A method for screening the active ingredients in a preparation of *Gynostemma pentaphyllum*

Abstract

The present invention relates to the technical field of traditional Chinese medicine research, and specifically relates to a method for screening effective substances of Jingu Lian preparation. The present invention is based on a rat adjuvant arthritis (AA) model and is intervened with Jingu Lian preparation to verify its pharmacological activities such as anti-inflammatory and joint protection; the components in the plasma after Jingu Lian preparation enters the blood are identified by high-resolution mass spectrometry technology; a "blood component-target-disease" network is constructed by combining network pharmacology to preliminarily screen out the effective component group and core targets for treating rheumatoid arthritis; at the same time, plasma metabolomics technology is used to screen out the differential metabolites related to rheumatoid arthritis and the metabolites significantly reversed by Jingu Lian preparation; finally, an association analysis is carried out between the effective component group and the significantly reversed differential metabolites to screen and identify the potential effective substances of Jingu Lian preparation to exert its pharmacological effects. The present invention clarifies the multi-component synergistic regulation mechanism of Jingu Lian preparation from the perspective of the improvement process of in vivo metabolite levels and integrated network analysis, and provides a scientific basis for the research and development and quality control of its new clinical compound preparations.

Description

Technical Field

[0001] This invention relates to the field of traditional Chinese medicine research technology, specifically a method for screening the active ingredients in Jin Gu Lian preparations. Background Technology

[0002] Currently, Western medicine's treatment of rheumatoid arthritis (RA) mainly focuses on relieving pain, reducing inflammation, and improving overall bodily function. Commonly used drugs include slow-acting antirheumatic drugs (SAIDs), TNF-α blockers, IL-1 blockers, and anti-B-cell monoclonal antibodies. However, long-term use of these Western medications often leads to significant side effects for patients.

[0003] Traditional Chinese medicine (TCM) treatments for "arthralgia" possess anti-inflammatory and analgesic effects, as well as immunosuppressive and immunomodulatory effects, exhibiting good long-term efficacy and minimal side effects. Jin Gu Lian preparations are traditional Miao medicine compound preparations composed of five herbs: *Clematis chinensis*, *Prunus persica* leaf, *Spatholobus suberectus*, *Illicium verum*, and *Imperata cylindrica*. They have the effects of dispelling wind and dampness, reducing swelling and relieving pain, and are mainly used clinically for joint swelling and pain caused by rheumatic obstruction. However, existing research is insufficient to elucidate the mechanism of action of Jin Gu Lian preparations in synergistic regulation of rheumatoid arthritis by its multiple components, limiting the further research and application of its novel clinical compound preparations.

[0004] To address the shortcomings of the existing technologies, the present invention aims to provide a method for screening the active ingredients of Jin Gu Lian preparations based on the combined analysis of network pharmacology and metabolomics. This method can rapidly screen the core active ingredients of Jin Gu Lian preparations for treating rheumatoid arthritis and reveal their targets and metabolic pathways. Summary of the Invention

[0005] To address the aforementioned technical problems in the existing technology, this invention provides a method for screening the active substances in Jin Gu Lian preparations, comprising the following steps:

[0006] Step S1: Establish a rat adjuvant-induced arthritis model and determine the efficacy of Jin Gu Lian preparation in treating rheumatoid arthritis through in vivo pharmacodynamic evaluation;

[0007] Step S2: Ultra-high performance liquid chromatography-high resolution tandem mass spectrometry was used to analyze the in vivo and in vitro chemical components of the Jin Gu Lian preparation, and at the same time, the plasma components entering the blood of rats after gavage administration were analyzed.

[0008] Step S3: Based on blood-entering components and rheumatoid arthritis disease targets, network pharmacology is used to screen the core targets of drugs and diseases, perform GO function and KEGG pathway enrichment analysis, construct a "blood-entering component-target-pathway-disease" network, and screen the effective component group involved in disease regulation.

[0009] Step S4: Use metabolomics research methods to perform full-spectrum analysis on rat plasma after gavage, screen for disease biomarkers of rheumatoid arthritis, and differential metabolites that significantly reverted after intervention with Jin Gu Lian preparation, and enrich key metabolic pathways of drug action.

[0010] Step S5: Integrate the core targets and active ingredient groups obtained in Step S3, and the biomarkers and metabolic pathways obtained in Step S4 to construct a correlation network and identify the pharmacodynamic substances of Jin Gu Lian preparations for treating rheumatoid arthritis.

[0011] Furthermore, the in vivo efficacy evaluation in step S1 includes: observation of the general condition of rats, monitoring of body weight, determination of arthritis index, pathological examination of ankle joints, and detection of the levels of 5-HT, IL-1β, IL-6, PGE2, RF, and TNF-α inflammatory factors in plasma.

[0012] Furthermore, the in vivo chemical composition analysis in step S2 includes the following steps:

[0013] (1) Preparation of test solution in vivo: Take the plasma of rats after gavage administration of Jin Gu Lian preparation, add 4 times the volume of methanol to precipitate protein, let stand at -20 ℃ for 30 min, vortex for 3 min, centrifuge at 4 ℃, collect the supernatant, blow dry with N2, dissolve the residue in 50% methanol, vortex for 3 min, centrifuge at 4 ℃, and take the supernatant for analysis.

[0014] (2) Chromatographic conditions:

[0015] Chromatographic analysis conditions: Ultra-high performance liquid chromatography (UHPLC) system was used. Column: Hypersil gold, 2.1 × 150 mm, 1.9 μm; Column temperature: 40 ℃; Injection volume: 2 µL; Mobile phase ratio and flow rate: Phase A 0.1% formic acid-water, Phase B 0.1% formic acid-acetonitrile; Flow rate: 0.3 mL / min; Gradient elution program: 0–2 min, 2% B; 2–20 min, 2%–15% B; 20–30 min, 15%–25% B; 30–40 min, 25%–40% B; 40–50 min, 40–98% B; 50–52 min, 98% B); Mass spectrometry analysis conditions: MS system: Q Exactive Plus MS; Detection mode: ESI. + & ESI - .

[0016] Furthermore, the in vitro chemical composition analysis in step S2 includes the following steps:

[0017] (1) Preparation of in vitro test solution: Take an appropriate amount of Jin Gu Lian preparation, add 50% methanol to dilute to 30 mg of raw drug per ml, mix well, sonicate for 30 min, filter, centrifuge the filtrate, take the supernatant, and filter through a filter membrane to obtain the solution.

[0018] (2) Chromatographic conditions:

[0019] Chromatographic analysis conditions: Ultra-high performance liquid chromatography (UHPLC) system was used. Column: Hypersil gold, 2.1 × 150 mm, 1.9 μm; Column temperature: 40 ℃; Injection volume: 2 µL; Mobile phase ratio and flow rate: Phase A 0.1% formic acid-water, Phase B 0.1% formic acid-acetonitrile; Flow rate: 0.3 mL / min; Gradient elution program: 0–2 min, 2% B; 2–20 min, 2%–15% B; 20–30 min, 15%–25% B; 30–40 min, 25%–40% B; 40–50 min, 40–98% B; 50–52 min, 98% B); Mass spectrometry analysis conditions: MS system: Q Exactive Plus MS; Detection mode: ESI. + & ESI - .

[0020] Furthermore, the mass spectrometry conditions are as follows:

[0021]

[0022] Further, step S3 includes the following steps:

[0023] (1) Predict the target of the drug-drug-inducing components in Jin Gu Lian preparations from the Swisstarget Prediction database, screen rheumatoid arthritis disease targets from Genecard, TTD, DisGeNET, OMIM and TCMSP databases, obtain the intersection targets of drug targets and disease targets after standardization and deduplication, and draw Venn diagram.

[0024] (2) Using the protein interaction data collected in the String database as the background network, the relevant target points of Jin Gu Lian preparation involved in the regulation of RA were mapped to the String database to construct the protein interaction network of Jin Gu Lian preparation for the treatment of RA. The screening condition was interaction score>0.4. The network was analyzed and visualized using Cytoscape. The protein interaction network is shown in the figure. The core targets were obtained by sorting by degree value and using degree>10.31 as the standard.

[0025] (3) GO functional enrichment analysis and KEGG pathway enrichment analysis were performed on the core targets to identify the key biological processes and signaling pathways of Jin Gu Lian preparations in the treatment of rheumatoid arthritis.

[0026] (4) Construct a “blood-entry component-target-pathway-disease” network for Jin Gu Lian preparations and screen effective component groups involved in disease regulation;

[0027] Further, step S4 includes the following steps:

[0028] (1) Untargeted metabolomics analysis of rat plasma in each group was performed using UHPLC / Q Exactive Plus MS. The effects of modeling and drug administration were verified by PCA and OPLS DA analysis.

[0029] (2) Using VIP>1, P<0.05 and FC≥1.2 / ≤0.67 as conditions, biomarkers that can represent the characteristics of the course of rat adjuvant arthritis model were screened. Endogenous components that showed significant differences between the model group and the healthy control group, but showed significant regression in the drug treatment group were used as biomarkers to draw Venn diagrams and identify a total of potential plasma biomarkers.

[0030] (3) Using VIP>1, P<0.05 and FC≥1.2 / ≤0.67 as conditions, we screened differential metabolites that showed significant regression after intervention with Jin Gu Lian preparations;

[0031] (4) Use the MetaboAnalyst platform to enrich the metabolic pathways of the callback biomarkers and identify key metabolic pathways.

[0032] Preferably, the key metabolic pathways are histidine metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, and phenylalanine metabolism.

[0033] Furthermore, the integration and correlation analysis in step S5 is as follows: using the Pearson analysis method to perform correlation analysis on blood-entering components and differential metabolites, and screening highly correlated components as potential pharmacologically active substances; using Metscape software to construct a target metabolic network and identify key proteins and key metabolites.

[0034] Preferably, the pharmacologically active substances are: kaempferol glucuronide, baicalin, apigenin 7O glucuronide, vitexin glucosinolate, and nobiletin; the key proteins are PTGS2 and ALOX5, and the key metabolites are 5,6 DHET, 11,12 EET, and Phosphatidylcholine.

[0035] Furthermore, the screening method of the present invention can also be applied to the screening of active ingredients in drugs for treating rheumatoid arthritis.

[0036] Compared with the prior art, the technical effects of this invention are reflected in:

[0037] 1. This invention is based on the integrated strategy of the theory of holistic effects of traditional Chinese medicine. It combines multiple methods such as serum pharmacology, network pharmacology, and metabolomics. Specifically, it uses liquid chromatography-mass spectrometry to perform qualitative analysis on the components of the extract in the preparation and the chemical components in the plasma after entering the bloodstream. Network pharmacology and metabolomics are used to analyze and preliminarily screen the active substances. Based on the study of the anti-RA effect and related mechanism of Jin Gu Lian preparation, the active substances of Jin Gu Lian preparation have been screened.

[0038] 2. This invention integrates multiple analytical methods to highlight the multi-component and multi-target effects of traditional Chinese medicine from the perspective of its overall effects, and explores its pharmacodynamic material basis and mechanism of action in the fight against rheumatoid arthritis (RA), which is beneficial for guiding its rational clinical application.

[0039] 3. Based on a combination of analytical methods, network pharmacology, and metabolomics, this invention screened and verified the pharmacodynamic material basis of Jin Gu Lian preparations in the fight against RA. From the perspective of the overall effect of traditional Chinese medicine on the body and the dialectical unity of thought, it elucidated the mechanism of action of Jin Gu Lian preparations in the fight against RA based on the concept of multi-component-multi-target interactive regulation, laying a scientific foundation for the rational clinical application and modern development of Jin Gu Lian preparations.

[0040] 4. This invention uses liquid chromatography-mass spectrometry (LC-MS) to detect and analyze the components of extracts from *Gynostemma pentaphyllum* preparations and the chemical components in plasma after entering the bloodstream. The detection method has high sensitivity and good reproducibility. Attached Figure Description

[0041] Figure 1 A graph showing changes in body weight before and after treatment.

[0042] Figure 2 Graph showing the changes in arthritis index of rats in each group before and after treatment.

[0043] Figure 3 Changes in organ coefficients in rats before and after treatment (¯x ± s, n = 10)

[0044] Figure 4 Pathological sections of the ankle joints of rats in each group (×40)

[0045] Figure 5 The effects of these substances on the secretion of 5-HT, IL-1β, IL-6, PGE2, RF, and TNF-α in rat plasma (ng / L, n = 10) were investigated.

[0046] Figure 6 High-resolution mass spectrometry-negative ion mode for in vivo component analysis of Jin Gu Lian capsules

[0047] Figure 7High-resolution mass spectrometry-positive ion mode for in vivo component analysis of Jin Gu Lian capsules

[0048] Figure 8 Venn diagram of disease-component intersection targets

[0049] Figure 9 Molecular network diagram of RA-related genes in Jin Gu Lian capsules - blood-entering components

[0050] Figure 10 A protein interaction network diagram for Jin Gu Lian capsules in the treatment of RA.

[0051] Figure 11 GO and KEGG analysis plots for intersection targets

[0052] Figure 12 Network diagram of "Jin Gu Lian Capsules - Blood-entering components - Targets - Pathways - Diseases"

[0053] Figure 13 PCA distribution map for all samples

[0054] Figure 14 Comparison of plasma metabolic profiles from different groups of samples

[0055] Figure 15 OPLS-DA model of CON vs MOD plasma samples (20, 25, 30, 35 days)

[0056] Figure 16 Venn diagram of the intersection of CON vs MOD (20, 25, 30, 35 days)

[0057] Figure 17 Metabolite clustering heatmaps for CON vs MOD (20, 25, 30, 35 days)

[0058] Figure 18 OPLS-DA model for JGLM vs MOD plasma samples (25, 30, 35 days).

[0059] Figure 19 Heatmap analysis of differentially expressed metabolites in plasma from the CON, MOD, and JGLM groups.

[0060] Figure 20 A network of "target-metabolites" related to the arachidonic acid metabolic pathway. Detailed Implementation

[0061] To make the technical solutions and effects of the embodiments of the present invention clearer, the technical solutions in the embodiments are described clearly and completely. The embodiments described below are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art in conjunction with the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0062] Unless otherwise specified, the instruments, reagents, and materials used in the following embodiments are all conventional instruments, reagents, and materials already available in the prior art and can be obtained through legitimate commercial channels. Unless otherwise specified, the experimental methods and detection methods used in the following embodiments are all conventional experimental methods and detection methods already available in the prior art.

[0063] The instruments and reagents used in the following examples and comparative examples are as follows:

[0064] Instruments: Agilent 1290 UPLC ultra-high performance liquid chromatograph, Agilent Q-TOF 6545 LC / MS high resolution tandem mass spectrometer, Vanquish Horizon ultra-high performance liquid chromatograph, Q Exactive Plus high resolution mass spectrometer, electronic balance, ultrasonic cleaner, high-speed centrifuge, nitrogen-air integrated machine, nitrogen blowing concentration device, etc.

[0065] Reagents: Sodium carboxymethyl cellulose, Freund's complete adjuvant, sodium heparin, disodium EDTA, hematoxylin-eosin staining agent, 5-HT, IL-1β, IL-6, PGE2, rheumatoid factor, TNF-α detection kit; mass spectrometry grade methanol, formic acid, acetonitrile, L-2-chlorophenylalanine, etc. Jin Gu Lian capsules are commercially available Jin Gu Lian capsules.

[0066] Example 1: In vivo pharmacodynamic test of Jin Gu Lian capsules against rheumatoid arthritis

[0067] 1.1 Modeling and Drug Administration

[0068] 1.1.1 Establishment of the AA model: SD rats were acclimatized for one week before model induction. Rats were randomly divided into four groups of 10 rats each: a control group (Control, abbr.CON), a model group (Model, abbr.MOD), a positive control group (Tripterygium wilfordii glycoside tablets, Positive, abbr.POS, 102 mg / kg), and a Jin-Gu-Lian group (JGLM 363 mg / kg, calculated as crude drug, three times the clinically equivalent dose of Jin-Gu-Lian capsules). Inflammation was induced by intradermal injection of 0.1 mL of complete Freund's adjuvant (CFA) into the right hind paw pad. The control group received the corresponding saline injection. Rats were re-immunized after 7 days, and the model was established for 21 days. Oral administration began on day 21 of model induction and continued for 15 days, twice daily. The CON and MOD groups received the same volume of 0.5% CMC-Na.

[0069] 1.1.2 Preparation of test solution: Take an appropriate amount of Jin Gu Lian capsules, add 50% methanol to dilute to approximately 30 mg / mL (based on crude drug weight), mix well, sonicate for 30 min, filter, centrifuge at 12000 rpm for 10 min, and obtain the supernatant. Before analysis, all solutions were filtered through a 0.22 μm filter.

[0070] 1.2 Experimental Methods and Results

[0071] 1.2.1 General Situation Assessment

[0072] Before modeling and on days 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 and 35 after modeling, the body weight of rats in each group was measured, and changes in their mental state, activity level, food intake, coat color and feces were observed.

[0073] Before modeling, all rats in the groups were in good health, with normal diets and good mental state. Several hours after modeling, obvious redness and swelling began to appear on the rats' paws. Starting on the first day after modeling, redness and swelling began to appear on the ankles and toes, becoming increasingly severe and causing difficulty in movement. After the second immunization, multiple joints in the rats became swollen and deformed, exhibiting varying degrees of deformity and restricted movement. The trends in body weight changes in each group are shown below. Figure 1 In the CON group, the rats showed a continuous increase in body weight, while the MOD group showed a slower increase. After intervention with Tripterygium wilfordii polyglycoside tablets and Jin Gu Lian capsules, the degree of paw swelling in the rats decreased, and the increase in body weight continued to rise.

[0074] 1.2.2 Determination of arthritis index (paw swelling, bare joint circumference) in rats

[0075] Before rat modeling and on days 5, 7, 10, 15, 20, 25, 30, and 35 after modeling, the volume (mL) of the ankle joint and toes of each group of rats was measured using a toe volume measuring instrument, and the circumference (cm) of the rat ankle joint was measured using a soft measuring tape.

[0076] In the CON group, paw volume and ankle circumference remained almost unchanged throughout the experiment. In the other groups, swelling began on the modeling side approximately 3 hours after modeling. Following re-immunization, paw volume peaked on day 10 in all modeling groups, and the values ​​remained relatively stable after day 20. After treatment, compared to the MOD group, paw volume and ankle circumference were significantly reduced in all treatment groups, and the JGLM and POS groups showed comparable levels of reduction in paw swelling. Results are shown below. Figure 2 .

[0077] 1.2.3 Effects of Jin Gu Lian Capsules on Rat Organs

[0078] On day 35 after modeling, the body weight of rats in each group was measured. After blood collection, they were sacrificed, and the heart, liver, spleen, lung, kidney, thymus, and adrenal glands were separated. The organ coefficient was calculated according to the formula: organ coefficient = organ weight / body weight.

[0079] Compared with the CON group, the MOD group showed a significant increase in the immune organ coefficient. After administration, compared with the MOD group, the Jin Gu Lian capsule group had a significant restorative effect on immune organs (P<0.05), while having no significant effect on organs such as the heart, liver, and kidneys. Results are shown below. Figure 3 See Table 1.

[0080] Table 1. Changes in organ coefficients in different groups of rats (mg / g, ± s, n = 10)

[0081]

[0082] Note: vs CON ** P<0.01; vs MOD, # P<0.05, ## P<0.01

[0083] 1.2.4 Pathological analysis of bare joints in rats

[0084] The right hind foot ankle joint of rats in each group was fixed with paraformaldehyde, decalcified with EDTA-2Na, dehydrated with anhydrous ethanol, embedded in paraffin, sectioned, and stained with hematoxylin and eosin (HE).

[0085] Morphological changes in the ankle joints of rats in each group were evaluated using HE staining (×40x). Pathological examination showed that the joint structure of rats in the CON group was normal, with synovial cells arranged in a single layer, and no inflammatory cell infiltration or neovascularization was observed. Compared with the CON group, the MOD group showed obvious inflammatory symptoms, including a large proliferation of synovial macrophages, fibroblast proliferation forming fibrous tissue, and extensive inflammatory cell infiltration in the synovial tissue. Compared with the MOD group, the lesions in rats treated with Tripterygium wilfordii polyglycoside tablets and Jin Gu Lian capsules were significantly reduced. From a pathological perspective, Jin Gu Lian capsules can improve the course of RA and have a good intervention effect. Results are shown below. Figure 4 .

[0086] 1.2.5 Changes in 5-HT, IL-1β, IL-6, PGE2, RF, and TNF-α in rat plasma

[0087] Blood samples were collected from the tail vein of each group 1 hour after drug administration on days 20, 25, 30 and 35 after modeling. The samples were placed in EP tubes pre-coated with heparin sodium, centrifuged for 10 min (4 ℃, 6000 rpm), and the supernatant was collected to obtain plasma samples. The plasma samples were obtained by microplate reader according to the instructions and the contents of 5-HT, IL-1β, IL-6, PGE2, RF and TNF-α in the plasma were calculated.

[0088] The research results show that ( Figure 5 (See Table 2) Compared with the CON group, the MOD group showed significantly increased levels of 5-HT, IL-1β, IL-6, PGE2, RF, and TNF-α (P<0.001), indicating successful model establishment. Compared with the MOD group, both Jin Gu Lian capsules and Tripterygium wilfordii polyglycoside tablets significantly reduced the levels of these six indicators in rat plasma (P<0.05, P<0.01), indicating that both Jin Gu Lian preparations and Tripterygium wilfordii polyglycoside tablets have anti-RA effects, and there was no significant difference between the two, suggesting that their therapeutic effects are comparable.

[0089] Table 2 Effects on the secretion of 5-HT, IL-1β, IL-6, PGE2, RF, and TNF-α in rat plasma (ng / L, n = 10)

[0090]

[0091] Note: vs CON *** P<0.001; vs MOD, ### P<0.001, ## P<0.01, # P<0.05

[0092] Example 2: Detection of in vivo and in vitro chemical components of Jin Gu Lian capsules

[0093] 2.1 Sample Preparation

[0094] (1) Preparation of stock solution for in vitro analysis: Preparation of test solution: Take an appropriate amount of Jin Gu Lian capsules, add 50% methanol to dilute to about 30 mg / mL (based on crude drug weight), mix well, sonicate for 30 min, filter, centrifuge at 12000 rpm for 10 min, and obtain supernatant. Before analysis, all solutions were filtered through a 0.22 μm filter.

[0095] (2) Preparation of in vivo samples and establishment of the AA model: SD rats were randomly divided into 4 groups, with 110 rats in each group, including control group, model group, positive drug group, and Jin Gu Lian group. 0.1 mL of complete Freund's adjuvant was injected intradermally into the right hind paw pad of the rats to induce inflammation. The control group was injected with the corresponding physiological saline. The rats were immunized again after 7 days, and the model was established for 21 days. On the 21st day, the drug was administered by gavage twice a day until the 35th day.

[0096] Preparation of Jin Gu Lian capsule gavage solution: Weigh an appropriate amount of Jin Gu Lian capsules and dilute with 0.5% CMC-Na to obtain the Jin Gu Lian preparation gavage solution; the Tripterygium wilfordii polyglycoside group gavage solution is prepared by diluting with 0.5% CMC-Na.

[0097] (3) Collection and preparation of plasma samples: Plasma from the model group and the drug-treated group on day 35 was collected. 0.5 mL of different samples from the same group were taken and mixed to obtain mixed blank plasma and mixed drug-containing plasma, respectively. 1 mL of each mixed plasma was taken, and 4 times the volume of methanol was added to precipitate the protein. The mixture was allowed to stand at -20 ℃ for 30 min, vortexed for 3 min, and centrifuged at 15000 rpm at 4 ℃ for 10 min. The supernatant was collected, dried under N2 at 37 ℃, and the residue was diluted to 200 μL of 50% methanol. The mixture was vortexed for 3 min, centrifuged at 15000 rpm at 4 ℃ for 10 min, and the supernatant was injected for analysis and stored in a -80 ℃ refrigerator.

[0098] 2.2.2 Chromatographic and Mass Spectrometry Conditions

[0099] Chromatographic analysis conditions: Ultra-high performance liquid chromatography (UHPLC) system: Vanquish Horizon; UHPLC column: Hypersil Gold, 2.1 × 150 mm, 1.9 μm; column temperature: 40 ℃; injection volume: 2 µL; mobile phase ratio and flow rate: Phase A 0.1% formic acid-water, Phase B 0.1% formic acid-acetonitrile; flow rate: 0.3 mL / min; gradient elution program as follows: 0–2 min, 2% B; 2–20 min, 2%–15% B; 20–30 min, 15%–25% B; 30–40 min, 25%–40% B; 40–50 min, 40–98% B; 50–52 min, 98% B. Mass spectrometry analysis conditions: MS system: Q Exactive Plus MS; detection mode: ESI. + & ESI - Mass spectrometry conditions are shown in Table 3.

[0100] Table 3 Mass Spectrometry Conditions

[0101] Tab. 3 Mass spectrometry conditions

[0102]

[0103] 2.2.3 Chemical composition analysis of *Gynostemma pentaphyllum* preparations based on UHPLC / Q Exactive Plus MS

[0104] Based on the in vitro chemical composition analysis results of the *Gynostemma pentaphyllum* preparation, and using Compound Discoverer 3.2 software, the structures of the components of *Gynostemma pentaphyllum* that enter the bloodstream were identified. Ion chromatograms are shown below. Figure 6 , 7 As shown in Table 4, the results confirmed the migration of 27 prototype components in the blood of AA rats after oral administration of *Gynostemma pentaphyllum*. The quality accuracy deviation was less than 5 ppm, and all identified compounds were confirmed by reference standards.

[0105] Table 4. Plasma translocation components (original form) in AA model rats after oral administration of Jin Gu Lian preparation.

[0106]

[0107]

[0108] Example 3: Based on the chemical components in plasma after entering the bloodstream and the disease targets of rheumatoid arthritis, network pharmacology was used to screen key targets and key compounds for the anti-rheumatoid arthritis efficacy of Jin Gu Lian Fang, and experimental verification was conducted.

[0109] 3.1 Screening of active ingredients and prediction of related targets in Jin Gu Lian preparations

[0110] The established UHPLC / Q Exactive Plus MS method was used to perform high-resolution mass spectrometry analysis on Jin Gu Lian capsule samples. Compound Discoverer 3.2 software was used to identify the structures of the Jin Gu Lian components entering the bloodstream. Combining relative molecular mass deviation <5 ppm, isotope abundance ratio, nitrogen rule, and Xcalibur primary and secondary mass spectrometry information, the compound structures were confirmed by using databases such as Chemispider and m / zVault, as well as literature, supplemented by the mzLogic algorithm and Mass Frontier 7.0. A chemical component database was established based on the plasma pharmacochemical analysis results of Jin Gu Lian preparations. 2D structures of the components entering the bloodstream were obtained from PubChem, and Swisstarget Prediction was used to predict the target sites. Genecard, TTD, DisGeNET, OMIM, and TCMSP databases were searched using "Rheumatoidarthritis" as the keyword to screen for human rheumatoid arthritis (RA)-related targets, and disease targets were integrated after deduplication.

[0111] 3.2 Screening of disease targets for rheumatoid arthritis

[0112] Twenty-seven blood-entering components were searched in the Swisstarget Prediction database to obtain chemical component targets (Probability>0). After screening and removing duplicates, 258 targets were obtained. RA was searched using the Genecard (Score>20), Omim, TTD, TCMSP, and DisGeNet (Score≥0.1) databases. After gene name normalization using Uniprot (https: / / www.uniprot.org / ) and removing duplicates, a total of 1197 relevant targets were obtained. The intersection of component targets and disease targets yielded 39 intersection targets, and a Venn diagram was plotted. (See...) Figure 8 .

[0113] 3.3 Constructing the "blood-entering components-components-diseases" network of Jin Gu Lian capsules and obtaining effective components.

[0114] like Figure 9 The molecular network of "traditional Chinese medicine-components-disease-related targets" of Jin Gu Lian preparation for the treatment of RA was shown. Twelve components of Jin Gu Lian preparation, including rutin, quercetin, akebia phenylethanol glycoside B, vitexin glucoside, hydroxyisogeniposide, nobiletin, and saponin, are involved in the regulation of RA. These 12 components constitute the effective component group of Jin Gu Lian preparation for the treatment of RA. The compound information is shown in Table 5.

[0115] Table 5. Information on the effective compound groups of Jin Gu Lian preparations for treating RA.

[0116]

[0117] 3.4 Construction and Analysis of PPI Network of Anti-rheumatoid Arthritis Targets in Jin Gu Lian Preparations

[0118] Using protein-protein interaction data from the String database as the background network, 39 relevant targets of Jin Gu Lian preparations involved in the regulation of RA were mapped to the String database to construct a protein-protein interaction network for the treatment of RA by Jin Gu Lian preparations. The screening criterion was an interaction score > 0.4. The network was analyzed using Cytoscape and visualized. The protein-protein interaction network is shown below. Figure 10 Sort by degree value, and use degree > 10.31 as the standard to obtain core targets. It is speculated that the Jin Gu Lian preparation mainly affects the following 14 targets in the treatment of RA: TNF, EGFR, TLR4, PTGS2, MMP9, IL2, ICAM1, PTPRC, ITGB1, MMP2, MMP3, RELA, ALOX5, and ITGB2.

[0119] 3.5 Discovery of the potential pathway for the treatment of RA by Jin Gu Lian preparations

[0120] Intersecting targets were imported into R software for GO and KEGG enrichment analysis to interpret the biological processes (abbr. BP), cellular components (abbr. CC), and molecular functions (abbr. MF) involved in the targets. Figure 11 A), and obtain the main participating signaling pathways ( Figure 11 B). Results showed that the Jin Gu Lian preparation involved in the treatment of RA through collagen degradation, regulation of inflammatory responses, and regulation of reactive oxygen species (ROS) metabolism, primarily involving the following signaling pathways: rheumatoid arthritis, IL-17 signaling pathway, arachidonic acid metabolism, tumor necrosis factor signaling pathway, lipid and atherosclerosis pathway, and NF-κB signaling pathway. Based on these results, a network of "Jin Gu Lian preparation - blood-entering components - targets - pathways - diseases" was constructed, see [link to relevant documentation]. Figure 12 .

[0121] Example 4 Identification of pharmacodynamic biomarkers

[0122] 4.1 Experimental Methods and Results

[0123] 4.1.1 Collection and Pretreatment of Plasma Samples

[0124] Whole blood samples were collected from 10 rats each in the normal control group, model group, positive drug group, and drug-treated group. The samples were centrifuged in heparinized sterile centrifuge tubes at 3000 rpm for 20 min at 4°C. The supernatant plasma was collected and stored at -80°C for later use. 50 μL of thawed plasma from each group was added to the same centrifuge tube, vortexed, and then rapidly aliquoted into 1.5 mL centrifuge tubes at 100 μL per tube as plasma quality control (abbr. QC) samples.

[0125] 4.2 Establishment and validation of non-targeted metabolomics analysis methods

[0126] 4.2.1 Chromatographic and Mass Spectrometry Conditions

[0127] Chromatographic analysis conditions: Ultra-high performance liquid chromatography (UHPLC) system: Vanquish Horizon; UHPLC column: Hypersil Gold, 2.1 × 150 mm, 1.9 μm; mobile phase: two phases, A and B: 0.1% formic acid in water (A) and 0.1% formic acid-acetonitrile:methanol (2:3, v / v, B); flow rate: 0.3 mL / min; column temperature: 40 ℃; injection volume: 5 μL; gradient elution program: 0–2 min, 2% B; 2–10 min, 2%–100% B; 10–12 min, 100% B. Mass spectrometry analysis conditions: Mass spectrometry system: QExactive Plus MS; detection mode: ESI+ & ESI-; mass spectrometry conditions are shown in Table 6.

[0128] Table 6 Mass Spectrometry Parameters

[0129]

[0130] The typical peak diagrams of plasma samples in ESI+ & ESI- modes show that the established method can detect various polar components such as amino acids and fatty acids, and the detection method can meet the requirements of subsequent analysis.

[0131] 4.2.3 Validation of Analytical Methods

[0132] (1) Methodological validation: A QC sample was prepared according to the above method, and injected five times consecutively. The BPC (Base Peak Chromatogram) obtained from the five injections was compared. Pearson correlation analysis was performed using the common peak area of ​​the five QC samples as an indicator. The results showed that the BPC peak intensity and retention time basically overlapped, and the correlation was >0.9, indicating a high positive correlation, which showed that the instrument precision was good.

[0133] (2) Method repeatability: Five QC samples were prepared in parallel using the same method. The chromatographic peaks of BPC obtained by injection were superimposed and compared, and the peak area of ​​the internal standard in the five samples was examined. The results showed that the peak intensity and retention time of BPC basically overlapped, and the RSD of the internal standard peak area was 11.88%, indicating good method repeatability.

[0134] The QC samples analyzed during the injection process were analyzed. Pearson correlation analysis showed that the correlation between all samples was >0.9, indicating good repeatability and stability of the system. The Hotelling's T2 range of the overall sample was examined, and all samples were within the 99% confidence interval. The PCA analysis showed that the QC samples were closely clustered together, indicating that the metabolometry analysis method has good stability and repeatability and high data quality.

[0135] 4.3 Metabolite profiles in AA rat plasma

[0136] To investigate changes in overall metabolism, the observations were first analyzed using PCA. (See the PCA score plot.) Figure 13 As shown in the figure, the general characteristics of plasma samples in each group were observed, and a significant grouping trend was shown among the CON group, MOD group, and JGLM group. The results indicated that, compared to the CON group, the endogenous metabolites in both the MOD and JGLM groups changed. After the model was successful (MOD_20 d), as the experimental days progressed (25 days → 30 days → 35 days), the MOD group exhibited a certain degree of self-healing, and its metabolic profile shifted towards that of the CON group. Figure 14 A). Under supervised mode, the MOD group with different number of days was distributed on both sides of the axis, showing significant differences compared to the CON group. Figure 14 B). After administration of the Jin Gu Lian preparation, the JGLM group showed a significant regression (25 days → 30 days → 35 days), exhibiting a counter-clockwise regression to the quadrant of the CON group under unsupervised mode. Figure 13 , Figure 14 D), the degree of pullback is similar to that of the POS group, and under the supervised OPLS-DA model, the grouping trend is more significant. Figure 14 E). At day 35 of the experiment, the drug-treated group approached the CON group ( Figure 14 H), PCA results showed that the Jin Gu Lian preparation group and the CON group were on the same side and had some overlap, indicating that the Jin Gu Lian preparation had a tendency to correct towards the normal side.

[0137] 4.4 Screening and pathway analysis of biomarkers in AA rat plasma

[0138] To visualize the progression of rheumatoid arthritis (RA), differentially expressed metabolites were analyzed at 20, 25, 30, and 35 days after successful modeling. The intersection of these metabolites was used as biomarkers for RA. Results analysis was conducted at day 35 as an example, using the OPLS-DA score plot (…). Figure 15 As can be seen from M), there is a clear separation trend between the MOD group and the CON group. The results indicate that the established data model has good discriminative and predictive abilities (R²X = 0.713, R²Y = 0.996, Q²...). 2 = 0.987), which can be further used to screen differentially expressed metabolites in plasma. In addition, to prevent model overfitting, a permutation test of 200 iterations was used to examine the model quality; the results are shown in […]. Figure 15 (N, R) 2 = 0.666, Q 2 = -0.417), all Q2 (R2) on the left are lower than the original value on the right or Q 2 The blue regression line intersects the left ordinate at or below zero, indicating that the established data model is effective and does not exhibit overfitting, making it suitable for screening differentially expressed metabolites. The S+V-plot is obtained by combining the VIP value with the S-plot. Figure 15 The volcano map was obtained by calculating the P-value and FC.

[0139] Biomarkers representing the disease course of the model were screened based on criteria of VIP>1, P<0.05, and FC≥1.2 / ≤0.67. The intersection of these criteria identified 152 potential plasma biomarkers related to RA, as shown in the Venn diagram. Figure 16 Of the 76 endogenous metabolites upregulated and 76 downregulated, the majority were phospholipids, fatty acids, and amino acids. Qualitative analysis was performed using CD software, and the biomarkers were matched with the Human Metabolome Database (abbr.HMDB, http: / / www.hmdb.ca / ) and mzCloud (https: / / www.mzcloud.org / ) for biomarker names and HMDB numbers. Results with a score >60 were considered reliable. Furthermore, cluster analysis was performed on all significant metabolites, such as heatmaps. Figure 17 As shown in the figure, the CON group and the MOD group can be clearly divided into two clusters, which is consistent with the results of the PCA model, indicating that the screened metabolites are reasonable.

[0140] Enrichment and pathway analyses were performed using Metaboanalyst 5.0. In plasma, these RA-regulated metabolites were associated with 23 metabolic pathways, including histidine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glycerophospholipid metabolism, phenylalanine metabolism, and arachidonic acid metabolism (Table 7).

[0141] Table 7. Metabolic pathways related to RA disease regulation

[0142]

[0143] 4.4 Screening and pathway analysis of differentially regulated metabolites after intervention with AA Jin Gu Lian preparation

[0144] Differential metabolite screening: The analytical method was the same as above. An OPLS-DA model was established between the MOD group and the JGLM group, and good separation was shown in each group. Figure 18 A, E, I). The arrangement results indicate that the model is not overfitting ( Figure 18 B, F, J). With VIP>1 ( Figure 18 C, G, K), P<0.05 and FC≥1.2 / ≤0.67 are the screening criteria. Figure 18 Following treatment with Jin Gu Lian preparations (JGLM), 27 (25 days), 47 (30 days), and 78 (35 days) metabolites in the plasma of AA rats were significantly reduced, including phospholipids, fatty acids, and amino acids. It is speculated that Jin Gu Lian preparations exert their therapeutic effect on RA by collectively regulating the imbalance of multiple metabolites, including fatty acids, amino acids, and glycerophospholipids. Furthermore, the regulatory effect becomes more extensive with prolonged administration.

[0145] Metabolic pathway analysis: Cluster analysis was performed on all metabolites with significant regression, such as cluster heatmaps. Figure 19 As shown in the figure, it can be clearly seen that most of the dysregulated metabolites were restored to some extent after drug administration, which is consistent with the results of the PCA model, indicating that the screened metabolites are reasonable. In order to determine the metabolic pathways that JGLM may regulate in RA model rats, differentially expressed metabolites were submitted to the Metaboanalyst 5.0 platform. Further analysis was conducted on the biological pathways involved in metabolism and their significance. The results showed that in plasma, these regulated metabolites were significantly associated with histidine metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and other metabolic pathways (Table 8).

[0146] Table 8. Pathways potentially involved in the effects of Jin Gu Lian preparations on AA model rats.

[0147]

[0148] 4. The pharmacodynamic basis of Jin Gu Lian preparations in the treatment of RA

[0149] Taking a 35-day period as an example, a correlation analysis was performed on the peak areas of 78 differentially metabolites and 12 active ingredients from Jin Gu Lian preparations for treating RA to screen for potential pharmacodynamic substances. The results showed that five highly correlated blood-entering components (JGLM_35d) were identified: kaempferol glucuronide, baicalin, apigenin-7-O-glucuronide, vitexin glucosinolate, and noriheptacorline, which can be considered potential pharmacodynamic substances of Jin Gu Lian preparations for treating RA.

[0150] 4.6 Obtaining key proteins and metabolites using Metscape software

[0151] Using Metscape software, 32 targets of 5 potential pharmacodynamic substances and 78 differentially expressed metabolites were categorized as "Genes" and "Compounds," respectively. Metabolic pathways were used as the "Query" to construct a "target-metabolism" network diagram, focusing on key proteins and signaling pathways in the treatment of rheumatoid arthritis (RA) with Jin Gu Lian preparations. Results showed 31 pathways related to the potential pharmacodynamic targets and metabolites of Jin Gu Lian preparations. Among these, the "arachidonic acid metabolic pathway" appeared in both metabolomics and network pharmacology analyses. The arachidonic acid-related "target-metabolism" network is shown below. Figure 20 The key proteins in Jin Gu Lian preparations for treating RA include PTGS2 and ALOX5, which are involved in regulating three endogenous metabolites: 5,6-DHET, 11,12-EET, and Phosphatidylcholine.

[0152] 4.7 Discussion

[0153] This study used UHPLC / Q Exactive Plus MS high-resolution mass spectrometry to analyze and obtain the characteristic spectra of endogenous substances in rats under normal physiological and RA pathological conditions. Through multivariate statistical analysis to screen differentially expressed metabolites between groups, and combined with relevant literature, it is speculated that RA mainly affects bodily functions by interfering with amino acid synthesis and metabolism, glycerophospholipid, lipid and fatty acid metabolism, and the synthesis and metabolism of pyrimidines and purines. This leads to disorders in amino acid metabolism, abnormal lipid metabolism, oxidative stress imbalance, and energy metabolism disorder, constituting the biochemical metabolic basis for the development and progression of RA.

[0154] (1) Pharmacodynamic evaluation of Jin Gu Lian preparations on adjuvant-induced arthritis (AA) in rats

[0155] RA is a chronic, systemic, autoimmune disease in humans, mainly characterized by erosive arthritis. Adjuvant-induced arthritis (AA) is the most classical animal model of RA. In this study, an AA rat model was established, and the rats in the model group showed obvious toe swelling, joint deformity, and a significant increase in the organ coefficients of immune organs. After intervention with the Jingu Lian preparation, the foot volume and ankle circumference of the rats were significantly reduced, and the degree of swelling elimination was comparable to that of the positive drug Tripterygium glycosides tablets. The pathological results further confirmed that the Jingu Lian preparation could effectively inhibit the proliferation of synovial macrophages and the infiltration of inflammatory cells, and significantly reduce joint lesions. In terms of the regulation of inflammatory mediators, the Jingu Lian preparation significantly reduced the overexpression of pro-inflammatory factors such as 5-HT, IL-1β, IL-6, PGE2, RF, and TNF-α in the plasma of AA rats, indicating its clear anti-RA and immunomodulatory effects. At the same time, this formula had no obvious effect on the main organs such as the heart, liver, and kidneys, reflecting its good safety.

[0156] (2) Exploration of the network pharmacology mechanism based on the ingredients in blood

[0157] To analyze the "multi-target" mechanism of the Jingu Lian preparation, 27 orally absorbed prototype components were confirmed by UHPLC / Q Exactive Plus MS technology in this study. By constructing a "traditional Chinese medicine-ingredient-disease-related target" network, an effective ingredient group for treatment consisting of 12 ingredients such as rutin, quercitrin, and nobiletin was identified. KEGG pathway enrichment analysis showed that the Jingu Lian preparation was mainly involved in regulating the processes of collagen catabolism, inflammatory response, and reactive oxygen metabolism through multiple pathways such as the IL-17 signaling pathway, arachidonic acid metabolism, tumor necrosis factor (TNF) signaling pathway, and NF-κB signaling pathway. This indicates that the anti-rheumatic effect of the Jingu Lian preparation does not rely on a single target, but is achieved by synergistically inhibiting multiple classical inflammatory and immune cascade reaction pathways.

[0158] (3) Overall metabolic homeostasis callback targeting plasma metabolomics

[0159] The pathogenesis of rheumatoid arthritis (RA) is accompanied by significant metabolic disorders. This study screened 152 potential biomarkers highly correlated with RA disease progression in the plasma of AA rats, mainly involving abnormalities in amino acid, fatty acid, and phospholipid metabolism. PCA and OPLS-DA trajectory analyses showed that with prolonged administration (from 25 to 35 days), the metabolic profile of the Jin Gu Lian preparation group exhibited a significant counter-clockwise regression, gradually approaching that of the normal control group. Specifically, at day 35, 78 dysregulated metabolites were significantly regressed. Pathway analysis indicated that the Jin Gu Lian preparation extensively intervened in histidine metabolism, glycerophospholipid metabolism, and arachidonic acid metabolism. This suggests that the Jin Gu Lian preparation can inhibit the production of inflammatory mediators at the metabolic source by regulating the imbalance of amino acid and lipid metabolism and reducing oxidative stress.

[0160] (4) Joint analysis revealed the core active pharmaceutical substances of Jin Gu Lian preparation in the fight against RA.

[0161] Correlation analysis between 78 differentially expressed metabolites and 12 active ingredients identified five highly correlated core potential pharmacodynamic substances: kaempferol glucuronide, baicalin, apigenin-7-O-glucuronide, vitexin glucosinolate, and nonotrimonin. The "target-metabolism" network diagram further revealed that the "arachidonic acid metabolic pathway" is the key link between these two omics findings. These five core components directly regulate the levels of endogenous metabolites such as 5,6-DHET, 11,12-EET, and phosphatidylcholine by acting on the two key target proteins PTGS2 and ALOX5.

[0162] Based on the unique theories of Miao medicine, this study takes the in vivo material basis of drugs as the starting point, and focuses on metabolomics and network pharmacology. It explores the pharmacodynamic material basis of "Jin Gu Lian preparation" from the perspective of the improvement process of metabolite levels in vivo and the integration network analysis, and reveals the relationship between its active ingredients and pharmacological effects in treating rheumatic arthralgia.

[0163] Finally, it should be noted that the above embodiments are merely representative examples of the present invention. Obviously, the technical solution of the present invention is not limited to the above embodiments, and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should be considered within the scope of protection of this invention.

Claims

1. A method for screening the active ingredients of a Jin Gu Lian preparation, the screening method comprising: Step S1: Establish a rat adjuvant-induced arthritis model and determine the efficacy of Jin Gu Lian preparation in treating rheumatoid arthritis through in vivo pharmacodynamic evaluation; Step S2: Ultra-high performance liquid chromatography-high resolution tandem mass spectrometry was used to analyze the in vivo and in vitro chemical components of the Jin Gu Lian preparation, and at the same time, the plasma components entering the blood of rats after gavage administration were analyzed. Step S3: Based on blood-entering components and rheumatoid arthritis disease targets, network pharmacology is used to screen the core targets of drugs and diseases, perform GO function and KEGG pathway enrichment analysis, construct a "blood-entering component-target-pathway-disease" network, and screen the effective component group involved in disease regulation. Step S4: Use metabolomics research methods to perform full-spectrum analysis on rat plasma after gavage, screen for disease biomarkers of rheumatoid arthritis, and differential metabolites that significantly reverted after intervention with Jin Gu Lian preparation, and enrich key metabolic pathways of drug action. Step S5: Integrate the core targets and active ingredient groups obtained in Step S3, and the biomarkers and metabolic pathways obtained in Step S4 to construct a correlation network and identify the pharmacodynamic substances of Jin Gu Lian preparations for treating rheumatoid arthritis.

2. The screening method according to claim 1, characterized in that, The in vivo efficacy evaluation in step S1 includes: observation of the general condition of rats, body weight monitoring, arthritis index measurement, ankle joint pathological examination, and plasma 5-HT and IL-1 levels. β IL-6, PGE2, RF, TNF-α α Detection of inflammatory factor levels.

3. The screening method according to claim 1, characterized in that, The in vivo and in vitro chemical composition analysis in step S2 includes the following steps: (1) Preparation of test solution in vivo: Take the plasma of rats after gavage administration of Jin Gu Lian preparation, add 4 times the volume of methanol to precipitate protein, let stand at -20 ℃ for 30 min, vortex for 3 min, centrifuge at 4 ℃, collect the supernatant, blow dry with N2, dissolve the residue in 50% methanol, vortex for 3 min, centrifuge at 4 ℃, and take the supernatant for analysis. Preparation of in vitro test solution: Take an appropriate amount of Jin Gu Lian preparation, add 50% methanol to dilute to 30 mg of raw drug per ml, mix well, sonicate for 30 min, filter, centrifuge the filtrate, take the supernatant, and filter through a filter membrane to obtain the solution. (2) Chromatographic conditions: Chromatographic analysis conditions: An ultra-high performance liquid chromatography (UHPLC) system was used, with an octadecylsilane-bonded silica column as the packing material; 0.1% formic acid-water was used as mobile phase A, and 0.1%–0.5% formic acid-acetonitrile was used as mobile phase B, with gradient elution. The gradient elution program was as follows: 0–2 min, 2% B; 2–20 min, 2%–15% B; 20–30 min, 15%–25% B; 30–40 min, 25%–40% B; 40–50 min, 40–98% B; 50–52 min, 98% B; flow rate: 0.3–0.5 mL / min. Mass spectrometry analysis conditions: MS system: Q Exactive Plus MS; Detection mode: ESI + & ESI - .

4. The screening method according to claim 3, characterized in that, The mass spectrometry conditions are as follows:

5. The method for screening pharmacodynamic substances according to claim 1, characterized in that, Step S3 includes the following steps: (1) Predict the target of the drug-drug-inducing components in Jin Gu Lian preparations from the Swisstarget Prediction database, screen rheumatoid arthritis disease targets from Genecard, TTD, DisGeNET, OMIM and TCMSP databases, obtain the intersection targets of drug targets and disease targets after standardization and deduplication, and draw Venn diagram. (2) Using the protein interaction data collected in the String database as the background network, the relevant target points of Jin Gu Lian preparations involved in the regulation of RA were mapped to the String database to construct the protein interaction network of Jin Gu Lian preparations for the treatment of RA. The screening condition was interaction score>0.

4. The network was analyzed and visualized using Cytoscape. The protein interaction network is shown in the figure. The core targets were obtained by sorting by degree value and using degree>10.31 as the standard. (3) GO functional enrichment analysis and KEGG pathway enrichment analysis were performed on the core targets to identify the key biological processes and signaling pathways of Jin Gu Lian preparations in the treatment of rheumatoid arthritis. (4) Construct a network of "blood-entering components-targets-pathways-diseases" for Jin Gu Lian preparations and screen effective component groups involved in disease regulation.

6. The method for screening pharmacodynamic substances according to claim 1, characterized in that, Step S4 includes the following steps: (1) Untargeted metabolomics analysis of rat plasma in each group was performed using UHPLC / Q Exactive Plus MS. The effects of modeling and drug administration were verified by PCA and OPLS-DA analysis. (2) With VIP>1, P Using the criteria of <0.05 and FC≥1.2 / ≤0.67, biomarkers that can represent the disease course characteristics of the rat adjuvant arthritis model were screened. Endogenous components that showed significant differences between the model group and the healthy control group, but showed significant regression in the drug-treated group, were used as biomarkers to draw Venn diagrams, and a total of potential plasma biomarkers were identified. (3) Using VIP>1, P<0.05 and FC≥1.2 / ≤0.67 as conditions, we screened differential metabolites that showed significant regression after intervention with Jin Gu Lian preparations; (4) Use the MetaboAnalyst platform to enrich the metabolic pathways of the callback biomarkers and identify key metabolic pathways.

7. The method for screening pharmacodynamic substances according to claim 6, characterized in that, The key metabolic pathways are histidine metabolism, glycerophospholipid metabolism, arachidonic acid metabolism, and phenylalanine metabolism.

8. The method for screening pharmacodynamic substances according to claim 1, characterized in that, The integration and correlation analysis in step S5 is as follows: Pearson analysis is used to perform correlation analysis between blood-entering components and differential metabolites, and highly correlated components are screened as potential pharmacological substances; Metscape software is used to construct a target-metabolism network to identify key proteins and key metabolites.

9. The method for screening pharmacodynamic substances according to claim 8, characterized in that, The pharmacologically active substances are: kaempferol glucuronide, baicalin, apigenin-7-O-glucuronide, vitexin glucosinolate, and noriheptacorlin; the key proteins are PTGS2 and ALOX5, and the key metabolites are 5,6-DHET, 11,12-EET, and Phosphatidylcholine.

10. The use of a screening method as described in any one of claims 1-9 in a medicament for treating rheumatoid arthritis.

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