations. Figure 12 This represents the change in the number of hydrogen bonds between small molecules and proteins during the simulation. The periodic change in the number of hydrogen bonds over the simulation period indicates a dynamic equilibrium at the binding interface, suggesting that the hydrogen bond network is resilient.

[0100] Example 2: Cellular experimental verification of the protective effect of Citreorosein against OGD-induced cardiomyocyte injury.

[0101] 1. Experimental Materials

[0102] 1.1 Experimental Cells

[0103] H9C2 rat cardiomyocytes were derived from Pronosei Biotechnology and cultured under the following conditions: DMEM + 10% FBS + 1% PS, 37℃, 5% CO2, and saturated humidity.

[0104] 1.2 Experimental Reagents and Consumables

[0105] Table 7 Experimental Reagents

[0106]

[0107] 1.3 Experimental Apparatus

[0108] Table 8 Experimental Instruments

[0109]

[0110] 2 Experimental Methods

[0111] 2.1 Basic Cell Culture Operations

[0112] 2.1.1 Cell resuscitation

[0113] Remove the frozen cells from the liquid nitrogen and immediately place them in a 37°C water bath, shaking the cryovial rapidly for 2 minutes to thaw them quickly. Add 9 mL of complete culture medium to a 15 mL centrifuge tube beforehand, then quickly add the thawed cells to the 15 mL centrifuge tube. Centrifuge, discard the supernatant, and seed the cells into culture flasks. Incubate the cells in a 37°C, saturated humidity, 5% CO2 cell culture incubator.

[0114] 2.1.2 Cell passage

[0115] Remove the cells, discard the culture medium in the flask, add 3 mL of sterile PBS buffer and rinse repeatedly, discard the PBS buffer, add 1 mL of trypsin to each flask, spread the trypsin solution evenly on the cell layer, gently shake the cell culture flask, and place it in a 37℃ incubator for 3 min to digest. Add 1 mL of complete culture medium to stop the digestion of trypsin, and use a pipette tip to repeatedly rinse the bottom of the culture flask to wash off any undetached cells. Transfer the liquid to a 15 mL centrifuge tube, centrifuge at 1000 r / min for 5 min, carefully discard the liquid in the centrifuge tube, and resuspend the precipitated cells with 2 mL of complete culture medium. Observe the cells under an inverted microscope, adjust the appropriate cell density, and seed them into culture flasks. Incubate at 37℃, saturated humidity, 5% CO2 in a cell culture incubator.

[0116] 2.1.3 Cell cryopreservation

[0117] Prepare cryovials in advance and label the cells with cell information. When the cells in the culture flask (25 cm2) reach 90% confluence, digest the cells with trypsin, centrifuge the cells and discard the supernatant. Resuspend the cells in 550 μL of basal culture medium and add a mixture of 400 μL FBS and 50 μL DMSO to the cryovial and mix by inversion. After the cells in the cryovial are cooled to 4℃ (20 min), -20℃ (30 min), and -80℃ (overnight), transfer them to liquid nitrogen for cryopreservation.

[0118] 2.2 Exploration of safe drug intervention concentrations

[0119] H9C2 cells with good growth and a confluence of 90% were harvested. After trypsin digestion, a single-cell suspension of 5 × 10⁴ cells / mL was prepared using complete culture medium and seeded into 96-well plates (100 μL / well). After overnight culture and cell adhesion, the culture medium was discarded, and different concentrations of Citreorosein (0, 1, 2, 4, 8, 10, 20, 40, 60 μM) were added for 24 h, with five replicates per group. After the intervention, 10 μL of CCK-8 solution was added to each well, and the cells were incubated in an incubator. After 1 h, the OD value at 450 nm was measured using a microplate reader. Concentrations that did not significantly affect cell survival were screened.

[0120] 2.3 Methods for constructing cardiomyocyte injury models

[0121] A cardiomyocyte injury model was constructed using the oxygen-glucose deprivation (OGD) method.

[0122] 2.4 Relevant Indicator Testing

[0123] 2.4.1 Experimental Grouping

[0124] Control group: H9C2 cells were cultured normally for 24 hours;

[0125] OGD group: H9C2 cells were cultured normally for 18 hours, then cultured under hypoxia for 6 hours.

[0126] OGD+Citreorosein group: H9C2 cells were treated with different concentrations of Citreorosein (5, 10, 20, 40 μM) for 24 h, and after 18 h of intervention, they were cultured under hypoxia for 6 h.

[0127] 2.4.2 CCK8 assay for cell proliferation

[0128] H9C2 cells with good growth and a confluence of 90% were harvested. After trypsin digestion, a single-cell suspension of 5 × 10⁴ cells / mL was prepared using complete culture medium and seeded into 96-well plates (100 μL / well, i.e., 5 × 10³ cells / well). After culturing at 37°C and 5% CO₂ for 24 h, the culture medium was discarded, and the cells were treated according to the experimental groups in 2.4.1, with 5 replicates per group. After the treatment, the culture medium was discarded, and 100 μL of prepared 10% CCK-8 solution was added to each well. The cells were then incubated in an incubator, and the OD value at 450 nm was measured using a microplate reader after 1 h.

[0129] 2.4.3 The kit was used to detect the LDH content in the cell supernatant.

[0130] (1) After H9C2 cells were treated according to the experimental grouping in 2.4.1, the cell supernatant of each group was collected, and the LDH content of the cell supernatant of each group was detected and analyzed using a kit.

[0131] (2) Detection method: After collecting cells, add 0.3 mL of physiological saline (or PBS) to each cell sample (the number of cells should not be less than 10⁶, the more the better). Sonicate the cells in an ice-water bath (200-300W power, run for 5 seconds, pause for 15 seconds, repeat 3-5 times), centrifuge at 4000 rpm for 10 minutes, and collect the supernatant for testing. The sample is tested using the undiluted solution. The Nanjing Jiancheng LDH reagent kit (catalog number: A020-2-2) is used for detection.

[0132] ① Operating steps

[0133] Table 9 Operating Procedures

[0134]

[0135] ② Definition and calculation formula of LDH in cell samples: Definition: 1 unit is defined as the production of 1 µmol of pyruvate in the reaction system when the sample is reacted with the matrix at 37°C for 15 minutes; Calculation formula: LDH activity in cell supernatant (U / L) = [(A assay - A control) / (A standard - A blank)]C ​​standard × N × 1000; Note: N: dilution factor of the sample before testing; C standard: concentration of standard solution, 0.2 μmol / mL; 1000: unit conversion, mL→L.

[0136] 2.4.4 ELISA detection of NT-proBNP, CK-MB, and cTnI levels in cell supernatant

[0137] (1) After H9C2 cells were treated according to the experimental grouping in 2.4.1, the cell supernatant of each group was collected, and the contents of NT-proBNP, CK-MB and cTnI in the cell supernatant of each group were detected and analyzed using a kit;

[0138] (2) Rat N-terminal probrain natriuretic peptide (NT-proBNP) enzyme-linked immunosorbent assay kit (Jining Biotechnology, JN6036).

[0139] (3) Rat creatine kinase isoenzyme MB (CK-MB) enzyme-linked immunosorbent assay kit (Jining Biotechnology, catalog number JN5496).

[0140] (4) Rat cardiac troponin I (cTn-I) enzyme-linked immunosorbent assay kit (Jining Biotechnology, JN5079).

[0141] 2.5 Data Statistics

[0142] All data are expressed as mean ± standard deviation. The results indicate that SPSS 19.0 software was used for statistical analysis of each group of data. One-way ANOVA was employed, and a p-value < 0.05 indicated a significant difference. GraphPad Prism 5.0 was used to assist in graphing.

[0143] 3 Experimental Results

[0144] 3.1 Exploration of safe drug intervention concentrations

[0145] (1) Detection of H9C2 cell proliferation after intervention with different concentrations of Citreorosein

[0146] Combined with Table 10 and Figure 13A bar chart of cell viability was used to assess cell survival after treatment with different concentrations of citreorosein, with the dimethyl sulfoxide (DMSO) group serving as the control group. Results showed that: ① Control group (0 μM / DMSO): cell viability was approximately 98%–100%, serving as the baseline; ② Low concentration range (1–20 μM): at concentrations of 1 μM, 2 μM, 4 μM, 8 μM, 10 μM, and 20 μM, cell viability remained between 98% and 105%, showing no significant difference from the control group, indicating that this concentration range had almost no inhibitory effect on cell survival; Medium-high concentration (40 μM): survival rate decreased slightly to approximately 96%–98%, still close to the control group level, indicating mild toxicity; ③ High concentration (60 μM): survival rate decreased significantly to approximately 90%, the lowest among all concentrations, indicating that a concentration of 60 μM began to produce significant toxicity to cells.

[0147] Table 10 Results of H9C2 cell proliferation assay after intervention with different concentrations of Citreorosein ( (n=5)

[0148]

[0149] Note: △ Compared with Citreorosein (0 μM), P<0.05; ▲ Compared with DMSO group, P<0.05; ▽ Compared with Citreorosein (1 μM), P<0.05; ▼ Compared with Citreorosein (2 μM), P<0.05; ☆ Compared with Citreorosein (4 μM), P<0.05; ★ Compared with Citreorosein (8 μM), P<0.05; ◇ Compared with Citreorosein (10 μM), P<0.05; ◆ Compared with Citreorosein (20 μM), P<0.05; ○ Compared with Citreorosein (40 μM), P<0.05.

[0150] 3.2 Results of the detection of the effect of different concentrations of Citreorosein on cell proliferation

[0151] Combined with Table 11 and Figure 14 The results showed that: ① The model was successfully established: after OGD treatment, the cell survival rate decreased from 100% to about 83%, and the difference from the control group was extremely significant (p<0.001), indicating that the damage model was effectively constructed; ② The protective effect of Citreorosein: with increasing concentration (5→20μM), the cell survival rate gradually increased; the protective effect reached statistical significance at a concentration of 20μM (p=0.041), which was its optimal protective concentration.

[0152] Table 11 Results of H9C2 cell proliferation detection under different interventions ( (n=5)

[0153]

[0154] Note: △ Compared with Control, P<0.05; ▲ Compared with OGD, P<0.05; ▽ Compared with OGD+Citreorosein (5μM), P<0.05; ▼ Compared with OGD+Citreorosein (10μM), P<0.05; ☆ Compared with OGD+Citreorosein (20μM), P<0.05; ★ Compared with OGD+Citreorosein (40μM), P<0.05.

[0155] 3.3 Results of LDH content detection in cell supernatant

[0156] The LDH release assay is one of the gold standards for in vitro assessment of cytotoxicity / protective effects. It directly reflects the degree of cell membrane integrity impairment and is highly positively correlated with the degree of cell necrosis. The results of this experiment corroborate previous cell viability assays, further confirming the protective effect of citreorosein against OGD-induced cell damage. (See Table 12 and...) Figure 15 The results showed that: ① The model was successfully established: LDH release increased from 130 U / L to 350 U / L after OGD treatment, which was significantly different from the control group (p<0.001), indicating that the cell damage model was successfully established; ② The protective effect of Citreorosein: With increasing concentration (5→20μM), LDH release gradually decreased, dropping to 240 U / L at 20μM, significantly reducing cell damage; all treatment groups were significantly different from the OGD group (p<0.001), proving that it has a clear cell protective effect.

[0157] Table 12 LDH activity analysis in cell supernatants of each group ( )

[0158]

[0159] Note: △ Compared with Control, P<0.05; ▲ Compared with OGD, P<0.05; ▽ Compared with OGD+Citreorosein (5μM), P<0.05; ▼ Compared with OGD+Citreorosein (10μM), P<0.05; ☆ Compared with OGD+Citreorosein (20μM), P<0.05.

[0160] 3.4 ELISA detection of NT-proBNP, CK-MB, and cTnI levels in cell supernatant

[0161] Combined with Table 13 and Figure 16This study demonstrates the release levels of three classic myocardial injury biomarkers in an OGD-induced cardiomyocyte injury model, as well as the protective effect of citreorosein (CK-MB: a classic early biomarker of myocardial injury, mainly found in the cytoplasm of cardiomyocytes, rapidly released when cardiomyocytes die; cTn-I: the most specific biomarker for myocardium, the "gold standard" for diagnosing acute myocardial infarction, whose level is directly related to the degree of myocardial necrosis; NT-proBNP: mainly secreted by ventricular myocytes, reflecting ventricular wall stress and dysfunction, and an important indicator for assessing heart failure and myocardial load).

[0162] Table 13 Analysis of the content of various indicators in cell supernatant of each group ( )

[0163]

[0164] Note: △ Compared with Control, P<0.05; ▲ Compared with OGD, P<0.05; ▽ Compared with OGD+Citreorosein (5μM), P<0.05; ▼ Compared with OGD+Citreorosein (10μM), P<0.05; ☆ Compared with OGD+Citreorosein (20μM), P<0.05; ★ Compared with OGD+Citreorosein (40μM), P<0.05.

[0165] 4. Results Analysis

[0166] 4.1 Effects of different concentrations of citreorosein on the safe concentration range of H9C2 cells

[0167] (1) Analysis of the proliferation of H9C2 cells after intervention with different concentrations of Citreorosein showed that the safe concentration range of Citreorosein for damaged myocardium was: ① In the concentration range of 0~20μM, Citreorosein had no significant effect on cell survival and could be regarded as a relatively safe experimental concentration; ② Toxicity threshold: When the concentration increased to 60μM, the cell survival rate showed a statistically significant decrease, indicating that the concentration was close to or reached the cytotoxicity threshold; ③ Control significance: The results of the DMSO group were consistent with those of the 0μM group, which verified that the solvent itself did not interfere with cell survival and the experimental results were reliable.

[0168] 4.2 Effects of different concentrations of citreorosein on OGD-induced H9C2 cell viability

[0169] The significant decrease in cell viability after OGD treatment indicates the successful establishment of the OGD-induced cardiomyocyte injury model. Citreorosein intervention improved the viability of damaged cells to varying degrees, suggesting that it can alleviate OGD-induced cardiomyocyte injury. Citreorosein at 20 μM showed the most significant protective effect.

[0170] 4.3 Effect of different concentrations of citreorosein on LDH content in cell supernatant

[0171] OGD treatment significantly increased LDH release, indicating impaired cell membrane integrity and successful model establishment. Intervention with citreorosein and TAXIFOLIN both reduced LDH levels, suggesting that both can alleviate OGD-induced cell damage. Citreorosein at 20 μM showed a more significant reducing effect, which was corroborated by cell viability assay results.

[0172] 4.4 Effects of different concentrations of Citreorosein on the levels of NT-proBNP, CK-MB, and cTnI in cell supernatant

[0173] All three myocardial injury markers were significantly elevated in the OGD group (p<0.001), validating the effectiveness of the OGD myocardial injury model. Citreorosein (5–20 μM) dose-dependently reduced the levels of the three markers, significantly alleviating OGD-induced myocardial injury and exerting a protective effect on damaged myocardium. Among them, the protective effect of the 20 μM Citreorosein group was more significant, and the trends of the three indicators were basically consistent with the results of LDH release and cell viability detection.

[0174] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention, enabling those skilled in the art to understand and apply it. However, it should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several simple deductions or substitutions can be made without departing from the inventive concept, without requiring creative effort. Therefore, any simple improvements made to the present invention by those skilled in the art based on the disclosure of this invention should be within the scope of protection of this invention.

Claims

1. Application of citreorosein, a medicinal and edible active ingredient, in the preparation of drugs for the prevention and treatment of myocardial injury.

2. The application according to claim 1, characterized in that, Myocardial injury refers to the damage to the heart muscle that accompanies the development and progression of heart failure.

3. The application according to claim 2, characterized in that, Myocardial injury is cardiomyocyte damage induced by hypoxia and / or hypoglycemia.

4. A pharmaceutical composition for preventing and treating myocardial injury, characterized in that, It contains the active pharmaceutical ingredient Citreorosein and pharmaceutically acceptable excipients.

5. The pharmaceutical composition according to claim 4, characterized in that, Pharmaceutically acceptable excipients include one or more of the following: fillers, binders, disintegrants, solubilizers, lubricants, stabilizers, or diluents.

6. A method for screening citreorosein, a medicinal and edible active ingredient for the prevention and treatment of myocardial injury, characterized in that, Includes the following steps: Gene expression profile data of heart failure disease samples and control samples were obtained, and differential expression analysis was performed on the gene expression profile data to obtain a set of differentially expressed genes related to heart failure. Construct a database of active ingredients that are both medicinal and edible, which includes the names of substances that are both medicinal and edible, the names of active ingredients, and the corresponding molecular structure information of the active ingredients. By predicting the molecular-protein interactions between the active ingredients in the database of active ingredients from the same source as food and medicine and the differentially expressed genes associated with heart failure, we can obtain gene-active ingredient-molecular structure information relationship pairs. Toxicological and drug-likeness evaluations were conducted on the active ingredients in the gene-active ingredient-molecular structure information relationship pairs to screen and obtain candidate key active ingredients. Molecular docking verification was performed on the candidate key active ingredients and their corresponding gene-encoded proteins. Based on the toxicological evaluation results, drug property evaluation results, and molecular docking verification results, the medicinal and edible active ingredient Citreorosein was identified from the candidate key active ingredients.

7. The screening method according to claim 6, characterized in that, Gene expression profile data were obtained from the GSE57338 dataset. Differential expression analysis was performed using the limma method, with the selection criteria being |log2FC|>0.5 and P.value<0.05, to obtain a set of differentially expressed genes related to heart failure.

8. The screening method according to claim 6, characterized in that, The construction of the database of active ingredients that are both food and medicine includes: using traditional Chinese medicine that is both food and medicine as the search object, screening active ingredients with oral bioavailability ≥30% and drug-likeness index ≥0.18, and obtaining the SMILES molecular structure information of the active ingredients.

9. The screening method according to claim 6, characterized in that, Molecular-protein interaction prediction was performed using the GraphBAN model. Based on the intersection of prediction results from BindingDB, BioSNAP, and KIBA models, gene-active ingredient-molecular structure information relationship pairs with an interaction probability greater than 0.5 were selected as candidate relationship pairs.

10. The screening method according to claim 6, characterized in that, The toxicological assessment criteria were that all five indicators—hERG channel blockade, human hepatotoxicity, ocular corrosivity, respiratory toxicity, and acute oral toxicity in rats—were negative, which was used to determine whether there was no significant toxicity or low toxicity.

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