Method for screening hypoglycemic core pharmacodynamic substance from brown coal source fulvic acid

Abstract

The invention relates to the technical field of screening of pharmacodynamic substances of natural products, and particularly discloses a method for screening a hypoglycemic core pharmacodynamic substance from brown coal source fulvic acid. The method integrates a double-layer progressive screening strategy and a double verification mechanism, and comprises the following steps: firstly, carrying out double screening through chemical analysis and network pharmacology in combination with topology parameters and literature evidences so as to lock a core pharmacodynamic compound; performing quantitative screening based on the target interaction network to determine a core target; finally, verification is completed through molecular docking and in-vitro alpha-glucosidase inhibition experiments. According to the method, nine core pharmacodynamic compounds such as gallic acid and protocatechuic acid are successfully screened, and a scientific basis and a technical path are provided for clarification of a multi-target synergistic effect mechanism and development of hypoglycemic functional products.

Description

Technical Field

[0001] This invention relates to the field of natural product drug development, specifically a method for systematically screening the core hypoglycemic active ingredients from a complex, multi-component system of fulvic acid derived from lignite. This method is based on a two-layer progressive screening strategy and employs a dual validation mechanism to ensure the accuracy and reliability of the screening results. Background Technology

[0002] In the global public health field, the prevalence of type 2 diabetes mellitus (T2DM) has become a serious challenge. According to data released by the International Diabetes Federation, the number of adults with diabetes worldwide reached 537 million in 2021 and is expected to continue to grow in the future. The pathophysiological basis of T2DM is a chronic hyperglycemic state caused by the combined effects of insulin resistance and progressive decline in pancreatic β-cell function. This persistent hyperglycemic state can trigger a cascade of serious microvascular and macrovascular complications, such as diabetic nephropathy, retinopathy, cardiovascular disease, and neuropathy. These complications are the main causes of increased disability and mortality rates among patients, as well as a greater social burden on healthcare.

[0003] Currently, the clinical management of type 2 diabetes mellitus (T2DM) mainly relies on chemically synthesized drugs, including biguanides (represented by metformin), sulfonylureas, meglitinides, alpha-glucosidase inhibitors, thiazolidinediones, SGLT2 inhibitors, and GLP-1 receptor agonists. These drugs constitute a relatively complete hypoglycemic treatment system. However, their clinical application is still accompanied by significant limitations. First, adverse drug reactions are common, such as the risk of hypoglycemia caused by sulfonylureas and the common gastrointestinal reactions of biguanides. Second, secondary failure is a common challenge in long-term treatment. More importantly, most of the above-mentioned drugs follow a single-target mode of action, making it difficult to effectively intervene in the complex pathological network of T2DM, which is composed of multiple metabolic pathway disorders. Therefore, the development of novel treatment strategies that can achieve multi-target synergistic regulation while possessing high safety, good tolerability, and cost-effectiveness has become a research frontier and urgent need in this field.

[0004] Against this backdrop, natural products, due to their diverse array of active ingredients, exhibit unique advantages as sources of multi-target therapeutic drugs. Fulvic acid, as the component with the smallest molecular weight, best water solubility, and highest bioactivity among humic acids, is gradually gaining widespread attention from researchers. It is a mixture of natural organic macromolecules that can be extracted from mineral sources such as lignite, weathered coal, and peat. Chemically, fulvic acid is a condensation system composed of aromatic rings, aliphatic chains, and various oxygen-containing functional groups such as carboxyl, phenolic hydroxyl, and carbonyl groups, linked by complex chemical bonds. Its molecular weight distribution is relatively wide, typically ranging from hundreds to thousands of Daltons.

[0005] Modern pharmacological studies have shown that fulvic acid possesses a variety of biological activities, including anti-inflammatory, antioxidant, gut microbiota regulation, and metabolic effects. Given that chronic low-grade inflammation, oxidative stress damage, and gut microbiota dysbiosis are recognized as key drivers of type 2 diabetes mellitus (T2DM), the multidimensional biological activities of fulvic acid make it an important candidate for potential hypoglycemic drugs. Some animal model studies have also preliminarily confirmed that exogenous administration of fulvic acid can improve glucose tolerance in experimental subjects and reduce their fasting blood glucose levels. However, despite its promising application prospects, the transformation of fulvic acid from a natural active substance into a clinical drug still faces a fundamental technical bottleneck: the pharmacodynamic material basis and precise molecular mechanism of its hypoglycemic activity have not yet been systematically elucidated.

[0006] The root of this bottleneck lies in the extreme chemical complexity of fulvic acid itself. It is not a single chemical entity, but a supramolecular system formed by the self-aggregation of hundreds or even thousands of structurally and similarly structured small organic molecules, such as phenolic acids, benzoquinones, fatty acids, amino acids, and small polysaccharides, through secondary bond forces such as hydrogen bonds and van der Waals forces. Faced with such a highly complex chemical matrix, the classic "separation-purification-structure identification-activity verification" research paradigm in natural product chemistry demonstrates significant inapplicability. Traditional activity-tracking separation methods typically involve extensive solvent extraction, multi-step column chromatography, and high-performance liquid chromatography (HPLC) preparation, a process that is time-consuming, costly, and has low recovery rates. More importantly, this research paradigm often disrupts the objectively existing synergistic relationships between the endogenous components of fulvic acid. Extensive research evidence shows that the overall efficacy of a natural mixture is often greater than the sum of the activities of its individual components. During forced separation, the component playing the primary role in efficacy is physically separated from the components playing an auxiliary or moderating role, resulting in a significant reduction or even complete loss of activity in the final monomeric compound. The direct consequence of this research status quo is that related fulvic acid products on the market can only use the crude indicator of "total humic acid content" as a quality control standard, and cannot establish a scientific quality evaluation system based on core active ingredients, thus seriously restricting their progress towards standardized drugs or high-end functional foods.

[0007] To address these challenges, researchers have attempted to incorporate modern analytical techniques and bioinformatics methods. High-throughput analytical techniques such as ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) can provide in-depth analysis of the chemical composition of fulvic acid, identifying hundreds or even thousands of chromatographic peaks and constructing its chemical composition profile. Meanwhile, network pharmacology, as a systems biology tool, can predict the potential biological activities of fulvic acid by constructing "component-target-pathway" interaction networks. Despite its promising prospects, existing technological approaches still reveal inherent limitations in integrated applications, which can be summarized in the following three aspects.

[0008] First, there is a mechanistic disconnect between chemical analysis and functional prediction. While techniques like UPLC-MS / MS can accurately provide chemical information about "what's in the system," they cannot answer the core question of "which component is effective." Relying solely on network pharmacology for prediction is highly dependent on the completeness and accuracy of existing databases, resulting in a high false positive rate. Conversely, relying solely on existing literature for screening may miss novel active molecules not included in the database. The massive amounts of data generated by chemical analysis have failed to effectively drive and optimize functional prediction models; there is a lack of a preliminary screening and validation bridge based on experimental data between the two.

[0009] Second, the screening strategy suffers from a "flattened" approach and a lack of hierarchical focus. Existing methods often employ a "one-off" or "indiscriminate" parallel screening strategy, attempting to evaluate all components identified by chemical analysis equally. When applied to complex systems like fulvic acid, this flattened approach easily leads to information overload, making it difficult to effectively distinguish key signals from background noise. It fails to establish a hierarchical, progressive screening system from "all components" to "candidate component groups," and then from "all potential targets" to "core functional targets," resulting in dispersed research resources and low screening efficiency.

[0010] Third, the "isolation" of technical steps in the research process. In current research practice, key steps such as chemical composition analysis, computer target prediction, molecular docking simulation, and in vitro / in vivo pharmacodynamics verification are often conducted as independent modules, lacking systematic integration. The component lists provided by chemists, the prediction reports given by bioinformaticians, and the verification work of experimental biologists fail to form a complete chain of evidence—a mutually driving, progressively advancing, and closed-loop correction of "prediction-computation-experiment"—that is not achieved. This working model results in insufficient internal logical coherence throughout the research process, thus limiting the persuasiveness and credibility of the final conclusions.

[0011] In summary, the core technical challenge that has long been addressed in this field is how to construct a systematic, precise, and efficient method to penetrate the complex system of fulvic acid, accurately identify and obtain the pharmacodynamic substances that exert the core hypoglycemic effect, and elucidate the molecular mechanism of their multi-target synergistic action. An ideal methodological system must organically integrate computational chemistry analysis and experimental biological verification; it must establish a two-tiered progressive screening strategy from macroscopic to microscopic and from overall to local levels; and it must construct a closed-loop evidence chain covering the entire process of "chemical analysis-virtual screening-experimental verification." Only in this way can we scientifically and reliably answer the key scientific question: "Which chemical components constitute the material basis for the hypoglycemic activity of fulvic acid, and through which key biological targets and signaling pathways do they achieve a synergistic hypoglycemic effect?" This will provide a solid theoretical foundation and key technical support for developing innovative fulvic acid-based hypoglycemic drugs with controllable quality, clear efficacy, and well-defined mechanisms. Summary of the Invention

[0012] In response to the technical bottleneck mentioned in the background art, which is that existing methods cannot systematically, efficiently and accurately screen out the core hypoglycemic active substances from the complex system of lignite-derived fulvic acid and elucidate its multi-target synergistic mechanism, this invention aims to provide a novel and systematic screening method to solve this problem.

[0013] To achieve the above objectives, this invention provides a method for screening core hypoglycemic agents from lignite-derived fulvic acid. The core concept of this method lies in the organic integration of three key elements: a "two-layer progressive screening strategy," a "dual verification mechanism," and a "closed-loop evidence chain." Its logical hierarchy is as follows: First, through a first-layer screening, the core active pharmaceutical compound is identified from the complete chemical composition of fulvic acid; then, through a second-layer screening, the potential targets of the core compound are further focused on the core target; finally, through multi-level experimental verification, the multi-target synergistic mechanism is systematically, efficiently, and accurately elucidated.

[0014] The method is implemented through the following steps: Step 1: Systematic analysis and identification of the chemical components of fulvic acid.

[0015] This step employs high-precision mass spectrometry to perform in-depth analysis of the chemical composition of humic acid from lignite sources.

[0016] The detailed operating procedure is as follows: Weigh 25 mg of fulvic acid sample and place it in a centrifuge tube containing two homogenization beads. Add 500 μL of an extraction buffer (methanol, acetonitrile, and water in a volume ratio of 2:2:1, pre-added with an isotope-labeled internal standard). After vortexing, homogenize the sample at 35 Hz for 4 minutes, followed by sonication in an ice-water bath for 5 minutes. Repeat this homogenization-sonication cycle three times. After extraction, incubate the sample at -40°C for 1 hour. Take 300 μL of the supernatant and filter it using a 0.22 μm filter plate at 6 psi for 180 seconds. Collect the filtrate for analysis.

[0017] Chromatographic analysis was performed using a Vanquish ultra-high performance liquid chromatograph equipped with a Waters ACQUITY UPLC BEHAmide column (2.1 mm × 50 mm, 1.7 μm). The sample pan temperature was maintained at 4 °C, and the injection volume was set to 2 μL.

[0018] Mass spectrometry was performed using an Orbitrap Exploris 120 mass spectrometer, controlled by Xcalibur software. Key ion source parameters were set as follows: sheath gas flow rate 50 Arb, auxiliary gas flow rate 15 Arb, and capillary temperature 320℃. The spray voltages in positive and negative ion modes were +3.8 kV and -3.4 kV, respectively. The primary mass spectrometry resolution was set to 60,000 eV, and the secondary mass spectrometry resolution was set to 15,000 eV. Collision energies were set in a stepped manner of 20 / 30 / 40 eV.

[0019] The raw data was converted to mzXML format using ProteoWizard software, and peak extraction and alignment were performed using R language packages. The processed data was then compared with a mass spectrometry database to identify compounds.

[0020] Step 2: Intersection analysis of target prediction and disease target.

[0021] The SMILES (Simplified Molecular Input Line Entry System) structural information of the compounds identified in Step 1 was obtained, and target prediction was performed using three independent databases: SwissTargetPrediction, SuperPred, and SEA (SimilarityEnsemble Approach). All prediction results were standardized using the UniProt database, and after merging and deduplication, a set of potential targets related to fulvic acid was obtained.

[0022] Meanwhile, using "Type 2 diabetes" and "T2DM" as keywords, a systematic search was conducted in four authoritative databases: DisGeNET (screening criteria: scoreGDA≥0.9 and scoreVDA≥0.9), DrugBank, GeneCards (screening criteria: Relevance score>35), and TTD to obtain relevant disease targets for type 2 diabetes. After merging and deduplicating, a set of disease targets was constructed.

[0023] The target set of fulvic acid components and the target set of diseases were imported into the Venny platform, and the intersection of the two was taken to obtain potential common targets for fulvic acid in the treatment of type 2 diabetes mellitus (T2DM). Subsequently, this common target was imported into the STRING database to construct a protein-protein interaction (PPI) network, with the species set to Homo sapiens (human) and the confidence level set to 0.9.

[0024] Step 3: Functional and pathway enrichment analysis of co-acting targets.

[0025] To further explore the biological functions and involved signaling pathways of the co-acting targets, enrichment analysis was performed in this step. The co-acting targets obtained in step two were imported into the DAVID database, with the species set as *Homo sapiens* and a significance threshold of P < 0.05. GO (Gene Ontology) functional enrichment analysis (covering three dimensions: biological process BP, molecular function MF, and cellular component CC) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis were conducted. The top 10 GO entries and top 20 KEGG pathways with the smallest P-values ​​were selected for data visualization.

[0026] Step 4: Dual screening of core pharmacodynamic compounds.

[0027] This step uses a dual screening process to identify the core pharmacodynamic compounds, ensuring the reliability and accuracy of the screening results.

[0028] The first screening step is quantitative screening using network topology: the common targets obtained in step two, the chemical components identified in step one, and their corresponding relationships are imported into Cytoscape software to construct a binary network of "fulvic acid-component-target". The degree value of each compound node in the network is calculated using the CytoNCA plugin and sorted in descending order of degree value. Compounds with the highest degree values ​​are selected as candidate core pharmacodynamic compounds.

[0029] The second screening step involves supporting literature evidence: a literature search is conducted on the above candidate compounds to determine their relevance to blood glucose control. The search strategy is: "Compound Name AND (Blood Glucose Control OR Diabetes OR Hypoglycemic OR Diabetes OR Antidiabetic)". Compounds with supporting evidence from animal experiments, in vitro experiments, studies demonstrating a clear mechanism of action, or relevant research reports are given priority.

[0030] Step 5: Quantitative screening of core targets.

[0031] Only compounds that meet both of the above screening criteria are ultimately identified as core pharmacodynamic compounds.

[0032] For the core pharmacodynamic compounds screened in step four, this step aims to further accurately identify their core targets. The target prediction and intersection analysis process from step two is repeated, that is, the intersection of the predicted targets of the core pharmacodynamic compounds and the targets for type 2 diabetes is taken to construct a new PPI network. The CytoNCA plugin is used to calculate three key topological parameters for each target node in the network: degree, betweenness centrality, and proximity centrality.

[0033] The screening criteria were set as follows: the degree value of the target must be higher than 1.5 times the median degree value of the entire network, and at least one of its betweenness centrality and proximity centrality must be higher than the network average. Targets that meet these stringent criteria are defined as core targets. GO and KEGG enrichment analyses were then performed on the selected core targets, and a five-layer association network of "fulvic acid - core components - core targets - key pathways - disease" was finally constructed.

[0034] Step Six: Molecular docking verification.

[0035] To verify the interaction between the core compound and the core target at the molecular level, molecular docking simulations were performed in this step. The three-dimensional structure of the core compound was obtained from the PubChem database, and the protein crystal structure of the core target was obtained from the RCSB PDB database. The protein structure was preprocessed using PyMOL software, including water molecule removal, removal of the original ligand, and hydrogenation. Docking calculations were performed using AutoDock software, with binding free energy as the primary evaluation metric, and the lowest energy conformation was selected for binding mode analysis.

[0036] Step 7: Verification of α-glucosidase inhibition experiment.

[0037] To further verify the in vitro hypoglycemic activity of the screened core compound and fulvic acid as a whole, this step performed a classic α-glucosidase inhibitory activity test. Four experimental groups were set up in a 96-well plate: blank group, control group, sample blank group, and sample group, with six parallel wells in each group. PBS buffer, the inhibitor to be tested, pNPG substrate, and α-glucosidase enzyme solution were added sequentially according to the preset volumes. All reaction wells were pre-incubated at 37°C for 10 minutes, then the enzyme solution was added to start the reaction, which was continued for 20 minutes and then terminated with sodium carbonate solution. The absorbance of each well was measured at 405 nm using a microplate reader. The inhibition rate was calculated according to a specific formula, and inhibition rate-concentration curves were plotted by setting different concentration gradients to preliminarily verify its hypoglycemic activity.

[0038] As a preferred embodiment of the present invention, the core pharmacodynamic compounds screened by the above-mentioned systematic method specifically include: gallic acid, protocatechuic acid, 3,4-dihydroxyphenylacetic acid, γ-linolenic acid, vanillic acid, gentianic acid, guanidinoacetic acid, quinic acid, and quinolinic acid.

[0039] Compared with the prior art, the method provided by the present invention achieves the following significant beneficial effects: (1) A highly efficient and precise two-layer progressive screening system was constructed. This invention used UPLC-MS / MS technology to resolve 229 chemical components from fulvic acid, and based on a dual screening mechanism supported by network topology analysis and literature review, accurately identified 9 core pharmacodynamic compounds. Furthermore, quantitative topology analysis was used to screen core targets from a large number of potential targets. This two-layer progressive strategy achieves a step-by-step focus from all components to core components, and from all targets to core targets, significantly improving the efficiency and accuracy of the screening.

[0040] (2) A complete closed-loop verification system of "prediction-computation-experiment" has been formed. From target prediction (obtaining 496 common interaction targets) to molecular docking (confirming that the core compound and the core target have good binding free energy and stable binding mode), and then to in vitro enzyme inhibition experiment (confirming the concentration-dependent α-glucosidase inhibition effect of fulvic acid and the core compound), a multi-level and mutually corroborating chain of evidence has been formed to ensure the scientific nature and final reliability of the screening results.

[0041] (3) The system elucidates the synergistic mechanism of multiple targets and provides a scientific basis for the standardized development of products. KEGG enrichment analysis shows that the core targets are significantly enriched in 20 pathways closely related to glucose metabolism, such as the PI3K-Akt signaling pathway, the MAPK signaling pathway, and the insulin signaling pathway. The constructed five-layer association network of "fulvic acid-core components-core targets-key pathways-disease" intuitively and systematically demonstrates its synergistic mechanism of "multi-component, multi-target, and multi-pathway". In addition, the nine core pharmacodynamic compounds screened can be used as characteristic component indicators of fulvic acid products to establish a precise quality control method based on pharmacodynamic substances, providing solid technical support for the standardization and modernization of related products. Attached Figure Description

[0042] Figure 1 The image shows the UPLC-MS / MS total ion chromatogram of the fulvic acid sample in positive ion mode in the examples.

[0043] Figure 2 The image shows the total ion chromatogram of the fulvic acid sample in negative ion mode using UPLC-MS / MS in the examples.

[0044] Figure 3 Venn diagram of predicted targets for fulvic acid chemical composition and targets associated with type 2 diabetes.

[0045] Figure 4 A protein-protein interaction network diagram constructed for 496 co-acting targets.

[0046] Figure 5 A bar chart showing the results of GO functional enrichment analysis for 496 co-acting targets.

[0047] Figure 6 Bubble chart showing the enrichment analysis results of KEGG signaling pathways for 496 common target sites.

[0048] Figure 7 Venn diagram of predicted targets for 9 core pharmacodynamic compounds and targets related to type 2 diabetes.

[0049] Figure 8 A protein interaction network diagram constructed for the intersection targets of the core compound and type 2 diabetes.

[0050] Figure 9 Bar chart showing the GO functional enrichment analysis results of the core compound and the target of type 2 diabetes. Figure 10 Bubble chart showing the enrichment results of the KEGG signaling pathway between the core compound and the target of type 2 diabetes.

[0051] Figure 11This is a multi-layered network diagram of "fulvic acid - core components - core targets - key pathways - diseases".

[0052] Figure 12 This is a schematic diagram of the molecular docking binding pattern between the core compound vanillic acid and the core target NFKB1.

[0053] Figure 13 This is a schematic diagram of the molecular docking binding pattern between the core compound gallic acid and the core target NFKB1.

[0054] Figure 14 This is a schematic diagram of the molecular docking binding pattern between the core compound 3,4-dihydroxyphenylacetic acid and the core target NFKB1.

[0055] Figure 15 This is a schematic diagram of the molecular docking binding pattern between the core compound γ-linolenic acid and the core target CYP3A4.

[0056] Figure 16 This is a schematic diagram of the molecular docking binding pattern between the core compound gentioic acid and the core target ESR2.

[0057] Figure 17 The figure shows the concentration-dependent curve of humic acid's inhibition rate of α-glucosidase in the examples.

[0058] Figure 18 This is a concentration-dependent curve of the inhibition rate of α-glucosidase by the core compound gallic acid in the examples.

[0059] Figure 19 The graph shows the concentration-dependent inhibition rate of α-glucosidase by the core compound 3,4-dihydroxyphenylacetic acid in the examples. Detailed Implementation

[0060] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be noted that the following embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0061] The following examples fully demonstrate how to use the method of the present invention to systematically screen core pharmacologically active substances with hypoglycemic activity from a complex mixture of lignite-derived fulvic acid. The entire process covers chemical component identification, target prediction and intersection, network analysis, core compound and target screening, functional pathway enrichment, molecular docking, and in vitro activity verification. Each step is interconnected and progressive, ultimately forming a complete chain of evidence. Example 1

[0062] Standardized preparation of humic acid from lignite: To ensure the reliability and reproducibility of subsequent research results, the research subjects were first prepared in a standardized manner. In this embodiment, lignite from the Pengzu area of ​​Eshan County, Yunnan Province, was used as raw material, and fulvic acid samples were prepared strictly in accordance with the "Method for producing humic acid and its salts by oxidative degradation of lignite" disclosed in the authorized patent ZL200810233669.X. Example 2

[0063] Systematic identification of the chemical components of fulvic acid: This step is a prerequisite for constructing the entire methodology. Fulvic acid, as a key active component of humic acid, has an extremely complex chemical composition, making it difficult for traditional methods to achieve systematic coverage. This embodiment employs ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) to perform in-depth analysis of the small molecule components of fulvic acid under high-resolution mass spectrometry and dual-mode positive and negative ion acquisition.

[0064] Sample pretreatment: Accurately weigh 25 mg of fulvic acid sample under low-temperature conditions and place it in an EP tube containing two homogenization beads. Add 500 μL of extraction buffer, which is a mixture of methanol, acetonitrile, and water in a 2:2:1 volume ratio, and add an isotope-labeled internal standard for subsequent data correction. After vortexing the sample for 30 seconds, homogenize it in a homogenizer at 35 Hz for 4 minutes to ensure thorough dispersion of fulvic acid particles. Immediately afterward, transfer it to an ice-water bath and sonicate for 5 minutes to utilize the cavitation effect of ultrasound to promote component dissolution. Repeat the above "homogenization-sonication" cycle three times to maximize extraction efficiency. After extraction, place the EP tube at -40°C for 1 hour to precipitate large molecules such as proteins, reducing their interference with mass spectrometry analysis. Finally, aspirate 300 μL of the supernatant and filter it through a 0.22 μm filter plate under positive pressure at 6 psi for 180 seconds. Collect the filtrate for subsequent analysis.

[0065] Chromatographic and mass spectrometric conditions: Chromatographic analysis was performed on a Vanquish ultra-high performance liquid chromatograph (UHPLC) from Thermo Fisher Scientific, using a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 50 mm, 1.7 μm). Mobile phase A was an aqueous solution containing 25 mmol / L ammonium acetate and 25 mmol / L ammonia, and mobile phase B was acetonitrile. The sample pan temperature was maintained at 4 °C, the injection volume was 2 μL, and a gradient elution program was used to achieve efficient separation of the compounds.

[0066] Mass spectrometry was performed using an Orbitrap Exploris 120 high-resolution mass spectrometer, controlled by Xcalibur software (version 4.4). Ion source parameters were set as follows: sheath gas flow rate 50 Arb, auxiliary gas flow rate 15 Arb, and capillary temperature 320℃. The spray voltages in positive and negative ion modes were +3.8 kV and -3.4 kV, respectively. Data-dependent acquisition (DDA) was used, with a first-stage full scan resolution of 60,000 (m / z 100-1500) and a second-stage mass spectrometry resolution of 15,000, employing a stepped collision energy (20 / 30 / 40 eV) to obtain rich fragment information.

[0067] Data processing and results: The acquired raw data were converted to mzXML format using ProteoWizard software (version V3.0.24054), and then peak extraction and alignment were performed using relevant packages in the R language environment. The preprocessed data were compared with the BiotreeDB mass spectrometry database (version V3.0), and compounds were accurately identified by comprehensively considering precise molecular weight, retention time, and secondary fragment ion information. The total ion current chromatograms of the experimental samples in positive and negative ion modes are shown below. Figure 1 and Figure 2 As shown.

[0068] Through the above process, this embodiment identified a total of 229 chemical components from the fulvic acid sample, specifically including: Tetradecyl sulfate, 5-Aminolevulinic acid, 2-Aminobutyric acid, Lactate, 4-Hydroxybenzoic acid, p-Toluquinone, Phenylephrine, beta-Alanine, 3-Hydroxyisovaleric acid, Citraconic acid, Benzoic acid, Glycine, Carnitine, Trigonelline, 2,6-Dimethoxybenzoic acid、Adenine、DEHP、4-Aminobutyric acid、Proline、Oxalic acid、3-Sulfopropanoic acid、Acetylglycine、2-Furoic acid、Butanoic acid、N1-Acetylspermidine、2-Aminoadipic acid、Glutamate、Maltose、Arecaidine、beta-Guanidinopropionic acid、Sarcosine、Imidazoleacetic acid、Homoveratric acid、Nonaethylene glycol、Azelaic acid、Nicotinate、Cyclopropylacetic acid、Propylgallate、Maleic acid、Palmitic acid、Salicylicacid、2-Hydroxybutyric acid、Stearic acid、Guandioacetic acid、Betaine、Stachydrine、N1-Methyl-2-pyridone-5-carboxamide、2-Hydroxypyridine、3-Hydroxy-2-methylpyridine、3-Pyridinemethanol、Glutaric acid、Malonic acid、2-Aminoisobutyric acid、3-Hydroxybenzoic acid、3-Hydroxypyruvic acid、4-Acetamidobutyric acid、Pyrrolidine、Hexaethylene glycol、Picoline、2-Methyl-3-hydroxybutyric acid、2,4-Dihydroxybenzoic acid、Patulin、Synephrine、Trehalose、Creatine、Nipecotic acid、4-(dimethylamino)butanoate、Hygric acid、Creatinine、Pentaethylene glycol、Syringaldehyde、Malic acid、Quinolinic acid、3-Dehydroquinic acid、3-Pyridol、Aspartate、5-Aminopentanoic acid、4-Guanidinobutyric acid、3-Amino-4-hydroxybenzoic acid、Dimethylglycine、L-Leucyl-L-alanine、Tetraethylene glycol、2-Piperidone、Quinic acid、Threonic acid、Glycolate、3-Hydroxyvaleric acid、Alanine、Lysine、EDTA、3-Succinoylpyridine、3-Pyridylacetic acid、Eplerenone、Oleamide、Citramalic acid、Glyceric acid、Oroticacid、2-Hydroxy-6-methoxybenzoic acid、Sucrose、Isonicotinic acid、2,2-Dimethyl-3,4-dihydro-2H-1,4-benzoxazin-3-one、Phenacetin、Galactose、Levulinic acid、Xylose、Lumichrome、3-Furoic acid、Gentisic acid、3,4-Dihydroxyphenylacetic acid、Telmisartan、3-Hydorxy-3-methylglutaric acid、gamma-Linolenic acid、Monomethylfumarate、2-Aminopurine、Gentisaldehyde、Griffonilide、Cytosine、(2R)-6-Oxo-2-piperidinecarboxylic acid、Zoxazolamine、Laurocapram、2-Hydroxyhexanedioic acid、alpha-Linolenic acid、Betaine aldehyde、Methylguanidine、2-Methylcyclopentane-1,3-dione、Mevalonic acid、Piperonylic acid、4-Hydroxyphenylpyruvic acid、2,2-Dimethylsuccinic acid、Pipecolic acid、Daminozide、Tyrosine、Trolox、Pimelic acid、Methylsuccinic acid、2-Hydroxy-2-methylbutyric acid、N1-Methyl-4-pyridone-3-carboxamide、Imidazol-1-yl-acetic acid、Amifampridine、2-Pyrimidinemethanamine、Daltogen、Acetylisoeugenol、Ethylmalonic acid、Vanillic acid、Acetoin、Melibiose、Miglitol、Phloroglucinol、SB-3CT、N-Methylglutamic acid、Normetanephrine、Phosphorylcholine、Prolylalanine、1-Methylpyrrolidine、Fomepizole、2,4-Dihydroxybutanoic acid、Protocatechuic acid、1,3,7-Trimethyluric acid、Pyridoxal(Vitamin B6)、2-Amino-4-methoxyphenol、Tramiprosate、3-Hydroxypropionic acid、Acetylcarnitine (Car(2:0))、Sorbose、Fagomine、Acetylcholine、2,4-Hexadienoic-acid、Docebenone、Embelin、2-Hydroxy-3-methylbutyric acid、5-Hydroxymethylfurfural、4-Hydroxyproline、3-AMINO-2-PIPERIDONE、Kynurenic acid、Pseudoginsenoside RT5、N,N-Diethyl-2-aminoethanol、2,2,6,6-Tetramethyl-4-piperidinyl 2-methylacrylate、4-Oxohexanoic acid、Sulfadimethoxine、3-hydroxybenzaldehyde、4-Hydroxybenzaldehyde、3-Methylbut-2-enoic acid、2-Ketocaproic acid、Ketoleucine、3-Methyl-2-oxovaleric acid、Flopropione、5-Hydroxyindole-3-acetic acid、4-Aminobenzoic acid、Isocitric acid、Terephthalic-Acid、Dimethylmalonic acid、4-Ketopimelic acid、N-Benzyloxycarbonylglycine、2,6-Dihydroxybenzoic acid、1-Methyl-6-oxo-1,6-dihydropyridine-3-carboxylic acid, 2-(Aminomethyl)pyrazine, 3-Methylcrotonylglycine, Diacetyl, Citric acid, 6-Hydroxynicotinic acid, Theophylline, N6-Acetyllysine, Tropine, Olprinone (Hydrochloride), 3-Methyl-1H-pyrazole-4-carbaldehyde, 8-Aminooctanoic acid, Fructose, 2-Hydroxy-4-methoxybenzoic acid, Indolelactic acid, Protoporphyrin IX, 3-Methoxy-4-hydroxyphenylglycol sulfate, 5-ketocaproate, 1,7-Dimethylxanthine, Dihydrothymine, Homogentisic The compounds include phenolic acids, 2',4',6'-Trihydroxyacetophenone, Theobromine, Flurandrenolide, Lawsone, Nortropine, 1-Methyl-6-phenyl-1H-imidazo[4,5-b]pyridin-2-amine, 3,5-Dihydroxybenzyl alcohol, Galactaric acid, 2-Ketobutyricacid, Paroxetine (Drug), Sulfachloropyridazine, Evocarpine, Enoxacin (hydrate), and Tris(2,4-di-tert-butylphenyl)phosphate. These compounds exhibit diverse structural types, encompassing phenolic acids, organic acids, fatty acids, and amino acids and their derivatives, providing a solid material foundation for subsequent target prediction and pharmacodynamic substance screening. Example 3

[0069] Intersection analysis of target prediction and disease target: This step aims to connect chemical substances with biological activities. First, the two-dimensional structures of 229 compounds were drawn using ChemDraw 20.0 software, and SMILES information was extracted using its built-in tools. To overcome potential algorithmic bias from a single database, a multi-database cross-validation strategy was employed. The SMILES information of the 229 compounds was input into three databases: SwissTargetPrediction, SuperPred, and SEA. The predicted target names were mapped to genes using the Uniprot database, standardized to standard gene names, and then merged and deduplicated, resulting in 1692 potential targets for fulvic acid.

[0070] Meanwhile, using "Type 2 diabetes" and "T2DM" as keywords, we searched for type 2 diabetes-related targets in four databases: DisGeNET (scoreGDA≥0.9 and scoreVDA≥0.9, yielding 186 targets), DrugBank (yielding 139 targets), GeneCards (relevance score>35, ​​yielding 1598 targets), and TTD (yielding 99 targets). After merging and deduplicating, we finally obtained 1800 type 2 diabetes-related targets.

[0071] 1692 component targets and 1800 disease targets were imported into the Venny 2.1.0 platform for Venn diagram analysis. For example... Figure 3 As shown, the intersection of the two yielded 496 targets. These 496 targets were considered potential targets for fulvic acid in the treatment of type 2 diabetes. To further explore their interaction, they were imported into the STRING database (species: Homo sapiens, confidence level: 0.9) to construct a protein-protein interaction (PPI) network. The results are as follows: Figure 4 As shown. Example 4

[0072] Functional and pathway enrichment analysis: To interpret the 496 co-acting targets from a biological perspective, this embodiment employs GO and KEGG enrichment analysis. The targets were imported into the DAVID database, with the species set as "Homo sapiens" and a significance threshold of P < 0.05.

[0073] GO enrichment analysis yielded 700 significantly enriched entries. The top 10 entries with the smallest p-values ​​are visualized as follows: Figure 5 As shown, it mainly involves biological processes such as the positive regulation of the ERK1 / 2 cascade and the insulin receptor signaling pathway.

[0074] KEGG pathway enrichment analysis results ( Figure 6The results showed that the target was mainly enriched in 20 pathways, including metabolic pathways, lipid and atherosclerosis, PI3K-Akt signaling pathway, and MAPK signaling pathway. Example 5

[0075] Dual screening of core pharmacodynamic compounds:

[0076] This embodiment employs a dual screening mechanism that combines network topology analysis with literature evidence verification.

[0077] The first screening step involved network topology analysis: 496 common target sites, 229 components, and their corresponding relationships were imported into Cytoscape 3.9.1 software to construct a binary network of "fulvic acid-component-target site". The degree value of each compound node was calculated using the CytoNCA plugin, and the compounds were sorted in descending order of degree value, with the top-ranked compounds selected as candidates.

[0078] The second screening step involves verifying evidence from the literature: For candidate compounds, a systematic search is conducted in databases such as PubMed, Web of Science, and CNKI, using the strategy of "compound name AND (hypoglycemic OR diabetes OR hypoglycemic OR diabetes OR antidiabetic)".

[0079] This screening mechanism employs an AND logic, meaning that only compounds that simultaneously meet the criteria of ranking highly in network topology and having supporting literature are identified as core pharmacodynamic compounds. Through this dual screening, nine core pharmacodynamic compounds were ultimately identified: gallic acid, protocatechuic acid, 3,4-dihydroxyphenylacetic acid, gamma-linolenic acid, vanillic acid, gentisic acid, guanidoacetic acid, quinic acid, and quinolinic acid. Example 6

[0080] Quantitative screening and mechanism de-emphasis of core targets: For the nine core compounds, this step involves more precise target localization. The target prediction and intersection process is repeated to obtain the intersection targets between each core compound and type 2 diabetes (e.g., ...). Figure 7 As shown). Merge all intersecting target points and construct a PPI network (as shown). Figure 8The CytoNCA plugin is used to calculate three topological parameters: degree (DC), betweenness centrality (BC), and proximity centrality (CC).

[0081] The screening criteria for core targets were set as follows: the degree value was higher than 1.5 times the network median, and at least one of the betweenness centrality and proximity centrality was higher than the network average. The screening results are shown in Table 1. Core targets include NFKB1, CYP3A4, IGF1R, ESR2, PPARG, etc.

[0082] GO and KEGG enrichment analyses were performed again on the core target. Figure 9 , Figure 10 The results showed that it focused more on functional biological processes. Finally, fulvic acid, nine core compounds, core targets, the top 20 key pathways, and type 2 diabetes were jointly introduced into Cytoscape 3.9.1 to construct a five-layer association network of "fulvic acid-component-target-pathway-disease". Figure 11 ). Example 7

[0083] Molecular docking verification: To verify the direct binding ability of the core compound to the core target at the molecular level, this embodiment employs molecular docking technology. The three-dimensional structure of the core compound was downloaded from the PubChem database, and the protein structure of the core target was downloaded from the RCSB PDB database. The protein was preprocessed using PyMOL software, including the removal of water molecules and protoligands, and the addition of polar hydrogen atoms. Docking calculations were performed using AutoDock software, with binding free energy as the primary evaluation metric. In drug development, a binding energy less than -5.0 kcal / mol generally indicates good binding activity, while less than -7.0 kcal / mol indicates relatively strong binding activity.

[0084] docking results (partial visualizations such as...) Figure 12-16 (See Table 2 for complete data) The data shows that the binding energy between the vast majority of core compounds and the core targets is below -5.0 kcal / mol. For example, the binding energy between γ-linolenic acid and CYP3A4 is as low as -8.56 kcal / mol, exhibiting extremely strong binding activity.

[0085] Table 1. Core targets corresponding to core compounds Example 8

[0086] α-glucosidase inhibition experiment verification: To ultimately verify the hypoglycemic activity of fulvic acid and its core compounds, this embodiment employs an α-glucosidase inhibition experiment. The experimental principle is based on the catalytic hydrolysis of the substrate pNPG by α-glucosidase to generate yellow p-nitrophenol (PNP), which exhibits a characteristic absorption at 405 nm. The inhibition rate is calculated using the formula: Inhibition rate (%) = [(Control A - Blank A) - (Sample A - Blank A)] / (Control A - Blank A) × 100%.

[0087] The experiment was conducted in 96-well plates, with blank control, control group, sample blank control, and sample group (6 replicates per group). The sample loading protocol is shown in Table 3. Experimental results ( Figure 17-19 The results showed that fulvic acid and its core compounds (such as gallic acid and 3,4-dihydroxyphenylacetic acid) all exhibited concentration-dependent inhibitory effects on α-glucosidase, directly confirming their in vitro hypoglycemic activity.

[0088] Experimental results (such as) Figures 17 to 19 As shown in the figure, fulvic acid and its core compounds (gallic acid and 3,4-dihydroxyphenylacetic acid as examples) exhibit concentration-dependent inhibitory effects on α-glucosidase, which directly verifies their hypoglycemic activity in in vitro models. These experimental results corroborate the aforementioned screened core compounds, forming a complete chain of evidence from theoretical prediction to experimental verification.

[0089] Table 2 Molecular docking results

[0090] In summary, the method of the present invention is fully described above. This method first clarifies the chemical composition of fulvic acid using UPLC-MS / MS technology, identifying 229 components. Then, a dual screening mechanism is employed to determine nine core pharmacodynamic compounds, and further, quantitative network analysis is used to identify their core targets. Based on this, combined with functional enrichment analysis, molecular docking, and in vitro enzyme inhibition experiments, the synergistic hypoglycemic mechanism of fulvic acid—combining multiple components, multiple targets, and multiple pathways—is systematically revealed.

[0091] The contribution of this invention lies in the fact that it not only systematically elucidates the hypoglycemic pharmacodynamic material basis and molecular mechanism of fulvic acid from lignite for the first time, but more importantly, the nine core compounds screened can serve as key biomarkers for their quality control, thus providing a direct technical path and scientific basis for developing new hypoglycemic drugs or functional products of fulvic acid with controllable quality and clear mechanisms.

[0092] It should be noted that the above embodiments are merely one specific implementation method of the present invention. Those skilled in the art, based on their understanding of the spirit and principles of the present invention, can make adaptive adjustments to specific parameters, database selection, software version, experimental conditions, and raw material sources, etc., all of which should fall within the protection scope of the present invention.

[0093] Table 3. Dosage of each reactant added (unit: μL) .

Claims

1. A method for screening core hypoglycemic pharmacologically active substances from lignite-derived fulvic acid, characterized in that, The method is based on a two-layer progressive screening strategy and a dual verification mechanism, and includes the following steps: Step 1: Conduct chemical composition analysis on lignite-derived humic acid to systematically determine its chemical composition; Step 2: Predict the target sites of each component in the chemical composition and perform intersection analysis with the target sites of type 2 diabetes-related diseases to obtain common target sites; Step 3: Based on the correspondence between the common target and each component in the chemical composition, construct a "component-target" interaction network; Step 4: Identify the core pharmacodynamic compound from the chemical composition through a first-level screening process. This step further includes: (a) First screening: Based on network topology parameters, the compound nodes in the "component-target" interaction network are quantitatively evaluated and ranked, and the top-ranked compounds are selected as candidate compounds; (b) Second screening: The candidate compounds are searched and verified for relevant literature evidence on hypoglycemia, and compounds with sufficient literature support are screened out; (c) Determination: The compound that simultaneously satisfies the conditions in steps (a) and (b) is determined as the core pharmacodynamic compound; Step 5: Identify the core target from the target sites of the core pharmacodynamic compound through a second-layer screening, including: constructing a target-protein interaction network of the core pharmacodynamic compound and performing quantitative screening based on network topology parameters to identify the core target.

2. The method according to claim 1, characterized in that, The criteria for quantitative screening based on network topology parameters in step five are: the degree value of the target point is higher than 1.5 times the median degree value of the network, and at least one of its betweenness centrality and proximity centrality is higher than the average value of the network.

3. The method according to claim 1, characterized in that, The following verification steps are also included: Step Six: Perform molecular docking verification between the core pharmacodynamic compound and the core target; Step 7: Verify the hypoglycemic activity of the core pharmacological compound through an in vitro α-glucosidase inhibition experiment.

4. The method according to claim 1, characterized in that, In step one, ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) is used for chemical composition analysis; In step two, targets are predicted using the SwissTargetPrediction, SuperPred, and SEA databases, and disease targets related to type 2 diabetes are obtained using the DisGeNET, DrugBank, GeneCards, and TTD databases.

5. The method according to any one of claims 1 to 4, characterized in that, The core pharmacologically active compounds screened out include: gallic acid, protocatechuic acid, 3,4-dihydroxyphenylacetic acid, γ-linolenic acid, vanillic acid, gentianic acid, guanidinoacetic acid, quinic acid, and quinolinic acid.

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