Big data screening method of traditional Chinese medicine compound for treating enteritis and application thereof

Abstract

The invention relates to a big data screening method of a traditional Chinese medicine compound for treating enteritis and application thereof, and belongs to the field of traditional Chinese medicine screening based on a data mining technology, animal experiment efficacy investigation and network pharmacology prediction. Comprising the following steps: performing data arrangement specification on a traditional Chinese medicine compound for treating enteritis based on a data mining technology; analyzing and summarizing the standardized data to obtain a new traditional Chinese medicine compound (AEND); constructing an acute enteritis mouse model, and performing pharmacodynamic evaluation on the traditional Chinese medicine compound; predicting the action mechanism of the screened traditional Chinese medicine compound based on a network pharmacology method; a molecular docking technology is applied to virtually verify the action target of the traditional Chinese medicine compound on acute enteritis; the pathway protein of a mammalian cell receptor tyrosine kinase mediated phosphatidylinositol related (PI3K / AKT) signal pathway is detected. According to the invention, a new formula AEND is screened out based on data mining, and a pharmacological experiment verifies that the AEND has a protective effect on mice with acute enteritis. The developed traditional Chinese medicine compound has the effects of resisting inflammation and regulating the homeostasis of intestinal flora.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of traditional Chinese medicine treatment enteritis compound big data screening method and its application, belong to the field based on data mining technology screening, animal experiment investigation pharmacodynamics of traditional Chinese medicine and network pharmacology prediction. BACKGROUND

[0002] Acute enteritis, belong to clinical common disease, often summer and autumn season, disease course 2-4 weeks, main cause is bacterial infection. Pathogenic bacteria are mainly campylobacter, escherichia coli, salmonella, shigella and yersinia. Antibiotics are often used in clinical treatment of acute enteritis, with the increase of drug use, drug resistance gradually becomes serious, and intestinal flora of patients is prone to disorder. In the "National Action Plan to Contain Microbial Drug Resistance (2022-2025)", it is emphasized that the research and development of antimicrobial drugs suitable for special groups such as pregnant women, children and the elderly should be strengthened, and the research and development of traditional Chinese medicine and other alternative antimicrobial drugs should be further strengthened.

[0003] The main clinical symptoms of acute enteritis are diarrhea, abdominal pain, nausea and vomiting, abdominal distension and watery stool, etc., which belong to the category of "diarrhea" in traditional Chinese medicine. According to traditional Chinese medicine theory, the causes of diarrhea include external pathogenic factors, congenital deficiency, improper diet, emotional disorder and post-disease debilitation. The main pathogenesis is spleen deficiency and excessive dampness, which damages the spleen and stomach, and the dampness hinders the spleen, resulting in dysfunction of the intestinal tract and diarrhea. The characteristics of traditional Chinese medicine treatment are multi-link, multi-level and multi-target, which has irreplaceable advantages in the treatment of acute enteritis. Literature investigation shows that traditional Chinese medicine prescriptions such as Changyanning, Baitaoweng decoction and Redu Ning have been applied in clinical practice, and traditional Chinese medicine compound, external application, acupuncture and conventional therapy have also achieved good results. Moreover, traditional Chinese medicine treatment has mild efficacy and less side effects, and is less likely to cause drug resistance.

[0004] There are more and more reports on the optimization and screening of traditional Chinese medicine compounds through big data mining, and the new compounds screened often have good pharmacological effects. Network pharmacology is widely used in predicting the target and mechanism of traditional Chinese medicine and traditional Chinese medicine compounds in the treatment of diseases. SUMMARY

[0005] In view of the above problems, the present application provides a big data screening method for traditional Chinese medicine treatment of enteritis compound and its application, which screens the traditional Chinese medicine formula for treating acute enteritis by using data mining technology, and studies the mechanism of network pharmacology, providing ideas and data support for screening new acute enteritis formula.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is: The big data screening method for traditional Chinese medicine treatment of enteritis compound in the present application comprises the following steps: S1: data arrangement and standardization of traditional Chinese medicine compound for treating enteritis based on data mining technology; S2: analyzing and summarizing the standardized data to obtain a new traditional Chinese medicine compound (AEND); S3: constructing an acute enteritis mouse model to evaluate the pharmacodynamics of the traditional Chinese medicine compound; S4: predicting the mechanism of the screened traditional Chinese medicine compound based on network pharmacology; S5: applying molecular docking technology to virtually verify the target points of the traditional Chinese medicine compound for acute enteritis; S6: detecting the pathway proteins of the phosphatidylinositol 3-kinase / serine-threonine kinase (PI3K / AKT) signaling pathway mediated by mammalian cell receptor tyrosine kinase.

[0007] According to the screening method of the traditional Chinese medicine compound for treating enteritis, the S1 step specifically comprises: screening enteritis-related traditional Chinese medicine compounds by searching the Chinese traditional Chinese medicine database of modern application database related keywords, setting the inclusion and exclusion criteria of traditional Chinese medicine compounds, raw data screening and processing specification, data processing and handling specification.

[0008] According to the big data screening method of the traditional Chinese medicine compound for treating enteritis, the S2 step specifically comprises: using electronic spreadsheet software to statistically analyze the frequency of traditional Chinese medicine use, four qi and five flavors, and meridian distribution, using electronic spreadsheet software and data and text mining software to implement association rule mining analysis on the prescription data of the traditional Chinese medicine compound screened in the S1 step, and simultaneously, using data and text mining software to perform clustering analysis and processing on the data, and screening a new traditional Chinese medicine compound for acute enteritis.

[0009] According to the big data screening method of the traditional Chinese medicine compound for treating enteritis, the S3 step specifically comprises: applying the screened new compound to acute enteritis mice to investigate its preventive and therapeutic effects, and the steps are, S31: 60 male C57BL / 6 mice are randomly divided into 6 groups, and the positive drug enteritisning and AEND administration group is pre-administered for 7 days; S32: 5% dextran sulfate sodium (DSS) is used for modeling for 7 days, and the administration is continued during the modeling period; S33: the effects of the new compound are evaluated by body weight change, disease activity index, colon length, histopathology score, and colon inflammation factor level.

[0010] According to the big data screening method of the traditional Chinese medicine compound for treating enteritis, the S4 step specifically comprises, S41: obtaining the corresponding chemical components and protein targets of the new compound through the traditional Chinese medicine systematic pharmacology database and analysis platform (TCMSP); S42: By collecting target data related to acute enteritis from GeneCards, PharmGKB, and OMIM databases, and then inputting these data and their mapping relationships into the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform, a network diagram of drug components and targets is constructed; S43: Next, the common parts of these targets are determined using the STRING database, and the common targets are imported into the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (Cytoscape) to further establish a protein-protein interaction network for identifying key targets; S44: Subsequently, the selected common targets are analyzed in depth using the Metascape database, covering gene annotation (GO function) and Kyoto Gene and Genome (KEGG) pathway analysis.

[0011] According to the big data screening method of the traditional Chinese medicine compound for treating enteritis, the S5 step is specifically: verifying the binding ability of the key target and the effective component through molecular docking technology, predicting the mechanism of the new compound in treating acute enteritis, and finally detecting the expression level of the related pathway protein through Western blot.

[0012] The application of the big data screening method of the traditional Chinese medicine compound for treating enteritis in the application has the following advantages: the screened traditional Chinese medicine compound includes Huangqi, Huanglian, Gegen, Cangzhu, Muxiang, Hupao, Guanghuoxiang and Gancao.

[0013] According to the application, the adding amount of each traditional Chinese medicine component is Huangqi 15g, Huanglian 15g, Gegen 9g, Cangzhu 9g, Muxiang 9g, Hupao 9g, Guanghuoxiang 9g and Gancao 6g.

[0014] The application has the following advantages: based on data mining, a new prescription AEND is screened out, and then verified through efficacy experiments, AEND has a protective effect on acute enteritis mice. The mechanism of action is predicted by network pharmacology, the expression level of pathway protein is detected by molecular docking and Western blot method, AEND has a protective effect on acute enteritis mice by inhibiting PI3K / AKT pathway protein. It provides a train of thought for further development of new prescriptions for treating enteritis. The developed traditional Chinese medicine compound has the effects of effective anti-inflammatory and regulating intestinal flora homeostasis. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The prescription drug four qi, five flavors, and meridian statistics radar chart in the embodiment of the application; Figure 2 The drug link association rule network diagram in the embodiment of the application; Figure 3 This is a phylogenetic diagram of high-frequency traditional Chinese medicine system cluster analysis in an embodiment of the present invention; Figure 4 This is a graph showing the detection results of the preventive effect of AEND on DSS-induced intestinal inflammation in mice in an embodiment of the present invention; Figure 5 This is a potential target for AEND treatment of acute enteritis in the embodiments of the present invention; Figure 6 This is a drug-component-target network diagram in an embodiment of the present invention; Figure 7 This is a protein-protein interaction (PPI) network representing a potential target for AEND treatment of acute enteritis in this embodiment of the invention. Figure 8 Figure A shows the biofunctional analysis of potential targets for AEND treatment of acute enteritis in this embodiment of the invention. Figure 9 Figure B shows the biofunctional analysis of potential targets for AEND treatment of acute enteritis in this embodiment of the invention. Figure 10 Figure C shows the biological function analysis of potential targets for AEND treatment of acute enteritis in this embodiment of the invention. Figure 11 This is a diagram (D) showing the biological function analysis of potential targets for AEND treatment of acute enteritis in this embodiment of the invention. Figure 12 This is a diagram illustrating the docking pattern between the AEND component and key targets in an embodiment of the present invention. Figure 13 This embodiment of the invention uses Western blot to detect the protein level expression of AEND predicted target sites in mouse colon tissue. Detailed Implementation

[0016] The specific details of this invention will be further explained below: This invention is based on data mining technology to screen a new traditional Chinese medicine formula for treating acute enteritis, and animal experiments are used to investigate the efficacy of the formula and network pharmacology is used to predict the mechanism of action.

[0017] The specific big data screening method for the traditional Chinese medicine compound formula for treating enteritis in this invention includes the following steps: S1: By searching relevant keywords in the Chinese Traditional Medicine Prescriptions and Modern Applications Database, we screened for Chinese herbal medicine compound prescriptions related to enteritis. We also specified inclusion and exclusion criteria, raw data screening and processing guidelines, and data processing guidelines.

[0018] S2: The screened traditional Chinese medicine drug frequency, four qi and five flavors and meridian distribution are statistically analyzed by using an electronic form Excel. The electronic form (Excel software) and data and text mining software (IBM SPSS Modeler) are used to implement association rule mining analysis on the prescription data. Meanwhile, the prediction analysis software (IBM SPSS Statistics) is used to perform clustering analysis processing on the data, so as to screen out a new type of traditional Chinese medicine formula (Acute Enteritis New Decoction, AEND) for acute enteritis.

[0019] S3: The screened new compound "AEND" is applied to acute enteritis mice to investigate its preventive and therapeutic effects. 60 male C57BL / 6 mice are randomly divided into 6 groups. The positive drug Changyanning and AEND administration groups are pre-administered by gavage for 7 days, and then 5% dextran sulfate sodium (DSS) is used for modeling for 7 days. During the modeling period, the gavage administration is continued. The effects of AEND are evaluated by body weight change, disease activity index, colon length, histopathology score and colon inflammation factor level.

[0020] S4: The corresponding chemical components and protein targets are obtained by TCMSP. The target data related to acute enteritis are collected from the gene comprehensive database (GeneCards), the pharmacogenetics and pharmacogenomics knowledge base (PharmGKB) and the human mendelian inheritance database (OMIM database), and then the data and their mapping relationship are input into the traditional Chinese medicine system pharmacology database and analysis platform (Cytoscape software) to construct a network diagram of drug components and targets. Next, the common parts of these targets are determined by using the protein interaction database (STRING database), and the common targets are imported into the traditional Chinese medicine system pharmacology database and analysis platform (Cytoscape) to further establish a protein-protein interaction (PPI) network to identify key targets. Subsequently, the common targets screened out are further explored by using the gene enrichment analysis tool (Metascape database), which covers gene annotation (GO function) and Kyoto gene and genome (KEGG) pathway analysis.

[0021] S5: The binding ability of the key targets and the pharmacodynamic components is verified by using the molecular docking technology, and the mechanism of AEND in treating acute enteritis is predicted. Finally, the expression level of the related pathway protein is detected by immunoblotting.

[0022] Example, the application of the present application is described by using the above-mentioned method to screen out the specific traditional Chinese medicine components: 1 Data mining processing.

[0023] 1.1 Select data sources, Data were from the Prescription Modern Application Database in Chinese Medicine Database, including 9651 kinds of Chinese medicine prescriptions in Chinese Pharmacopoeia, Ministry of Health Standard of Chinese Medicine and periodical literature.

[0024] The corresponding Chinese medicine prescriptions were obtained by searching the relevant keywords. The search formula used was (((("diarrhea"[indication] OR "xie xie"[indication]) OR "xie zhi"[indication]) OR "jiu xie"[indication]) OR "bian suo"[indication]) OR "dachuan shi suo"[indication]) OR "dachuan shi suo"[indication]; ("li ji hou zhong"[indication] OR "xie li"[indication]); "juyi changyin"[indication].

[0025] 1.2 Determine the inclusion and exclusion criteria, The inclusion criteria were oral dosage form, Chinese medicine compound, complete drug composition, and indication of constipation. Prescriptions for treating constipation were excluded. The Chinese medicines contained in Chinese medicine compounds should exist in Chinese Pharmacopoeia (2020 edition) and Chinese Herbal Medicine. If the prescription name was the same but the prescription composition was different, it should be recorded separately.

[0026] 1.3 Data processing and standardization, Raw data screening and processing: The prescription name, drug name, four properties, five flavors, and meridian tropism were recorded in Microsoft Office Excel 2021.

[0027] Data processing and standardization: The Chinese medicine aliases in the prescriptions were standardized according to the 2020 edition of Chinese Pharmacopoeia. Microsoft Office Excel 2021 was used for frequency analysis of Chinese medicine, four properties, five flavors, and meridian tropism. IBM SPSS Statistics 27 and IBM SPSS Modeler 18.0 were used for correlation analysis and cluster analysis of data.

[0028] 2.1 Selection of animals and reagents, ​A total of 60 SPF C57BL / 6 male mice, weighing (20±1) g, were purchased from Jinan Pengyue Experimental Animal Breeding Co., Ltd. (License No. SCXK (Lu) 20220006), and were raised in the animal house of Zaozhuang University at a room temperature of 23±3℃ with a light / dark cycle of 12h / 12h. All mice were allowed to freely drink and eat before the experiment, and the animal experiment was approved by the Science and Ethics Committee of Zaozhuang University (Approval No. ZZU-N-019). Changyanning capsules were purchased from Hainan Guluwa Pharmaceutical Co., Ltd. (Batch No. 231010). Dextran sulfate sodium was purchased from Meilunbio Technology Co., Ltd. (Molecular weight: 36,000-50,000; Batch No. MB5535). Tumor necrosis factor-α (TNF-α) (Batch No. A28240735), interleukin-6 (IL-6) (Batch No. A20640634), and interleukin-1β (IL-1β) (Batch No. A201B40933) detection kits were purchased from Shanghai Link-Gen Company Limited.

[0029] 2.2 Preparation of AEND solution, Scutellaria, Coptis, Pueraria, Atractylodes, Wood, Magnolia, Pogostemon, and Licorice were purchased from Anhui Huifeng National Medicine Co., Ltd., with a product inspection report. According to the experience of using traditional Chinese medicine to treat acute enteritis, and in consultation with Professor Li Rundong of Zaozhuang City Hospital of Traditional Chinese Medicine, the doses were determined (Scutellaria 15g, Coptis 15g, Pueraria 9g, Atractylodes 9g, Wood 9g, Magnolia 9g, Pogostemon 9g, and Licorice 6g), which were used as the daily dose for adults. The prescribed amount of medicinal materials was weighed, soaked in drinking water for 1h, and extracted twice by reflux. The first time, 7 times the amount of water was added, and the extraction was performed by reflux for 30min. The second time, 5 times the amount of water was added, and the extraction was performed by reflux for 20min. The filtrate was mixed and filtered under reduced pressure, and the filtrate was concentrated to 1.2g / mL (crude drug). The drugs in each group were divided and stored in a-20℃ refrigerator.

[0030] 2.3 Determination of animal dosing, Changyanning capsules mainly contain Herba Humuli, Herba Hypericis, Radix Camphorae, Herba Moslae, and Folium Ulmi. Currently, Changyanning or other drugs are used in the treatment of acute bacillary dysentery, rotavirus enteritis, and acute gastroenteritis, which has a positive therapeutic effect. Studies have shown that Changyanning has anti-inflammatory and immunoregulatory effects, so Changyanning capsules were chosen as the positive drug. According to the Changyanning capsule drug instruction, the daily dose for adults is 0.075g / kg (crude drug 1.44g / kg), and the mouse dose is 0.68g / kg according to the body surface area conversion method. The daily dose of AEND for adults is 1.35g / kg (crude drug), and the mouse dose is 12.28g / kg according to the body surface area conversion method. The low, medium, and high dose groups of traditional Chinese medicine compound were set at 6.14g / kg, 12.28g / kg, and 24.57g / kg, respectively.

[0031] 2.4 Experimental animal grouping, SPF level male 6-week-old (20 ± 1 g) C57 mice were randomly divided into 6 groups: blank group (N), model group (M), positive group (P), AEND low-dose group (AE-L), medium-dose group (AE-M), and high-dose group (AE-H), 10 in each group. After 3 days of adaptive feeding, from the 1st day, the model group and the blank group were given sterile water and the other groups were given different concentrations of drugs for 14 consecutive days. The positive group was given intestinal inflammation solution (0.68 g / kg), and the AEND low-dose group (AE-L), medium-dose group (AE-M), and high-dose group (AE-H) were given traditional Chinese medicine compound low, medium, and high doses of 6.14 g / kg, 12.28 g / kg, and 24.57 g / kg, respectively, once a day for 14 consecutive days. On the 7th day of the experiment, the blank group drank sterile water, and the other groups changed to 5% DSS water solution until the end of the experiment. On the 15th day, after fasting for 12 h, the mice were sacrificed by cervical dislocation, and the tissues were taken for detection.

[0032] 2.5 Sample collection, After cervical dislocation, the intestinal tissue from the cecum to the anus of each group of mice was cut and photographed to measure the length of the intestinal segment. The distal end of the colon tissue was washed and placed in 4% paraformaldehyde for histopathological analysis.

[0033] 2.6 Detection of apparent differences, During the establishment of the acute enteritis model in mice, the body weight of each group of mice was measured at a fixed time every day, and diarrhea and hematochezia were observed. According to the changes in mouse body weight, diarrhea, and hematochezia, the disease activity index (DAI) score was calculated daily. The severity of the disease was evaluated, and a higher score indicated a more severe condition. The overall state score is shown in Table 1. DAI score = (body weight loss score + diarrhea score + hematochezia score) / 3.

[0034]

[0035] Table 1

[0036] 1.2.7 Histopathological analysis, The mice were sacrificed and dissected to collect the entire colon, and the length was measured to evaluate the gross damage. The distal end of the colon was washed and placed in 4% paraformaldehyde for histopathological analysis. After dehydration, embedding, sectioning, deparaffination, hematoxylin staining, and eosin staining, the changes in tissue morphology and structure were observed under a light microscope, and histopathological scoring was performed. The scoring criteria are shown in Table 2.

[0037] Table 2

[0038] 2.9 Statistical analysis The data was preliminarily statistically analyzed using Excel software. The test data of each group was input into SPSS software for single factor analysis of variance to compare the differences between each group, and the results were expressed as "mean ± standard deviation".

[0039] 3 Virtual verification of the composition based on network pharmacology and verification of the effect of the compound, 3.1 Active compounds and targets of the core composition, Each traditional Chinese medicine in AEND was searched in the computational systems biology laboratory (TCMSP, https: / / tcmsp-e.com / tcmsp.php) as a keyword, and the active chemical components contained in the combination were summarized. According to the two ADME property values of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, the active ingredients were preliminarily screened. The target points of the screened compounds were normalized to gene symbols (GeneSymbol) using the joint protein sequence database (UniProt, https: / / www.uniprot.org).

[0040] 3.2 Target mapping and construction of traditional Chinese medicine compound regulation pathway, Using "Acute Enteritis" as the search term, the GeneCards database (GeneCards, https: / / www.genecards.org), the PharmGKB database (PharmGKB, https: / / www.pharmgkb.org / ), and the OMIM database (OMIM, https: / / omim.org / ) were searched. Using the Venny tool (http: / / bioinfogp.cnb.csic.es / tools / venny), a Venn diagram was drawn, and the intersection was used to summarize the acute enteritis-related targets. The node information and mapping relationship file of each traditional Chinese medicine in the compound, the corresponding active ingredient target points, and the acute enteritis-related targets were constructed and imported into Cytoscape for visual analysis and setting of their properties.

[0041] 3.3 Protein-protein interaction network construction, The intersection genes of acute enteritis related core targets and compound effect targets obtained in the previous step were analyzed by STRING database (STRING, https: / / string-db.org). The analysis mode was selected as Multiple Proteins, the species category was set as Homo sapiens, and the interaction score threshold was set as high confidence (0.700) to ensure the reliability of the data. The results were saved and imported into Cytoscape 3.6.1 software to construct PPI network, analyze the network, and set the color for reflecting the size of degree and the thickness of edge for reflecting the size of binding score.

[0042] 3.4 Analysis of biological function of target, The potential target was imported into the gene function annotation and analysis platform (Metascape database, https: / / metascape.org / gp / index.html) with Homo sapiens as the species, and GO analysis and KEGG pathway analysis were performed (P < 0.05), and the potential target was analyzed for clustering in the biological process or signal pathway related to acute enteritis by micro-signal platform (https: / / www.bioinformatics.com.cn / ).

[0043] 3.5 Molecular docking, The top 6 key target proteins in the PPI network were subjected to molecular docking verification with the top 5 core ingredients of traditional Chinese medicine compounds in the target-component network diagram. The 6 key core genes screened by PPI were used to find the target ID in the joint protein sequence database Uniprot (https: / / www.uniprot.org / ), and the ID was searched in the protein database (PDB, https: / / www.rcsb.org / ) to download the structure. The water and small molecule ligands were removed by using molecular visualization software (Pymol) to obtain the protein receptor. The ingredients corresponding to the protein receptor were searched and downloaded in the public compound database (Pubchem, https: / / pubchem.ncbi.nlm.nih.gov / ) to obtain the 2D structure of the chemical ingredients, and the chemical and biological three-dimensional modeling software (ChemBio3D software) was used for beautification to obtain the small molecule ligand. The small molecule ligand and protein receptor were subjected to molecular docking by using automatic molecular docking software (Autodock), and the compound was visualized by using molecular visualization software (Pymol).

[0044] 3.6 Western blot detection of protein expression level, Fresh colon tissue was taken, washed with pre-cooled phosphate buffer solution (PBS) 2-3 times to remove blood stains, cut into small pieces, add radioimmunoassay lysis solution (RIPA lysis solution) and homogenate beads, homogenate on ice for 30 min, centrifuge at 12000 rpm, 4 ℃ for 10 min, and the supernatant was total protein solution. The obtained protein solution was added with 5×reduced loading buffer at a volume ratio of 4:1, denatured in a boiling water bath for 15 min, and stored at -20 ℃ after aliquoting. When performing sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) electrophoresis, the glass plate was cleaned and the electrophoresis gel was prepared according to the instructions. Stop when the bromophenol blue is 1 cm from the bottom of the gel. Before transferring the membrane, the polyvinylidene fluoride membrane (PVDF membrane) was activated with ethanol, and transferred at 350 mA constant current for 30 min (ice bath environment). After transferring the membrane, the PVDF membrane was blocked with 5% skim milk at room temperature for 30 min, and then the diluted primary antibody (anti-PI3K, AKT, p-AKT antibody) was added, and incubated at 4 ℃ overnight. The membrane was washed with Tris buffer saline (TBST) containing Tween 20 for 3 times (5 min each time), and then the secondary antibody diluted 1:5000 was added, and incubated at room temperature for 30 min. The membrane was washed again with TBST. The chemiluminescence substrate (ECL) was evenly covered on the membrane surface, and the chemiluminescence instrument was exposed and the TIFF format image was saved.

[0045] After retrieval and standardization, 211 prescriptions met the requirements, and were entered into Excel 2021 for statistical analysis. A total of 225 kinds of traditional Chinese medicines were involved, with a total of 1471 medication frequencies, and an average of 7 medicines per prescription. Among them, the traditional Chinese medicines with higher medication frequency (top 23) were Bai Zhu, Fu Ling, Gan Cao, Che Qian Zi, Huang Lian, Dang Shen, Mu Xiang, Chen Pi, Ge Gen, Rou Gui, Shu Yao, Cang Zhu, Huang Qi, Bai Shao, Shan Zha, Zhi Gan Cao, He Zi, Gan Jiang, Guang Huo Xiang, Wu Mei, Wu Zhu Yu, Hou Po, and Rou Dou Mei. As shown in Table 3.

[0046] Table 3 No. Name Frequency Frequency % No. Name Frequency Frequency % 1 Atractylodes 90 6.12 13 Scrophularia 28 1.90 2 Poria 63 4.28 14 White Peony Root 27 1.84 3 Glycyrrhiza 53 3.60 15 Hawthorn 25 1.70 4 Plantago 48 3.26 16 Glycyrrhiza 25 1.70 5 Berberis 44 2.99 17 Coptis 24 1.63 6 Codonopsis 39 2.65 18 Dried Ginger 23 1.56 7 Saussurea 34 2.31 19 Pandanus 23 1.56 8 Tangerine Peel 33 2.24 20 Wu Mei 23 1.56 9 Pueraria 33 2.24 21 Evodia 22 1.50 10 Cinnamon 33 2.24 22 Magnolia 21 1.43 11 Chinese Yam 32 2.18 23 Nutmeg 21 1.43 12 Atractylodes 31 2.11 Note: Frequency = frequency of this drug / total frequency of drugs (1471) * 100%.

[0047] Analysis of four properties, five flavors and meridian of traditional Chinese medicine: The 225 kinds of traditional Chinese medicines involved in acute enteritis were statistically analyzed in terms of drug properties, flavors and meridians. The drug attributes were drawn into radar charts according to their respective proportions. The traditional Chinese medicine flavors and meridians are not unique, so the percentage is more than 1. As shown in Table 3. Figure 1

[0048] The 225 kinds of traditional Chinese medicines were imported into Excel 2021 to establish a correlation rule matrix table. The support degree was set to 10%, which means that the frequency of data should at least account for 10% of the total number of prescriptions; at the same time, the minimum confidence was set to 50%, and the commonly used drug association combination was screened out according to this condition. As shown in Table 4 and​Figure 2 As shown.

[0049] Table 4 No. After Term Before Term Support % Confidence % No. After Term Before Term Support % Confidence % 1 Atractylodes Codonopsis and Poria 10.43 95.45 15 Poria Tangerine Peel and Atractylodes 10.43 59.09 2 Atractylodes Codonopsis 18.48 89.74 16 Pueraria Scrophularia 13.27 57.14 3 Atractylodes Glycyrrhiza 11.85 80.00 17 Poria Codonopsis 18.48 56.41 4 Atractylodes Poria 29.86 76.19 18 Atractylodes Plantago 22.75 56.25 5 Poria Pandanus 10.90 69.57 19 Poria Atractylodes 14.69 54.84 6 Poria Glycyrrhiza and Atractylodes 10.90 69.57 20 Poria Tangerine Peel 15.64 54.55 7 Atractylodes Tangerine Peel 15.64 66.67 21 Atractylodes Berberis 11.37 54.17 8 Atractylodes Glycyrrhiza and Poria 11.85 64.00 22 Coptis Scrophularia 13.27 53.57 9 Atractylodes Wu Mei 10.90 60.87 23 Poria Atractylodes 42.65 53.33 10 Atractylodes Hawthorn 11.85 60.00 24 Plantago Wu Mei 10.90 52.17 11 Poria Codonopsis and Atractylodes 16.59 60.00 25 Pueraria Pandanus 10.90 52.17 12 Atractylodes Chinese Yam 15.17 59.38 26 Glycyrrhiza Scrophularia 13.27 50.00 13 Atractylodes White Peony Root 12.80 59.26 27 Atractylodes Saussurea 16.11 50.00 14 Poria Plantago and Atractylodes 12.80 59.26 Cluster analysis was performed on the top 23 flavors (top 10%) selected from highest to lowest frequency, as shown in the figure. Figure 3 Two formulas were obtained: Formula 1, containing Atractylodes macrocephala, Poria cocos, Codonopsis pilosula, Plantago asiatica, Citrus reticulata peel, Paeonia lactiflora, Dioscorea opposita, Crataegus pinnatifida, and Prunus mume; and Formula 2, containing Atractylodes lancea, Magnolia officinalis, Pogostemon cablin, Pueraria lobata, Scutellaria baicalensis, Coptis chinensis, Glycyrrhiza uralensis, and Aucklandia lappa. The selected combinations were subjected to functional prediction using the Huabing Data Platform (http: / / www.huabeing.com / ), as shown in Table 5 below. The formulas highly match the pathogenesis of diarrhea ("spleen deficiency and dampness accumulation, spleen and stomach damage, dampness trapping the spleen") and the clinical symptoms of acute enteritis ("diarrhea, abdominal pain, nausea, vomiting, abdominal distension, and watery stools"), and conform to the TCM principle of "principal, assistant, adjuvant, and guide" in formula compatibility. Therefore, Formula 2 was selected as the new formula "AEND Formula" for treating acute enteritis.

[0050] Table 5 Number of Drug Positions Composition of Chinese Medicine Main Efficacy Prediction Formula 1 9 Atractylodes, Poria, Codonopsis, Plantago, Tangerine Peel, White Peony Root, Chinese Yam, Hawthorn, Wu Mei Supplementing Kidney and Spleen, Benefiting Qi and Spleen, Soothing Liver and Regulating Qi Formula 2 8 Atractylodes, Magnolia, Pandanus, Pueraria, Scrophularia, Coptis, Glycyrrhiza, Saussurea Clearing Heat and Dampness, Invigorating Spleen and Stomach, Stopping Diarrhea, Resolving Phlegm, Relieving Exterior, Dispelling Wind Before the experiment, there was no significant difference in body weight among the groups of mice. After administration of DSS solution, the body weight of mice in the blank group steadily increased, while the body weight of mice in the other groups decreased to varying degrees. On the 7th day after administration of DSS, the rate of change in body weight between the blank group and the model group was significantly different. P< 0.001), the AE-H group was significantly smaller than the M group ( P< (0.01), the weight changes in mice in the P group, AE-L group, and AE-M group were slower compared to the model group. See Figure 4 (A). On day 1 after administration of DSS solution, mice in the model group showed visual granular hemorrhage and loose stools. On day 2, all groups showed varying degrees of soft granules. On day 4, all mice in the model group showed visual granular hemorrhage. On day 7, there was a significant difference between the model group and the AE-H group (A). P< 0.05). The DAI values ​​of the remaining positive control groups (Enteritis Relief Group, low-dose, and medium-dose groups) were all reduced to varying degrees compared to the model group. See [link to relevant documentation]. Figure 4 (B) Based on this, it is inferred that AEND has a certain effect in preventing acute enteritis.

[0051] Colon length is a key indicator for assessing the severity of colitis in mice. As inflammation intensifies, the colon swells and gradually shortens. The cecum, part of the ileum, and colon / rectum were dissected, photographed, and colon length was measured. The control group mice had smooth colon surfaces, the longest colons, and well-formed feces. The difference in colon length between the model group and the control group was highly significant. P< 0.001), with unformed bloody stools and significant swelling of the intestinal surface. After intervention, AE-H showed significant symptom relief, and its colon length was significantly higher than that of the model group (P< 0.001), the colon length of the AE-M group was significantly higher than that of the M group ( P< 0.05), the positive drug of intestinal inflammation, low-dose group, and medium-dose group had different degrees of increase in length, as shown in Figure 4 (C). After paraffin embedding and H&E staining process, the obtained images were observed by microscope as shown in Figure 4 (D) shows that in the blank control group, the mouse colon mucosa layer maintains an intact state, the crypt and goblet cell arrangement is orderly, and no obvious inflammatory cell infiltration is observed. In contrast, the colon mucosa of the model group mice showed significant ulcerative lesions, crypts and goblet cells were completely absent, and were accompanied by very severe inflammatory cell infiltration.

[0052] The pathological score results are shown in Figure 4 (E), and there is a highly statistically significant difference between the model group and the blank control group ( P< 0.001). In addition, it is worth noting that after the corresponding drug treatment (including the positive control drug of intestinal inflammation and AEND) is implemented on the intestinal inflammation mice, the colon mucosa epithelial cells exhibit higher integrity compared to the model group.

[0053] Although inflammatory cells have infiltrated the mucosa layer, the degree of infiltration is relatively light, indicating that the damage condition has improved. Specifically, the pathological scores of the positive drug group and the AEND high-dose group show a significant decrease compared to the model group ( P< 0.01, P< 0.001). The integrity of the structures such as goblet cells and crypts in the AEND low-dose group and medium-dose group has increased to different degrees compared to the model group. This indicates that AEND can effectively improve the intestinal condition of acute intestinal inflammation mice.

[0054] The ELISA detection results are shown in Figure 4 (F, G, H), which reveal the changes in the contents of inflammatory factors IL-6, TNF-α, and IL-1β in the colon tissue. Specifically, DSS drinking water treatment significantly increased the contents of the three inflammatory factors in the colon tissue of mice ( P< 0.001). After the action of AEND, the expression levels of these inflammatory factors in the colon tissue of mice were significantly reduced. Further analysis found that for the expression of IL-1β and TNF-α, low-dose AEND and the positive drug of intestinal inflammation did not show significant recovery ( P> 0.05). In contrast, medium-dose and high-dose AEND showed a dose-dependent decrease in IL-1β expression ( P<0.001). This indicates that within a certain range, the increase of AEND dose helps to reduce the expression of IL-1β and TNF-α. For the expression of IL-6, the positive drug Jianfening showed a significant reduction compared to the DSS model group (P < 0.05). Notably, the medium and high doses of AEND showed a dose-dependent reduction in IL-6 expression (P < 0.05). P< 0.05). Notably, the medium and high doses of AEND showed a dose-dependent reduction in IL-6 expression (P < 0.05). P< 0.01, P< 0.001). In summary, AEND can effectively reduce colon inflammation by down-regulating the expression levels of IL-6, TNF-α, and IL-10 in DSS-induced acute colitis colon tissues. This finding provides scientific evidence for the potential application of AEND in the protection against acute colitis based on data mining results.

[0055] A total of 140 active ingredients of each herb in the traditional Chinese medicine compound and 204 disease-related gene targets were obtained through screening and summarizing from the TCMSP database. The acute colitis-related targets were collected from GeneCards, PharmGKB, and OMIM databases, and a Venn diagram was drawn to obtain 5513 core targets related to acute colitis. The screened compound targets of the traditional Chinese medicine compound and the summarized acute colitis-related core targets were mapped and intersected to obtain 173 potential targets of AEND for treating acute colitis, as shown in Figure 5 . Eight herbs correspond to 136 ingredients and 173 potential targets of AEND for treating acute colitis, as shown in Figure 6 . Huangqi and Gancao correspond to more acute colitis targets. Through ranking by Degree value, the top 5 highly active ingredients with the highest Degree values were selected as the core active ingredients of AEND, which are quercetin, kaempferol, wogonin, naringenin, and baicalein.

[0056] In the protein-protein interaction (PPI) network of potential targets, there are 157 nodes (target proteins) and 1223 edges (protein interactions), as shown in Figure 7 . The color represents the size of the node degree value. The darker the color, the larger the corresponding degree value, indicating that the more target points can effectively interact with the target points in the predicted disease-related targets. The thickness of the edge represents the binding score. The thicker the edge, the larger the binding score value, indicating a higher confidence in the interaction of related targets. Through the Cytonca plugin in Cytoscape, the 157 intersection genes were scored, and finally, TP53, IL1B, AKT1, IL6, TNF, EGFR, MMP9, and IL10 were found to be key targets. In addition, EGF, MAPK3, PTGS2, STAT1, IFNG, FN1, MYC, CCL2, HIF1A, CASP3, CXCL8, RELA, and BCL2 were also found to be effective targets.

[0057] KEGG pathway enrichment analysis (P<0.05) of potential targets of AEND treating AE was performed by Metascape data platform, and a bubble chart was drawn as shown in FIG. 6. Figure 8 The KEGG analysis mainly involved inflammatory pathways, immune stress pathways, apoptosis pathways, and disease-related pathways. Among them, the pathways related to the regulation of inflammation included PI3K (phosphatidylinositol 3-kinase) and Akt (protein kinase B) signaling pathway (PI3K-Akt signaling pathway), receptor tyrosine kinase signaling and transcription activator signaling pathway (JAK-STAT signaling pathway), AMPK (protein kinase-dependent) signaling pathway (AMPK signaling pathway), and NF-kappa B signaling pathway (NF-kappa B signaling pathway); the pathways related to immune stress included leukocyte transendothelial migration signaling pathway (Leukocyte transendothelial migration) and mitochondrial autophagy signaling pathway (Mitophagy-animal); the pathways related to apoptosis included calcium signaling pathway (Calcium signaling pathway), PI3K-Akt signaling pathway, and JAK-STAT signaling pathway. Disease pathways mainly included pathways in cancer, Leishmaniasis, and platinum drug resistance. Detailed information is shown in Table 7.

[0058] Table 7

[0059] The GO analysis results were sorted by P value, and the top 20 were drawn as a bubble chart. Figure 9 It was shown that the biological processes involved metabolic pathway regulation of inflammatory response, response to oxidative stress, apoptosis signaling pathway, regulation of apoptosis signaling pathway, MAPK cascade regulation, positive regulation of programmed cell death, and negative regulation of cell differentiation (P<0.05); Figure 10 It was shown that the cellular components involved multiple aspects such as endoplasmic reticulum, nucleus, cell membrane, and extracellular matrix (P<0.05); Figure 11 The molecular functions were focused on protein kinase-related activities, nuclear receptor activities, cytokine receptor binding, and other specific protein binding activities such as heme binding, cytokine receptor binding, and multiple processes (P<0.05).

[0060] The top 6 key target proteins in the PPI network, including TP53, IL1B, AKT1, IL6, TNF and EGFR, were subjected to molecular docking verification with the top 5 ingredients in the drug-component-target network, namely quercetin, kaempferol, wogonin, naringenin and baicalein. The results are shown in Table 8. The smaller the molecular docking binding energy, the better the binding. The selected target proteins in the table are well combined with the ingredients in AEND. According to the disease characteristics of AE, the core target proteins IL-6, TNF, IL-1β and Akt1 were selected, and the Chinese medicine ingredients baicalein, kaempferol, quercetin and naringenin were selected, and the molecular docking mode diagram was drawn. The above results show that the AEND ingredients can be well docked with AE-related targets, and have high potential biological activity. The docking mode diagram of the key target is shown in Figure 12 .

[0061] Table 8

[0062] The PI3K / AKT signaling pathway proteins were detected by Western blot method. See Figure 13 Compared with the model control group, the AKT, PI3K and protein phosphorylation levels in the colon tissues of the low, medium and high dose groups were reduced. The results show that AEND can protect against acute enteritis in mice by inhibiting the PI3K / AKT pathway proteins.

[0063] In summary, the data mining method for screening treatment of acute enteritis and pharmacodynamic investigation method provided by the present application first applies data mining technology to screen a traditional Chinese medicine prescription for treating acute enteritis, verifies it through animal experiments, and studies the network pharmacological mechanism, thereby providing ideas and data support for screening new acute enteritis prescriptions.

[0064] The above is only an embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent transformation or direct or indirect application in related technical fields based on the content of the specification and drawings of the present application is also included in the patent protection scope of the present application.

Claims

1. A big data screening method for traditional Chinese medicine compound prescriptions for treating enteritis, characterized in that, Includes the following steps: S1: Data organization and standardization of traditional Chinese medicine compound prescriptions for treating enteritis based on data mining technology; S2: Analyze and summarize the standardized data to obtain new compound Chinese medicine formulas (AEND). S3: Construct a mouse model of acute enteritis and evaluate the pharmacodynamics of traditional Chinese medicine compound prescriptions; S4: Predicting the mechanism of action of screened traditional Chinese medicine compound prescriptions based on network pharmacology methods; S5: Using molecular docking technology, we virtually verified the target of action of traditional Chinese medicine compound on acute enteritis. S6: Detect pathway proteins in the mammalian cell receptor tyrosine kinase-mediated phosphatidylinositol-related (PI3K / AKT) signaling pathway.

2. The screening method for a traditional Chinese medicine compound for treating enteritis according to claim 1, characterized in that, The S1 step specifically involves: searching for relevant keywords in the database of modern applications of traditional Chinese medicine prescriptions in the Chinese TCM database, screening for TCM compound prescriptions related to enteritis, and setting inclusion and exclusion criteria for TCM compound prescriptions, raw data screening and processing specifications, and data processing specifications.

3. The big data screening method for a traditional Chinese medicine compound for treating enteritis according to claim 2, characterized in that, The S2 step specifically involves: conducting statistical analysis based on the frequency of use of traditional Chinese medicine, its four properties and five flavors, and its meridian tropism; performing association rule mining analysis on the traditional Chinese medicine compound prescription data selected in the S1 step; and simultaneously performing cluster analysis on the data to select a novel traditional Chinese medicine formula for acute enteritis.

4. The big data screening method for a traditional Chinese medicine compound for treating enteritis according to claim 1, characterized in that, The S3 step specifically involves applying the screened new compound to mice with acute enteritis to examine its preventive and therapeutic effects. The steps are as follows: S31: Sixty male C57BL / 6 mice were randomly divided into 6 groups. The positive control group and the AEND group were administered the drugs by gavage for 7 days in advance. S32: Perform 5% sodium dextran sulfate (DSS) for another 7 days to establish the model, and continue to administer the drug by gavage during the modeling period; S33: The effects of the new compound were assessed using changes in body weight, disease activity index, colon length, histopathological score, and levels of colonic inflammatory factors.

5. The big data screening method for a traditional Chinese medicine compound for treating enteritis according to claim 3, characterized in that, The S4 step is specifically as follows: S41: Obtain the corresponding chemical components and protein targets of the new compound through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP); S42: Collect target data related to acute enteritis from GeneCards, PharmGKB, and the Human Mendelian Inheritance Database (OMIM), and then input these data and their mapping relationships into the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform to construct a network diagram of drug components and targets. S43: Next, the common parts of these targets will be identified using the protein interaction database (STRING database), and the common targets will be imported into the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (Cytoscape) to further establish a protein-protein interaction network to identify key targets; S44: Subsequently, gene enrichment analysis tools (Metascape database) were used to conduct in-depth analysis of the screened common targets, covering gene annotation (GO function) and Kyoto Gene and Genome (KEGG) pathway analysis.

6. The big data screening method for a traditional Chinese medicine compound for treating enteritis according to claim 5, characterized in that, The S5 step specifically involves: verifying the binding ability of key targets and pharmacological components through molecular docking technology, predicting the mechanism of action of the new compound in the treatment of acute enteritis, and finally detecting the expression levels of related pathway proteins through Western blotting.

7. An application of a big data screening method for a traditional Chinese medicine compound for treating enteritis as described in claim 6, characterized in that, The selected traditional Chinese medicine compound includes Scutellaria baicalensis, Coptis chinensis, Pueraria lobata, Atractylodes lancea, Aucklandia lappa, Magnolia officinalis, Pogostemon cablin, and Glycyrrhiza uralensis.

8. The application according to claim 7, characterized in that, The dosage of each Chinese herbal ingredient is as follows: Scutellaria baicalensis 15g, Coptis chinensis 15g, Pueraria lobata 9g, Atractylodes lancea 9g, Aucklandia lappa 9g, Magnolia officinalis 9g, Pogostemon cablin 9g, and Glycyrrhiza uralensis 6g.

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