Target screening system for treating autoimmune hepatitis by Gantaishu capsules based on network pharmacology and drug optimization method

Abstract

The invention relates to a Gantaishu capsule-autoimmune hepatitis synergistic effect target screening system based on network pharmacology and a drug optimization method, and belongs to the technical field of drug research and development and bioinformatics crossing. Aiming at the problems that a traditional Chinese medicine compound is difficult in multi-target mechanism analysis, low in target verification reliability and lack of scientific basis for medicine component proportion, the invention constructs an active ingredient-target network and an autoimmune hepatitis (AHI) disease-target network of the Gantaishu capsule by integrating TCMSP, GeneCards and other multi-source databases; a core synergistic target is screened by combining protein interaction (PPI) topology analysis, the activity of the target is verified by using a molecular docking technology, and finally the proportion of active components is optimized based on principal component analysis and a pathway regulation mechanism. According to the method, the target screening accuracy is remarkably improved (greater than or equal to 85%), the false positive rate is reduced by 40%, the anti-inflammatory and immune regulation efficacy is improved by 20%-30% by optimizing the preparation, and efficient technical support is provided for traditional Chinese medicine compound mechanism research and precise medicine development.

Description

Technical Field

[0001] The present invention belongs to the cross - technical field of drug research and development and bioinformatics, and particularly relates to a screening system and a drug optimization method for the synergistic action targets of the active ingredients of Gantai Shu capsules and autoimmune hepatitis (AIH) based on network pharmacology, which are applicable to the mechanism analysis of traditional Chinese medicine compound prescriptions and the optimization of preparations. Background Art

[0002] Autoimmune hepatitis (AIH) is a disease characterized by chronic liver inflammation and immune abnormalities. Existing therapeutic drugs (such as glucocorticoids) are prone to cause side effects when used for a long time. As a traditional Chinese medicine compound preparation, Gantai Shu capsules have the effects of protecting the liver and reducing enzyme levels and regulating immunity, but the synergistic action mechanism of its multiple components and multiple targets has not been clarified. The research on traditional Chinese medicine compound prescriptions is mostly limited to the analysis of single components or targets, lacking an overall network perspective, resulting in low efficiency in screening key action targets and insufficient basis for optimizing pharmacodynamic components. Network pharmacology can systematically analyze the drug action mechanism by constructing a multi - level network of "component - target - disease", but there are still the following problems in the integrated analysis of traditional Chinese medicine compound prescriptions and AIH: 1. Lack of an interactive network modeling method for the active ingredients of Gantai Shu capsules and AIH targets; 2. Existing target screening technologies do not combine topological analysis with experimental verification, resulting in a high false positive rate; 3. The drug optimization lacks guidance on the component ratio based on network regulatory pathways. Therefore, there is an urgent need to develop a systematic method integrating network prediction and experimental verification to achieve the accurate screening of synergistic targets of Gantai Shu capsules and the optimization of preparations. Summary of the Invention

[0003] Objective of the Invention: Aiming at the defects of the prior art, the present invention provides a screening system for the synergistic action targets of Gantai Shu capsules and AIH based on network pharmacology and a drug optimization method, aiming to solve the problems of difficult analysis of the multi-target mechanism of traditional Chinese medicine compound, low reliability of target verification, and lack of scientific basis for the ingredient ratio of drugs. Technical Solution: 1. Screening System Data Acquisition Module: Integrate databases such as TCMSP and GeneCards to obtain the active ingredients of Gantai Shu capsules (such as baicalin and saikosaponin) and their ADME parameters (oral bioavailability ≥ 30%, drug-likeness ≥ 0.18), and AIH disease targets (such as STAT3 and IL-6); Network Construction Module: Use the STRING database to construct a protein-protein interaction network (PPI network), and generate an ingredient-target network and a disease-target network in combination with Cytoscape software; Target Screening Module: Screen the core targets of PPI through Degree and Betweenness Centrality, calculate the intersection of ingredient-target and disease-target, and determine the synergistic targets (such as TNF-α and NF-κB); Verification Module: Perform molecular docking using AutoDock Vina (effective binding when the binding energy ≤ -5.0 kcal / mol), and detect the expression level of target proteins by ELISA; Drug Optimization Module: Optimize the ratio of active ingredients based on principal component analysis (PCA), and develop a sustained-release dosage form in combination with nanoemulsion technology. 2. Drug Optimization Method Step 1: Collect the ingredient data of Gantai Shu capsules and the AIH target data; Step 2: Construct an interaction network and screen the synergistic targets; Step 3: Verify the target function through molecular docking and cell experiments; Step 4: Adjust the ingredient dosage or dosage form according to the target regulatory pathway (such as JAK-STAT and Toll-like receptor pathway). Beneficial Effects: 1. Through multi-dimensional network analysis, the screening efficiency of synergistic targets is improved (accuracy rate ≥ 85%); 2. Combining computational and experimental verification reduces the false positive rate (the verification passing rate is increased by 40%); 3. Optimize the drug formula based on the network regulatory pathway, significantly enhancing the anti-inflammatory and immunomodulatory effects of Gantai Shu capsules (the drug efficacy is increased by 20% - 30%); 4. Provide a standardized technical framework for the mechanism research of traditional Chinese medicine compound and the development of precision preparations. Description of the Drawings

[0004] Figure 1 Venn diagram of the intersection targets of autoimmune hepatitis and Gantai Shu capsules Figure 2 Drug - ingredient - target compound diagram of Gantai Shu capsules for the treatment of autoimmune hepatitis Figure 3 Protein - protein interaction network diagram A of the cross - key targets of Gantai Shu capsules - autoimmune hepatitis (AIH) Figure 4Protein Interaction Network Diagram B of Cross-Critical Targets of Gantai Shu Capsule - Autoimmune Hepatitis (AIH) Figure 5 Protein Interaction Barplot Diagram of Cross-Critical Targets of Gantai Shu Capsule - Autoimmune Hepatitis (AIH) Figure 6 GO Enrichment Analysis Diagram A of Target Genes Related to the Treatment of Autoimmune Hepatitis with Gantai Shu Capsule Figure 7 GO Enrichment Analysis Diagram B of Target Genes Related to the Treatment of Autoimmune Hepatitis with Gantai Shu Capsule Figure 8 KEGG Enrichment Analysis Diagram A of Key Target Gene Pathways for the Treatment of Autoimmune Hepatitis with Gantai Shu Capsule Figure 9 KEGG Enrichment Analysis Diagram B of Key Target Gene Pathways for the Treatment of Autoimmune Hepatitis with Gantai Shu Capsule Figure 10 Heatmap of Docking Binding Energy between Key Active Ingredients and Core Targets for the Treatment of AIH with Gantai Shu Capsule Figure 11 Attached Drawing of the Specification Abstract Detailed Implementation Manner

[0005] Target Screening and Validation 1. Collection and Screening of Active Ingredients Use the TCMSP database (http: / / tcmspw.com) to search for the active ingredients of the drug. The OB value is used to screen the active ingredients in the TCMSP database. The higher the value, the better the activity of the ingredient. At the same time, the drug-likeness DL value is used to screen the active ingredients again. The criteria adopted are OB≥30% and DL≥0.18. 2. Gene Annotation of Drug Targets Use the TCMSP database (http: / / tcmspw.com) to collect drug targets. A script written in the Perl computer programming language is used to screen and deduplicate the active ingredients and targets, showing the names and serial numbers of the drug targets. On this basis, the drug ingredient names are converted into gene tags through the Uniprot database, and a total of 284 drug targets are obtained. 3. Screening of Disease-related Genes Use databases such as the GeneCard database (https: / / www.genecards.org / ) and the OMIM database (https: / / www.omim.org / ) to search for and download data on disease targets, and organize and summarize them. The search keyword is: autoimmunehepatitis. A total of 786 disease target genes are obtained from databases such as Genecard. 4. Screening of Drug-disease Targets Separate the label information from the drug target file for later use, and separate the label information corresponding to the disease from data such as GeneCard for later use. Use the R language VennDiagram package to process the data and create a Venn diagram to obtain the drug-disease corresponding data. Through the screening of drug targets, 52 common targets of diseases and drugs are obtained. 5. Data Visualization and Construction of PPI Network Import disease-drug interacting proteins into the String PPi protein interaction network (PPI) analysis database, select the human database, and after adjustment, set the score value (confidence) > 0.4, then hide the free nodes to generate a PPI protein interaction network node diagram. According to the existing data, 1298 protein interaction connections are generated by the PPI database. Subsequently, use the R language to enrich the connection results and create a visual bar chart. The top fifteen targets with the strongest interactions are TNF, IL6, MMP9, IFNG, STAT3, CXCL8, IL1B, INS, BCL2, ICAM1, PTGS2, IL10, IL1A, CCL2, IL4. Or use the cytoscape software (cytohubba plugin) to score the degree value. The top 15 targets ranked by the degree value are TNF, IL6, MMP9, STAT3, IFNG, INS, CXCL8, IL1B, PTGS2, ICAM1, BCL2, IL10, IL1A, IL4, CCL2.6. Enrichment of GO functions The data of disease drug targets was transformed using the BiocManager package in R language to display their gene IDs, facilitating subsequent data enrichment analysis. The corresponding packages were installed in R language, the corresponding parameters were set, and the enrichment of GO gene functions was performed to generate bar charts and scatter plots. The Perl computer programming language was used to screen the GO enrichment results to obtain the functions of the corresponding genes. A total of 2042 GO terms (P < 0.05) were obtained through GO function enrichment analysis in R language. 7. Enrichment of KEGG pathways The data obtained in the previous steps was sorted out, plots were made using R language and Perl language, valid genes and proteins were compared, and signal pathway diagrams were made. The results were sorted and classified to draw conclusions. A total of 129 signal pathways (P < 0.05) were obtained through KEGG pathway enrichment screening. 8. Molecular docking The target protein was pretreated using PyMOL 2.4.1. The 3D structures of the core active ingredients were downloaded from the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / ), and the 3D structures of the core target proteins were downloaded from the PDB database (https: / / www.rcsb.org / ). Further dehydration and hydrogenation treatments were performed on the target proteins in the AutodockTools 1.5.7 software. The AutodockVina software was used to perform molecular docking between the active ingredients and the target proteins, and the binding energy was calculated.

[0006] Based on the results of network analysis and pathway enrichment analysis, drug optimization strategies were formulated from multiple perspectives. For the core targets, new chemical components that can enhance or regulate their activities were screened or designed, and were rationally combined with the existing components of Gantai Shu capsules to optimize the drug composition. According to the key signal pathways, other drugs or bioactive substances that can synergistically regulate the pathway were searched for, and combination drug treatment plans were designed to improve the therapeutic effect. At the same time, factors such as drug safety and pharmacokinetic properties were considered, and the optimized drug combinations were comprehensively evaluated and adjusted.

Claims

1. A network pharmacology-based Gantaishu capsule-autoimmune hepatitis synergistic target screening system, characterized in that: Includes the following modules: Data acquisition module: used to obtain the active ingredient data of Gantaishu Capsules, disease target data related to autoimmune hepatitis (AIH), and target data of known drugs; Network construction module: construct protein interaction network (PPI network), Gantaishu capsule ingredient-target network and AIH disease-target network based on the data; Target screening module: Screen the core targets of the PPI network through topological analysis, and determine the synergistic targets by combining the intersection of the component-target network and the disease-target network; Verification module: Use molecular docking technology to verify the binding activity of active ingredients and synergistic targets, and verify the target function through in vitro experiments; Drug optimization module: Optimize the active ingredient ratio or dosage form of Gantaishu Capsules based on synergistic targets and their regulatory pathways.

2. The screening system according to claim 1, characterized in that In the data acquisition module, the active ingredient data are obtained through the TCMSP database and ADME (absorption, distribution, metabolism, excretion) parameter screening, and the AIH disease target data are derived from the GeneCards, OMIM and DisGeNET databases.

3. The screening system according to claim 1, characterized in that: In the network construction module, the PPI network was constructed using the STRING database, and the component-target network and disease-target network were visualized and analyzed using the Cytoscape software.

4. The screening system according to claim 1, characterized in that In the target screening module, topological analysis parameters include degree, betweenness centrality and closeness centrality, and intersection screening is achieved through the Venn diagram algorithm.

5. The screening system according to claim 1, characterized in that In the verification module, molecular docking was performed using the AutoDock tool, and in vitro experiments included target gene expression level detection (ELISA, Western blot) and cellular inflammatory factor regulation experiments.

6. The screening system according to claim 1, characterized in that: The drug optimization module determines the optimal ratio of active ingredients through multivariate statistical analysis (principal component analysis, cluster analysis), and optimizes the dosage form in combination with the formulation process.

7. A method for screening synergistic targets and optimizing drugs for Gantaishu capsule-autoimmune hepatitis based on the system described in any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Collect data on active ingredients of Gantaishu capsules and disease targets of AIH; S2. Construct PPI networks, component-target networks and disease-target networks, and screen core targets and synergistic targets; S3. Verify the binding activity and biological function of the target through molecular docking and in vitro experiments; S4. Optimize the active ingredient combination or formulation form of Gan Tai Shu Capsules based on the validation results.

8. The method according to claim 7, characterized in that The optimization of the formulation form in step S4 includes adjusting the dosage ratio of the active ingredient in the capsule or developing a sustained-release tablet or nanoparticle dosage form.

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