Medicine composition of erianin and sorafenib and application of medicine composition in preparation of anti-liver cancer medicine

Abstract

The invention discloses an application of a pharmaceutical composition of erianin (ERI) and sorafenib (SOR) in preparation of an anti-liver cancer drug. The invention further discloses a pharmaceutical composition which comprises ERI and SOR, and the specific molar ratio of ERI to SOR is optimized and determined through an in-vitro experiment. The composition is developed based on multi-mode screening (network pharmacology prediction, molecular docking and molecular dynamics simulation) of STAT3 targets, and in-vitro experiments prove that the composition has synergistic anti-tumor activity and can be used for preparing anti-liver cancer drugs. The invention further discloses a development method of the ERI-SOR composition integrated with multi-mode computational simulation. Experiments prove that ERI and SOR both show a remarkable synergistic effect (combination index (CI) lt, 1) at the concentration of 0.5-16 [mu] M, CI at the optimal ratio (1: 1, 4 [mu] M + 4 [mu] M) reaches 0.617, the cell proliferation inhibition effect is remarkable compared with that of a single drug, and the excellent synergistic effect is shown.

Description

Technical Field

[0001] This invention belongs to the field of biopharmaceutical technology, specifically to a pharmaceutical composition of Erianin (ERI) and sorafenib (SOR) and its use in the preparation of anti-liver cancer drugs. This invention integrates network pharmacology prediction, molecular docking, molecular dynamics simulation, and in vitro experimental verification through multimodal computational simulation methods. Through systematic target screening and synergistic effect analysis, it provides an efficient technical path for the development of drug combinations. background

[0002] Hepatocellular carcinoma, as the main pathological type of primary liver cancer (accounting for 75% to 85%), has the following biological characteristics: limited early diagnostic markers, and most cases have already entered the advanced stage when diagnosed; at the same time, it shows high heterogeneity, and abnormal activation of specific molecular pathways can be detected in about 50% to 60% of cases. Existing experimental data show that there are significant differences in the sensitivity of hepatocellular carcinoma cell lines to targeted drugs. In the current field of drug research and development, molecular targeted preparations represented by SOR face the challenge of declining drug response rates - the median progression-free survival in phase III clinical trials is about 6 months, and the response rate in cell experiments is less than 30%. Existing technologies have the following limitations: targeted drugs are limited by the heterogeneity of tumor cell molecular characteristics; combination drug regimens lack precise dose optimization based on computational simulation; and drug combination screening systems require more systematic verification methods.

[0003] ERI is a bibenzyl phenolic compound (molecular formula C 18 H 22 O5, molecular weight 318.3643Da), has the following characteristics: a broad spectrum of biological activities, including antioxidant, anti-inflammatory, and tumor cell behavior regulation effects; experiments have shown that it has growth regulatory effects on various tumor cell lines such as hepatocellular carcinoma, colorectal cancer, and breast cancer; and molecular mechanisms can affect the activity state of signaling pathways such as STAT3, PI3K / AKT / mTOR, and ROS / JNK. Of particular note, the combination of ERI and drugs such as oxaliplatin can produce a synergistic effect, the mechanism of which may involve the regulation of drug transporter expression and intervention in the JAK2 / STAT3 pathway. However, the following unresolved issues remain regarding the mechanism of ERI's effect on SOR drug sensitivity: the specificity of the target has not been fully elucidated; the optimal combination ratio requires systematic optimization; and the molecular dynamics binding characteristics need to be verified. Summary of the Invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide an application of a pharmaceutical composition of ERI and SOR in the preparation of anti-liver cancer drugs.

[0005] The technical problem that the present invention also aims to solve is to provide a pharmaceutical composition of ERI and SOR.

[0006] The final technical problem to be solved by the present invention is to provide a method for developing an ERI-SOR combination that integrates multimodal computational simulation. By establishing a target prediction system based on network pharmacology, combining molecular docking and molecular dynamics simulation to verify the binding affinity and conformational stability of ERI with the target, and using in vitro cell experiments to determine the drug ratio combination with synergistic effect (combination index (CI) <1), the technical problem of the lack of a systematic verification method for drug combination development in the existing technology is solved.

[0007] Technical solution: In order to solve the above technical problems, the present invention provides the use of a pharmaceutical composition of ERI and SOR in the preparation of anti-liver cancer drugs.

[0008] Wherein, the concentration of the ERI is 0.5 to 16 μM, and the concentration of the SOR is 0.5 to 16 μM.

[0009] Wherein, the drug is a drug that inhibits the phosphorylation level of STAT3 protein and / or the expression level of Cyclin D1 protein.

[0010] The present invention also includes a pharmaceutical composition comprising ERI and SOR, wherein the concentration of ERI is 0.5 to 16 μM, and the concentration of SOR is 0.5 to 16 μM.

[0011] Wherein, the concentration of ERI in the pharmaceutical composition is 4 μM, and the concentration of SOR is 4 μM.

[0012] Wherein, the molar ratio of ERI to SOR in the pharmaceutical composition is 1:1.

[0013] The pharmaceutical composition showed a synergistic effect (CI<1) in in vitro experiments.

[0014] The present invention also includes a method for developing ERI-SOR compositions integrating multimodal computational simulations, comprising the following steps:

[0015] 1) ERI structure files were imported into the SwissTargetPrediction database and the ComparativeToxicogenomics Database (CTD) to predict potential targets. Genes associated with SOR resistance in hepatocellular carcinoma were obtained from the GeneCards database. Fifty common targets were obtained by intersection, and a protein-protein interaction (PPI) network was constructed. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis were used to reveal the biological characteristics of common targets. The top 10 core interacting targets were screened using the cytoHubba plug-in of Cytoscape software using the maximum neighborhood component (MNC), maximum clique centrality (MCC), and node degree algorithms, respectively. Multidimensional intersection analysis was performed with differentially expressed SOR resistance genes in hepatocellular carcinoma from the GSE94550 dataset, ultimately identifying STAT3 as a key regulatory target.

[0016] 2) The protein crystal structure of STAT3 (PDB code: 6njs) was docked with ERI to evaluate binding ability. The complex system was simulated by molecular dynamics for 100 ns using GROMACS software to verify its binding stability and dynamic behavior.

[0017] 3) The synergistic effect of ERI-SOR combination was verified by in vitro cell experiments: CCK-8 assay was used to detect the effects of ERI alone and in combination with SOR on Huh7 R The inhibitory effect on cell proliferation was detected by Western blot to detect changes in STAT3 protein phosphorylation level, STAT3 total protein and / or Cyclin D1 protein expression level to verify the target mechanism.

[0018] Among them, the specific steps of predicting potential targets in step 1) are: importing the chemical structure file of ERI into the SwissTargetPrediction database, setting the Probability threshold to 0.1, and predicting the top 100 targets most likely related to ERI; searching the CTD database with "Erianin" as the keyword to obtain ERI related targets; taking the predicted targets of the two databases and removing duplicates to obtain the potential targets of ERI; using "sorafenib resistant hepatocellular carcinoma" as the keyword, searching the hepatocellular carcinoma SOR resistance-related genes from the GeneCards database, and screening hepatocellular carcinoma SOR resistance targets with a Relevance score ≥ 10; performing intersection analysis on ERI targets and hepatocellular carcinoma SOR resistance targets using the Venny 2.1 online tool to obtain 50 common targets; as an optimal method, the steps of constructing the PPI network and screening the core targets in step 1) are: importing the obtained 50 common targets into the STRING database, setting the species to "Homo sapiens”, with an interaction score threshold of ≥0.4, a PPI network was constructed and imported into Cytoscape software, and the top 10 core targets were screened using the MNC, MCC, and Degree algorithms of the cytoHubba plug-in, respectively.

[0019] Among them, the GEO data analysis in step 1) and the key target screening process for ERI-regulated SOR resistance in hepatocellular carcinoma are as follows: the parental cell group (GSM2478034, GSM2478035, GSM2478036) and the resistant cell group (GSM2478028, GSM2478029, GSM2478030) in the GSE94550 dataset were selected, and SOR resistance differential genes (SRDEGs) (|logFC|>1 and adj.P<0.05) were screened out using the limma package. The SOR resistance differential genes (SRDEGs) (|logFC|>1 and adj.P<0.05) were cross-validated with the top 10 core targets screened by the MNC, MCC and Degree algorithms, respectively. Finally, STAT3 was identified as the core target of ERI-regulated SOR resistance in hepatocellular carcinoma.

[0020] Among them, the specific steps of the molecular dynamics simulation experiment in step 2) are: using the CHARMM36 force field and TIP3P water model to perform system construction (adding boxes, water models, ions), energy minimization, NVT / NPT balance, 100ns molecular dynamics simulation, and analysis of key parameters of the molecular dynamics simulation of the ERI-STAT3 complex, including the root mean square deviation (RMSD) of the protein backbone, the root mean square fluctuation (RMSF) of the binding site residues, the radius of gyration (Rg) of the complex structure, the stability of the hydrogen bond between ERI and Gln644 residues, and changes in the free energy landscape.

[0021] The in vitro cell experiment in step 3) includes the use of CCK-8 method to detect the effect of ERI alone and in combination with SOR on Huh7 R The inhibitory effect on cell proliferation and the synergistic effect of the combined drug are evaluated. In step 3), the in vitro cell experiment includes a Western blot assay to detect changes in the levels of key signaling pathway proteins such as STAT3 and p-STAT3 in cells before and after treatment with ERI alone and in combination with SOR.

[0022] The present invention constructed a network pharmacology model by integrating the SwissTargetPrediction, CTD, and GeneCards multi-source databases, systematically screened potential targets for ERI to regulate SOR resistance in hepatocellular carcinoma, and used the Venny 2.1 tool for multi-source data integration to obtain 50 drug-disease co-targets. GO / KEGG enrichment analysis showed that these targets were significantly enriched in specific signaling pathways. The cytoHubba plug-in of Cytoscape software was further used to screen the top 10 core interacting genes using the MNC, MCC, and Degree algorithms, respectively. Multidimensional cross-validation was performed in combination with the differential gene expression profile of the GSE94550 data set (|logFC|>1, adj.P<0.05), and ultimately STAT3 was determined to be the core target of ERI to regulate SOR resistance in hepatocellular carcinoma. Molecular docking experiments (PDB ID: 6njs, binding energy -5.9 kcal / mol) and molecular dynamics simulations (GROMACS2023.2, 100 ns trajectory) confirmed that ERI and STAT3 can form a stable complex system (hydrogen bond occupancy > 80%). In vitro experiments have shown that the ERI-SOR combination (optimal molar ratio 1:1, concentration 0.5-16 μM) has a positive effect on the expression of STAT3 in specific cell models (Huh7). R ) showed a synergistic effect (CI<1). The multimodal screening method established in the present invention provides a new technical strategy for the development of drug combinations.

[0023] The present invention will focus on the following three dimensions: (1) Composition ratio optimization: using systematic experimental design to systematically examine the synergistic effects of different concentration combinations and establish a standardized in vitro evaluation method; (2) Mechanism of action research: in-depth analysis of drug-target interaction characteristics through molecular docking and 100ns molecular dynamics simulation; (3) Technology system expansion: developing a combination screening prediction model based on computational simulation to improve R&D efficiency. The innovation of the present invention is mainly reflected in: for the first time, a three-in-one drug combination development technology system of "multi-source data integration-computational simulation prediction-in vitro experimental verification" has been established. This system has the characteristics of standardized methods, repeatable results, and strong universal application. It not only provides technical support for the development of ERI-SOR compositions, but its methodological framework can also be directly transferred to the combination research of other natural products and chemical drugs, providing a new technical path and research paradigm for innovative drug development.

[0024] Beneficial Effects: Compared with existing technologies, the present invention has the following significant advantages: First, by innovatively integrating multiple technologies such as network pharmacology prediction, molecular docking, molecular dynamics simulation, and in vitro experimental verification, a comprehensive drug combination development methodology has been constructed, achieving a full research process from target prediction to synergistic effect verification. Experimental data showed that ERI and SOR exhibited significant synergistic effects (CI <1) in the concentration range of 0.5 to 16 μM. The optimal ratio (1:1, 4 μM + 4 μM) achieved a CI value of 0.617, with a significantly greater cell proliferation inhibition effect than either drug alone, demonstrating excellent synergistic effect. Second, the multimodal research method of "database screening-computational simulation-in vitro validation" established by the present invention not only addresses technical challenges in traditional drug combination development, such as inaccurate target prediction and lack of theoretical basis for ratio optimization, but also significantly improves R&D efficiency through standardized operating procedures. This methodology is highly universal and reproducible, and its technical approach and implementation plan can be directly transferred and applied to the development and research of other drug combinations, providing new technical means and methodological references for innovative drug development. In addition, the interdisciplinary technology integration strategy adopted in this study has opened up new ideas for the combined application research of natural products and modern chemical drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Analysis of the plant origin, structural formula, and target of ERI for reversing SOR resistance in hepatocellular carcinoma. (A) Morphological characteristics of the medicinal plant Dendrobium chrysotoxum (left) and the chemical structure of ERI (right); (B) Venn diagram of drug-disease target intersection; (C) PPI network of 50 common targets constructed using the STRING database.

[0026] Figure 2Functional enrichment analysis of common drug-disease targets. (A) Biological Process (BP): Regulation of tumorigenesis (regulation of cell proliferation; positive regulation of inflammatory response; activation of angiogenesis); (B) Cellular Component (CC): Subcellular localization (plasma membrane; nucleoplasm; protein kinase complex); (C) Molecular Function (MF): Regulation of kinase activity (protein kinase binding; phosphatase receptor activity; phosphotransferase activity); (D) KEGG Pathway: Core cancer signaling network (PI3K-Akt signaling pathway; pathways in cancer; viral oncogenic pathways).

[0027] Figure 3 Huh7 (parent) and Huh7 based on GSE94550 dataset R (SOR resistance) differential gene expression profiling analysis of hepatocellular carcinoma cell lines. (A) Huh7 and Huh7 R Sample transcriptome data normalization. Left: Unnormalized data distribution; Right: Normalized data distribution; (B) Volcano plot of genome-wide gene expression differences, SRDEGs screening criteria: |logFC|>1 and adj.P<0.05; (C) Hierarchical clustering heat map showing the expression patterns of the top 30 up-regulated genes (yellow markers) and the top 30 down-regulated genes (blue markers), revealing that Huh7 R Significant transcriptional differences relative to Huh7.

[0028] Figure 4 Network topology-based core target screening and JAK-STAT3 pathway visualization. (A) Top 10 hub targets screened by the MNC algorithm (node ​​size corresponds to degree value, warm color gradient indicates p.adject significance); (B) Top 10 hub targets screened by the MCC algorithm (visualization parameters are the same as in A); (C) Top 10 hub targets screened by the Degree algorithm (visualization parameters are the same as in A); (D) Multidimensional data integration and cross-validation confirmed STAT3 as a core hub target; (E) JAK-STAT3 signaling pathway map created using the "pathview" package.

[0029] Figure 5 Molecular docking diagrams of ERI and STAT3 receptor. (A) Molecular docking diagram of ERI-STAT3 receptor visualized using AutoDockTools 1.5.7 software (double spheres represent hydrogen bonds); (B) Two-dimensional interaction diagram of ERI-STAT3 receptor generated using LigPlot+ v2.2.9, showing key amino acid residues and hydrogen bonds (green dashed lines represent hydrogen bonds); (C) Molecular docking diagram of ERI-STAT3 receptor rendered using PyMOL 3.1.4.1 software (red dashed lines represent hydrogen bonds).

[0030] Figure 6Molecular dynamics simulation analysis of the ERI-STAT3 complex (100 ns trajectory). (A) STAT3 protein backbone RMSD convergence curve (0-100 ns, fluctuation amplitude <0.3 nm); (B) STAT3 protein residue-specific RMSF map (blue curve, fluctuation amplitude <0.2 nm for residues 640-650 in the binding region); (C) ERI-STAT3 complex Rg dynamic change curve (1.5-3.5 nm, black curve indicates main chain compactness); (D) ERI-STAT3 complex solvent accessible surface area (SASA) fluctuation curve (260-280 nm 2 , the hydrophobic core stabilizes and ensures the structural integrity of the protein); (E) Time series distribution of the number of hydrogen bonds in the ERI-STAT3 complex (ERI and Gln 644 Continuous hydrogen bond formation, occupancy >80%); (F) Free energy landscape of the ERI-STAT3 complex (blue area: low-energy stable state, red area: high-energy transition state); (G) Covariance matrix of atomic motions of the ERI-STAT3 complex (dark blue: strong positive correlation, bright red: strong negative correlation); (H) Global conformational free energy landscape of the ERI-STAT3 complex (blue gradient area: high-probability conformation, red outer edge area: transient high-energy conformation).

[0031] Figure 7 This is an in vitro validation of the effect of ERI on reversing the SOR resistance in liver cancer. (A) SOR monotherapy against Huh7 and Huh7 R Dose-effect curve of cells; (B) ERI single drug to Huh7 R Cell proliferation inhibition curve; (C) ERI-SOR combination (molar ratio 1:1) inhibition of Huh7 R CompuSyn software calculated and plotted the synergistic inhibitory effect of different concentrations of ERI combined with SOR on Huh7 cells. R Scatter plot of CI value of cells and drug combination effect, Figure 7 The blue circles in D are from left to right Figure 7 The horizontal axis in C corresponds to the concentrations from left to right (0.5+0.5, 1.0+1.0, 2+2, 4+4, 8+8, 16+16). (E) Western blot analysis of changes in p-STAT3 phosphorylation, total STAT3 expression, and the ratio of p-STAT3 to STAT3 after treatment with the optimal concentration combination of ERI and SOR. (F) Western blot analysis of changes in CyclinD1 protein expression after treatment with the optimal concentration combination of ERI and SOR. **P < 0.01 indicates statistically significant differences compared with the control group. DETAILED DESCRIPTION

[0032] Statistical analysis and mapping in the examples of the present invention were performed using GraphPad Prism 10.4.0 and CompuSyn 3.0.1. Data between groups were compared using one-way analysis of variance (ANOVA) supplemented by Tukey's multiple comparison test. CI values ​​were calculated according to the Chou-Talalay method. Continuous variables were expressed as mean ± standard error (SEM) to reflect the estimated error of the mean. Data normality was assessed by the Shapiro-Wilk test (P>0.05 was considered to be in accordance with the normal distribution). Statistical significance thresholds were set as: *P<0.05 or **P<0.01.

[0033] Example 1

[0034] 1. Drug and disease target prediction

[0035] The ERI used in the present invention is extracted from the medicinal plant Dendrobium chrysotoxum Lindl., and its plant morphological characteristics and the chemical structure of ERI are as follows: Figure 1 As shown in A. First, the chemical structure file of ERI (sdf format) was obtained from the PubChem database. The structure file was imported into the SwissTargetPrediction database (http: / / www.swisstargetprediction.ch / ), the Probability threshold was set to 0.1, and 100 potential targets of ERI were predicted. At the same time, the CTD database (https: / / ctdbase.org / ) was searched with "Erianin" as the keyword to obtain 131 related targets. The predicted targets of the above two databases were merged and deduplicated. Subsequently, "sorafenib resistant hepatocellular carcinoma" was used as the search term to screen liver cancer SOR resistance-related genes with a Relevance score ≥ 10 in the GeneCards database (https: / / www.genecards.org / ), and a total of 1112 disease targets were obtained.

[0036] 2. Constructing a drug-disease target network

[0037] The Venny 2.1 online tool (https: / / bioinfogp.cnb.csic.es / tools / venny / ) was used to perform intersection analysis on potential ERI targets and SOR resistance-related genes in hepatocellular carcinoma, and 50 common targets were obtained. The results were visualized using a Venn diagram ( Figure 1B). Subsequently, the common targets were imported into the STRING database platform (https: / / cn.string-db.org / ), the species was set to "Homo sapiens", and the interaction score threshold was set to ≥0.4 to construct the PPI network. The interaction relationship between targets was systematically analyzed ( Figure 1 C), and export PPI network data for subsequent functional enrichment and core target screening.

[0038] 3. GO and KEGG pathway enrichment analysis

[0039] In RStudio software, we used R packages such as "clusterProfiler", "org.Hs.eg.db", "enrichplot", "ggplot2" and "pathview" to systematically perform GO functional annotation and KEGG pathway enrichment analysis on the 50 common targets, and drew visualization charts related to BP, CC, MF and pathways. Functional enrichment analysis showed that the 50 common targets of ERI and SOR-resistant hepatocellular carcinoma were significantly enriched at multiple functional levels: in BP, they were mainly enriched in tumor-related processes such as regulation of cell proliferation, inflammatory response and angiogenesis ( Figure 2 A); in CC, it is significantly enriched in subcellular structures such as membrane, chromosome telomeric region and protein kinase complex ( Figure 2 B); in MF, it mainly involves catalytic functions such as protein kinase activity, phosphatase binding, and modification of phosphorus-containing groups ( Figure 2 C); at the KEGG pathway level, it was significantly enriched in classic oncogenic signaling networks such as the PI3K-Akt signaling pathway and cancer-related pathways ( Figure 2 D) These multidimensional analysis results systematically indicate that ERI may reverse SOR resistance by synergistically regulating key biological processes such as cell proliferation, inflammatory microenvironment, and epigenetic modification.

[0040] 4. GEO data analysis and differential expression analysis

[0041] The Series Matrix File of the microarray dataset GSE94550 and the platform file GPL17586-45144 were obtained from the GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ). Experimental design: GSM2478034, GSM2478035, and GSM2478036 were selected as the parental cell control group (Huh7), and GSM2478028, GSM2478029, and GSM2478030 were selected as the SOR-resistant group (Huh7 R ). First, the probe matrix (probeMatrix) was converted to the gene matrix (geneMatrix) through the Perl script, and the expression level of the genes corresponding to the multiple probes was averaged. Then, in the RStudio environment, the normalizeBetweenArrays function was used to normalize the data, and the limma package was applied to perform differential expression analysis. The screening criteria were SRDEGs with |logFC|>1 and corrected P<0.05. Finally, the volcano map was drawn by the "ggplot2" package, and the hierarchical clustering heat map was generated by the "pheatmap" package for visualization. The quality control of the standardization process was demonstrated by displaying the distribution characteristics of the original and standardized data through the sample histogram ( Figure 3 A), the difference analysis results are shown in the volcano plot ( Figure 3 B) and heatmap ( Figure 3 C) Intuitive presentation, where significantly up-regulated genes are marked in yellow and down-regulated genes are marked in blue.

[0042] 5. Drug-disease core target screening

[0043] The present invention uses Venny 2.1 online analysis tool (https: / / bioinfogp.cnb.csic.es / tools / venny / ), first imports PPI network data into Cytoscape software, and uses the MNC ( Figure 4 A), MCC( Figure 4 B) and Degree( Figure 4 C) Three topological algorithms were used to screen out the top 10 core interaction targets among the common drug-disease targets; these core targets were then subjected to multidimensional cross-validation analysis with SRDEGs, ultimately determining that STAT3 is the key core target for ERI to regulate SOR resistance in hepatocellular carcinoma. To further analyze the biological function of STAT3, this study used the "pathview" package based on the RStudio platform to perform a systematic visualization analysis of the JAK-STAT3 signaling pathway, fully presenting the molecular regulatory network mediated by this pathway and its biological functional characteristics ( Figure 4 D).

[0044] 6. Molecular docking experiment

[0045] The present invention first obtains the 2D chemical structure of ERI from the PubChem database, performs energy optimization using the MM2 force field in ChemBio3D software, and saves it as a mol2 format file. Simultaneously, the STAT3 protein crystal structure (PDB code: 6njs) is downloaded from the PDB database and processed using PyMOL 3.1.4.1 software to obtain PDB format files of the protein receptor and its original ligand KQV. The following key steps are completed in AutoDockTools 1.5.7 software: (1) dehydration, hydrogenation, and Gasteiger charge assignment of the STAT3 protein receptor; (2) hydrogenation, Gasteiger charge assignment, and selection of rotatable dihedral angles (Torsions) of the ERI molecule; and (3) the processed protein receptor and ligand are saved in pdbqt format, respectively. Based on the binding site of the original ligand KQV, the Grid Box module is used to accurately locate the STAT3 protein active pocket and generate a gpf format parameter file. The binding affinity of ERI to STAT3 calculated by AutoDock Vina software was -5.9 kcal / mol, indicating moderate binding strength. Key interaction analysis showed that ERI formed a stable hydrogen bond with the Gln residue at position 644 of the STAT3 protein. 2D / 3D visualization results were finally generated using AutoDockTools 1.5.7, PyMOL 3.1.4.1, and LigPlot+ v2.2.9 software ( Figure 5 AC), clearly showing the binding mode and molecular interaction characteristics of the ERI-STAT3 complex.

[0046] 7. Molecular dynamics simulation experiment

[0047] Based on the molecular docking results, the present invention uses GROMACS2023.2 software to perform a systematic molecular dynamics simulation of the ERI-STAT3 complex system. First, SPDBV_4.10 is used to improve the STAT3 protein structure (PDB code: 6njs), and the missing atoms are supplemented and saved in PDB format; the ERI molecules (mol2 format) obtained by docking and the protein receptor are converted into topology files and merged. The simulation uses the CHARMM36 force field and the TIP3P water model. The main steps include: system construction (adding solvent box, water molecules and ions), energy optimization (balancing charge, energy minimization), equilibrium simulation (NVT / NPT pre-equilibrium) and 100ns production simulation. Trajectory analysis calculated parameters such as RMSD, RMSF, Rg, SASA, number of hydrogen bonds and stability, and plotted the covariance matrix and free energy landscape.

[0048] Figure 6 A (RMSD curve, horizontal axis 0-100 ns, vertical axis 0-0.4 nm) shows that the root mean square deviation of STAT3 main chain atoms stabilizes at 0.15-0.35 nm (red curve) after 60 ns, indicating that the complex reaches conformational equilibrium; Figure 6 In B (RMSF spectrum, horizontal axis residues 0-700, vertical axis 0-0.6 nm), the 0-200 segment fluctuates significantly, showing high flexibility (peak near 0.5 nm), while the fluctuation near the binding site Gln644 (640-650) is <0.2 nm, indicating that the local rigidity of the binding interface is enhanced; Figure 6 C (Rg spectrum, horizontal axis 0-100 ns, vertical axis 0-4 nm) shows that the total Rg is stable at 3.25-3.5 nm (black curve), and the Rg differences in the x / y / z axes (red / green / blue curves) reflect the structural anisotropy; Figure 6 D(SASA curve, horizontal axis 0~100ns, vertical axis 250~290nm 2 ) showed that the SASA of the complex was stable (270 ± 13 nm 2 ), the collaborative RMSD / RMSF / Rg data jointly support the structural integrity during the 100ns simulation; Figure 6 E (hydrogen bond timing analysis diagram, horizontal axis 0–100 ns, vertical axis 0–5 bond numbers) shows that Gln644-ERI forms a persistent interaction (hydrogen bond occupancy >80%); Figure 6 F (free energy landscape, PC1: 0.0–0.36, PC2: 3.37–3.48, ΔG = 0–10 kcal / mol) showed that the dominant conformation was concentrated in PC1 = 0.11–0.24 (ΔG < 5 kcal / mol); Figure 6 G (covariance matrix diagram, atomic range: abscissa 1-521, ordinate 3-523, r = 0.00-0.40) revealed a strong positive correlation (r > 0.3) among the local regions of STAT3 in the ERI-STAT3 complex, showing a trend of cooperative motion, suggesting that ERI may regulate the conformational dynamics of STAT3 through allosteric effects; Figure 6 The global conformational free energy landscape (PC1: -4.98–5.45, PC2: -4.79–3.71) further confirmed the concentration of low-energy conformations (blue gradient) and the transient nature of high-energy conformations (red edge). The results indicate that ERI anchors the STAT3 active site via hydrogen bonds (donor-acceptor distance <0.35 nm), forming a kinetically stable (RMSD <0.35 nm) and thermodynamically favorable (ΔG <5 kcal / mol) complex.

[0049] Example 2 In vitro cell experiment verification

[0050] 1. Reagents and chemicals

[0051] ERI and SOR used in the experiment were purchased from Shanghai MacLean Biochemical Technology Co., Ltd. (purity ≥98%); high-glucose Dulbecco's modified Eagle's medium (High-glucose DMEM), penicillin-streptomycin double antibody solution, and phosphate-buffered saline (PBS) were purchased from Wuhan Sevier Biotechnology Co., Ltd.; fetal bovine serum (FBS) was provided by Shanghai Nova Pharmaceutical Technology Co., Ltd.

[0052] 2. Cell culture

[0053] Huh7 was purchased from Nanjing KeyGen Biotechnology Development Co., Ltd. (Cat. No. KGG3309-1) and maintained in DMEM complete culture medium (supplemented with 10% FBS and 1% penicillin-streptomycin). The SOR-resistant hepatocellular carcinoma cell line Huh7 was established using a concentration gradient method. R The initial induction concentration of SOR was 0.5 μM, and the concentration was gradually increased. The concentration was increased every 1-2 weeks. The concentration was increased by about 1.5 times each time. During this period, the appropriate concentration was adjusted according to the specific state of the cells, and finally a liver cancer SOR-resistant cell line Huh7 with a resistance index of 5.63 was obtained. R The drug-resistant cell line Huh7 R The cells were cultured in a medium containing 0.2 μM SOR to maintain their drug resistance. R All cells were cultured in a complete DMEM medium at 37°C in a cell culture incubator containing 5% CO2 for subsequent experiments.

[0054] 3. Cell viability assay and drug combination evaluation

[0055] Huh7 and Huh7 in the logarithmic growth phase R After digestion with 0.25% trypsin, the cells were counted using a hemocytometer and the cell density was adjusted to 3 × 10 3 Cells were plated at 400 nm / well in a 96-well culture plate. When the cells adhered and grew to approximately 65% ​​confluence, they were treated with different concentrations of ERI (0.5-16 μM), SOR (0.5-16 μM) alone, or in combination (fixed ratio 1:1, each concentration combination) for 48 hours. After treatment, the CCK-8 kit (Biosharp, Cat. No. BS350B) was used to perform the experimental procedures according to the instructions. The absorbance (OD value) of each well was measured at a wavelength of 450 nm using a microplate reader. The relative cell viability was calculated using the following formula: [(OD value of the drug-treated group - OD value of the blank control group) / (OD value of the untreated control group - OD value of the blank control group)] × 100%.

[0056] In order to evaluate the effect of ERI combined with SOR on Huh7 R The synergistic effect of cells was calculated using CompuSyn software based on the Chou-Talalay method. This method uses the median effect equation fa / fu=(D / D m ) m Calculate the drug effect, where fa is the inhibition rate (CCK-8 assay) at a specific concentration D (D, derived from the concept of "Dose" in the dose-effect relationship in pharmacology, specifically refers to Concentration in cell experiments), fu = 1-fa is the survival rate, D m The half-maximal inhibitory concentration (IC 50 ), m is the slope of the dose-effect curve. By inputting different fa values, the corresponding drug concentration (D) can be obtained, and then the CI value can be calculated: CI=(D1 / D X1 )+(D2 / D X2 )+α(D1D2 / D X1 D X2 ), where D X1 、D X2 The specific level of pharmacodynamic inhibition produced by a single drug (such as ED 50 , ED 75 The concentrations of each drug when the two drugs are used together to produce the same specific inhibition level (equal specific inhibition levels) are calculated. D1 and D2 are the actual concentrations of each drug when the two drugs are used in combination to produce the same specific inhibition level. α is the drug interaction pattern coefficient. A value of 0 indicates that the two drug mechanisms of action are mutually exclusive (independent), and a value of 1 indicates that the effects are non-mutually exclusive (possible overlapping or synergistic mechanisms). The nature of the interaction is determined based on the CI value: CI < 0.3 (strong synergy), 0.3 ≤ CI < 0.7 (moderate synergy), 0.7 ≤ CI < 1 (mild synergy), CI = 1 (additive), and CI > 1 (antagonistic). Subsequently, the ERI and SOR concentrations that produce the optimal CI value are selected for subsequent experiments.

[0057] The results of drug sensitivity test showed that after 48 hours of SOR treatment, the drug-resistant strain Huh7 R The half inhibitory concentration (IC 50 =14.82 μM) compared with parental Huh7 cells (IC 50 =2.63μM) was significantly increased (P<0.01), and the drug resistance index reached 5.63, indicating that a liver cancer cell model with stable SOR drug resistance characteristics was successfully constructed ( Figure 7 A) CCK-8 assay showed that ERI alone could inhibit Huh7 in a dose-dependent manner. R Cell proliferation, its IC 50 The value was 5.87 μM ( Figure 7B). CI analysis revealed that the combination of ERI (4 μM) and SOR (4 μM) exhibited a significant synergistic anti-tumor effect (CI = 0.617). This optimal combination (molar ratio 1:1) was selected for subsequent molecular mechanism studies ( Figure 7 C, Figure 7 D).

[0058] 4. Western blot experiment

[0059] Before the experiment, a mixture of protease and phosphatase inhibitors (Shanghai Biyuntian Biotechnology Co., Ltd., Catalog No.: P1045) was added to the RIPA lysis buffer (Wuhan Saiwei Biotechnology Co., Ltd., Catalog No.: G2002-100ML) in proportion to prepare a lysis working solution with a final concentration of 1%. R After cells were thoroughly lysed with lysis buffer, they were centrifuged at 13,000 rpm for 10 minutes at 4°C to pellet cell debris. Extracted proteins were accurately quantified using a BCA protein concentration assay kit (Shanghai Beyotime Biotechnology Co., Ltd., Catalog No. P0012S). Proteins were mixed with 5× loading buffer and denatured at 100°C for 10 minutes. Proteins were separated by SDS-PAGE gel electrophoresis and transferred to a PVDF membrane. The membrane was blocked with 3% BSA for 2 hours and washed three times with TBST buffer. The blocked PVDF membrane was incubated overnight at 4°C with primary antibodies (STAT3, Immunoway, Catalog No. YM8325; p-STAT3, Immunoway, Catalog No. YP0251; β-actin, Immunoway, Catalog No. YT0099; and Cyclin D1, Immunoway, Catalog No. YT1173). The next day, goat anti-rabbit secondary antibody (Immunoway, Cat. No. RS0002) was used to incubate at room temperature for 2 hours, and then ultrasensitive ECL chemiluminescent substrate (Nanjing Jiyi Biotechnology Co., Ltd., Cat. No. BL523A) was added. The protein blot signal was detected by ChemiDoc MP chemiluminescent imaging system, and the expression level of the target protein was semi-quantitatively analyzed. Western blot analysis further confirmed that ERI alone could specifically downregulate Huh7. R The phosphorylation level of STAT3, a key protein in the STAT3 signaling pathway, and the expression of Cyclin D1, a proliferation-related protein, were significantly inhibited by the ERI / SOR combination group ( Figure 7 E, Figure 7 F).

[0060] This study integrates network pharmacology prediction, molecular docking simulation, and in vitro experimental verification to construct a complete ERI-SOR drug combination development and evaluation system. The results show that within a concentration range of 0.5 to 16 μM (six isocratic concentration gradients), the drug combination exhibits a significant synergistic anti-proliferative effect (CI < 1). Among them, the synergistic effect of the 1:1 ratio (4 μM + 4 μM) is the most prominent, with a CI as low as 0.617, indicating that the combination's cell proliferation inhibition effect is significantly superior to that of single-drug treatment.

Claims

1. Application of the pharmaceutical composition of ERI and SOR in the preparation of anti-liver cancer drugs.

2. The use according to claim 1, characterized in that The concentration of the ERI is 0.5 to 16 μM, and the concentration of the SOR is 0.5 to 16 μM.

3. The use according to claim 1, characterized in that The drug is a drug that inhibits the phosphorylation level of STAT3 protein and / or the expression level of Cyclin D1 protein.

4. A pharmaceutical composition, characterized in that The pharmaceutical composition comprises ERI and SOR, wherein the concentration of ERI is 0.5 to 16 μM, and the concentration of SOR is 0.5 to 16 μM.

5. The pharmaceutical composition according to claim 4, characterized in that The concentration of ERI in the pharmaceutical composition is 4 μM, and the concentration of SOR is 4 μM.

6. The pharmaceutical composition according to claim 4, characterized in that The molar ratio of ERI to SOR in the pharmaceutical composition is 1:

1.

7. A method for developing ERI-SOR compositions integrating multimodal computational simulations, characterized in that: The following steps are involved: 1) ERI structure files were imported into the SwissTargetPrediction database and the ComparativeToxicogenomics Database to predict potential targets. Genes associated with SOR resistance in hepatocellular carcinoma were obtained from the GeneCards database. Fifty common targets were obtained by intersection analysis to construct a protein-protein interaction network. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology functional enrichment analysis were used to reveal the biological characteristics of common targets. The top 10 core interacting targets were screened using the cytoHubba plug-in of Cytoscape software using the maximum neighborhood component, maximum clique centrality, and node degree algorithms, respectively. Multidimensional intersection analysis was performed with differentially expressed genes in hepatocellular carcinoma SOR resistance from the GSE94550 dataset, ultimately identifying STAT3 as a key regulatory target. 2) Molecular docking of the STAT3 protein crystal structure with ERI was performed to evaluate binding ability, and 100 ns molecular dynamics simulation of the complex system was performed using GROMACS software to verify its binding stability and dynamic behavior; 3) The synergistic effect of ERI-SOR combination was verified by in vitro cell experiments: CCK-8 assay was used to detect the effects of ERI alone and in combination with SOR on Huh7 R The inhibitory effect on cell proliferation was detected by Western blot to detect changes in STAT3 protein phosphorylation level, STAT3 total protein and / or Cyclin D1 protein expression level to verify the target mechanism.

8. The method for developing ERI-SOR compositions integrating multimodal computational simulation according to claim 7, wherein: The specific steps of predicting potential targets in step 1) are as follows: import the chemical structure file of ERI into the SwissTargetPrediction database, set the Probability threshold to 0.1, and predict the top 100 targets most likely related to ERI; search the Comparative Toxicogenomics Database database using "Erianin" as the keyword to obtain ERI-related targets; union the predicted targets of the two databases and remove duplicates to obtain the potential targets of ERI; use "sorafenib resistant hepatocellular carcinoma" as the keyword to search the hepatocellular carcinoma SOR resistance-related genes from the GeneCards database and screen the hepatocellular carcinoma SOR resistance targets with a Relevance score ≥ 10; and use Venny 2.1 Online tools were used to perform intersection analysis on ERI targets and hepatocellular carcinoma SOR resistance targets to obtain 50 common targets. Preferably, the steps of constructing a protein-protein interaction network and screening core targets in step 1) are as follows: the 50 common targets obtained were imported into the STRING database, the species was set to "Homo sapiens", the interaction score threshold was ≥0.4, the protein-protein interaction network was constructed and imported into the Cytoscape software, and the top 10 core targets were screened using the maximum neighborhood component, maximum clique centrality, and node degree algorithms of the cytoHubba plug-in, respectively.

9. The method for developing ERI-SOR compositions integrating multimodal computational simulation according to claim 7, wherein: The GEO data analysis in step 1) and the key target screening process for ERI-regulated SOR resistance in hepatocellular carcinoma are as follows: the parental cell groups GSM2478034, GSM2478035, GSM2478036 and the resistant cell groups GSM2478028, GSM2478029, GSM2478030 in the GSE94550 dataset were selected, and SOR resistance differential genes were screened out using the limma package. The genes were cross-validated with the top 10 core targets screened by the maximum neighborhood component, maximum clique centrality, and node degree algorithms, respectively. Finally, STAT3 was identified as the core target for ERI-regulated SOR resistance in hepatocellular carcinoma.

10. The method for developing ERI-SOR compositions integrating multimodal computational simulation according to claim 7, wherein: The specific steps of the molecular dynamics simulation experiment in step 2) are as follows: using the CHARMM36 force field and the TIP3P water model to perform system construction, energy minimization, NVT / NPT equilibrium, and 100 ns molecular dynamics simulation, and analyzing the key parameters of the molecular dynamics simulation of the ERI-STAT3 complex, including the root mean square deviation of the protein backbone, the root mean square fluctuation of the binding site residues, the gyration radius of the complex structure, the stability of the hydrogen bond between ERI and Gln644 residues, and the changes in the free energy landscape.

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