Method for determining quality markers of WD liver fibrosis treated by hepingling based on tracking liver components

Abstract

The application provides a method for determining quality markers of WD liver fibrosis treated by Ganduoling based on tracking liver components, and belongs to the technical field of active ingredient identification of traditional Chinese medicine compounds. First, UHPLC-Orbitrap-MS is used to detect in-vitro chemical components of Ganduoling and prototype components entering the liver tissue of WD model rats, and the liver tissue of WD model rats is detected as a blank control, and according to the principle that the'retention time, peak area difference fold FC value is greater than 10 or the model group is not detected', the prototype components entering the liver are screened; then, network pharmacology and molecular docking technology are used, and finally, 17 components such as ferulic acid, aloin, chrysophanol and the like are determined as the quality markers of WD liver fibrosis treated by Ganduoling. The application provides a scientific experimental basis for clarifying the pharmacodynamic material basis of WD liver fibrosis treated by Ganduoling, optimizing the quality control standard and developing new drugs, and provides a further reference for clinical medication and new drug development.

Description

Technical Field

[0001] This invention belongs to the technical field of identification of prototype components of traditional Chinese medicine compound prescriptions, specifically relating to a method for determining the quality markers of liver fibrosis in WD treatment based on tracking liver-entering components. Background Technology

[0002] Hepatolenticular degeneration (HLD), also known as Wilson's disease (WD), is an autosomal recessive inherited disorder of copper metabolism caused by a mutation in the ATP7B gene. Its main clinical features are liver damage and neuropsychiatric symptoms. In Traditional Chinese Medicine (TCM), liver fibrosis in WD falls under the categories of "accumulation" and "hypochondriac pain." TCM believes its core pathogenesis is "liver and kidney yin deficiency, phlegm and blood stasis," where phlegm and blood stasis bind together, obstructing the liver meridians and leading to the proliferation of fibrous connective tissue.

[0003] The hospital-prepared drug Gandouling (approval number: Wan Yao Zhi Zi Z20050071) from the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine is composed of six Chinese herbs: Coptis chinensis, Salvia miltiorrhiza, Spatholobus suberectus, Rheum palmatum, Curcuma longa, and Curcuma zedoaria. It has been clinically applied to the treatment of Wilson's disease with good results. Gandouling promotes bile secretion from the liver, allowing copper to enter the intestines and be excreted with the bile, reducing copper damage to the liver and other systems. It is generally believed that the therapeutic effect of compound Chinese medicine formulas is attributed to the large number of Chinese herbal components extracted from them. However, due to the diversity and complexity of Chinese herbal components, the screening and analysis of their quality markers (active ingredients) face many challenges. Encouragingly, liver tissue pharmacology combined with network pharmacology based on ultra-high performance liquid chromatography-orbitrap-MS (UHPLC-Orbitrap-MS) technology provides an opportunity to identify the distribution of active ingredients in the liver, the target organ, of complex Chinese herbal components.

[0004] The combination of ultra-high performance liquid chromatography (UHPLC) and electrostatic field orbital trap high-resolution mass spectrometry (Orbitrap-HRMS) offers advantages such as ultra-high resolution, high quality precision, and wide dynamic range, and has been widely used in the qualitative analysis of chemical components of traditional Chinese medicine and in the medicinal chemistry research of liver tissue. This invention aims to leverage the high throughput, high sensitivity, and high resolution advantages of UHPLC-Orbitrap-MS, combined with network pharmacology and molecular docking techniques, to determine the quality biomarkers for hepatol in the treatment of WD liver fibrosis. This will provide scientific experimental evidence for elucidating the pharmacodynamic material basis of hepatol in the treatment of WD liver fibrosis, optimizing quality control standards, and developing new drugs, thus providing further reference for clinical medication and new drug development. Summary of the Invention

[0005] The purpose of this invention is to propose a method for determining the quality biomarkers of hepatoretin in treating WD liver fibrosis based on tracking the components entering the liver. By tracking and analyzing the original components of hepatoretin entering the liver, the high throughput, high sensitivity and high resolution advantages of UHPLC-Orbitrap-MS technology are utilized, and network pharmacology and molecular docking technology are combined to determine the quality biomarkers of hepatoretin in treating WD liver fibrosis.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for determining the quality biomarkers of hepatol in treating WD liver fibrosis based on tracking liver-entering components involves first detecting the in vitro chemical components of hepatol and the original components in the liver tissue of WD model rats using UHPLC-Orbitrap-MS, while simultaneously detecting liver tissue from WD model rats as a blank control. Based on "retention time (RT), peak area difference fold (FC) > 10 (drug group VS model group) or no detection in the model group", the original components entering the liver are screened as potential quality biomarkers of hepatol. Then, through network pharmacology and molecular docking technology, the quality biomarkers of hepatol in treating WD liver fibrosis are finally determined.

[0008] The in vitro chemical composition characterization steps for liver beta-carotene are as follows:

[0009] ① Preparation of the test solution

[0010] Grind the liver bean extract tablets and pass them through a 60-gauge sieve. Take 0.5 g of liver bean extract powder and place it in a stoppered conical flask. Accurately add 50 mL of methanol, seal tightly, weigh, sonicate for 30 min, remove, cool, weigh again, replenish the lost weight with methanol, shake well, filter, and collect the filtrate to obtain the final product.

[0011] ②Chromatographic conditions

[0012] A Vanquish ultra-high performance liquid chromatograph was used, with a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 μm, Waters, USA). Phase A of the liquid chromatography was aqueous, containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and phase B was acetonitrile. The sample pan temperature was 4 ℃. The injection volume was 2 μL, the column temperature was 30 ℃, and the injection flow rate was 0.50 mL / min.

[0013] A gradient elution program was used: from the initial time to 0.25 min, mobile phase B was maintained at 95%; from 0.25 min to 3.50 min, mobile phase B was gradually reduced from 95% to 65%; from 3.50 min to 4.00 min, mobile phase B was gradually reduced from 65% to 40%; from 4.00 min to 4.50 min, mobile phase B was maintained at 40%; from 4.50 min to 4.55 min, mobile phase B was gradually increased from 40% to 95%; and from 4.55 min to 6.00 min, mobile phase B was maintained at 95%.

[0014] ③Mass spectrometry conditions

[0015] The Orbitrap Exploris 120 mass spectrometer was used with a sheath gas flow rate of 50 Arb, an auxiliary gas flow rate of 15 Arb, a capillary temperature of 320 °C, a full scan MS resolution of 60,000, an MS / MS resolution of 15,000, collision energies (SNCE) of 20 / 30 / 40, and spray voltages of 3.8 kV for positive ions and -3.4 kV for negative ions.

[0016] ④Component identification

[0017] The raw data was converted to mzXML format using ProteoWizard, and then subjected to baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. The identification of compounds was based on precise mass number, secondary fragments, and isotope distribution, and qualitative analysis was performed using R packages and the database was BiotreeDB (V3.0).

[0018] ⑤ Identification results of in vitro chemical components of liverwort

[0019] A total of 238 components were detected, including 14 amino acids, 103 phenylpropanoids, 25 alkaloids, 44 terpenoids, 13 lipids, 6 anthraquinones, and 33 other components.

[0020] The steps for identifying the prototype components of liver tissue from rats in the WD model are as follows:

[0021] ① Construction and administration of WD model rats

[0022] After one week of acclimatization, SD rats in both the model group and the treatment group were given 0.185% copper sulfate aqueous solution and 1 g / kg of copper sulfate diet daily for 12 weeks to replicate the WD model rats. Starting from the seventh week, liver bean tablets were ground into fine powder and a suspension was prepared with distilled water. Rats in the treatment group were given 2.916 g / kg / d suspension by gavage, and rats in the model group were given 10 mL / kg / d distilled water by gavage for 6 weeks.

[0023] ② Collection and processing of rat liver tissue samples

[0024] Two hours after the last administration, rats were anesthetized intraperitoneally, and the whole liver was immediately removed. The surface blood was rinsed with physiological saline, the moisture was absorbed with filter paper, and the liver was stored at -80℃ for later use. At the same time, rats in the model group were removed as blank controls.

[0025] The liver sample (25±1 mg) was placed on clean dry ice and ground with liquid nitrogen at 60 Hz for 30 s. It was then centrifuged twice at 12000 rpm at 4 ℃ and dried under low temperature. The dried sample was reconstituted with 500 μL of extraction buffer (methanol:acetonitrile:water = 2:2:1). The supernatant was collected into a vial for analysis.

[0026] ③ Identification of the original components of liver tissue in rats with WD model

[0027] Liver tissue samples from the model group and the drug-treated group were analyzed under the above-mentioned detection conditions. Raw data were converted to mzXML format using ProteoWizard. Baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization were performed on the liver-derived drug, model group, and drug-treated group samples. Compound identification was based on precise mass number, secondary fragmentation, and isotope distribution, using the R package for qualitative analysis. The database used was BiotreeDB (V3.0). The analysis was based on the principle that "simultaneous detection of [a specific compound] in both the drug-treated group and the liver-derived drug group..." Furthermore, the retention time (RT) was consistent, the peak area difference fold (FC value) between the treatment group and the model group was >10, or the model group was not detected. Twenty-one prototype components that entered the liver tissue were selected, namely: catechin, tetrahydroberberine, curcumin epoxide, zeulane, sucrose, valine, verrucoside, N-transferoyltyramine, isoflavone, aloe-emodin, isoglycyrrhizin, salvianolic acid A, emodin-8-glucoside, lotus palm glycoside, mannitol, ferulic acid, anthraquinone, pentadecanoic acid, rhein, kaempferol, and citric acid.

[0028] The steps in network pharmacology research are as follows:

[0029] ① Prediction of potential targets for liver fibrosis in the treatment of WD

[0030] Twenty-one hepatic precursor components with liver-targeting distribution characteristics were selected as the subjects of network pharmacology research. Target prediction was performed on these 21 precursor components using the SwissTargetPrediction database, with the species set as Homosapiens, resulting in a total of 348 potential drug targets.

[0031] Using Wilson disease, hepatolenticular degeneration, hepatic fibrosis, and liver fibrosis as keywords, the GeneCards database was searched, yielding a total of 35,674 targets. After deduplication, 16,429 potential disease-related targets were identified. By taking the intersection of drug targets and disease targets, 321 common targets were identified.

[0032] ② Construction of Protein-Protein Interaction (PPI) Network

[0033] We obtained 321 common targets, imported them into the STRING database, constructed a protein-protein interaction (PPI) network, set the species to Homo sapiens, and set the confidence threshold to medium (0.400); removed 6 targets that had no interaction relationship, and retained 315 targets for subsequent analysis.

[0034] ③KEGG pathway enrichment analysis

[0035] To further reveal the signal transduction pathways involved by core targets, KEGG pathway enrichment analysis was performed on 315 interacting targets using the Metascape platform. With p<0.05 as the significance threshold, a minimum gene count of 3, and an enrichment factor > 1.5 as the screening criteria, a total of 186 pathways were obtained. The top 20 KEGG pathways with the most significant enrichment were selected for further analysis based on a comprehensive ranking of the number of enriched genes (Count) and the significance level (-Log10(P)).

[0036] ④ Screening of core targets

[0037] Existing literature has confirmed that the cAMP pathway, which ranks highly in KEGG enrichment results, is associated with the pathogenesis of liver fibrosis. Forty-three targets from the cAMP pathway were imported into the STRING database to construct a PPI network. The PPI network data was then imported into Cytoscape 3.10.4 software for visualization and topology analysis. Using the Network Analyzer tool, a comprehensive screening criterion of degree, betweenness centrality, and closeness centrality was calculated for network nodes. The top ten targets based on these three criteria were selected, ultimately identifying five core targets: Grin2b, Drd2, Adrb2, Camk2a, and Cacna1c.

[0038] ⑤ “Component-Core Target” Mapping and Core Prototype Component Identification

[0039] To clarify the material basis for the key pharmacological effects of the 21 hepatic prototype components, a two-way mapping relationship table of "hepatic component-core target" was constructed, which mapped 5 core targets to the 21 hepatic prototype components. By counting and sorting the number of core targets connected to each component, 20 hepatic prototype components were finally selected as the subjects of subsequent molecular docking research.

[0040] The molecular docking verification steps are as follows:

[0041] To verify the reliability of the network pharmacology prediction results, molecular docking verification was performed on the 20 core components and 5 core targets selected. The crystal structures of the corresponding target proteins were downloaded from the RCSB PDB database, and the 3D molecular structures of the core components were downloaded from the PubChem database. Molecular docking simulation was performed using CB-DOCK2. Finally, 17 binding energies less than -6 kcal / mol were selected as quality markers for the treatment of WD liver fibrosis with Hesperidin: catechin, tetrahydroberberine, curcumin epoxide, zeulane, sucrose, valine, verrucoside, N-transferoyltyramine, isoflavone, aloe-emodin, isoglycyrrhizin, salvianolic acid A, emodin-8-glucoside, sennaoside, ferulic acid, anthraquinone, and kaempferol.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] 1. This invention, for the first time, combines a liver tissue pharmacological method based on UHPLC-Orbitrap-MS high-resolution mass spectrometry with network pharmacology and molecular docking technology for the systematic analysis of hepatic active ingredients in the treatment of WD liver fibrosis with Gandouling. Orbitrap-MS technology boasts ultra-high resolution (≥70,000 FWHM), high quality precision (≤3 ppm), and a wide dynamic range, enabling more accurate and sensitive identification of trace amounts of the original components of traditional Chinese medicine compound in liver tissue. Furthermore, network pharmacology and molecular docking technology are used for active ingredient screening. Compared to traditional serum pharmacology, this method directly targets the target organ while excluding interfering components from the targeted distribution, more accurately reflecting the drug exposure level of active ingredients in fibrotic liver. This provides clear candidate compounds for subsequent quality marker confirmation and pharmacodynamic mechanism studies.

[0044] 2. This invention provides a scientific experimental basis for elucidating the pharmacodynamic material basis of hepatoretin for treating WD liver fibrosis, optimizing quality control standards, and developing new drugs, and provides further reference for clinical drug use and new drug development. Attached Figure Description

[0045] Figures 1-21These are mirror images of catechin, tetrahydroberberine, zeylanone, curcumin epoxide, sucrose, valine, verrucoside isoflavone, N-transferoyltyramine, isoflavonic acid, aloe-emodin, isoglycyrrhizin, salvianolic acid A, emodin-8-glucoside, lotus palm glycoside, mannitol, ferulic acid, anthraquinone, pentadecanoic acid, rhein, kaempferol, and citric acid.

[0046] Figure 22 Total ion chromatogram (TIC peak chromatogram) for screening differentially metabolites of liver-derived ...

[0047] Figure 23 Total ion chromatogram (TIC peak chromatogram) for screening differentially metabolites of liver-derived ...

[0048] Figure 24 This represents the intersection of components and disease targets.

[0049] Figure 25 A summary of KEGG pathways.

[0050] Figure 26 The target point is the intersection of degree, betweenness centrality, and closeness centrality.

[0051] Figure 27 The diagram shows the PPI network of 43 pathway targets.

[0052] Figure 28 , 29 Figures 30, 31, and 32 show the molecular docking diagrams of the five core targets and some core components, respectively. Detailed Implementation

[0053] This invention proposes a method for determining the quality biomarkers of hepatol in treating WD liver fibrosis based on tracking the components entering the liver. First, the in vitro chemical components of hepatol and the active components in the liver tissue of WD model rats are detected by UHPLC-Orbitrap-MS, while the liver tissue of WD model rats is detected as a blank control. Based on "retention time (RT), peak area difference fold (FC) > 10 (drug group VS model group) or no detection in the model group", the original components entering the liver are screened as potential quality biomarkers of hepatol. Then, through network pharmacology and molecular docking technology, the quality biomarkers of hepatol in treating WD liver fibrosis are finally determined.

[0054] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings.

[0055] Example 1

[0056] In vitro chemical characterization of liver-derived protein

[0057] ① Preparation of the test solution

[0058] Take the in-house preparation of Hesaooling tablets (batch number: 20250728) from the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, grind it and pass it through a No. 60 sieve. Take 0.5 g of Hesaooling powder, place it in a stoppered conical flask, accurately add 50 mL of methanol, seal tightly, weigh, and sonicate (power 250 W, frequency 40 kHz) for 30 min. Remove, cool, weigh again, replenish the lost weight with methanol, shake well, filter, and take the filtrate to obtain the product.

[0059] ②Chromatographic conditions

[0060] A Vanquish ultra-high performance liquid chromatograph was used, with a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 μm, Waters, USA). Phase A of the liquid chromatography was aqueous, containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and phase B was acetonitrile. The sample pan temperature was 4 ℃. The injection volume was 2 μL, the column temperature was 30 ℃, and the injection flow rate was 0.50 mL / min.

[0061] A gradient elution program was used: from the initial time to 0.25 min, mobile phase B was maintained at 95%; from 0.25 min to 3.50 min, mobile phase B was gradually reduced from 95% to 65%; from 3.50 min to 4.00 min, mobile phase B was gradually reduced from 65% to 40%; from 4.00 min to 4.50 min, mobile phase B was maintained at 40%; from 4.50 min to 4.55 min, mobile phase B was gradually increased from 40% to 95%; and from 4.55 min to 6.00 min, mobile phase B was maintained at 95%.

[0062] ③Mass spectrometry conditions

[0063] The Orbitrap Exploris 120 mass spectrometer was used with a sheath gas flow rate of 50 Arb, an auxiliary gas flow rate of 15 Arb, a capillary temperature of 320 °C, a full scan MS resolution of 60,000, an MS / MS resolution of 15,000, collision energies (SNCE) of 20 / 30 / 40, and spray voltages of 3.8 kV for positive ions and -3.4 kV for negative ions.

[0064] ④Component identification

[0065] The raw data was converted to mzXML format using ProteoWizard, and then subjected to baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. Compound identification was based on precise mass number, secondary fragments, and isotopic distribution, using R packages for qualitative analysis and BiotreeDB (V3.0) as the database.

[0066] ⑤ Identification results of in vitro chemical components of liverwort

[0067] A total of 238 components were detected, including 14 amino acids (such as valine, D-proline, D-arginine, etc.), 103 phenylpropanoids (such as salvianolic acid A, catechins, isoflavonic acid, etc.), 25 alkaloids (such as tetrahydroberberine, norcodonine, nicotinamide, etc.), 44 terpenoids (such as zeulane, curcumin epoxide, isocurcumol, etc.), 13 lipids (such as pentadecanoic acid, palmitic acid, stearic acid, etc.), 6 anthraquinones (such as aloe-emodin, emodin, emodin-8-glucoside, etc.), and 33 other components.

[0068] Example 2

[0069] Identification of the original components of liver tissue in rats with WD model

[0070] ① Construction and administration of WD model rats:

[0071] This protocol was approved by the Animal Experiment Ethics Committee of Anhui University of Traditional Chinese Medicine (AHUCM-rats-2025049). SPF-grade SD rats, weighing 200±50 g, were purchased from Zhejiang Vital River Laboratory Animal Technology Co., Ltd. (License No.: SCXK(Zhejiang)2024-0001). The rats were housed at 18-22 ℃, relative humidity 40-60%, and a 12-hour light-dark cycle, with free access to food and water for one week for acclimatization.

[0072] Both the model group and the treatment group received 0.185% copper sulfate aqueous solution and 1 g / kg of copper sulfate diet (1 kg of copper sulfate diet contains 1 g of copper) daily for 12 weeks to replicate the WD model rats. Starting from the seventh week, liver bean tablets (batch number: 20250728) were ground into fine powder and a suspension was prepared with distilled water. The treatment group rats were given 2.916 g / kg / d of the suspension by gavage (10 mL per kg rat per day), and the model group rats were given the same dose of distilled water by gavage for 6 weeks.

[0073] ② Collection and processing of rat liver tissue samples:

[0074] Two hours after the last administration, rats were anesthetized intraperitoneally, and the whole liver was immediately removed. The surface blood was rinsed with physiological saline, the moisture was absorbed with filter paper, and the liver was stored at -80℃ for later use. At the same time, rats in the model group were removed as blank controls.

[0075] The liver sample (25±1mg) was placed on clean dry ice and ground with liquid nitrogen at 60 Hz for 30 s. It was then centrifuged twice at 12000 rpm at 4 ℃ and dried under low temperature. The dried sample was reconstituted with 500 μL of extraction buffer (methanol:acetonitrile:water = 2:2:1). The supernatant was collected into a vial for analysis.

[0076] ③Chromatographic conditions

[0077] Same chromatographic conditions as in Example 1.

[0078] ④ Mass spectrometry conditions

[0079] Same mass spectrometry conditions as in Example 1.

[0080] ⑤ Identification of in vitro components of liver tissue from WD model rats

[0081] Liver tissue samples from the model group and the drug-treated group were analyzed under the above-mentioned detection conditions. Raw data were converted to mzXML format using ProteoWizard. Baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization were performed on the liver-derived compound, model group, and drug-treated group samples. Compound identification was based on precise mass number, secondary fragmentation, and isotope distribution. Qualitative analysis was performed using the R package and the BiotreeDB (V3.0) database. Based on the criteria of simultaneous detection in the drug-treated group and the liver-derived compound group with consistent retention times (RT), a peak area difference fold (FC) > 10 between the drug-treated group and the model group, or no detection in the model group, 21 liver-derived compound precursor components were screened for entry into liver tissue. These components are: catechins (derived from rhubarb and chicken blood vine), tetrahydroberberine (derived from Coptis chinensis), curcumin epoxide (derived from Curcuma zedoaria), zebufenone (derived from Curcuma zedoaria), sucrose (derived from rhubarb), valine (derived from Salvia miltiorrhiza), and verbascoside. Ketones (derived from *Spatholobus suberectus*), N-trans-feruloyltyramine (derived from *Coptis chinensis* and *Spatholobus suberectus*), isoflavonic acid (derived from *Salvia miltiorrhiza*), aloe-emodin (derived from rhubarb, *Spatholobus suberectus*, and *Salvia miltiorrhiza*), isoliquiritigenin (derived from *Spatholobus suberectus*), salvianolic acid A (derived from *Salvia miltiorrhiza*), emodin-8-glucoside (derived from rhubarb), lotus palmitoside (derived from rhubarb), mannitol (derived from *Coptis chinensis*), ferulic acid (derived from *Coptis chinensis* and *Salvia miltiorrhiza*), anthraquinone (derived from rhubarb), pentadecanoic acid (derived from turmeric), emodin (derived from rhubarb, *Coptis chinensis*, and *Salvia miltiorrhiza*), kaempferol (derived from *Salvia miltiorrhiza*), citric acid (derived from rhubarb). Mirror images of each substance are shown below. Figure 1-21 As shown.

[0082] The total ion chromatogram (TIC peak chromatogram) of liver-injecting components screened by UHPLC-Orbitrap-MS technology in positive ion mode is shown below. Figure 22 As shown, by comparing the drug administration group and the model group, based on the criteria that "the drug administration group has a chromatographic peak corresponding to hescarnitine and the difference multiple (FC value > 10), or the model group does not detect it", a total of 8 prototype components containing hescarnitine were screened out and marked in the figure, and listed in Table 1.

[0083] Total ion chromatogram (TIC peak chromatogram) of liver-injecting components screened by UHPLC-Orbitrap-MS technology in negative ion mode. Figure 23 As shown, by comparing the drug administration group and the model group, based on the criteria that "the drug administration group has a chromatographic peak corresponding to hescarnitine, and the model group does not detect it, or the difference multiple (FC value > 10)", a total of 13 prototype components containing hescarnitine components were screened out and marked in the figure, and listed in Table 2.

[0084] Table 1. Screening of liver-entering components of Hesperidin using UHPLC-Orbitrap-MS in positive ion mode

[0085] 1 sucrose <![CDATA[C 12 H 22 O 11 ]]> 307.1020266 <![CDATA[[M+H-2H2O] + ]]> rhubarb 2 Valine <![CDATA[C 15 H 11 NO2]]> 235.1650001 <![CDATA[[2M+H] + ]]> Salvia miltiorrhiza 3 (-)-Catechins <![CDATA[C 13 H 12 O4]]> 291.0861012 <![CDATA[[M+H] + ]]> Rhubarb, Chicken Blood Vine 4 N-transferoyltyramine <![CDATA[C 18 H 19 NO4]]> 336.1230545 <![CDATA[[M+Na] + ]]> Coptis chinensis, Spatholobus suberectus 5 Versicolor isoflavones <![CDATA[C 16 H 12 O5]]> 285.0756413 <![CDATA[[M+H] + ]]> Chicken Blood Vine 6 Zephyranone <![CDATA[C 15 H 18 O3]]> 246.1256987 <![CDATA[[M] + ]]> Curcuma zedoaria 7 Tetrahydroberberine <![CDATA[C 19 H 17 NO4]]> 370.1320876 <![CDATA[[M+HCOO+2H] + ]]> Coptis chinensis 8 Curcuma zeolite <![CDATA[C 15 H 18 O3]]> 293.1362786 <![CDATA[[M+HCOO+2H] + ]]> Curcuma zedoaria

[0086] Table 2. Screening of liver-entering components of Hesao-Ling using UHPLC-Orbitrap-MS in negative ion mode

[0087] 1 Citric acid <![CDATA[C6H8O7]]> 383.0497686 <![CDATA[[2M-H] - ]]> rhubarb 2 Mannitol <![CDATA[C6H 14 O6]]> 203.0559915 <![CDATA[[M+Na-2H] - ]]> Coptis chinensis 3 Isoflavonic acid <![CDATA[C 10 H 10 O4]]> 193.0506831 <![CDATA[[M-H] - ]]> Salvia miltiorrhiza 4 ferulic acid <![CDATA[C 10 H 10 O4]]> 193.0506584 <![CDATA[[M-H] - ]]> Coptis chinensis, Salvia miltiorrhiza 5 Lotus palm glycoside <![CDATA[C 23 H 26 O 11 ]]> 515.0965916 <![CDATA[[M+K-2H] - ]]> rhubarb 6 Tanshinone A <![CDATA[C 26 H 22 O 10 ]]> 493.1111798 <![CDATA[[M-H] - ]]> Salvia miltiorrhiza 7 emodin-8-glucoside <![CDATA[C 21 H 20 O 10 ]]> 430.0863655 <![CDATA[[M-2H] 2- ]]> rhubarb 8 Aloe-emodin <![CDATA[C 15 H 10 O5]]> 269.0456259 <![CDATA[[M-H] - ]]> Rhubarb, Spatholobus suberectus, Salvia miltiorrhiza 9 Kaempferol <![CDATA[C 15 H 10 O6]]> 285.0406273 <![CDATA[[M-H] - ]]> Salvia miltiorrhiza 10 Isoliquiritin <![CDATA[C 15 H 12 O4]]> 275.0925284 <![CDATA[[M-H2O-H] - ]]> Chicken Blood Vine 11 Dananthraquinone <![CDATA[C 14 H8O4]]> 239.0349272 <![CDATA[[M-H] - ]]> rhubarb 12 rhein <![CDATA[C 15 H 10 O4]]> 253.0507369 <![CDATA[[M-H] - ]]> Rhubarb, Coptis chinensis, Salvia miltiorrhiza 13 Pentadecanoic acid <![CDATA[C 15 H 30 O2]]> 241.2172933 <![CDATA[[M-H] - ]]> turmeric

[0088] Example 3

[0089] Network pharmacology research

[0090] ① Prediction of potential targets for liver fibrosis in the treatment of WD

[0091] Twenty-one hepatic precursor components with liver-targeting distribution characteristics were selected as subjects for network pharmacology research. To explore their potential mechanisms of action, target prediction was performed on these 21 precursor components using the SwissTargetPrediction database, with the species set as Homo sapiens. Targets with high probability were screened, resulting in a total of 348 potential drug targets.

[0092] Using Wilson disease, hepatolenticular degeneration, hepatic fibrosis, and liver fibrosis as keywords, a search of the GeneCards database yielded 35,674 targets. After deduplication, 16,429 potential disease-related targets were identified. Taking the intersection of drug targets and disease targets, 321 common targets were identified (e.g., ...). Figure 24 (As shown in the image). This suggests that the liver-entering components of Liver-Galloxan may synergistically regulate disease progression through multiple targets.

[0093] ②Construction of Protein-Protein Interaction (PPI) Network

[0094] We obtained 321 common targets, imported them into the STRING database, constructed a protein-protein interaction (PPI) network, set the species to Homo sapiens, and set the confidence threshold to medium (0.400). We removed 6 targets with no interaction relationship and retained 315 targets for subsequent analysis.

[0095] ③KEGG pathway enrichment analysis

[0096] To further elucidate the signal transduction pathways involved by core targets, KEGG pathway enrichment analysis was performed on 315 interacting component-disease targets using the Metascape platform. Using p < 0.05 as the significance threshold, a minimum gene count of 3, and an enrichment factor > 1.5 as the screening criteria, a total of 186 pathways were obtained. The pathways were ranked according to the number of enriched genes (Count) and significance level (-Log10(P)), and the top 20 most significantly enriched KEGG pathways were selected for further analysis. Among them, the cAMP signaling pathway was associated with liver fibrosis, such as... Figure 25 As shown in Table 3.

[0097] Table 3. Top 20 KEGG pathway enrichment analyses

[0098] 1 Neuroactive ligand-receptor interaction -99.59145362 23.8687631 2 Neuroactive ligand signaling -93.89634982 35.69704076 3 Calcium signaling pathway -54.30989928 20.68818898 4 cAMP signaling pathway -40.88824644 18.51494592 5 Serotonergic synapse -31.43212401 24.53932367 6 Hormone signaling -29.18218663 15.10766109 7 Inflammatory mediator regulation of TRP channels -25.61828478 23.59057239 8 Gap junction -13.68265244 16.40074906 9 Rap1 signaling pathway -9.113622481 7.344234801 10 Dopaminergic synapse -25.09480938 19.02322473 11 Amphetamine addiction -16.90775429 22.56489533 12 Cocaine addiction -14.67841593 25.81723356 13 Alcoholism -8.818420867 7.642233857 14 Parkinson disease -6.847886383 5.446517413 15 Glutamatergic synapse -23.82558293 20.13333333 16 Pathways of neurodegeneration - multiple diseases -20.77790663 7.501064815 17 Alzheimer disease -15.05671932 7.022451317 18 Nicotine addiction -12.56504966 26.10785908 19 Circadian entrainment -11.8455246 14.04490263 20 Long-term potentiation -11.39008777 17.42885572

[0099] ④ Screening of core targets

[0100] Forty-three targets from the cAMP pathway were imported into the STRING database to construct a PPI network. The PPI network data was then imported into Cytoscape 3.10.4 software for visualization and topology analysis. Using the Network Analyzer tool, a comprehensive selection criterion of degree, betweenness centrality, and closeness centrality was used to calculate network node values. The top ten targets based on these three criteria were selected, ultimately identifying five core targets: Grin2b, Drd2, Adrb2, Camk2a, and Cacna1c. Figure 26 , 27 As shown.

[0101] ⑤ “Component-Core Target” Mapping and Core Prototype Component Identification

[0102] To clarify the material basis for the key pharmacological effects of the 21 hepatic prototype components, a two-way mapping table of "hepatic component-core target" was constructed, mapping 5 core targets to the 21 hepatic prototype components. By counting and sorting the number of core targets connected to each component, 20 components were finally screened out. The components obtained may be the main material basis for the therapeutic effect of Hepatol on WD liver fibrosis, and are also the key research objects for subsequent molecular docking.

[0103] Example 4

[0104] Molecular docking verification

[0105] To verify the reliability of the network pharmacology prediction results, molecular docking verification was performed on the 20 screened core components and 5 core targets. Crystal structures of the corresponding target proteins were downloaded from the RCSB PDB database and saved as PDB files. Ligand molecule preparation: 3D molecular structures (SDF format) of the core components were downloaded from the PubChem database. Docking process and result analysis: Molecular docking simulation was performed using CB-DOCK2, and the docking results are shown in Table 4. Figures 28-32 Visualize the binding pattern between the core target with the lowest binding energy and the active ingredient of hesperidin.

[0106] Molecular docking results showed that the binding energies between the 17 core components of Hedlow and their core targets were all less than -6 kcal / mol. These components were: catechin, tetrahydroberberine, curcumin epoxide, zeulane, sucrose, valine, verbascoside, N-transferuloyltyramine, isoflavonic acid, aloe-emodin, isoglycyrrhizin, salvianolic acid A, emodin-8-glucoside, sennaoside, ferulic acid, anthraquinone, and kaempferol. This indicates that these 17 components are the key prototype components of Hedlow for treating WD liver fibrosis and were ultimately identified as quality markers for Hedlow in treating WD liver fibrosis.

[0107] Table 4. Information on the core hepatic precursor components and molecular docking of Liver-Activated Tonifying Agent

[0108] 1 Isoflavonic acid 2 Camk2a -6.4 Drd2 -6.5 2 Tanshinone A 3 Grin2b -9.2 Drd2 -6 Adrb2 -6.7 3 Aloe-emodin 4 Grin2b -8.5 Camk2a -7.7 Drd2 -8.1 Adrb2 -10.7 4 ferulic acid 3 Camk2a -6.2 Drd2 -7.2 Grin2b -7.2 5 emodin-8-glucoside 3 Drd2 -9 Grin2b -10.5 Camk2a -8.7 6 Dananthraquinone 2 Camk2a -7.8 Drd2 -7.9 7 rhein 2 Camk2a -8.1 Drd2 -8.2 8 Kaempferol 3 Drd2 -8.7 Adrb2 -9.4 Camk2a -8.3 9 Lotus palm glycoside 1 Drd2 -8.6 10 Isoliquiritin 2 Camk2a -7.5 Drd2 -8.4 11 (-)-Catechins 3 Drd2 -7.6 Camk2a -7.9 Adrb2 -8.6 12 Tetrahydroberberine 2 Drd2 -9.4 Cacna1c -7.6 13 Curcuma zeolite 1 Drd2 -7.4 14 Zephyranone 1 Drd2 -7.6 15 sucrose 1 Grin2b -6.4 16 Versicolor isoflavones 3 Camk2a -8.1 Drd2 -8.3 Grin2b -9.1 17 N-transferulotyramine 2 Grin2b -9.2 Drd2 -8.5

Claims

1. A method for determining quality biomarkers for liver fibrosis in WD treatment based on tracking liver-entering components, characterized in that, First, the in vitro chemical components of Hedonicol and the original components in the liver tissue of WD model rats were detected by UHPLC-Orbitrap-MS. At the same time, the liver tissue of WD model rats was detected as a blank control. Based on "retention time (RT), peak area difference fold FC value >10 (drug group VS model group) or no detection in the model group", the original components in the liver were screened as potential quality markers of Hedonicol. Then, through network pharmacology and molecular docking technology, the quality markers of Hedonicol in the treatment of WD liver fibrosis were finally determined.

2. The method for determining the quality markers of liver fibrosis in WD treatment based on tracking liver-entering components as described in claim 1, characterized in that, The steps for characterizing the in vitro chemical components of liver-derived protein are as follows: ① Preparation of the test solution Grind liver bean tablets and pass them through a 60-mesh sieve. Take 0.5 g of liver bean powder and place it in a stoppered conical flask. Accurately add 50 mL of methanol, seal tightly, weigh, sonicate for 30 min, remove, cool, weigh again, replenish the lost weight with methanol, shake well, filter, and collect the filtrate to obtain the final product. ②Chromatographic conditions A Vanquish ultra-high performance liquid chromatograph was used, with a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 μm, Waters, USA). Phase A of the liquid chromatography was aqueous, containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and phase B was acetonitrile. The sample pan temperature was 4 ℃; the injection volume was 2 μL; the column temperature was 30 ℃; and the injection flow rate was 0.50 mL / min. A gradient elution program was used: from the initial time to 0.25 min, mobile phase B was maintained at 95%; from 0.25 min to 3.50 min, mobile phase B was gradually reduced from 95% to 65%; from 3.50 min to 4.00 min, mobile phase B was gradually reduced from 65% to 40%; from 4.00 min to 4.50 min, mobile phase B was maintained at 40%; from 4.50 min to 4.55 min, mobile phase B was gradually increased from 40% to 95%; and from 4.55 min to 6.00 min, mobile phase B was maintained at 95%. ③Mass spectrometry conditions Orbitrap Exploris 120 mass spectrometer, sheath flow rate 50 Arb, auxiliary flow rate 15 Arb, capillary temperature 320℃, full scan MS resolution 60000, MS / MS resolution 15000, collision energy SNCE 20 / 30 / 40, spray voltage 3.8 kV for positive ions and -3.4 kV for negative ions; ④Component identification The raw data was converted to mzXML format using ProteoWizard, and then subjected to baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. The identification of compounds was based on precise mass number, secondary fragments, and isotopic distribution, and qualitative analysis was performed using R packages and the database was BiotreeDB (V3.0). ⑤ Identification results of in vitro chemical components of liverwort A total of 238 components were detected, including 14 amino acids, 103 phenylpropanoids, 25 alkaloids, 44 terpenoids, 13 lipids, 6 anthraquinones, and 33 other components.

3. The method for determining the quality markers of liver fibrosis in WD treatment based on tracking liver-entering components as described in claim 2, characterized in that, The steps for identifying the precursor components of hesperidin in liver tissue of rats with WD model are as follows: ① Construction and administration of WD model rats After one week of acclimatization, SD rats in both the model group and the treatment group were given 0.185% copper sulfate aqueous solution and 1 g / kg copper sulfate diet daily for 12 weeks to replicate the WD model rats. Starting from the seventh week, liver bean tablets were ground into fine powder and a suspension was prepared with distilled water. Rats in the treatment group were given 2.916 g / kg / d suspension by gavage, and rats in the model group were given 10 mL / kg / d distilled water by gavage for 6 weeks. ② Collection and processing of rat liver tissue samples: Two hours after the last administration, rats were anesthetized intraperitoneally, and the whole liver was immediately removed. The surface blood was rinsed with physiological saline, the moisture was absorbed with filter paper, and the liver was stored at -80℃ for later use. At the same time, rats in the model group were removed as blank controls. The liver sample (25±1 mg) was placed on clean dry ice and ground with liquid nitrogen at 60 Hz for 30 s. It was then centrifuged twice at 12000 rpm at 4 ℃ and dried under low temperature. The dried sample was reconstituted with 500 μL of extraction buffer (methanol:acetonitrile:water = 2:2:1). The supernatant was collected into a vial for analysis. ③ Identification of the original components of hepatinin in liver tissue of rats with WD model Liver tissue samples from the model group and the drug-treated group were analyzed under the above-mentioned detection conditions. Raw data were converted to mzXML format using ProteoWizard. Baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization were performed on the liver-derived compound, model group, and drug-treated group samples. Compound identification was based on precise mass number, secondary fragmentation, and isotope distribution, using R packages for qualitative analysis. The database used was BiotreeDB (V3.0). The identification was based on the simultaneous detection of compounds in the drug-treated group and the liver-derived compound group. "If the retention time (RT) is consistent, the peak area difference fold (FC value) between the treatment group and the model group is >10, or the model group is not detected", 21 liver-derived components that can enter liver tissue were screened out, namely: catechin, tetrahydroberberine, curcumin epoxide, zeulane, sucrose, valine, verrucoside, N-transferoyltyramine, isoflavone, aloe-emodin, isoglycyrrhizin, salvianolic acid A, emodin-8-glucoside, lotus palm glycoside, mannitol, ferulic acid, anthraquinone, pentadecanoic acid, rhein, kaempferol, and citric acid.

4. The method for determining the quality markers of liver fibrosis in WD treatment based on tracking liver-entering components as described in claim 3, characterized in that, The steps in network pharmacology research are as follows: ① Prediction of potential targets for liver fibrosis in the treatment of WD Twenty-one hepatic precursor components with liver-targeting distribution characteristics were selected as the subjects of network pharmacology research. Target prediction was performed on these 21 precursor components using the SwissTargetPrediction database, with the species set as Homosapiens, resulting in a total of 348 potential drug targets. Using Wilson disease, hepatolenticular degeneration, hepatic fibrosis, and liver fibrosis as keywords, the GeneCards database was searched, yielding a total of 35,674 targets. After deduplication, 16,429 potential disease-related targets were obtained. By taking the intersection of drug targets and disease targets, 321 common targets were identified. ② Construction of Protein-Protein Interaction (PPI) Network We obtained 321 common targets, imported them into the STRING database, constructed a protein-protein interaction (PPI) network, set the species to Homo sapiens, and set the confidence threshold to medium (0.400); removed 6 targets that had no interaction relationship, and retained 315 targets for subsequent analysis; ③KEGG pathway enrichment analysis To further elucidate the signal transduction pathways involved by core targets, KEGG pathway enrichment analysis was performed on 315 interacting targets using the Metascape platform. Using p < 0.05 as the significance threshold, a minimum gene count of 3, and an enrichment factor > 1.5 as the screening criteria, a total of 186 pathways were obtained. The top 20 KEGG pathways with the most significant enrichment were selected for further analysis based on a comprehensive ranking of enriched gene count and significance level (-Log10(P)). ④ Screening of core targets Existing literature has confirmed that the cAMP pathway, which ranks highly in KEGG enrichment results, is associated with the pathogenesis of liver fibrosis. Forty-three targets from the cAMP pathway were imported into the STRING database to construct a PPI network. The PPI network data was then imported into Cytoscape 3.10.4 software for visualization and topology analysis. Using the Network Analyzer tool, the degree, betweenness centrality, and closeness centrality of network nodes were calculated as a comprehensive screening criterion. The top ten targets based on these three criteria were selected, ultimately identifying five core targets: Grin2b, Drd2, Adrb2, Camk2a, and Cacna1c. ⑤ "Component-Core Target" Mapping and Core Prototype Component Identification To clarify the material basis for the key pharmacological effects of the 21 hepatic prototype components, a two-way mapping relationship table of "hepatic component-core target" was constructed, which back-mapped 5 core targets to 21 hepatic prototype components. By counting and sorting the number of core targets connected to each component, 20 hepatic prototype components were finally selected as the subjects of subsequent molecular docking research.

5. The method for determining the quality markers of liver fibrosis in WD treatment based on tracking liver-entering components as described in claim 4, characterized in that, The molecular docking verification steps are as follows: To verify the reliability of the network pharmacology prediction results, molecular docking verification was performed on the 20 core components and 5 core targets selected. The crystal structures of the corresponding target proteins were downloaded from the RCSB PDB database, and the 3D molecular structures of the core components were downloaded from the PubChem database. Molecular docking simulation was performed using CB-DOCK2. Finally, 17 binding energies less than -6 kcal / mol were selected as quality markers for the treatment of WD liver fibrosis with Hesperidin: catechin, tetrahydroberberine, curcumin epoxide, zeulane, sucrose, valine, verrucoside, N-transferoyltyramine, isoflavone, aloe-emodin, isoglycyrrhizin, salvianolic acid A, emodin-8-glucoside, sennaoside, ferulic acid, anthraquinone, and kaempferol.

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