Method for screening target of anshenjujing decoction in treating diabetic nephropathy and application thereof

Abstract

The present application belongs to the technical field of biological medicine, and particularly relates to a target screening method and application of Anshen Juting decoction for treating diabetic nephropathy. The present application systematically reveals the mechanism of Anshen Juting decoction for treating diabetic nephropathy by integrating serum and on-target drug chemistry, network pharmacology and molecular docking verification. The method accurately identifies 9 core on-blood and on-target components, determines 7 potential core targets of Anshen Juting decoction for treating diabetic nephropathy through target prediction and screening, constructs a "drug-on-blood component-target-mechanism pathway" network, finds out the core pathway of Anshen Juting decoction for treating diabetic nephropathy, and screens 5 active components with high affinity. The present application solves the problems of difficult analysis of multi-target mechanism of traditional Chinese medicine compound, low reliability of target verification and lack of scientific basis for drug component proportioning.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and specifically relates to the target screening method and application of Anshen Jujing Decoction in the treatment of diabetic nephropathy. Background Technology

[0002] Currently, diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease in my country. Research on traditional Chinese medicine (TCM) treatment of DKD has made significant progress in recent years, with its "multi-component, multi-target, multi-pathway" characteristics highly consistent with the complex pathogenesis of DKD. An Shen Ju Jing Tang, a formula developed by Professor Pi Chiheng, a master of traditional Chinese medicine, based on years of clinical experience in treating proteinuria, is recorded in "Pi Chiheng's Academic Thoughts and Clinical Experience in Nephrology." In long-term clinical practice and treatment observation, it has achieved certain results in delaying the progression of DKD. The formula consists of Codonopsis pilosula, Astragalus membranaceus, Salvia miltiorrhiza, Euryale ferox, Ulmus pumila, Mantis religiosa ootheca, Sepia officinalis, Schisandra chinensis, and Carthamus tinctorius. Based on five key pathogenesis principles, this formula heavily utilizes Codonopsis pilosula and Astragalus membranaceus; it primarily focuses on tonifying Qi and nourishing the original essence, strengthening the kidneys and removing blood stasis, while also eliminating dampness and toxins; it addresses both deficiency and excess, treating both the root cause and symptoms, effectively reducing proteinuria in DKD patients. However, the material basis and target of this compound's efficacy are not yet clear, and a scientific and systematic screening method is urgently needed.

[0003] Currently, existing technologies for screening targets in traditional Chinese medicine (TCM) compound prescriptions have the following shortcomings. First, target prediction lacks validation of in vivo exposure and tissue retention. Currently widely used network pharmacology methods rely heavily on reverse engineering of databases, which can generate candidate targets in high throughput, but do not fully consider the actual process of the drug in vivo, i.e., whether the components enter the bloodstream and whether they can target and distribute to kidney tissue. Due to the lack of dual screening of "blood-entering components" and "kidney-residing components," the false positive rate of the prediction results is high, and the biological reliability of target validation is limited. Second, there is a disconnect between the analysis of the material basis and the effector target. Serum pharmacology is mainly used to analyze in vivo exposed components, but it is difficult to determine whether these components actually reach the target organ, the kidney; while pharmacology research targeting kidney tissue is relatively scarce, making it difficult to establish a direct mapping relationship between "kidney-entering components and action targets." Due to the lack of systematic analysis of the actual pharmacogenomics of the target site, existing technologies have significant limitations in elucidating the synergistic mechanisms of multiple targets and cannot provide sufficient experimental evidence for drug component ratios.

[0004] To address the shortcomings of existing technologies, this invention provides a target screening method for Anshen Jujing Decoction in the treatment of diabetic nephropathy. Combining serum, targeted renal tissue pharmacology, and molecular docking techniques, this method systematically analyzes the material basis of Anshen Jujing Decoction in treating diabetic nephropathy. It solves the problems of difficulty in elucidating the multi-target mechanism of traditional Chinese medicine compound, low reliability of target verification, and lack of scientific basis for drug component ratios, thus providing a scientific basis for its clinical application and further development. Summary of the Invention

[0005] This invention proposes a target screening method and its application for treating diabetic nephropathy with Anshen Jujing Decoction, which solves the problems of difficulty in analyzing the multi-target mechanism of traditional Chinese medicine compound, low reliability of target verification, and lack of scientific basis for drug component ratio.

[0006] The technical solution of this invention is implemented as follows: The target screening method for An Shen Ju Jing Tang in the treatment of diabetic nephropathy includes constructing a "drug-blood-target-mechanism pathway" network, identifying nine core blood-entering and target components, including salvianolic acid A, codonopsis glycoside, salvianolic acid H, tanshinone, schisandrin A, red cloverin-7-O-glucopyranoside, verrucoside, chlorogenic acid, and apigenin; identifying seven key core targets, including MMP9, CASP3, MAPK1, HSP90AA1, IL6, AKT1, and TNF; and identifying core pathways as lipids and atherosclerosis, interleukin-17 signaling pathway (IL-17 signaling pathway), tumor necrosis factor signaling pathway (TNF signaling pathway), and advanced glycation end products-advanced glycation end products receptor signaling pathway (AGE-RAGE signaling pathway) in diabetic complications.

[0007] Using the above technical solution, a "drug-blood-target-mechanism pathway" network was constructed using Cytoscape 3.10.0 software. The CytoNCA module within this software was used to analyze and identify 9 core blood-entering and target components and 7 key core targets. Simultaneously, 37 potential key targets were imported into the DAVID database for KEGG signaling pathway analysis. The top 5 pathways (excluding cancer mechanisms) were analyzed, resulting in 4 core pathways. Compared to collecting data from relevant public databases, the "drug-blood-target-mechanism pathway" network can address the difficulties in elucidating the mechanisms of multiple targets in traditional Chinese medicine compound formulas, the low reliability of target validation, and the lack of scientific evidence for drug component ratios.

[0008] Optional, it also includes the identification of blood-entry and target-entry components. Among them, the blood-entry and target-entry components of Anshen Jujing Decoction were identified, and the structural information of 155 blood-entry and target-entry components was obtained.

[0009] Using the above technical solution, the Xcalibur qualitative analysis software is used to analyze and obtain the precise primary molecular weight and secondary fragment ion information of the measured compound. The primary molecular weight and secondary fragment ion information are then compared with a self-built or commercial Chinese medicine database to perform qualitative analysis.

[0010] Optionally, it also includes target prediction and screening, in which active targets are predicted by obtaining the metabolite structure of the blood-entering components, disease targets are screened by combining the database, the intersection of drug targets and disease targets is obtained, and potential common targets for Anshen Jujing Decoction in the treatment of diabetic nephropathy are obtained; among them, there are 592 (382+210) drug targets, 4724 (4514+210) disease targets, and 210 potential common targets.

[0011] Using the above technical solution, the target components of 155 components of Anshen Jujing Decoction were obtained by analyzing their blood-entry and target-entry points through the TCMS, Swiss Target Prediction, and SEA databases. Duplicate values ​​were removed, resulting in 592 (382+210) drug targets. A comprehensive search was conducted in databases such as GeneCards, OMIM, TTD, DrugBank, PharmGKB, and DisGeNET using specific keywords such as "diabetes," "nephropathy," "chronic kidney disease," and "diabetic nephropathy." To eliminate duplicate entries, the data from these databases were merged, resulting in 4724 (4514+210) related disease targets. Using the Venn diagram of the bioinformatics cloud platform, 210 potential co-active targets were identified as overlapping targets between Anshen Jujing Decoction and diabetic nephropathy.

[0012] Optionally, it also includes core target analysis, which involves protein-protein interaction analysis of potential co-acting targets to construct a "drug-blood-target-mechanism pathway" network and screen out potential key targets with a degree value >69.15, tight centrality >0.0028, and intermediate centrality >140.12. Among them, there are 37 potential key targets, namely NFE2L2, HMOX1, NQO1, MAPK1, EGFR, CASP3, SOX2, GSK3B, HSP90AA1, PTGS2, JUN, HSP90AB1, MMP9, KEAP1, BCL2, IL6, TNF, GPX4, BRAF, AKT1, PPARG, MAPK3, TGFB1, IL2, IFNG, APP, FOS, SQSTM1, GCLC, CREB1, CDKN2A, CCL2, APOE, IL1B, ICAM1, CXCL8, and PIK3CA.

[0013] Using the above technical solution and the CytoNCA module in Cytoscape 3.10.3 software, the medians of degree, tight centrality and betweenness centrality were obtained, which were 69.15, 0.0028 and 140.12 respectively, thereby identifying potential key targets.

[0014] Optionally, the screening criteria for core blood-entry components, target components, and key core targets are: degree value > 24, tight centrality > 0.0431, and intermediate centrality > 0.3700.

[0015] Using the above technical solution and the CytoNCA module in Cytoscape 3.10.3 software, the medians of degree, tight centrality and betweenness centrality values ​​were obtained, which were 24, 0.0431 and 0.3700 respectively, thereby determining the core blood-entering and target components and key core targets.

[0016] Optionally, the core pathways can be identified through GO functional enrichment analysis and KEGG signaling pathway analysis. GO functional enrichment analysis includes biological processes, cellular components, and molecular functions. Biological processes involve the positive regulation of nitric oxide biosynthesis, the positive regulation of transcription by RNA polymerase II, responses to exogenous stimuli, the positive regulation of gene expression, and the positive regulation of extracellular signal-regulated kinase 1 and 2 cascades (positive regulation of ERK1 and ERK2 cascades). Cellular components include cytosol, protein complexes, mitochondria, endoplasmic reticulum lumen, and ciliary matrix. Molecular functions include same-protein binding, enzyme binding, ubiquitin-protein ligase binding, polymerase II-specific DNA-binding transcription factor binding, and protein binding.

[0017] Optionally, the obtained core blood-entry and target components can be molecularly docked with the core target, and the binding energy S value and RMSD value can be calculated to screen out high-affinity components.

[0018] After adopting the above technical solution, the beneficial effects of the present invention are: This invention systematically reveals the mechanism of action of Anshen Jujing Decoction in treating diabetic nephropathy by integrating serum and target drug chemistry, network pharmacology, and molecular docking verification. This method accurately identified nine core blood-entering and target-entering components, and then determined seven potential core targets for Anshen Jujing Decoction in treating diabetic nephropathy through target prediction and screening. Furthermore, a "drug-blood-entering component-target-mechanism pathway" network was constructed, revealing that lipids and atherosclerosis, the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway (TNF signaling pathway), and the advanced glycation end products-advanced glycation end products receptor signaling pathway (AGE-RAGE signaling pathway) in diabetic complications are the core pathways for Anshen Jujing Decoction in treating diabetic nephropathy. Molecular docking verification screened out five active ingredients with high affinity: verbascoside, red cloverin-7-O-glucopyranoside, apigenin, salvianolic acid A, and salvianolic acid H. This invention not only elucidates the synergistic mechanism of multi-target and multi-pathway action of traditional Chinese medicine compound, providing a modern scientific explanation for the treatment of diabetic nephropathy with Anshen Jujing Decoction, but also provides an important basis for the quality control of traditional Chinese medicine and the optimization of clinical combination drug regimens.

[0019] Compared to traditional research methods, this invention significantly improves the efficiency and accuracy of screening active ingredients in traditional Chinese medicine, establishes a complete research paradigm from chemical characterization to mechanism verification, and screens or designs novel chemical components that can enhance or regulate the activity of core targets. These components are then rationally combined with existing ingredients in Anshen Jujing Decoction to optimize the drug composition. Furthermore, based on key signaling pathways, other drugs or bioactive substances that can synergistically regulate these pathways are sought, and combined drug regimens are designed to improve therapeutic efficacy. Attached Figure Description

[0020] 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.

[0021] Figure 1 The total ion chromatogram of the blood-entering components containing An Shen Ju Jing Tang; Figure 2 The total ion chromatogram of the target components containing An Shen Ju Jing Tang; Figure 3 Venn diagram showing the intersection target of diabetic nephropathy and An Shen Ju Jing Tang in this invention; Figure 4 This is a drug-component-target-mechanism pathway diagram of An Shen Ju Jing Tang for treating diabetic nephropathy in this invention; Figure 5This is a protein interaction network diagram of the key cross-targets of An Shen Ju Jing Tang in diabetic nephropathy in this invention; Figure 6 This is a GO enrichment analysis diagram of the biological process of the An Shen Ju Jing Tang treatment of diabetic nephropathy in this invention. Figure 7 This is a GO enrichment analysis diagram of cellular components of target genes related to the treatment of diabetic nephropathy using Anshen Jujing Decoction in this invention; Figure 8 This is a GO enrichment analysis diagram of the molecular functions of target genes related to the treatment of diabetic nephropathy using Anshen Jujing Decoction in this invention; Figure 9 This is a KEGG enrichment analysis diagram of key target gene pathways in the treatment of diabetic nephropathy using Anshen Jujing Decoction in this invention. Figure 10 This is a heat map showing the docking and binding energy between the key active ingredients and core target molecules of An Shen Ju Jing Tang in the treatment of diabetic nephropathy in this invention. Figure 11 This is a visualization of the docking results between the potential active ingredients and key target molecules of An Shen Ju Jing Tang in the treatment of diabetic nephropathy in this invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example This application discloses a target screening method for Anshen Jujing Decoction in the treatment of diabetic nephropathy. Using Anshen Jujing Decoction as the research object, this invention employs a combination of network pharmacology, molecular docking, and visualization to systematically analyze the active ingredients and mechanism of action of Anshen Jujing Decoction in treating diabetic nephropathy. Through serum and drug-target chemical analysis, the components entering the bloodstream and targeting the target are identified, constructing a "drug-bloodstream component-target-mechanism pathway" network to predict key core targets. Molecular docking and visualization are then used to verify their biological activity. Specific details are as follows.

[0024] I. Methods The target screening method for An Shen Ju Jing Tang in the treatment of diabetic nephropathy includes the following steps: S1. Identification of Blood-Entering and Target-Entering Components: The blood-entering and target-entering components of Anshen Jujing Decoction were identified, and structural information of 155 blood-entering and target-entering components was obtained. Using Xcalibur qualitative analysis software, the precise primary molecular weight and secondary fragment ion information of the measured compounds were obtained. The primary molecular weight and secondary fragment ion information were compared with self-built or commercial Chinese medicine databases for qualitative analysis. Figure 1 and Figure 2 The total ion chromatogram of the blood- and target-entry components of An Shen Ju Jing Tang is shown, among which... Figure 1 and Figure 2 In this context, A represents the detection result under positive ion mode. Figure 1 and Figure 2 The "B" in the diagram represents the detection result under negative ion mode. From... Figure 1 and Figure 2 In the study, mass spectrometry peaks of the blood-entering and target-entering components of An Shen Ju Jing Tang were obtained, and a total of 155 blood-entering and target-entering components were identified through component identification.

[0025] S2. Target Prediction and Screening: The blood-entry components and metabolite structures obtained in step S1 were imported into the PubChem database to obtain SDF structure files. Active targets were predicted using the SEA and SwissTargetPrediction databases. Disease targets were screened using the GeneCards, OMIM, and DrugBank databases. The intersection of drug targets and disease targets was obtained using Venny 2.1.0 software to identify potential common targets for Anshen Jujing Decoction in the treatment of diabetic nephropathy. Among them, there were 592 (382+210) drug targets, 4724 (4514+210) disease targets, and 210 potential common targets. Specifically, the blood-entry and target components of 155 components of Anshen Jujing Decoction were obtained using the TCMS, Swiss Target Prediction, and SEA databases. Duplicate values ​​were removed, thus identifying 592 drug targets. A comprehensive search was conducted in databases such as GeneCards, OMIM, TTD, DrugBank, PharmGKB, and DisGeNET using specific keywords such as "diabetes," "nephropathy," "chronic kidney disease," and "diabetic nephropathy." To eliminate duplicate entries, the data from these databases were merged, resulting in 4724 relevant disease targets. Figure 3 The Venn diagram of the bioinformatics cloud platform shown reveals 210 potential co-active targets that overlap between Anshen Jujing Decoction and diabetic nephropathy.

[0026] S3. Core Target Analysis: The potential co-acting targets obtained in step S2 are input into the String database for protein-protein interaction analysis to construct a "drug-blood-target-mechanism pathway" network. Potential key targets with a degree value > 69.15, tight centrality > 0.0028, and medium centrality > 140.12 are then screened using Cytoscape 3.10.3 software. Specifically, the median values ​​for degree, tight centrality, and medium centrality are obtained using the CytoNCA module in Cytoscape 3.10.3 software, which are 69.15, 0.0028, and 140.12 respectively, thus identifying potential key targets. Figure 4 Among them, there are 37 potential key targets, namely NFE2L2, HMOX1, NQO1, MAPK1, EGFR, CASP3, SOX2, GSK3B, HSP90AA1, PTGS2, JUN, HSP90AB1, MMP9, KEAP1, BCL2, IL6, TNF, GPX4, BRAF, AKT1, PPARG, MAPK3, TGFB1, IL2, IFNG, APP, FOS, SQSTM1, GCLC, CREB1, CDKN2A, CCL2, APOE, IL1B, ICAM1, CXCL8, and PIK3CA.

[0027] S4. Network Construction and Analysis: A "drug-blood-target-mechanism pathway" network was constructed using Cytoscape 3.10.3 software, and analyzed using the CytoNCA module in the software to screen out core blood-entering and target components and key core targets with a degree value > 24, tight centrality > 0.0431, and medium centrality > 0.3700. Specifically, using the CytoNCA module in Cytoscape 3.10.3 software, the medians of the degree value, tight centrality, and medium centrality were obtained, which were 24, 0.0431, and 0.3700, respectively, thus determining the core blood-entering and target components and key core targets.

[0028] Analysis Figure 5 Nine core components were identified that enter the bloodstream and target therapies, including salvianolic acid A, codonopsis glycoside, salvianolic acid H, tanshinone, schisandrin A, red cloverin-7-O-glucopyranoside, verrucoside, chlorogenic acid, and apigenin; analysis also revealed... Figure 5 There are seven key targets in the study, including MMP9, CASP3, MAPK1, HSP90AA1, IL6, AKT1, and TNF.

[0029] S5. Functional enrichment and pathway analysis: Perform GO functional enrichment analysis on the targets that intersect with step S2. Figures 6-8 ) and KEGG signaling pathway ( Figure 9 Analysis revealed the core pathways for Anshen Jujing Decoction in treating diabetic nephropathy. These core pathways included lipid and atherosclerosis, the interleukin-17 signaling pathway (IL-17 signaling pathway), the tumor necrosis factor signaling pathway (TNF signaling pathway), and the advanced glycation end products-advanced glycation end products receptor signaling pathway (AGE-RAGE signaling pathway) in diabetic complications. GO functional enrichment analysis included biological processes, cellular components, and molecular functions. Biological processes involved the positive regulation of nitric oxide biosynthesis, the positive regulation of transcription by RNA polymerase II, responses to exogenous stimuli, the positive regulation of gene expression, and the positive regulation of the extracellular signal-regulated kinase 1 and 2 cascades (positive regulation of the ERK1 and ERK2 cascades). Cellular components included cytosol, protein complexes, mitochondria, endoplasmic reticulum lumen, and ciliary matrix. Molecular functions included same-protein binding, enzyme binding, ubiquitin-protein ligase binding, polymerase II-specific DNA-binding transcription factor binding, and protein binding.

[0030] S6. Molecular docking verification: Perform molecular docking between the core blood-entry and target components obtained in step S4 and the core target of Anshen Jujing Decoction for treating diabetic nephropathy obtained in step S3, and calculate the binding energy S value and RMSD value. Figure 10 The heatmap shows that as the color changes from dark to light, it indicates that the compounds are more significantly connected to the key target sites. This allowed us to screen out five active ingredients with high affinity (dashed area): red cloverin-7-O-glucopyranoside, verrucoside, apigenin, salvianolic acid H, and salvianolic acid A.

[0031] S7. Molecular Docking Visualization: Further, the potential pharmacodynamic components and key targets obtained in step S6 are visualized and analyzed using Discovery Studio 2019 Client. Molecular docking using Discovery Studio 2019 Client mainly involves three steps: Structure Preparation: Hydrogenation and optimization of the receptor and ligand; Docking Execution: Defining the active site and selecting protocols such as CDOCKER or LibDock; Result Analysis: Viewing the optimal conformation and analyzing hydrogen bonds and other interactions through 2D / 3D plots. Figure 10 The heatmap results show that the color of the heatmap changes from dark to light, indicating that the degree of docking between the compound and the key core target is more significant. Figure 11 The degree of hydrogen bond interaction between the compounds and the key target sites also corroborates the results.

[0032] II. Experimental Verification 1. Identification of blood components This invention employs UPLC-Q-TOF-MS / MS technology to analyze rat serum and target (kidney) samples after administration of Anshen Jujing Decoction. Chromatographic conditions: ACQUITY UPLC HSS T3 (100 mm × 2.1 mm, 1.8 μm), column temperature 35℃, flow rate 0.3 mL / min, injection volume 2 μL. The mobile phase consisted of eluent A (0.15% formic acid aqueous solution) and eluent B (acetonitrile to methanol, volume ratio 4:1). Gradient elution was used, with the following elution program: 0-2 min, 5% B; 2-4 min, 5%-30% B; 4-8 min, 30%-50% B; 8-10 min, 50%-80% B; 10-14 min, 80%-100% B; 14-15 min, 100% B. Mass spectrometry conditions: Electrospray ionization (ESI), positive and negative ion mode scanning, mass scan range m / z 100-1200. By comparing the total ion chromatograms of blank serum and drug-containing serum, combined with a self-built compound database and literature reports on fragmentation patterns, a total of 155 blood-entering and target-entering prototype components and metabolites were identified, including salvianolic acid A, codonopsis glycoside, salvianolic acid H, tanshinone, schisandrin A, and red cloverin-7-O-glucopyranoside, etc.

[0033] 2. Target prediction and screening The chemical structures of the metabolites of 155 identified blood-borne and target-bound proto-products were imported into the PubChem database to obtain SDF files. Potential targets were predicted using the PharmMapper and SwissTargetPrediction platforms, resulting in 592 drug targets. Simultaneously, targets related to "diabetic nephropathy" were retrieved from the GeneCards, OMIM, and DrugBank databases, yielding 4724 disease targets after deduplication. The 592 drug targets and 4724 disease targets were overlaid, and a Venn diagram was constructed using Venny 2.1.0 software, as shown below. Figure 3 As shown, there are two types of targets: drug targets (592) and disease targets (4724). Both types of targets were obtained from the aforementioned website. The intersection of these two types of targets yielded 210 common targets, which were selected as potential drug targets for disease treatment. These 210 common targets were input into the STRING database to construct a "drug-blood-target-mechanism pathway" network. A minimum interaction score of 0.9 was set, and free nodes were hidden. Figure 4 It is a network graph with network lines (i.e., 7261 edges) connecting 210 nodes, which are 210 common target points.

[0034] 3. Core Target Analysis Network topology parameters were calculated using Cytoscape 3.10.3 software, and seven core targets were identified with a degree value >24, betweenness centrality >0.0431, and tight centrality >0.37002. These targets include MMP9, CASP3, MAPK1, HSP90AA1, IL6, AKT1, and TNF. These targets form tightly interacting modules in lipid and atherosclerosis, the IL-17 signaling pathway, the TNF signaling pathway, and the AGE-RAGE signaling pathway in diabetic complications. Among them, MAPK1 acts as a hub node, directly connecting with MMP9, CASP3, and others.

[0035] 4. Enrichment analysis of GO and KEGG GO and KEGG enrichment analyses were performed on 210 common targets using the DAVID database. Figures 6-8 As shown, GO analysis revealed that the biological process (BP) was significantly enriched in "positive regulation of transcription by RNA polymerase II" (GO: 0045944, FDR = 1.91 × 10⁻⁶). -8 "Positive Regulation of DNA Template Transcription" (GO:0045893, FDR=5.06×10) -9 ) and "positive regulation of gene expression" (GO:0010628, FDR=1.15×10 -9 Cellular components (CC) are mainly distributed in the nucleus (GO:0005634, FDR=1.08×10). -4 ), "cytosol" (GO:0005829, FDR=3.62×10), -4 ) and cytoplasm (GO:0005737, FDR=2.18×10) -3 Molecular function (MF) involves protein binding rate (GO:0005515, FDR = 1.83 × 10⁻⁶). -4 "Identical protein binding" (GO:0042802, FDR = 7.45 × 10⁻⁶) -10 ) and "enzyme binding" (GO:0019899, ​​FDR=6.14×10) -9 ).like Figure 9 As shown, KEGG pathway analysis revealed that the target was significantly enriched in the interleukin-17 signaling pathway and the advanced glycation end products-advanced glycation end products receptor signaling pathway. Literature reports that genes such as MAPK1, CASP3, and IL-6 in the interleukin-17 signaling pathway are closely related to renal fibrosis and renal inflammation, suggesting that Anshen Jujing Decoction may treat diabetic nephropathy through the interleukin-17 signaling pathway.

[0036] 5. Molecular docking Molecular docking of the aforementioned core targets was performed using MOE2019.01 software. Pretreatment steps included: removal of crystalline water molecules, hydrogenation, optimization of protonation state, and definition of the active pocket (using the original ligand Axitinib as a reference). Docking parameters were set as follows: force field of AMBER10, sampling method of rigid acceptor-flexible ligand, conformation number of 100, and scoring function of AffinitydG. Figure 10 , Figure 11 As shown in Table 1, the average binding energy (S value) of the nine components is <-6.0 kcal / mol (the lower the number, the more stable the binding). Among them, red cloverin-7-O-glucopyranoside showed the best binding characteristics when docking with the MAPK1 molecule (S = -12.0 kcal / mol). It formed hydrogen bonds with PRO-254, PRO-255, PRO-240, ALA-242, MET-247, GLN-227 and ALA-189 in the active cavity of MAPK1 (bond lengths of 2.85, 2.12, 2.81, 2.86, 2.83, 1.93 and 2.71, respectively), and carbon-hydrogen bonds with MET-247, GLU-241, TYR-248 and GLY-186 (bond lengths of 4.32, 3.46, 4.80 and 3.61, respectively), showing stronger binding stability.

[0037] Table 1. Visualization results of molecular docking between potential active ingredients and key core targets of An Shen Ju Jing Tang for treating diabetic nephropathy in this invention.

[0038]

[0039] Based on the above analysis results, this invention formulates drug optimization strategies from multiple perspectives, using network analysis and pathway enrichment analysis. For core targets, novel chemical components capable of enhancing or regulating their activity are screened or designed, and rationally combined with existing components of Anshen Jujing Decoction to optimize the drug composition. According to key signaling pathways, other drugs or bioactive substances capable of synergistically regulating these pathways are sought, and combination drug regimens are designed to improve the application of drug therapeutic effects.

[0040] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the technical solution of the present invention should be included within the protection scope of the present invention.

Claims

1. A target screening method for An Shen Ju Jing Tang in the treatment of diabetic nephropathy, characterized in that, This study included constructing a "drug-blood-target-mechanism pathway" network, identifying nine core blood-entering and target components, including salvianolic acid A, codonopsis glycoside, salvianolic acid H, tanshinone, schisandrin A, red cloverin-7-O-glucopyranoside, verrucoside, chlorogenic acid, and apigenin; identifying seven key core targets, including MMP9, CASP3, MAPK1, HSP90AA1, IL6, AKT1, and TNF; and identifying core pathways as lipids and atherosclerosis, the IL-17 signaling pathway, the TNF signaling pathway, and the AGE-RAGE signaling pathway in diabetic complications.

2. The target screening method for treating diabetic nephropathy with Anshen Jujing Decoction according to claim 1, characterized in that, It also includes the identification of blood-entry and target-entry components. Among them, the blood-entry and target-entry components of Anshen Jujing Decoction were identified, and structural information of 155 blood-entry and target-entry components was obtained.

3. The target screening method for treating diabetic nephropathy with Anshen Jujing Decoction according to claim 2, characterized in that, It also includes target prediction and screening. By obtaining the metabolite structure of the blood-entering components, active targets are predicted, disease targets are screened in combination with the database, the intersection of drug targets and disease targets is obtained, and potential common targets for Anshen Jujing Decoction in the treatment of diabetic nephropathy are obtained. Among them, there are 592 drug targets, 4724 disease targets, and 210 potential common targets.

4. The target screening method for treating diabetic nephropathy with Anshen Jujing Decoction according to claim 3, characterized in that, It also includes core target analysis, which involves protein-protein interaction analysis of potential co-acting targets, constructing a "drug-blood-target-mechanism pathway" network, and screening out potential key targets with a degree value >69.15, tight centrality >0.0028, and intermediate centrality >140.

12. Among them, there are 37 potential key targets, namely NFE2L2, HMOX1, NQO1, MAPK1, EGFR, CASP3, SOX2, GSK3B, HSP90AA1, PTGS2, JUN, HSP90AB1, MMP9, KEAP1, BCL2, IL6, TNF, GPX4, BRAF, AKT1, PPARG, MAPK3, TGFB1, IL2, IFNG, APP, FOS, SQSTM1, GCLC, CREB1, CDKN2A, CCL2, APOE, IL1B, ICAM1, CXCL8, and PIK3CA.

5. The target screening method for treating diabetic nephropathy with Anshen Jujing Decoction according to claim 1, characterized in that, The screening criteria for core blood-entering and target components and key core targets are: degree value > 24, tight centrality > 0.0431, and intermediate centrality > 0.3700.

6. The target screening method for treating diabetic nephropathy with Anshen Jujing Decoction according to claim 1, characterized in that, The core pathways were identified using GO functional enrichment analysis and KEGG signaling pathway analysis. GO functional enrichment analysis included biological processes, cellular components, and molecular functions. Biological processes involved the positive regulation of nitric oxide biosynthesis, the positive regulation of transcription by RNA polymerase II, responses to exogenous stimuli, the positive regulation of gene expression, and the positive regulation of the ERK1 and ERK2 cascade. Cellular components included cytosol, protein complexes, mitochondria, endoplasmic reticulum lumen, and ciliary basal body. Molecular functions included same-protein binding, enzyme binding, ubiquitin-protein ligase binding, polymerase II-specific DNA binding to transcription factors, and protein binding.

7. The target screening method for treating diabetic nephropathy with Anshen Jujing Decoction according to claim 1, characterized in that, It also includes performing molecular docking between the obtained core blood-entry and target components and the core target, calculating the binding energy S value and RMSD value, and screening out high-affinity components.

8. The application of a target screening method for An Shen Ju Jing Tang in the treatment of diabetic nephropathy, characterized in that... Used for predicting and screening the active ingredients of drugs and for predicting and identifying the targets of drug action.

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