Method for discussing core components, targets and action mechanisms of Zhibai Dihuang pills / decoction for treating early diabetic nephropathy based on directed PPI network and multiple network centrality algorithms

Abstract

The invention discloses core components, targets and action mechanisms of Zhibai Dihuang pills / decoction for treating early diabetic nephropathy based on a directed PPI network and a plurality of network centrality algorithms. Core components and targets of the Zhibai rehmannia pill / decoction for treating diabetic nephropathy are determined through the steps of screening active components of the Zhibai rehmannia pill / decoction, collecting target information corresponding to the active components, collecting disease related targets, obtaining drug-disease intersection genes, constructing a directed protein interaction network and the like. The action mechanism of the Zhibai rehmannia pill / decoction for treating diabetic nephropathy is researched. The invention provides a scientific theoretical basis for the treatment of diabetic nephropathy by the Zhibai Dihuang pill / decoction, and provides a new method for the clinical treatment and scientific research of the Zhibai Dihuang pill / decoction on diabetic nephropathy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pharmacology, and particularly relates to a core component, target and mechanism of action of Zhibai Dihuang pill / tang in treating early diabetic nephropathy based on a directed PPI network and multiple network centrality algorithms. BACKGROUND

[0002] Diabetic nephropathy (DN) is a kind of kidney disease caused by diabetes, and its pathological features include early glomerular hypertrophy, glomerular basement membrane thickening, podocyte injury, mesangial matrix expansion and renal tubular injury, and finally evolves into glomerular sclerosis and renal tubulointerstitial fibrosis, which has become the main cause of end-stage renal disease worldwide [1] . The main clinical manifestations of diabetic nephropathy are proteinuria, edema, hypertension, and renal function impairment.

[0003] Zhibai Dihuang pill / tang is derived from the Medical Classics of the Golden Mirror of Medicine [2] , which is composed of 8 kinds of traditional Chinese medicines, including Radix Rehmanniae Preparata, Anemarrhena Asphodeloides, Phellodendri Cortex, Corni Officinalis, Dioscoreae, Moutan Cortex, Poria and Alisma, and is one of the commonly used traditional Chinese medicines for treating diabetic nephropathy. In the prescription, Radix Rehmanniae Preparata, Dioscoreae and Corni Officinalis are the monarch drugs, which can treat the root cause by tonifying deficiency and nourishing kidney, and Radix Rehmanniae Preparata can nourish yin and tonify kidney and nourish blood; Dioscoreae can tonify spleen, lung, kidney and essence; Corni Officinalis can nourish liver and tonify kidney and astringe essence. Anemarrhena Asphodeloides and Phellodendri Cortex are the ministerial drugs, and Anemarrhena Asphodeloides can clear heat and drain fire, and nourish yin and moisten dryness; Phellodendri Cortex can clear heat and dry dampness, drain fire and remove steaming; Poria, Alisma and Moutan Cortex are the auxiliary drugs, and Poria can tonify qi and spleen, and drain water and dampness; Alisma can tonify kidney yin and drain water and dampness; Moutan Cortex can clear heat and cool blood, and activate blood and resolve stasis. The combination of the above drugs can nourish yin and clear heat, tonify spleen and kidney, and activate blood and resolve stasis [3] .

[0004] Network pharmacology is a multidisciplinary research field that integrates computational analysis and in vitro and in vivo experimental methods, and combines a large amount of information to discover new drug targets and molecular mechanisms [4] , and provides a new network model of “multiple targets, multiple actions and complex diseases”. Through the research means of computational biology and network analysis, and by using technologies such as omics, high-throughput screening and network visualization analysis, the relationship between biomolecules is established, and the regulation of traditional Chinese medicine on multiple signaling pathways and biological processes is analyzed from multiple angles, so as to provide direction for disease treatment and drug discovery [5] . Moreover, network pharmacology based on computer science provides a systematic research strategy for drug research, and through the analysis of multiple target actions and interaction mechanisms, this method has been widely applied in the pharmacological research of traditional Chinese medicine [6,7]Although network pharmacology research methods can utilize a "multi-target, multi-mechanism" strategy to explore the mechanism of action of Zhibai Dihuang Wan / Tang in the treatment of diabetic nephropathy, the PPI network in traditional network pharmacology is an undirected network, which limits the in-depth analysis of the hierarchical relationships between targets. Therefore, this method has certain limitations. To gain a deeper understanding of how complex Chinese medicines produce therapeutic benefits through the human body, this study analyzed directed PPI networks and used multiple network centrality algorithms to conduct an in-depth exploration of the mechanism of action of Zhibai Dihuang Wan / Tang in the treatment of diabetic nephropathy, aiming to determine a more accurate, objective, and comprehensive evaluation of drug targets. Summary of the Invention

[0005] Based on the above background, the present invention provides a method to explore the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in the treatment of early diabetic nephropathy based on a directed PPI network and multiple network centrality algorithms. This method screens and analyzes the core components, targets, and pathways involved in Zhibai Dihuang Wan / Decoction in the treatment of diabetic nephropathy using a directed PPI network and multiple network centrality algorithms, thereby exploring the potential mechanisms of action of Zhibai Dihuang Wan / Decoction in the treatment of diabetic nephropathy.

[0006] The technical solution provided by the present invention is:

[0007] Based on directed PPI networks and multiple network centrality algorithms, this study explored the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in the treatment of early diabetic nephropathy, including the following steps:

[0008] S1: Screening of active ingredients and targets of Zhibai Dihuang Wan / Tang:

[0009] The chemical components of Rehmannia glutinosa (SDH), Anemarrhena asphodeloides (ZM), Phellodendron chinense (HB), Cornus officinalis (SZY), Dioscorea opposita (SY), Paeonia suffruticosa (MDP), Poria cocos (FL), and Alisma orientalis (ZX) were searched in the BATMAN-TCM 2.0 database and screened for active ingredients to obtain the active ingredient information and related target information of Zhibai Dihuang Wan / Decoction.

[0010] S2: Obtaining diabetic nephropathy-related targets:

[0011] Search and save diabetic nephropathy-related targets in DrugBank, DisGeNET, GeneCards, OMIM database, and TTD database;

[0012] S3: Obtaining common drug-disease targets:

[0013] Intersecting the target of the active ingredient of the drug and the potential target of diabetic nephropathy to obtain the intersection target and draw a Venn diagram;

[0014] S4: Construction of directed protein-protein interaction (PPI) network:

[0015] Drug-disease common targets were entered into the STRING database for protein-protein interaction (PPI) network analysis. tsv files were downloaded and saved to obtain corresponding target data. These target genes were imported into the KEGG database to determine upstream and downstream links, and a directed PPI network was constructed using the Python programming environment.

[0016] S5: Screening core targets in the directed PPI network:

[0017] To quantitatively assess the importance of each target in the network, the study used multiple network centrality algorithms and calculated Katz centrality, HITS authority score, and PageRank score to comprehensively evaluate the interactions between targets. The top 10 drug targets were selected as the core targets for Zhibai Dihuang Wan / Tang in the treatment of diabetic nephropathy.

[0018] S6: Preliminary filtering and subnetwork analysis in directed PPI networks:

[0019] Perform preliminary filtering based on the ranking results of S5 and construct relevant sub-networks for analysis;

[0020] S7: Determine the core active ingredients of Zhibai Dihuang Wan / Decoction for the treatment of diabetic nephropathy:

[0021] The core target of Zhibai Dihuang Wan / Decoction for treating diabetic nephropathy is determined through step S5, and the drug component that acts on it is the core active ingredient.

[0022] This invention is the first to systematically study the core active ingredients and main mechanism of action of Zhibai Dihuang Wan / Decoction in treating diabetic nephropathy. The beneficial effects of this invention are as follows:

[0023] 1) The present invention applies bioinformatics technology in combination with computer network technology to determine the core components, targets and mechanisms of action of Zhibai Dihuang Wan / Decoction in the treatment of diabetic nephropathy. The directed PPI network can more accurately analyze the drug mechanism of action and identify key regulatory nodes, thereby improving the understanding of drug targets and signal regulatory pathways, avoiding the misjudgment that may be caused by traditional undirected network analysis. Katz centrality, HITS authority score and PageRank score are used to perform multi-dimensional scoring of targets, avoiding the misjudgment of key regulatory targets by traditional single algorithms. This provides a scientific theoretical basis for the clinical treatment and scientific research of Zhibai Dihuang Wan / Decoction in the treatment of diabetic nephropathy, thereby better analyzing the mechanism of action of traditional Chinese medicine and providing new strategies for drug screening and precision treatment.

[0024] 2) The present invention provides new ideas and breakthroughs for the clinical research on the combination of traditional Chinese and western medicine to treat diabetic nephropathy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Venn diagram of common targets of Zhibai Dihuang Wan / Tang and diabetic nephropathy

[0026] Figure 2 Directed PPI network graph

[0027] Figure 3 Directed PPI network graph after preliminary filtering

[0028] Figure 4 Core target network and subnetwork diagram DETAILED DESCRIPTION

[0029] In order to enable researchers in this field to better understand the technical solution of the present invention, the method for determining the core components, targets and mechanism of action of Zhibai Dihuang Wan / Decoction for treating diabetic nephropathy provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0030] S1: Screening of active ingredients and targets of Zhibai Dihuang Wan / Tang:

[0031] Chemical constituents of Zhibai Dihuang Wan / Tang (Zibai Dihuang Wan) / Tang were retrieved using the BATMAN-TCM database (http: / / bionet.ncpsb.org.cn / batman-tcm / ) using the search keywords "Shudihuang (SDH)", "Zhimu (ZM)", "Huangbai (HB)", "Zhuoyu (SZY)", "Yam (SY)", "Moongao (MDP)", "Fuling (FL)", and "Zixie (ZX)". Active constituents were screened for their activity using the criteria of "p value < 0.05", "score cutoff ≥ 0.84", and "druggable score ≥ 0.7". Target information from the identified active constituents was further standardized using the Uniprot database (http: / / www.uniprot.org / ), with the restriction that the species was human and that verified genes were available. The collected target protein information was converted into standardized gene names, ultimately establishing a database of potential targets for the active constituents of Zhibai Dihuang Wan / Tang (Zibai Dihuang Wan) / Tang.

[0032] A total of active ingredients that met the screening criteria were obtained through the BATMAN-TCM 2.0 database search, including: 20 active ingredients of Rehmannia glutinosa, 28 active ingredients of Anemarrhena asphodeloides, 57 active ingredients of Phellodendron chinense, 80 active ingredients of Cornus officinalis, 45 active ingredients of Dioscorea batatas, 23 active ingredients of Paeonia suffruticosa, 35 active ingredients of Poria cocos, and 9 active ingredients of Alisma orientalis. At the same time, the known target proteins corresponding to the above active ingredients were obtained: before all targets were merged and deduplicated, 166 targets related to the active ingredients of Rehmannia glutinosa, 355 targets related to the active ingredients of Anemarrhena asphodeloides, 533 targets related to the active ingredients of Phellodendron chinense, 814 targets related to the active ingredients of Cornus officinalis, 489 targets related to the active ingredients of Dioscorea batatas, 342 targets related to the active ingredients of Paeonia suffruticosa, 290 targets related to the active ingredients of Poria cocos, and 153 targets related to the active ingredients of Alisma orientalis were obtained. After merging and deduplicating the above targets, a total of 1352 targets were obtained;

[0033] S2: Obtaining diabetic nephropathy-related targets:

[0034] Using the keywords "diabetic kidney disease" and "diabetic nephropathy," we searched the TTD database (https: / / db.idrblab.net / ttd / ), DisGeNET (http: / / www.disgenet.org / ), GeneCards (https: / / www.genecards.org / ), DrugBank (https: / / go.drugbank.com / ), and OMIM (https: / / www.omim.org / ). We used a "relevance score ≥ 20" threshold for the GeneCards database to identify genes associated with diabetic nephropathy. We integrated the data from these databases and deleted duplicate gene information to identify target genes related to diabetic nephropathy.

[0035] S3: Obtaining common drug-disease targets

[0036] The Venny 2.1 online tool (https: / / bioinfogp.cnb.csic.es / tools / venny / ) was used to perform an intersection operation on the drug targets of Zhibai Dihuang Wan / Tang and the disease-related targets of diabetic nephropathy to determine the drug-disease intersection targets, i.e., the potential targets of Zhibai Dihuang Wan / Tang in the treatment of diabetic nephropathy, and the corresponding Venn diagram was drawn. Figure 1 ;

[0037] S4: Construction of directed protein-protein interaction (PPI) network:

[0038] The drug-disease intersection targets collected above were entered into the STRING database (https: / / cn.string-db.org / ). The species parameter was set to "Homo sapiens" in the "Multiple proteins" option, and a related search was performed. In subsequent steps, free nodes were hidden, only "Experiments" was selected in the "Active interaction sources" option, and the "Minimum required interaction score" option was set to "Highest confidence (0.900)." A PPI network analysis was performed to construct a PPI network diagram for the drug-disease intersection targets. The resulting .tsv file was downloaded and saved. The target genes whose upstream and downstream genes needed to be identified were obtained from the corresponding .tsv file. The target pairs obtained through STRING were then manually entered into the KEGG database (https: / / www.kegg.jp) for analysis to obtain the signaling pathways and corresponding upstream and downstream information for these target pairs. By integrating and analyzing the relevant signaling pathway diagrams, the upstream and downstream directions of the target pairs obtained from the STRING database were determined, and the basic data for the corresponding directed PPI network was finally compiled. Based on the Python programming environment, the NetworkX library was used to construct and analyze the directed PPI network of drug targets related to Zhibai Dihuang Wan / Tang. In the initial stage of the study, the Pandas library was used to read the Excel file, in which the first and second columns respectively represented the interaction relationship between targets, and the direction was from the first column to the second column. Subsequently, based on the acquired data, a directed PPI network was constructed. The nodes in the network symbolize the drug targets, and the edges represent the interaction relationship between targets. This study used the DiGraph() method in the NetworkX library to construct a directed graph. In order to display the network structure more intuitively, this study applied the spring_layout() method to generate a force-directed layout of the nodes, and used the Matplotlib library to draw the directed PPI network diagram, see Figure 2 ;

[0039] S5: Screening core targets in the directed PPI network:

[0040] To quantitatively assess the importance of each target in the network, this study employed multiple network centrality algorithms, calculating Katz centrality, HITS authority scores, and PageRank scores to comprehensively assess the target's role in signaling. Katz centrality measures the global influence of a node within the entire network, thereby identifying key regulatory targets; HITS authority scores identify core signaling regulators within the PPI network, enabling the identification of key receptor targets; and PageRank assesses the importance of signaling within the network, with higher scores indicating greater network influence. To improve scoring stability, the three centrality metrics were normalized using MinMaxScaler and a composite score was calculated: FinalScore = 0.4 × Katz + 0.3 × Authority + 0.3 × PageRank. Finally, all targets were ranked according to their FinalScore scores. The ranking results are shown in Table 1. The top 10 targets in Table 1, namely IKBKG, PIK3R1, TP53, MAPK1, EP300, IDE, CASP8, IKBKB, CHUK, and NFKBIA, are the key core targets of Zhibai Dihuang Wan / Tang in the treatment of diabetic nephropathy;

[0041] Table 1 Ranking of main targets

[0042]

[0043]

[0044] S6: Preliminary filtering and subnetwork analysis in directed PPI networks:

[0045] According to the ranking results of step S5, the top 70 targets were selected and the directed PPI network was reconstructed. Figure 3 .

[0046] Then, the top 30 targets were selected and the directed PPI network was rebuilt again. The three sub-networks with the largest number of nodes were selected from the network and the visual map was drawn. Figure 4 (A). The three sub-networks are: NF-κB (nuclear factor κB) signaling pathway is a core regulatory network that is widely present in cells and participates in the regulation of multiple biological processes. Its role covers immune response, inflammatory response, cell survival and apoptosis, tumorigenesis, etc. The results are shown in Figure 4 (B); The PI3K / AKT signaling pathway is an important regulatory network in cells, involved in core life activities such as cell proliferation, survival, metabolism, and migration. Its abnormalities are closely related to various diseases such as cancer, diabetes, and cardiovascular disease. Figure 4(C); The p53 / mTOR / EP300 pathway is a key molecular network regulating cell fate in tumor research, involving complex interactions among tumor suppressor genes, metabolic regulation, and epigenetic modifications. Figure 4 (D);

[0047] S7: Determine the core active ingredients of Zhibai Dihuang Wan / Decoction for the treatment of diabetic nephropathy:

[0048] Based on the top 10 key core targets for Bai Di Huang Wan / Decoction in treating diabetic nephropathy identified in step S5, the active pharmaceutical ingredients that act on them are identified as the core active ingredients of Zhi Bai Di Huang Wan / Decoction in treating diabetic nephropathy. The core active ingredients of Zhi Bai Di Huang Wan / Decoction in treating diabetic nephropathy are shown in Table 2.

[0049] Table 2 Drug-Active Ingredient-Core Target Table

[0050]

[0051]

[0052] The above description of the present invention and its embodiments is non-limiting. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by the above and, without departing from the purpose of the present invention, designs structures and embodiments similar to the technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

[0053] References

[0054] [1] Li Weiyu. Research progress on TCM syndrome differentiation and treatment of diabetic nephropathy. Journal of Modern Distance Education of Traditional Chinese Medicine. 2025, 23(03): 72-5.

[0055] [2] Fang Zhaohui, Lu Ruimin, Zhou Jinqiao, et al. Overview of famous prescriptions for treating diabetes mellitus in history. Journal of Clinical Traditional Chinese Medicine. 2010, 22(08): 684-7.

[0056] [3] Shi Aimei. Clinical study on Zhibai Dihuang Wan as an auxiliary treatment for diabetic nephropathy. Journal of New Chinese Medicine. 2022, 54(18): 6-10.

[0057] [4]ZHAO L, ZHANG H, LI N, et al. Network pharmacology, a promising approach to reveal the pharmacology mechanism of Chinese medicine formula [J]. JEthnopharmacol, 2023, 309(116306.

[0058] [5] Wang Dan, Liu Jianda, Hou Baoxu, et al. Analysis of the mechanism of action of Chuanxiong in treating spinal cord ischemia-reperfusion injury based on network pharmacology and molecular docking. Journal of Practical Drugs and Clinics. 2025, 28(02): 88-94.

[0059] [6]ZHAO S, LI S.Network-based relating pharmacological and genomicspaces for drug target identification[J].PloS one,2010,5(7):e11764.

[0060] [7]XIA QD,

Claims

1. This study explored the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction for the treatment of early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms. This approach, applied in network pharmacology, allows for a more accurate, objective, and comprehensive evaluation of drug targets. This approach can effectively assess and identify key drug core components and targets, contributing to a better understanding of drug mechanisms of action and the discovery of potential therapeutic pathways. The approach includes the following steps: S1: Screening of active ingredients and targets of Zhibai Dihuang Wan / Tang: The chemical components of "Rehmannia glutinosa", "Anemarrhena asphodeloides", "Phellodendron amurense", "Cornus officinalis", "Yam", "Paeonia suffruticosa", "Poria cocos", and "Alismatis" were searched in the BATMAN-TCM 2.0 database and screened for active ingredients to obtain information on active ingredients and related targets of Zhibai Dihuang Wan / Tang. S2: Obtaining diabetic nephropathy-related targets: Search and save diabetic nephropathy-related targets in DrugBank, DisGeNET, GeneCards, OMIM database, and TTD database; S3: Obtaining common drug-disease targets: Intersecting the target of the active ingredient of the drug and the potential target of diabetic nephropathy to obtain the intersection target and draw a Venn diagram; S4: Construction of directed protein-protein interaction (PPI) network: Drug-disease common targets were entered into the STRING database for protein-protein interaction (PPI) network analysis. The corresponding files were downloaded and saved to obtain relevant target data. These target genes were then imported into the KEGG database. Functional upstream and downstream regulatory directions were assigned to the protein interaction information obtained from the STRING database. A directed PPI network was then constructed using the Python programming environment. S5: Screening core targets in the directed PPI network: To quantitatively assess the importance of each target in the network, the study used multiple network centrality algorithms and calculated Katz centrality, HITS authority score, and PageRank score to comprehensively evaluate the interactions between targets. The top 10 drug targets were selected as the core targets for Zhibai Dihuang Wan / Tang in the treatment of diabetic nephropathy. S6: Preliminary filtering and subnetwork analysis in directed PPI networks: Perform preliminary filtering based on the ranking results of S5 and construct relevant sub-networks for analysis; S7: Determine the core active ingredients of Zhibai Dihuang Wan / Decoction for the treatment of diabetic nephropathy: The core target of Zhibai Dihuang Wan / Decoction for treating diabetic nephropathy is determined through step S5, and the drug component that acts on it is the core active ingredient.

2. The method of claim 1 for exploring the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in treating early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms, characterized in that: The TCM data described in step S1 was obtained from the Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine (BATMAN-TCM). Active ingredients were identified using "P value < 0.05," "Score cutoff ≥ 0.84," and "Druggable Score ≥ 0.7" as screening criteria. The targets of each active ingredient were then collected from the BATMAN-TCM database, with the condition "Known target proteins" being used.

3. The method of claim 1 for exploring the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in treating early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms, characterized in that: Using "diabetic kidney disease" and "diabetic nephropathy" as search keywords, we searched the TTD database, DisGeNET database, GeneCards database, DrugBank database, and OMIM database. The GeneCards database was set with a "relevance score ≥ 20" screening threshold to obtain genes related to diabetic nephropathy.

4. The method of claim 1 for exploring the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in treating early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms, characterized in that: In step S3, the drug-disease intersection targets are obtained using the Venny 2.1 website, namely, the potential targets of Zhibai Dihuang Wan / Decoction in the treatment of diabetic nephropathy.

5. The method of claim 1 for exploring the core components, targets and mechanisms of action of Zhibai Dihuang Wan / Decoction in treating early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms, characterized in that: In step S4, the PPI network search database was selected from the STRING database. The species parameter was set to "Homosapiens" in the "Multiple proteins" option, and a related search was performed. In subsequent steps, free nodes were hidden, only "Experiments" was selected in the "active interaction sources" option, and the "minimum required interaction score" option was set to "highest confidence (0.900)" for PPI network analysis. A PPI network diagram for drug-disease intersection targets was constructed. Target pairs obtained through STRING were manually entered into the KEGG database for analysis to obtain signaling pathways and their corresponding upstream and downstream information. By integrating and analyzing relevant signaling pathway diagrams, the upstream and downstream directions of the target pairs obtained from the STRING database were determined, and the basic data for the corresponding directed PPI network was finally compiled. Using the Python programming environment, a directed PPI network for drug targets related to Zhibai Dihuang Wan / Tang was constructed and analyzed.

6. The method of claim 1 for exploring the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in treating early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms, characterized in that: In step S5, multiple network centrality algorithms were used to calculate Katz centrality, HITS authority scores, and PageRank scores to comprehensively assess the role of the targets in signaling. The top 10 targets were selected and identified as key core targets for Zhibai Dihuang Wan / Decoction in the treatment of diabetic nephropathy.

7. The method of claim 1 for exploring the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in treating early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms, characterized in that: In step S6, the results of step S5 are sorted, and the top 70 targets are selected to reconstruct a directed PPI network. Then, the top 30 targets are selected and the directed PPI network is reconstructed again. The three sub-networks with the largest number of nodes are selected from the network and visualized.

8. The method of claim 1 for exploring the core components, targets, and mechanisms of action of Zhibai Dihuang Wan / Decoction in treating early diabetic nephropathy based on directed PPI networks and multiple network centrality algorithms, characterized in that: In step S7, the top 10 key core targets of Bai Di Huang Wan / Tang for treating diabetic nephropathy obtained in step S5 are used to determine the active ingredients of the drugs that act on them, which are the core active ingredients of Zhi Bai Di Huang Wan / Tang for treating diabetic nephropathy.

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