A method for screening targeted quality markers of traditional Chinese medicine and the application of Codonopsis pilosula targeted Q-Marker

Abstract

The present invention relates to the technical field of targeted screening of traditional Chinese medicines, and proposes a method for screening targeted quality markers of traditional Chinese medicines, comprising the following steps: S1, hotspot focusing: extracting common hotspot pathways and targets of the main pharmacological activities of traditional Chinese medicines, combining multiple algorithms to screen key targets and delineate the potential range of targeted Q-Markers; S2, virtual two-way fishing: utilizing molecular docking to virtually two-way fish for components and targets; S3, real fishing: preparing and characterizing functionalized magnetic nanoparticles of targets, obtaining components through real fishing, testing the fishing results, and determining the targeted Q-Markers. The above technical solution solves the problems of low accuracy, efficiency, and economy in related technologies, and provides more accurate screening results, a more efficient screening process, and a more economical screening method.

Description

Technical Field

[0001] The present invention relates to the technical field of targeted screening of traditional Chinese medicines, and in particular to a method for screening targeted quality markers of traditional Chinese medicines and the application of a Codonopsis pilosula targeted Q-Marker. Background Art

[0002] The core of Traditional Chinese Medicine (TCM) quality control lies in controlling its efficacy. TCM quality markers (Q-Markers) are a concept for TCM quality control based on the "property-efficacy-ingredient" theory. They combine traditional TCM efficacy with modern pharmacological research for quality control. The application of Q-Markers has advanced the development of efficacy-based TCM quality control. Network analysis, as a highly efficient analytical technique, has been successfully applied as a large-scale, rapid screening method for Q-Markers.

[0003] However, existing network analysis methods are not fully suitable for Q-marker screening of traditional Chinese medicines (TCMs). First, TCMs possess a rich array of traditional efficacy, and existing network analysis techniques for Q-marker screening of a single efficacy employ a "one-to-many" analysis model, focusing on efficacy versus ingredients. While this approach is a powerful tool for quality control of TCMs targeting specific functions, it cannot meet the "many-to-many" analysis requirements for simultaneous quality control of multiple efficacy. Furthermore, traditional TCM efficacy is often difficult to directly correspond to the terminology used in modern pharmacological research. Existing network analysis databases are often constructed using modern pharmacological activity terms, making it difficult to directly apply these databases to collect information on specific efficacy using standard network analysis methods. For example, Codonopsis pilosula, a major TCM herb, is known in ancient texts for its traditional efficacy of strengthening the spleen and lungs, nourishing blood, and promoting fluid production. Current pharmacological research indicates that it has therapeutic effects in immune, digestive, and lung diseases. Directly screening for Q-markers targeting only a single pharmacological activity presents challenges in collecting information, and comprehensive quality control cannot be achieved by screening for Q-markers targeting only a single pharmacological activity. Second, network analysis involves a large amount of multidimensional data, and commonly used analytical methods such as direct intersection analysis and single-parameter component screening cannot fully utilize this data. Third, network analysis lacks connection to actual experimental results, a significant technical shortcoming. Combining this with time-consuming and material-intensive experiments further weakens its inherent advantages of efficiency and speed. Summary of the Invention

[0004] The present invention proposes a method for screening targeted quality markers of traditional Chinese medicine, which solves the problems of low accuracy, efficiency and economy in related technologies, and provides more accurate screening results, a more efficient screening process and a more economical screening method.

[0005] The technical solutions of the present invention are as follows:

[0006] A method for screening targeted quality markers of traditional Chinese medicine, comprising the following steps:

[0007] S1. Hotspot Focus: Extract common hotspot pathways and targets of the pharmacological activity of traditional Chinese medicine, screen key targets and define the potential scope of targeted Q-Markers;

[0008] S2, virtual two-way fishing: using molecular docking to virtually fish components and targets;

[0009] S3. Real fishing: Prepare and characterize functionalized magnetic nanoparticles of the target, obtain the components through real fishing, detect the fishing results, and determine the targeted Q-Marker.

[0010] Preferably, the step S1 includes:

[0011] A1. Extraction of hot pathways and targets: Search the literature database using the names of traditional Chinese medicines as keywords to collect literature information. After deduplication, export the document containing the literature information. Collate the number of publications, the subject of the article, the keywords, and the publication year. Import the data into bibliometric analysis software, extract the data, select keywords related to pharmacology research for visualization analysis, and identify hot pathways and targets in the mechanism of action related to the various effects of traditional Chinese medicines.

[0012] A2. Screening of key targets: Obtain relevant targets of hotspot pathways and targets from the target information database, standardize target names, collect target information, obtain protein interaction network information from the database, implement network topology parameter analysis, calculate multiple network topology parameters for each target and perform visual analysis based on target scores, and select targets with high scores in the PPI network as key targets;

[0013] A3. Determination of the potential scope of targeted Q-Markers: Combined with the ingredient database and literature collection and the quality control ingredients of traditional Chinese medicine, the parameter data of absorption, distribution, metabolism and excretion of each ingredient were collected, imported into the software, and clustered. Based on the clustering results, the ADME characteristics of ingredients of different categories were analyzed, and the target information of the ingredients was obtained. The target information was standardized, and attention was paid to the ingredients with high repetition rates between the relevant targets and the key targets obtained in A2. The ingredients were further combined with the ADME characteristics of the category to which the ingredients belonged to select the ingredients as the potential scope of targeted Q-Markers.

[0014] Preferably, the step S2 includes:

[0015] B1. Molecular docking: Prepare the structural files of the key target of A2 and the components within the potential range of the targeted Q-Marker of A3, import them into the molecular docking software, perform docking, and visualize and analyze the docking effect;

[0016] B2. Algorithm analysis: Based on the docking result data in B1, score each component and classify them according to the scores to obtain the components in the excellent category. Further import the docking result data and component category labels into the software to analyze and screen targets closely related to the components in the excellent category.

[0017] Preferably, step S3 includes:

[0018] C1. Preparation of functionalized magnetic nanoparticles: Take nano-sized Fe3O4, add appropriate amount of anhydrous ethanol and deionized water, and after complete dispersion, add ammonia water and tetraethyl silicate while stirring. After the reaction is completed, magnetic attraction is performed, and the reaction solvent is removed. The lower layer of solid is washed with ultrapure water until neutral, and then washed with anhydrous ethanol, and vacuum dried to obtain Fe3O4@SiO2. Take Fe3O4@SiO2, add anhydrous ethanol, and after uniform dispersion, add 3-aminopropyltriethoxysilane while stirring. After the reaction is completed, magnetic separation is performed, and the lower layer of solid is washed with anhydrous ethanol. and ultrapure water, respectively, magnetically separated and vacuum dried to obtain Fe3O4@SiO2-NH2, Fe3O4@SiO2-NH2 was taken, anhydrous ethanol and phosphate buffered saline were added, and dispersed evenly, 50% glutaraldehyde solution was added and mixed evenly, magnetically separated after the reaction, washed with phosphate buffered saline, buffer was added and dispersed evenly, and the target protein solution obtained by screening with B2 was added, magnetically separated after the reaction, washed with buffer, and the remaining solid was the functionalized magnetic nanoparticles, which were stored in a suitable buffer at 4°C for future use;

[0019] C2. Determination of the immobilization yield of the target protein: The immobilization yield of the target protein was determined using a 2,2-biquinoline-4,4-dicarboxylic acid disodium protein kit. The standard was diluted with buffer to form a series of standard solutions of different concentrations. The Fe3O4@SiO2-NH2 and functionalized magnetic nanoparticles prepared in C1 were taken and added with buffer as sample solutions. The 2,2-biquinoline-4,4-dicarboxylic acid disodium working solution was added to each standard solution and sample solution, and the mixture was incubated at 37°C for 30 minutes. The absorbance at 562 nm was measured using a microplate reader to establish a standard curve and calculate the protein concentration and immobilization yield.

[0020] C3. Material characterization: Fourier transform infrared spectroscopy and scanning electron microscopy were used to characterize Fe3O4, Fe3O4@SiO2, Fe3O4@SiO2-NH2 and functionalized magnetic nanoparticles in C1, and the characteristics of functionalized magnetic nanoparticles were compared and analyzed;

[0021] C4, ligand fishing: Take the excellent components in B2, add methanol to make a mixed solution as the sample solution, mix the Fe3O4@SiO2-NH2 and functionalized magnetic nanoparticles in C1 with equal amounts of sample solution, incubate and magnetically separate, discard the supernatant, wash with buffer, add dissociation solution, mix evenly, dissociate and then perform magnetic separation to obtain eluate, and pass through a microporous filter membrane for later use;

[0022] C5. Detection of fishing results and determination of targeted Q-Marker: Accurately aspirate the sample solution before fishing and the eluate after fishing in C4 respectively, inject them into high performance liquid chromatography for analysis, compare the peak area of each component before and after fishing, calculate the component fishing rate, and screen the components whose fishing rate meets the five principles of Q-Marker as the targeted Q-Marker of traditional Chinese medicine.

[0023] Preferably, the step S1 further includes:

[0024] The target information database is: Kyoto Encyclopedia of Genes and Genomes;

[0025] The standardized target names are derived from the Uniprot database;

[0026] The method for obtaining PPI network information is: STRING 12.0 database;

[0027] The network topology parameter analysis software is: CytoScape 3.10.0;

[0028] The algorithm is: approximate ideal solution sorting method.

[0029] Preferably, the step S2 further includes:

[0030] The sources of the structure files are PubChem database and RCSB PDB database;

[0031] The software is Discovery Studio 2016;

[0032] The molecular docking process uses the Clean Protein module and the Prepare Protein module to pre-treat the target protein and expose the active pocket, uses the Minimize Ligands module to minimize the energy of the component conformations, and uses the CDocker module for docking. The docking effect is evaluated using binding energy as an indicator;

[0033] The component scoring algorithm is TOPSIS;

[0034] The software is SIMCA-P 14.1;

[0035] The analysis method for screening targets is OPLS-DA.

[0036] Preferably, in the step S3, the functionalized magnetic nanoparticles are Fe3O4@SiO2-COX-2 or Fe3O4@SiO2-MAPK14;

[0037] The suitable buffer is tris hydrochloride buffer;

[0038] The uniform dispersion method is ultrasound;

[0039] The washing times were 3 times.

[0040] Preferably, the step C1 is specifically as follows:

[0041] The raw material dosage and reaction conditions for the preparation of Fe3O4@SiO2 are as follows: 100 mg nano-Fe3O4, 80 mL anhydrous ethanol, 20 mL deionized water, 1.0 mL ammonia water, 0.3 mL TEOS, reaction for 6 h, and vacuum drying at 60°C for 12 h;

[0042] The raw material dosage and reaction conditions for the preparation of Fe3O4@SiO2-NH2 are as follows: 100 mg Fe3O4@SiO2, 200 mL anhydrous ethanol, 3 mL APTES, mixed reaction for 6 h, and vacuum dried at 50 °C for 6 h;

[0043] The raw material dosage and reaction conditions for the preparation of the functionalized magnetic nanoparticles based on Fe3O4@SiO2-NH2 are as follows: 10 mg Fe3O4@SiO2-NH2, 2.0 mL anhydrous ethanol, 2.0 mL PBS buffer, 200 μL 50% glutaraldehyde solution, 2.0 mL appropriate buffer, 100 μL COX-2 solution, 100 μL MAPK14 solution, and the reaction is carried out for 3 h.

[0044] The step C4 comprises:

[0045] The components in the excellent category are: codonopsis glycoside, codonopsis alcohol, codonopsis glycoside nin, luteolin, syringin and apigenin-7-O-glucoside; the concentration of the sample solution is: 40 μg / mL; the material dosage and reaction conditions are: 10 mg Fe3O4@SiO2-NH2, Fe3O4@SiO2-COX-2 and Fe3O4@SiO2-MAPK14, 2.0 mL sample solution, 2.0 mL dissociation solution, incubation for 120 minutes, and dissociation for 60 minutes.

[0046] Preferably, the step C5 comprises:

[0047] The HPLC analysis process is as follows: 10 μL of the sample solution before fishing, the eluate after fishing, and the reference solution are drawn up, respectively, and injected into an HPLC liquid chromatograph equipped with a diode array detector for determination, using a KROMASIL 100-5-C18 chromatographic column for detection, with an acetonitrile-water mobile phase, a flow rate of 0.8 mL / min, a detection wavelength of 254 nm, a column temperature of 25° C., and in the 0th to 5th minute of gradient elution, the volume content of the solvent acetonitrile in the mobile phase increases from 15% to 20%, from the 5th to 7th minute, the volume content of the solvent acetonitrile in the mobile phase increases from 20% to 30%, and from the 7th to 20th minute, the volume content of the solvent acetonitrile in the mobile phase increases from 30% to 60%.

[0048] A method for screening targeted quality markers of traditional Chinese medicine is applied to Codonopsis pilosula targeted Q-Marker, specifically:

[0049] D1. Preparation of test sample: Accurately measure 3 g of Codonopsis pilosula powder, add 25 mL of methanol, and ultrasonically extract for 30 min. Filter and evaporate to dryness. Add methanol to the residue and dilute to a 5 mL volumetric flask. Filter through a 0.45 μm membrane to prepare the test solution.

[0050] D2. Preparation of reference solution: Accurately weigh the reference substances of codonopsis pilosula, codonopsis glycoside, and codonopsis glycoside amine, and add methanol to prepare a mixed solution containing 50 μg of codonopsis pilosula, 50 μg of codonopsis pilosula, and 50 μg of codonopsis glycoside amine per 1 mL;

[0051] D3. Determination: Accurately aspirate 10 μL of each reference solution and test solution, inject them into an HPLC liquid chromatograph equipped with a DAD detector, and determine the results using an Agilent HC C18 column. The mobile phase is acetonitrile-water, the flow rate is 0.8 mL / min, the detection wavelength is 268 nm, the column temperature is 35°C, and the volume content of the solvent acetonitrile in the mobile phase increases from 5% to 7% from 0 to 6 minutes of gradient elution. From 6 minutes to 15 minutes, the volume content of the solvent acetonitrile in the mobile phase is maintained at 7%. From 15 minutes to 20 minutes, the volume content of the solvent acetonitrile in the mobile phase increases from 7% to 12%. From 20 minutes to 32 minutes, the volume content of the solvent acetonitrile in the mobile phase increases from 12% to 15%. From 32 minutes to From 40 min to 55 min, the volume content of acetonitrile solvent in the mobile phase increased from 15% to 20%. From 55 min to 65 min, the volume content of acetonitrile solvent in the mobile phase increased from 30% to 50%. From 65 min to 70 min, the volume content of acetonitrile solvent in the mobile phase increased from 50% to 70%. From 70 min to 90 min, the volume content of acetonitrile solvent in the mobile phase was maintained at 70%. The number of theoretical plates calculated according to the dangshen glycoside peak should not be less than 400,000.

[0052] The working principle and beneficial effects of the present invention are:

[0053] 1. The present invention is based on the research idea of using network analysis for Q-Marker screening, and adopts bibliometric methods to analyze the pharmacology-related research conducted on the multiple functions of traditional Chinese medicine in the past two decades, explores the hot pathways and targets in the mechanism of action behind the multiple functions of traditional Chinese medicine, collects relevant information with the hot pathways and targets as clues, and combines correlation analysis, cluster analysis, and comprehensive evaluation algorithms to perform dimensionality reduction analysis on the multidimensional data in the network analysis. By using the micro-analysis technology of virtual fishing and real fishing, combined with virtual calculation and actual experiments, the original "one-to-many" analysis mode for a single or a few functions is realized to "many-to-many" targeted Q-Marker screening for multiple functions of traditional Chinese medicine at the same time, meeting the quality control needs of traditional Chinese medicine based on multiple functions.

[0054] 2. The targeted Q-Marker screening mode directly conducts bibliometric analysis on the massive pharmacological activity research related to the multiple effects of traditional Chinese medicine, extracts and clusters keywords, and focuses on hot pathways. It transforms the information collection at the efficacy level in the existing network analysis into the information collection at the pathway and target level that is directly related to multiple effects. This perfectly solves the problems of the incompatibility between traditional Chinese medicine efficacy terms and modern databases during the information collection process and the complexity of processing large amounts of information on multiple effects.

[0055] 3. For the multi-dimensional data in network analysis, instead of directly taking the intersection or using only a single parameter as the screening basis, we use correlation analysis, cluster analysis and comprehensive evaluation algorithms to perform dimensionality reduction processing, so as to achieve short-term and efficient comprehensive processing of a large amount of data with multiple parameters, and provide new ideas for the full utilization of multi-dimensional data in network analysis.

[0056] 4. Innovatively combine real fishing technology with the characteristics of strong specificity, high efficiency, less sample pretreatment requirements, small sample volume, and recyclable materials with virtual fishing technology, introduce the network analysis screening process of Q-Marker, and combine it with real experiments while maintaining the high efficiency and economic advantages of network analysis, effectively making up for the shortcomings of virtual methods such as network analysis that lack effective, fast and economical real experiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The preferred embodiments will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0058] Figure 1 Flowchart of the TCM-targeted Q-Marker screening model.

[0059] Figure 2 Visualization results of bibliometric analysis of Codonopsis pilosula pharmacology related research;

[0060] A: Keyword clustering results; B: Publication time of the literature reporting each keyword; C: Keyword research heat density map; D: Enlarged map of keywords directly related to NF-κB; E: Enlarged map of keywords directly related to TLR4.

[0061] Figure 3 It is the PPI network of TLR4 / MAPK / NF-κB / COX-2 hotspot pathway targets of Codonopsis pilosula.

[0062] Figure 4 The results of OPLS-DA analysis of Codonopsis pilosula components based on ADME characteristics.

[0063] Figure 5 The molecular docking results of the components and key targets within the potential scope of Codonopsis pilosula targeting Q-Marker.

[0064] Figure 6 This is the OPLS-DA analysis result based on molecular docking results in the screening of Codonopsis pilosula-targeted Q-Marker.

[0065] Figure 7 is FT-IR spectrum;

[0066] A: Fe3O4; B: Fe3O4@SiO2; C: Fe3O4@SiO2-NH2; D: Fe3O4@SiO2-COX-2; E: Fe3O4@SiO2-MAPK14.

[0067] Figure 8 is the SEM image;

[0068] A: Fe3O4; B: Fe3O4@SiO2; C: Fe3O4@SiO2-NH2; D: Fe3O4@SiO2-COX-2; E: Fe3O4@SiO2-MAPK14.

[0069] Figure 9 This is the HPLC test result of real fishing in the screening of Codonopsis pilosula targeted Q-Marker;

[0070] A: Sample solution before fishing; B: Eluate after fishing with Fe3O4@SiO2-COX-2; C: Eluate after fishing with Fe3O4@SiO2-MAPK14; D: Eluate after fishing with Fe3O4@SiO2-NH2; 1: Codonopsis pilosula glycoside; 2: Codonopsis pilosula alcohol; 3: Codonopsis pilosula glycoside nin; 5: Luteolin; 7: Syringin; 11: Apigenin-7-O-glucoside.

[0071] Figure 10 This is the chromatogram of the extraction time investigation in the test sample preparation method;

[0072] A: 20min; B: 30min; C: 40min; D: 50min.

[0073] Figure 11 This is the chromatogram for investigation of solid-liquid ratio in the preparation method of the test sample;

[0074] A: 1:25; B: 1:30; C: 1:40; D: 1:50.

[0075] Figure 12 This is the chromatogram of the extraction solvent investigation in the test sample preparation method;

[0076] A: Methanol; B: A mixed solvent of methanol and ethyl acetate in equal proportions; C: Ethyl acetate.

[0077] Figure 13 This is the chromatogram for the investigation of the sampling amount in the preparation method of the test product;

[0078] A: 1g; B: 3g.

[0079] Figure 14 Chromatogram for the detection wavelength investigation in the selection of chromatographic conditions;

[0080] A: 254nm; B: 268nm; C: 344nm.

[0081] Figure 15 Chromatogram of mobile phase investigation for chromatographic condition selection;

[0082] A: acetonitrile-water; B: acetonitrile-0.05% phosphoric acid water.

[0083] Figure 16 A chromatogram for flow rate inspection during chromatographic condition selection;

[0084] A: 0.7mL / min; B: 0.8mL / min; C: 0.9mL / min; D: 1.0mL / min.

[0085] Figure 17 Chromatogram for testing column temperature during chromatographic condition selection;

[0086] A: 25℃; B: 30℃; C: 35℃; D: 40℃.

[0087] Figure 18 Chromatograms of chromatographic columns for chromatographic condition selection;

[0088] A: Agilent ZORBAX Eclipse XDB-C18; B: KROMASIL 100-5-C18; C: ThermoSyncrois AQ.

[0089] Figure 19 The HPLC chromatogram overlay and chemical characteristic maps of 15 batches of Codonopsis pilosula medicinal materials;

[0090] Among them, 20220705, 20220706, 20220707, 20220708, 20220709, 20220710, 20220711, 20220712, 20220713, 20220714, 20220715, 20220716, 20220717, 20220718, and YL024-20210201 are Codonopsis pilosula medicinal materials from different batches; R is the HPLC chemical characteristic spectrum of Codonopsis pilosula medicinal materials. DETAILED DESCRIPTION

[0091] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, they can also be understood as further technical solutions without paying creative work. In some figures, components with the same structure or function are schematically illustrated, or only one of them is marked. In this article, "one" not only means "only one", but also means "more than one", and "several" includes "two" and "more than two".

[0092] It should be noted that, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; and internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in the present invention based on the specific circumstances.

[0093] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0094] Reference Figures 1 to 19 , which is the first embodiment of the present invention, proposes

[0095] S1、Hotspot Focus:

[0096] A1. Extraction of hotspot pathways and targets: The Web of Science database was used as the information source for retrieval. The search terms were "Codonopsis Radix", "Dangshen", "Codonopsis pilosula", "Codonopsis pilosula (Franch.) Nannf.", "Codonopsis pilosula Nannf.var.modesta (Nannf.) LTShen", and "Codonopsis tangshen Oliv.". The retrieval period was January 1, 2004, to January 31, 2024. After deduplication, the literature information was exported in full record format as a plain text file as a sample for data analysis using Microsoft Excel. The 2021 software integrates and processes data such as the number of publications, disciplines of the articles, keywords, and years of publication. VOSviewer software is used to extract data from plain text files and conduct visual analysis. A total of 456 documents were collected, and 573 keywords were extracted, of which 139 keywords were related to pharmacological research. 17 keyword categories were clustered. Based on the keyword clustering and highlighting results, it was found that NF-κB and TLR4 were the focus of the research on the main pharmacological activities of Codonopsis pilosula. The two are closely related to the pharmacological research on immune regulation, gastrointestinal regulation, treatment of digestive system diseases, and treatment of lung diseases carried out around the various traditional effects of Codonopsis pilosula. Further analysis found that the TLR4 / MAPK / NF-κB pathway and the TLR4 / NF-κB / COX-2 pathway were more widely studied. Finally, the TLR4 / MAPK / NF-κB / COX-2 pathway was selected as the hot pathway in the research on the pharmacological activity related to the various traditional effects of Codonopsis pilosula.

[0097] A2. Screening of key targets: We searched for related targets of the hot pathway TLR4 / MAPK / NF-κB / COX-2 on the KEGG official website, combined the search results with literature reports, obtained target information, removed duplicate target information, and standardized target names using the Uniprot database. We submitted the targets to the STRING 12.0 database to obtain protein PPI network information, and used the built-in tools of CytoScape 3.10.0 to perform network topology parameter analysis. We then conducted further analysis in Matlab. In the R2021a software, the TOPSIS algorithm is used to calculate the average shortest path length, betweenness centrality, closeness centrality, clustering coefficient, degree value, neighborhood connectivity, topological coefficient and other parameters of each target. The relationship between targets in the PPI network is described based on the calculation results. The larger the target area and the more red it is, the higher the score, and the thicker the connecting line, the closer the association. The top 10 targets TNF, MAPK14, RELA, TLR4, IKBKB, COX-2, MAPK9, TLR2, MAP3K7, and MAPK13 in the PPI network are selected as key targets.

[0098] A3. Determination of the potential scope of targeted Q-Markers: Search the components and related targets of Codonopsis pilosula in TCMSP, collect potential targets of components in the Swiss Target Prediction website, further supplement the components and targets related to Codonopsis pilosula quality control based on literature reports, standardize target information using the Uniprot database, collect the physicochemical properties, lipophilicity, water solubility, pharmacokinetics, drug similarity, and medicinal chemistry-related characteristics of each component in the SwissADME website, and import the ADME-related data of each component into SIMCA-P 14.1 software was used to perform cluster analysis using the HCA algorithm. Based on the classification results, OPLS-DA analysis was further performed to generate a bioplot. The established model performed well. By analyzing the advantages and characteristics of ADME-related indicators of four different categories, ingredients were screened. Category 1 and category 4 showed advantages in gastrointestinal absorption, oral bioavailability, and blood-brain barrier permeability. Therefore, in the subsequent ingredient selection process, more ingredients belonging to category 1 and category 4 were selected. At the same time, the Codonopsis components were further screened based on the relevant targets of each component. Components with high probability values of relevant targets and high repetition rates between the relevant targets and the key targets described in A2 were selected. The potential range of Codonopsis-targeted Q-Markers was obtained, including codonopsis ginsenoside, codonopsis ginsenoside alcohol, codonopsis ginsenoside nin, luteolin, luteolin, codonopsis ginsenoside I, syringin, geniposide, atractylodes lactone III, oleanolic acid, and apigenin-7-O-glucoside.

[0099] Table 1 Potential scope of Codonopsis targeting Q-Markers based on component categories and related targets

[0100]

[0101]

[0102] S2. Virtual two-way fishing:

[0103] B1. Molecular docking: Molecular docking was performed sequentially between the components within the potential screening range of the Codonopsis pilosula-targeted Q-Marker and the 10 key targets. Preparation files for the components and targets were downloaded from the PubChem and RCSB PDB databases, respectively, and imported into Discovery Studio 2016 software. The target proteins were pretreated and the active pockets were exposed using the Clean Protein and Prepare Protein modules. The energy of the component conformations was minimized using the Minimize Ligands module. After completing the docking verification of the original ligand, docking was performed using the CDocker module. The ratio of the docked component to the original ligand was displayed by drawing a heat map using binding energy as an indicator. A larger value represents a better docking effect, and a value ≥0.8 is ideal.

[0104] B2. Algorithm analysis: In Matlab R2021a software, the TOPSIS algorithm was used to score each component based on the molecular docking result data. The components that were used as further technical solutions or equal to the median score were classified as the better category, including codonopsis pilosula, codonopsis pilosula alcohol, codonopsis pilosula glycoside, luteolin, syringin and apigenin-7-O-glucoside. The remaining components with lower scores were classified as the worse category. The molecular docking data and classification results were further imported into SIMCA-P 14.1 software, OPLS-DA analysis was carried out, and bioplot diagrams were generated. The established model performed well, and two targets COX-2 and MAPK14 that were more closely associated with the better category components were selected for subsequent experiments.

[0105] Table 2 TOPSIS comprehensive evaluation and component classification based on molecular docking

[0106]

[0107]

[0108] S3. Real fishing:

[0109] C1. Preparation of functionalized magnetic nanoparticles: 100 mg of nano-sized Fe3O4 was added to 80 mL of EtOH and 20 mL of deionized water, and ultrasonically dispersed for 20 min until completely dispersed. Then, 1.0 mL of ammonia water and 0.3 mL of LTEOS were added while stirring, and the reaction was carried out for 6 h. After the reaction was completed, magnetic suction was performed to remove the reaction solvent. The lower layer of solid was washed with ultrapure water until neutral, and then washed with EtOH three times. It was vacuum dried at 60 ° C for 12 h to obtain Fe3O4@SiO2. 100 mg of Fe3O4@SiO2 was added to 200 mL of EtOH, ultrasonically dispersed for 10 min, and 3 mL of ATES was added dropwise while stirring. The reaction was carried out for 6 h. After the reaction was completed, magnetic separation was performed. The lower layer of solid was washed with anhydrous ethanol and ultrapure water three times respectively. After magnetic separation, it was vacuum dried at 50 ° C for 6 h to obtain Fe3O4@SiO2-NH2. 20 mg of Fe3O4@SiO2-NH2 was taken and divided into two equal parts. 2.0 mL of EtOH, 2.0 mL PBS buffer, ultrasonic dispersion for 10 min, add 200 μL 50% glutaraldehyde solution to each portion, mix evenly, shake on a shaker for 3 h, magnetically separate after the reaction, wash 3 times with PBS buffer solution, then add 2.0 mL Tris-HCl buffer to each portion, ultrasonic dispersion for 10 min, add 100 μL COX-2 solution to one portion and 100 μMAPK14 solution to the other portion, both portions react for 3 h, magnetically separate after the reaction, wash 3 times with Tris-HCl buffer, and the remaining solid is the functionalized magnetic nanoparticles Fe3O4@SiO2-COX-2 and Fe3O4@SiO2-MAPK14, which are stored in Tris-HCl buffer at 4°C for future use.

[0110] C2. Determination of the immobilization yield of target protein: The BCA protein quantification kit was used for determination. The standard was diluted with PBS buffer into solutions of 1 μg / μL, 0.5 μg / μL, 0.25 μg / μL, 0.125 μg / μL, 0.0625 μg / μL and 0.03125 μg / μL, respectively. 25 μL of each solution was taken. Then 5 mg of Fe3O4@SiO2-NH2, Fe3O4@SiO2-COX-2 and Fe3O4@SiO2-MAPK14 were taken respectively, and 25 μL of PBS buffer was added as sample solutions. Then 200 μL of BCA working solution was added to each standard solution and sample solution, and the mixture was incubated at 37°C for 30 min. The absorbance at 562 nm was measured with a microplate reader. As a further technical solution, the stretching vibration of the Fe-O bond in Fe3O4, the absorption peak at 3420 cm-1 comes from the stretching vibration of OH. In the spectrum of the SiO2-modified material, the absorption peak near 1090 cm⁻¹ is attributed to the symmetric and asymmetric Si-O-Si vibrations, indicating that the SiO2 has successfully capped the Fe⁺O⁻¹. Furthermore, the infrared absorption peak at 1090 cm⁻¹ in the Fe⁺O⁻¹ spectrum is extremely weak, while the absorption peak of the immobilized enzyme exhibits a red-shift at this position, also indicating the successful formation of the Fe-O-Si bond. In the spectrum of the NH⁺-modified material, the absorption peak around 1380 cm⁻¹ is attributed to the stretching vibration of CN, indicating that the Fe⁺O⁻¹@SiO⁻¹ has been successfully modified with amino groups. Scanning electron microscopy (SEM) analysis reveals that the unmodified Fe⁺O⁻¹ magnetic nanoparticles are spherical, with a relatively regular shape, smooth surface, and uniform distribution, and a diameter of approximately 30-40 nm. With the addition of SiO⁺, NH⁺ clusters, and enzyme modification, the particle diameter gradually increases. The shapes of the Fe⁺O⁻¹@SiO⁻¹ and Fe⁺O⁻¹@SiO⁻¹-NH⁻¹ are complex and irregular. The surfaces of Fe3O4@SiO2-COX-2 and Fe3O4@SiO2-MAPK14 are more uneven than those of unmodified nanoparticles.

[0111] C4. Ligand fishing: Take an appropriate amount of each component in the better category described in B2, weigh them accurately, add methanol to make a mixed solution containing 40 μg of each component per 1 mL, and obtain the fishing sample solution. Mix 10 mg of Fe3O4@SiO2-NH2, Fe3O4@SiO2-COX-2 and Fe3O4@SiO2-MAPK14 with 2.0 mL of sample solution, incubate for 120 min, separate magnetically, discard the supernatant, wash 3 times with Tris-HCl buffer, add 2.0 mL of methanol, mix evenly, dissociate for 60 min, and then separate magnetically to obtain the eluate, which is filtered through a 0.22 μm microporous filter membrane.

[0112] C5. Detection of Targeted Q-Markers: Analysis was performed using a Shimadzu LC-20A Prominence high-performance liquid chromatograph using a KROMASIL 100-5-C18 column, acetonitrile as mobile phase A, and water as mobile phase B. The gradient elution program was: 15%-20% A (0-5 min); 20%-30% A (5-7 min); and 30%-60% A (7-20 min). The flow rate was 0.8 mL / min, and the column temperature was 25°C. Detection was performed using a DAD detector at a wavelength of 254 nm. 10 μl of each of the sample solution before catching and the eluate after catching with Fe3O4@SiO2-NH2, Fe3O4@SiO2-COX-2, and Fe3O4@SiO2-MAPK14 were precisely aspirated and injected into the liquid chromatograph for analysis. The peak areas of each component before and after catching were compared, and the component catching rate was calculated according to the following formula.

[0113]

[0114] Among them, Senzyme represents the peak area of the components contained in the eluate after being treated with the enzyme material, S0 represents the peak area of the components contained in the eluate after being treated with the non-enzyme material, SS represents the peak area of the components contained in the sample solution, Renzyme represents the fishing rate of the enzyme material, R0 represents the fishing rate of the non-enzyme material, and R represents the component fishing rate.

[0115] Table 3 Experimental results of real fishing

[0116]

[0117]

[0118] Note: NH2, COX-2, and MAPK14 represent the eluates after fishing with Fe3O4@SiO2-NH2, Fe3O4@SiO2-COX-2, and Fe3O4@SiO2-MAPK14, respectively.

[0119] With the exception of syringin, which was not detected by Fe3O4@SiO2-COX-2 due to its low affinity for the component syringin, all other components were detected. Apigenin-7-O-glucoside performed best in the detection results for both functionalized magnetic nanoparticles. Codonopsis arginine, codonopsis arginine alcohol, codonopsis arginine glycoside, and luteolin exhibited varying affinity for the different functionalized magnetic nanoparticles. Based on the actual detection results, a total of five components were identified. Further analysis based on the five principles of Q-Marker analysis eliminated luteolin and apigenin-7-O-glucoside, flavonoids commonly found in many plants and with low specificity. Ultimately, the Q-Markers targeting Codonopsis pilosula were determined to be codonopsis arginine, codonopsis arginine alcohol, and codonopsis arginine glycoside.

[0120] Reference Figures 1 to 19 , which is the second embodiment of the present invention, proposes

[0121] Step 1. Sample source

[0122] A total of 15 batches of Codonopsis pilosula were collected from Dong-E-E-Jiao Co., Ltd. Among them, 14 batches were purchased from Zhejiang Yingte and 1 batch was purchased from Gansu Xuanyuan Pharmaceutical.

[0123] Step 2. Optimization of test sample preparation method

[0124] The sample preparation method was examined with reference to the 2020 edition of the Pharmacopoeia of the People's Republic of China, Part IV, General Chapter 0512, High-Performance Liquid Chromatography. Ultrasonic extraction times of 20, 30, 40, and 50 minutes were tested, with 30 minutes showing the best extraction effect. Ultimately, 30 minutes was selected. The solid-to-liquid ratio was examined, with a solid-to-liquid ratio of 1:25 selected. Different extraction solvents, including methanol, ethyl acetate, and methanol-ethyl acetate, were tested, with methanol extracting more chromatographic peaks and better peak shapes. Ultimately, methanol was selected as the extraction solvent. Different sample sizes were examined, with 3 g yielding more chromatographic peaks and better peak shapes. Therefore, 3 g was chosen as the sample size.

[0125] Step 3. Selection of chromatographic conditions

[0126] This experiment examined the chromatograms at absorption wavelengths of 254nm, 268nm, and 344nm. At 268nm, the chromatographic peak response value and the peak height ratio of each peak were better, the number of chromatographic peaks was larger, and the separation was better, so 268nm was determined as the detection wavelength; acetonitrile-water and acetonitrile-0.05% phosphoric acid water mobile phases were examined. When water-acetonitrile was used as the mobile phase, the baseline was stable, so water-acetonitrile was used as the mobile phase; different flow rates were examined, and it was found that when the flow rate was 0.8mL / min, there were more chromatographic peaks and the peak shape was better; different column temperatures were examined, and it was found that when the column temperature was 35℃, there were more chromatographic peaks and the peak shape was better; different chromatographic columns were also examined, including Agilent ZORBAXEclipse XDB-C18, Thermo Syncrois AQ, and KROMASIL 100-5-C18, and it was found that when the KROMASIL 100-5-C18 column was used for HPLC analysis, the separation of each chromatographic peak was better, the peak shape was better, and the retention time was more ideal.

[0127] Step 4. Methodological review

[0128] Take Codonopsis pilosula samples, prepare test solutions according to the optimized method under item 2, and inject them continuously for 6 times. The similarity is examined using the fingerprint similarity software recommended by the Chinese Pharmacopoeia, and it is found that the HPLC chromatogram similarity is greater than 0.99, the precision is good, and it meets the experimental requirements; take Codonopsis pilosula samples, prepare test solutions according to the optimized method under item 2, and inject them at 0, 2, 4, 8, 16, and 24 hours, and the similarity is examined using the fingerprint similarity software recommended by the Chinese Pharmacopoeia, and it is found that the HPLC chromatogram similarity is greater than 0.99, and the test solution has good stability within 24 hours, which meets the experimental requirements; take 6 Codonopsis pilosula samples, prepare test solutions according to the optimized method under item 2, and inject them separately. The similarity is examined using the fingerprint similarity software recommended by the Chinese Pharmacopoeia, and it is found that the HPLC chromatogram similarity is greater than 0.99, the method repeatability is good, which meets the experimental requirements.

[0129] Step 5. Establishment of HPLC chemical fingerprint of Codonopsis pilosula

[0130] 3 g of 15 batches of Codonopsis pilosula powder were accurately measured, added with 25 mL of methanol, and ultrasonically extracted for 30 min. The mixture was filtered and evaporated to dryness. Methanol was added to the residue, the volume was adjusted to 5 mL, and the mixture was filtered through a 0.45 μm microporous filter membrane to serve as the test solution. Appropriate amounts of codonopsis pilosulae acetyl alcohol, codonopsis acetyl glycoside, and codonopsis acetyl glycoside reference standards were accurately weighed and methanol was added to prepare a mixed solution containing 50 μg of codonopsis pilosulae acetyl alcohol, 50 μg of codonopsis acetyl glycoside, and 50 μg of codonopsis acetyl glycoside per mL as the reference solution. 10 μL of each reference solution and test solution were accurately drawn and injected into a Huapu S6000 high performance liquid chromatograph equipped with a DAD detector for determination. The results were analyzed using an Agilent HC The detection was performed on a C18 column with acetonitrile as mobile phase A and water as mobile phase B; the gradient elution program was as follows: 0-6 min, 5%-7% A; 6-15 min, 7% A; 15-20 min, 7%-12% A; 20-32 min, 12%-15% A; 32-40 min, 15%-20% A; 40-55 min, 20%-30%; 55-65 min, 30%-50%; 65-70 min, 50%-70%; 70-90 min, 70%; the flow rate was 0.8 mL / min, the detection wavelength was 268 nm, the column temperature was 35°C, and the number of theoretical plates calculated based on the dangshen glycoside peak should be no less than 400,000.

[0131] Chromatograms of 15 batches of Codonopsis pilosula were recorded and analyzed using the fingerprint similarity software recommended by the Chinese Pharmacopoeia. Using chromatogram 20220705 as the reference, a time window of 0.5 min was set, and multi-point calibration and full-peak matching were performed on the chromatographic peaks to generate characteristic spectra for the 15 batches of Codonopsis pilosula. The characteristic spectra of the 15 batches of Codonopsis pilosula showed seven common peaks, with retention times of 44.837, 46.961, 48.021, 53.331, 60.918, 73.968, and 82.155 min, respectively. Each peak was numbered 1 to 7. Codonopsis pilosula-targeted Q-marker was identified based on the retention time of the reference substance. Peak 3 was identified as codonopsis linaloside, peak 4 was identified as codonopsis linaloside, and peak 5 was identified as codonopsis linalool.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for screening targeted quality markers of traditional Chinese medicine, characterized in that: The following steps are involved: S1. Hotspot Focus: Extract common hotspot pathways and targets of the pharmacological activity of traditional Chinese medicine, screen key targets and define the potential scope of targeted Q-Markers; S2, virtual two-way fishing: using molecular docking to virtually fish components and targets; S3, Real fishing: Prepare and characterize the functionalized magnetic nanoparticles of the target, obtain the components through real fishing, test the fishing results, and determine the targeted Q-Marker; The step S1 comprises: A1. Extraction of hot pathways and targets: Search the literature database using the names of traditional Chinese medicines as keywords to collect literature information. After deduplication, export the document containing the literature information. Collate the number of publications, the subject of the article, the keywords, and the publication year. Import the data into bibliometric analysis software, extract the data, select keywords related to pharmacology research for visualization analysis, and identify hot pathways and targets in the mechanism of action related to the various effects of traditional Chinese medicines. A2. Screening of key targets: Obtain relevant targets of hotspot pathways and targets from the target information database, standardize target names, collect target information, obtain protein interaction network information from the database, implement network topology parameter analysis, calculate multiple network topology parameters for each target and perform visual analysis based on target scores, and select targets with high scores in the PPI network as key targets; A3. Determination of the potential scope of targeted Q-Markers: Combined with the ingredient database and literature collection and the quality control ingredients of traditional Chinese medicine, collect parameter data on absorption, distribution, metabolism and excretion of each ingredient, import them into the software, and perform clustering. Based on the clustering results, analyze the ADME characteristics of ingredients in different categories, obtain ingredient target information, standardize the target information, focus on ingredients with high repetition rates between related targets and key targets obtained in A2, and further combine the ADME characteristics of the category to which the ingredients belong to select ingredients as the potential scope of targeted Q-Markers; The step S2 comprises: B1. Molecular docking: Prepare the structural files of the key target of A2 and the components within the potential range of the targeted Q-Marker of A3, import them into the molecular docking software, perform docking, and visualize and analyze the docking effect; B2. Algorithm Analysis: Based on the docking results data in B1, score each component and classify them according to the scores to obtain the components in the excellent category. Then, import the docking results data and component category labels into the software to analyze and screen targets closely associated with the components in the excellent category; The step S3 comprises: C1. Preparation of functionalized magnetic nanoparticles: Take nano-sized Fe3O4, add appropriate amount of anhydrous ethanol and deionized water, and after complete dispersion, add ammonia water and tetraethyl silicate while stirring. After the reaction is completed, magnetic attraction is performed, and the reaction solvent is removed. The lower solid layer is washed with ultrapure water until neutral, and then washed with anhydrous ethanol, and vacuum dried to obtain Fe3O4@SiO2. Take Fe3O4@SiO2, add anhydrous ethanol, and after uniform dispersion, add 3-aminopropyltriethoxysilane while stirring. After the reaction is completed, magnetic separation is performed, and the lower solid layer is washed with anhydrous ethanol and ultrapure water respectively. After separation, vacuum drying is performed to obtain Fe3O4@SiO2-NH2. Anhydrous ethanol and phosphate buffered saline solution are added to Fe3O4@SiO2-NH2, and the mixture is evenly dispersed. A 50% glutaraldehyde solution is added and mixed evenly. After the reaction is completed, magnetic separation is performed, the mixture is washed with phosphate buffered saline solution, and a buffer solution is added and evenly dispersed. The target protein solution obtained by screening with B2 is added. After the reaction is completed, magnetic separation is performed and the mixture is washed with a buffer solution. The remaining solid is the functionalized magnetic nanoparticle, which is stored in a suitable buffer solution at 4°C for future use; the suitable buffer solution is tris(hydroxymethylaminomethane) hydrochloride buffer solution. C2. Determination of the immobilization yield of the target protein: The immobilization yield of the target protein was determined using a 2,2-biquinoline-4,4-dicarboxylic acid disodium protein kit. The standard was diluted with buffer to form a series of standard solutions of different concentrations. The Fe3O4@SiO2-NH2 and functionalized magnetic nanoparticles prepared in C1 were taken and added with buffer as sample solutions. The 2,2-biquinoline-4,4-dicarboxylic acid disodium working solution was added to each standard solution and sample solution, and the mixture was incubated at 37°C for 30 min. The absorbance at 562 nm was measured using a microplate reader to establish a standard curve and calculate the protein concentration and immobilization yield. C3. Material characterization: Fourier transform infrared spectroscopy and scanning electron microscopy were used to characterize Fe3O4, Fe3O4@SiO2, Fe3O4@SiO2-NH2 and functionalized magnetic nanoparticles in C1, and the characteristics of functionalized magnetic nanoparticles were compared and analyzed; C4, ligand fishing: Take the excellent components in B2, add methanol to prepare a mixed solution as the sample solution, mix the Fe3O4@SiO2-NH2 and functionalized magnetic nanoparticles in C1 with an equal amount of sample solution, incubate and magnetically separate, discard the supernatant, wash with buffer, add dissociation solution, mix evenly, magnetically separate after dissociation, obtain eluate, and pass through a microporous filter membrane for use; the excellent components are: codonopsis ginsenoside, codonopsis ginsenoside alcohol, codonopsis ginsenoside nin, luteolin, syringin and apigenin-7-O-glucoside; C5. Detection of fishing results and determination of targeted Q-Marker: Accurately aspirate the sample solution before fishing and the eluate after fishing in C4 respectively, inject them into high performance liquid chromatography for analysis, compare the peak area of each component before and after fishing, calculate the component fishing rate, and screen the components whose fishing rate meets the five principles of Q-Marker as the targeted Q-Marker of traditional Chinese medicine.

2. A method for screening targeted quality markers of traditional Chinese medicine according to claim 1, characterized in that: The step S1 further includes: The target information database is: Kyoto Encyclopedia of Genes and Genomes; The sources of standardized target names are: Uniprot database; The ways to obtain PPI network information are: STRING 12.0 database; The network topology parameter analysis software is: CytoScape 3.10.0; The algorithm is: approximate ideal solution sorting method.

3. The method for screening targeted quality markers of traditional Chinese medicine according to claim 1, wherein: The step S2 further includes: The structural files are sourced from the PubChem database and the RCSB PDB database; The molecular docking software is Discovery Studio 2016; The molecular docking process uses the Clean Protein module and the Prepare Protein module to pre-treat the target protein and expose the active pocket. The Minimize Ligands module is used to minimize the energy of the component conformation. The CDocker module is used for docking, and the binding energy is used as an indicator to evaluate the docking effect. The component scoring algorithm is TOPSIS. The analytical method for screening targets was OPLS-DA, and the software SIMCA-P 14.1 was used to import the docking result data and component category labels in step B2.

4. The method for screening targeted quality markers of traditional Chinese medicine according to claim 1, wherein In the step S3, the functionalized magnetic nanoparticles are Fe3O4@SiO2-COX-2 or Fe3O4@SiO2-MAPK14; and the uniform dispersion method is ultrasound.

5. The method for screening targeted quality markers of traditional Chinese medicine according to claim 1, wherein: The step C1 is specifically as follows: the raw material dosage and reaction conditions for preparing Fe3O4@SiO2 are as follows: 100 mg of nano-sized Fe3O4, 80 mL of anhydrous ethanol, 20 mL of deionized water, 1.0 mL of ammonia water, 0.3 mL of TEOS, reaction for 6 h, and vacuum drying at 60°C for 12 h; the raw material dosage and reaction conditions for preparing Fe3O4@SiO2-NH2 are as follows: 100 mg of Fe3O4@SiO2, 200 mL of anhydrous ethanol, 3 mL of APTES, mixed reaction for 6 h, and vacuum drying at 50°C for 6 h; The raw material dosage and reaction conditions for the preparation of functionalized magnetic nanoparticles based on Fe3O4@SiO2-NH2 are as follows: 10 mgFe3O4@SiO2-NH2, 2.0 mL anhydrous ethanol, 2.0 mL PBS buffer, 200 μL 50% glutaraldehyde solution, 2.0 mL Tris-HCl buffer, 100 μL COX-2 solution, 100 μL MAPK14 solution, reaction time 3 h; The step C4 comprises: The sample solution concentration is 40 μg / mL; the material dosage and reaction conditions are: 10 mg Fe3O4@SiO2-NH2, Fe3O4@SiO2-COX-2 and Fe3O4@SiO2-MAPK14, 2.0 mL sample solution, 2.0 mL dissociation solution, incubation for 120 min, and dissociation for 60 min.

6. The method for screening targeted quality markers of traditional Chinese medicine according to claim 1, characterized in that: The step C5 comprises: The high performance liquid chromatography analysis process is as follows: 10 μL of the sample solution before fishing, the eluate after fishing, and the reference solution are respectively injected into an HPLC liquid chromatograph equipped with a diode array detector for determination. A KROMASIL100-5-C18 column is used for detection. The mobile phase is acetonitrile-water. The flow rate is 0.8 mL / min, the detection wavelength is 254 nm, the column temperature is 25°C, and the volume content of the solvent acetonitrile in the mobile phase increases from 15% to 20% from 0 to 5 minutes of gradient elution. From 5 minutes to 7 minutes, the volume content of the solvent acetonitrile in the mobile phase increases from 20% to 30%. From 7 minutes to 20 minutes, the volume content of the solvent acetonitrile in the mobile phase increases from 30% to 60%.

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