Method for analyzing action mechanism of trichosanthes kirilowii maxim, allium macrostemon and pinellia ternate decoction for treating heart failure

Abstract

The invention discloses a method for analyzing the action mechanism of a decoction of trichosanthes kirilowii maxim, allium macrostemon and pinellia ternate for treating heart failure (heart failure), and the multi-component, multi-target and multi-channel synergistic action mechanism of the prescription is systematically analyzed by integrating network pharmacology and metabonomics technologies. The method provides a systematic analysis strategy for research on a mechanism of treating complex diseases by a traditional Chinese medicine compound.

Description

Technical Field

[0001] This invention belongs to the field of traditional Chinese medicine technology, specifically relating to a method for analyzing the mechanism of action of Gualou Xiebai Banxia Decoction in treating heart failure. Background Technology

[0002] Heart failure (HF) is the end stage of various cardiovascular diseases, characterized by high morbidity, high readmission rates, and high mortality rates, making it one of the most important chronic cardiovascular diseases of the 21st century. While modern medical treatments (such as ACE inhibitors, beta-blockers, and diuretics) can alleviate symptoms, they suffer from significant side effects and individual variability in efficacy. Traditional Chinese medicine (TCM) offers advantages in treating HF through multi-target and holistic regulation. Among TCM formulations, the compound preparation Gualou Xiebai Banxia Tang (GXBD) has traditionally been used to treat chest tightness, and scientific literature has demonstrated its effectiveness in treating myocardial infarction. Modern pharmacological studies have confirmed its coronary artery dilation, anti-inflammatory, and cardioprotective effects. Since myocardial infarction is the main cause of HF, GXBD has potential therapeutic effects on HF; however, its specific mechanism of action remains unclear, hindering its international promotion and precision medicine application.

[0003] GXBD originates from the *Synopsis of Prescriptions of the Golden Chamber* and is composed of Trichosanthes kirilowii, Allium macrostemon, and Pinellia ternata, among other ingredients. It possesses the effects of promoting Yang, dispersing stagnation, resolving phlegm, and removing blood stasis, and is widely used clinically to treat chest pain (similar to coronary heart disease and heart failure). However, existing research largely focuses on clinical efficacy observations or single-component analyses, lacking an elucidation of its systemic mechanism of action. This makes it difficult to verify the multi-component, multi-target characteristics of traditional Chinese medicine compound formulas through traditional experiments. Furthermore, it is impossible to clearly identify key active ingredients, targets, and pathway networks; and the correlation between changes in metabolites after drug intervention and the improvement of heart failure is unclear.

[0004] Traditional pharmacological methods (such as animal models and molecular biology experiments) are time-consuming and labor-intensive, and it is difficult to fully elucidate the mechanisms of action of compound Chinese medicine formulas. This results in incomplete, unreliable, and clinically difficult elucidation of the "multi-component-multi-target-multi-pathway" mechanisms of action of traditional Chinese medicine compound formulas. Summary of the Invention

[0005] The present invention aims to at least partially solve the problems existing in the prior art. To this end, the present invention provides a method for analyzing the mechanism of action of Gualou Xiebai Banxia Decoction in treating heart failure.

[0006] According to one aspect of the present invention, a method for analyzing the mechanism of action of Gualou Xiebai Banxia Decoction in treating heart failure includes the following steps: (a) obtaining the potential active ingredients and their targets of Gualou Xiebai Banxia Decoction through network pharmacology analysis, and taking the intersection with the disease targets of heart failure to obtain the component-disease intersection target genes; (b) constructing a protein interaction network based on the intersection target genes, and screening core targets through topology analysis; (c) performing pathway enrichment analysis on the core targets to elucidate the drug action mechanism; and (d) detecting the drug action mechanism in a heart failure model after administration using metabolomics technology. (e) Metabolite changes, screening for differential metabolites; (c) Mapping the differential metabolites to the pathways obtained in step (c), establishing a dynamic association of "component-target-metabolite", and elucidating the causal mechanism by which drugs improve heart failure through target-regulated metabolic reprogramming; (d) Evaluating the binding affinity of the active ingredient in step (a) to the core target in step (b) through molecular docking, and verifying high-affinity component-target pairs based on binding energy thresholds; (e) Integrating the results of steps (a)-(f), and cross-validating the causal association between network pharmacology targets and metabolomics metabolite changes.

[0007] The screening thresholds for potential active ingredients in step (a) are: oral bioavailability (OB) > 30%, drug similarity (DL) > 0.18, and intestinal epithelial cell permeability (Caco-2) > -0.4.

[0008] In step (b), the selection of core targets is based on the median of degree centrality, closeness centrality, and betweenness centrality in topological analysis, and the targets are selected in descending order of degree centrality.

[0009] The metabolomics analysis in step (d) includes: (d1) preparation of Gualou Xiebai Banxia decoction: adding rice wine to the medicinal materials, decocting and concentrating, and then filtering; (d2) administering the drug to a heart failure animal model by gavage, collecting heart tissue and performing derivatization; (d3) detecting metabolites using liquid chromatography-mass spectrometry (LC-MS), and screening differentially expressed metabolites through multivariate statistical analysis.

[0010] In step (d2), 5-(diisopropylamino)amylamine (DIAAA) was used as the derivatization reagent: 5 μL of HOBt, 5 μL of DIAAA-TEA, and 5 μL of HATU were added sequentially to the dried sample. The reaction was carried out with shaking at room temperature for 1 min, and then terminated with 35 μL of acetonitrile. In step (d3), the LC-MS chromatographic conditions were as follows: column: octadecylsilane-bonded silica gel; mobile phase A: 0.1% formic acid aqueous solution, mobile phase B: 0.1% formic acid acetonitrile solution; gradient elution program: 0.0–0.5 min, mobile phase A from 98.0% → 95.0%, mobile phase B from 2.0% → 5.0%; 0.5–2.0 min, mobile phase A from 95.0% → 92.2%, mobile phase B from 5.0% → 92.2%. %→7.8%; 2.0~4.0 min, mobile phase A from 92.2%→91.0%, mobile phase B from 7.8%→9.0%; 4.0~6.0 min, mobile phase A from 91.0%→86.0%, mobile phase B from 9.0%→14.0%; 6.0~10.0 min, mobile phase A from 86.0%→77.0%, mobile phase B from 14.0%→23.0%; 10.0~13.0 min, mobile phase A from 92.2%→91.0%, mobile phase B from 7.8%→9.0%; 4.0~6.0 min, mobile phase A from 91.0%→86.0%, mobile phase B from 9.0%→14.0%; 6.0~10.0 min, mobile phase A from 86.0%→77.0%, mobile phase B from 14.0%→23.0%; 10.0~13.0 min, mobile phase A from 92.2%→7 ... Phase A decreased from 77.0% to 67.0%, and mobile phase B decreased from 23.0% to 33.0%; from 13.0 to 18.0 min, mobile phase A decreased from 67.0% to 53.0%, and mobile phase B decreased from 33.0% to 47.0%; from 18.0 to 22.0 min, mobile phase A decreased from 53.0% to 40.0%, and mobile phase B decreased from 47.0% to 60.0%; from 22.0 to 24.0 min, mobile phase A decreased from 40.0% to 10.0%. %, mobile phase B from 60.0% to 90.0%; 24.0–24.5 min, mobile phase A from 10.0% to 5.0%, mobile phase B from 90.0% to 95.0%; 24.5–26.9 min, mobile phase A from 5.0% to 5.0%, mobile phase B from 95.0% to 95.0%; 26.9–27.0 min, mobile phase A from 5.0% to 98.0%, mobile phase B from 95.0% to 2.0%.

[0011] The LC-MS mass spectrometry conditions in step (d3) were as follows: backflush nitrogen temperature 300℃; backflush nitrogen flow rate 11.0 L / min; nebulizer gas flow rate 25 psig; auxiliary nitrogen temperature 325℃; auxiliary nitrogen flow rate 11.0 L / min; capillary inlet voltage 3500 V; Agilent jet outlet voltage 500 V (+) in positive ion mode; source collision voltage 175 V; cone voltage 65.0 V; Octopole RF Peak 750 V; full scan mode; scan range 200–1700 m / z.

[0012] The multivariate statistical analysis in step (d3) included screening for differentially expressed metabolites between groups using analysis of variance (ANOVA) (P<0.05).

[0013] Step (f) molecular docking includes: (f1) obtaining the tertiary structure file of the core target and the molecular structure file of the active ingredient; (f2) calculating the binding energy using molecular docking software and screening component-target pairs with binding energy ≤ -5.0 kcal / mol; wherein, the target structure in step (f1) is derived from the Uniport database, and the active ingredient structure is generated by ChemDraw; step (f2) uses AutoDock Vina software for docking.

[0014] It also includes step (h): using animal models of heart failure to verify the improvement of pathological phenotypes and comparing the results with the integrated analysis to ensure that the mechanism prediction is consistent with the actual efficacy.

[0015] Existing technologies suffer from incomplete mechanism analysis due to data fragmentation: network pharmacology only predicts targets, and metabolomics only detects metabolites, lacking a dynamic correlation between the two. This invention, through KEGG pathway causal mapping, directly correlates network pharmacology predictions with metabolomics results, demonstrating that this component improves heart failure by regulating the HIF-1 and TNF-α pathways, as well as the pyruvate-lactate axis, TCA cycle, glutamate metabolism, and one-carbon metabolism pathways. Simultaneously, comprehensive molecular docking validation ensures target reliability. This achieves a complete mechanism analysis from chemical composition to clinical phenotype, overcoming the fragmented limitations of traditional research.

[0016] Traditional methods, due to the disconnect between computation and experimentation, fail to guide clinical application in predicting target outcomes. This invention, through HE staining and Sirius red staining experiments, confirms the therapeutic effect of GXBD on heart failure, while metabolomics reveals the inactivation of the fatty acid oxidation pathway it regulates. This "prediction → verification → correction" process not only reduces false positives but also identifies pyruvate, lactate, malic acid, fumaric acid, α-ketoglutarate, glutamate, glutamine, glutathione, methionine, serine, and glycine as efficacy biomarkers, directly guiding clinical monitoring. This establishes a rapid pathway from basic research to clinical translation, significantly improving the efficiency and precision of developing traditional Chinese medicine compound formulas. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1Cross-analysis of the heart failure disease target and the target of the Gualou Xiebai Banxia Decoction compound provided in the embodiments of the present invention; Venn plot (A) of the cross-analysis of the compound target and the disease target; Visualization plot of the heart failure disease target (B);

[0019] Figure 2 The following diagrams illustrate the PPI network analysis results (A), the top 10 most critical hub genes (B), and the top 9 KEGG pathway terms and their corresponding targets in the PPI network, as provided in this embodiment of the invention (C).

[0020] Figure 3 This is a component-disease-target pathway network diagram of Gualou Xiebai Banxia Decoction provided in an embodiment of the present invention;

[0021] Figure 4 Histopathological sections of the healthy group, the model group, and the Gualou Xiebai Banxia Decoction treatment group provided in the embodiments of the present invention;

[0022] Figure 5 The correlation analysis (A) and molecular docking analysis results (B) between the core target and potential active ingredients provided according to embodiments of the present invention are shown in the figure.

[0023] Figure 6 Figure (A), iPath analysis results (B), and pathway enrichment analysis results (C) of FBMN analysis results of mouse heart tissue after treatment with Gualou Xiebai Banxia Decoction according to embodiments of the present invention;

[0024] Figure 7 A graph showing the relevant pathways of potential biomarkers provided according to embodiments of the present invention and their differences between different groups;

[0025] Figure 8 The mechanism of action of GXBD in the treatment of heart failure in mice according to embodiments of the present invention. Detailed Implementation

[0026] The following examples are provided to help those skilled in the art better understand the present invention. It should be noted that the following examples are not intended to limit the scope of protection claimed by the present invention, but are merely illustrative. Unless otherwise specified, the raw materials, reagents, or devices mentioned in the following examples are commercially available or obtained through known existing methods.

[0027] According to an embodiment of the present invention, a method for analyzing the mechanism of action of Gualou Xiebai Banxia Decoction in treating heart failure is provided, comprising the following steps: (a) obtaining the potential active ingredients and their targets of Gualou Xiebai Banxia Decoction through network pharmacology analysis, and taking the intersection with the target of heart failure disease to obtain the component-disease intersection target gene; in this process, the potential active ingredients of Gualou Xiebai Banxia Decoction are obtained from the TCMSP database, and supplemented by literature review. The compound structure is input into the Swisstarget prediction and PharmMapper databases to obtain the target, and the protein target is calibrated to homo sapiens using the Uniport database; in the disease target databases DisGeNet, CTD, GeneCards and TCMSP, candidate targets of heart failure are searched with "Heart failure" as the keyword, and after deduplication, the intersection mapping is performed with the potential active ingredient target to obtain the component-disease intersection target gene, that is, the target of Gualou Xiebai Banxia Decoction in relieving heart failure.

[0028] Figure 1 Cross-analysis of GXBD compound targets and heart failure disease targets is presented. Figure 1 A is a Venn diagram of the cross-analysis of compound targets and disease targets, illustrating the overlap between compound targets in the GXBD drug formulation and disease targets in heart failure. The diagram shows 346 intersections, indicating that some compound targets in GXBD overlap with heart failure disease targets, meaning that GXBD drug components can directly act on relevant heart failure targets when treating heart failure. 322 intersections are solely targets of the GXBD drug formulation; these targets may target other diseases or symptoms and are not related to heart failure. 2784 intersections are solely targets of heart failure; these targets are only related to heart failure disease and may be characteristic molecular targets of heart failure. This diagram reflects whether GXBD drugs intersect with heart failure targets and reveals the potential mechanisms of action of GXBD on heart failure. Figure 1 B is a target perturbation plot of three traditional Chinese medicines in GXBD and their effects on heart failure. (Horizontal bar chart located in...) Figure 1The lower left (B) chart displays the total number of targets interfered with by each herbal ingredient. This information reflects the number of targets each herbal ingredient targets in its action, helping to understand their effects on heart failure. For example, if a herbal ingredient shows interference with multiple heart failure-related targets, it may have broader therapeutic potential, capable of modulating heart failure-related biological pathways in multiple ways. The circles and vertical lines on the horizontal axis represent the correlations or interactions between different parts (such as GXBD ingredients and heart failure targets). Circles represent certain targets, while vertical lines indicate direct or indirect connections between these targets. This helps to see how different drug ingredients work together to treat heart failure. The vertical bars above show the number of targets compared, indicating how many heart failure-related targets different herbal ingredients act on, helping to understand the specific role of each ingredient in the therapeutic effect.

[0029] (b) Construct a protein interaction network based on the intersection target genes and screen core targets through topology analysis. In this process, the intersection target points in step (a) are analyzed using the STRING database to obtain the target protein interaction network and its ".tsv" data. Cytoscape is used to perform topology analysis on the network nodes to determine the screening conditions for core targets and to perform screening analysis. (c) Pathway enrichment analysis is performed on the core targets to elucidate the drug action mechanism. In this process, the DAVID database is used to perform GO biological process and KEGG pathway enrichment analysis on the core targets to explain the mechanism of action of Gualou Xiebai Banxia Decoction in relieving heart failure. The screening thresholds for potential active ingredients in step (a) are: oral bioavailability (OB) > 30%, drug similarity (DL) > 0.18, and intestinal epithelial cell permeability (Caco-2) > -0.4. In step (b), the selection of core targets is based on the median of degree centrality, closeness centrality, and betweenness centrality in topological analysis, and the targets are selected in descending order of degree centrality.

[0030] Figure 2 The results of PPI (protein-protein interaction) network analysis of GXBD interference cross-targets in heart failure are presented. Figure 2 A shows the cross-target PPI network, which illustrates the protein-protein interactions between multiple cross-targets involved in the interference effect of GXBD on heart failure. Nodes represent different proteins or genes, while edges represent interactions between them. Different colored lines represent different interaction strengths or types, which can be represented by color gradients. Figure 2B shows the top 10 hub genes. This section presents the 10 most critical hub genes in the PPI network. Hub genes typically play an important bridging role in the network and may be key targets for intervention. The importance of hub genes in the PPI network is represented by the size of the graph and the density of network connections. Figure 2 C shows the first nine KEGG pathway terms and their corresponding targets. This section uses a chord diagram to illustrate the nine major KEGG pathways associated with GXBD and their corresponding targets. The layout of the chord diagram clearly shows the relationship between each pathway and its target. Each string connects a corresponding target to a pathway, and colors may represent different biological processes or mechanisms of action. Overall, the diagram illustrates the molecular mechanisms by which GXBD interferes with heart failure, revealing the roles of key targets and pathways, and helping to further understand its therapeutic potential. This information is of significant value for exploring the role of GXBD in heart failure and the biological significance of related targets.

[0031] Figure 3 This diagram illustrates the predicted targets and characteristics of compound GXBD (for the treatment of heart failure). The figure shows that the three main components of GXBD are associated with multiple biological pathways, covering areas such as cancer, diabetic complications, immune responses, and infections. Through interactions with different targets, GXBD may exert therapeutic effects in various diseases, including lipid metabolism, atherosclerosis, cancer, and tuberculosis, demonstrating its broad potential. The color differentiation in this diagram helps to more clearly see the different directions of action of the various components of GXBD.

[0032] Figure 4 Histopathological sections of the healthy group, the model group, and the Gualou Xiebai Banxia Decoction treatment group were presented. The results of HE staining and Sirius red staining experiments confirmed that GXBD has a therapeutic effect on heart failure.

[0033] Figure 5 The correlation analysis (A) and molecular docking analysis results (B) between the core target and potential active ingredients are shown. The correlation and molecular docking analysis results indicate that chrysoeriol, loliolide, H-TPI-OH and QM1A4NN056 are potential target compounds in GXBD.

[0034] (d) As is well known, the heart needs to continuously produce ATP to maintain its contractile function; if ATP production cannot be sustained, heart failure will occur. It has been reported that the heart primarily utilizes carboxylic acid metabolism for oxidative phosphorylation and energy production. Therefore, this invention uses metabolomics technology to detect changes in metabolites after administration to a heart failure model, and screens for differentially expressed metabolites, which helps to elucidate the mechanism of GXBD in treating HF; in this process, the metabolomics analysis includes the following steps:

[0035] Step 1. Preparation of Trichosanthes, Allium macrostemon and Pinellia decoction: Take 60.00g of Trichosanthes, 41.40g of Allium macrostemon, and 34.50g of Pinellia ternata. Soak in 2000mL of rice wine for 30min. Heat at 600W and decoct to 800mL. Filter the decoction through a No. 9 sieve (200 mesh) of the Chinese Pharmacopoeia. Concentrate the decoction under reduced pressure to 250mL of thick extract. Store at 4℃ for later use.

[0036] Step 2. Select male C57BL / 6 mice weighing 21g (±1g) and aged 4-6 weeks. All animals were housed in a 12:12 hour diurnal cycle with humidity of 40±5% and temperature of 22-24℃. Mice were allowed free access to food and water. All animal experiments were conducted in accordance with the relevant licensing requirements of the Macao Special Administrative Region Government and approved by the Animal Experiment Ethics Committee of Macau University of Science and Technology. After one week of acclimatization, the mice were randomly divided into 6 groups (n=8): control group (Ctrl), positive control group (POS), model group (Mod), low-dose group (L, GXBD-L+ISO), medium-dose group (M, GXBD-M+ISO), and high-dose group (H, GXBD-H+ISO). The Gualou Xiebai Banxia Decoction was administered by gavage, and isoproterenol (ISO) was administered by subcutaneous injection. Mice in the control group were subcutaneously injected with 1 mL / kg of physiological saline, while the other four groups were subcutaneously injected daily with 35 mg / kg of isoproterenol (ISO) in physiological saline solution to establish a mouse model of heart failure. Simultaneously, mice in the control and model groups were administered 1 mL / kg of physiological saline via gavage daily, while the low-dose (L group), medium-dose (M group), and high-dose (H group) groups were administered 5.4 g / kg, 10.8 g / kg, and 21.6 g / kg of Gualou Xiebai Banxia Decoction Extract, respectively, via gavage daily. After five weeks of treatment, the mice were euthanized. Accurately weigh 5 mg (±0.05 mg) of lyophilized heart tissue, add 70 μL of physiological saline to homogenize the tissue, mix the mixture with 280 μL of cold methanol / acetonitrile / H2O (6:3:1, v / v / v), vortex for 1 minute, and centrifuge at 13500 rpm for 10 minutes at 4 °C. Transfer the supernatant, repeat the operation twice, combine the supernatants, and dry under nitrogen. The nitrogen-dried sample was derivatized using DIAAA. Metabolomics analysis was performed using liquid chromatography-mass spectrometry.

[0037] Step 3. Perform targeted and non-targeted data processing on the spectral information from Step 2, conduct multivariate statistical analysis, and screen out metabolites with significant differences;

[0038] Step 4. Metabolites were enriched using network pharmacology, and the levels of different metabolites were compared among the control group, model group, and treatment group to elucidate the potential mechanism of action of Gualou Xiebai Banxia Decoction in treating heart failure.

[0039] In step 2, the DIAAA derivatization method is as follows: 5 μL of HOBt, 5 μL of DIAAA-TEA, and 5 μL of HATU are added sequentially to each dried sample. After shaking and mixing thoroughly, the reaction is carried out at room temperature for 1 min, and terminated with 35 μL of acetonitrile. The chromatographic conditions for liquid chromatography-mass spectrometry in step 2 include: stationary phase: a column packed with octadecylsilane-bonded silica gel; mobile phase: mobile phase A is water containing 0.1% (v / v) formic acid, and mobile phase B is a solution containing 0.1% (v / v) formic acid and acetonitrile; gradient elution is used: 0.0–0.5 min, mobile phase A from 98.0% → 95.0%, mobile phase B from 2.0% → 5.0%; 0.5–2.0 min, mobile phase A from 95% → 95%. 0%→92.2%, mobile phase B from 5.0%→7.8%; 2.0~4.0 min, mobile phase A from 92.2%→91.0%, mobile phase B from 7.8%→9.0%; 4.0~6.0 min, mobile phase A from 91.0%→86.0%, mobile phase B from 9.0%→14.0%; 6.0~10.0 min, mobile phase A from 86.0%→77.0%, mobile phase B from 14.0%→23.0%; 10. From 0 to 13.0 min, mobile phase A decreased from 77.0% to 67.0%, and mobile phase B decreased from 23.0% to 33.0%; from 13.0 to 18.0 min, mobile phase A decreased from 67.0% to 53.0%, and mobile phase B decreased from 33.0% to 47.0%; from 18.0 to 22.0 min, mobile phase A decreased from 53.0% to 40.0%, and mobile phase B decreased from 47.0% to 60.0%; from 22.0 to 24.0 min, mobile phase A decreased from 40.0% to 40.0%. %→10.0%, mobile phase B from 60.0%→90.0%; 24.0~24.5min, mobile phase A from 10.0%→5.0%, mobile phase B from 90.0%→95.0%; 24.5~26.9min, mobile phase A from 5.0%→5.0%, mobile phase B from 95.0%→95.0%; 26.9~27.0min, mobile phase A from 5.0%→98.0%, mobile phase B from 95.0%→2.0%.

[0040] In step 2, the mass spectrometry conditions for the liquid chromatography-mass spectrometry (LC-MS) method include: ESI ion source; backflushing nitrogen temperature of 300℃; backflushing nitrogen flow rate of 11.0 L / min; nebulizer gas flow rate of 25 psig; auxiliary nitrogen temperature of 325℃; auxiliary nitrogen flow rate of 11.0 L / min; capillary inlet voltage of 3500 V; Agilent jet outlet voltage in positive ion mode of 500 V(+); source collision voltage of 175 V; cone voltage of 65.0 V; Octopole RF Peak of 750 V; full scan mode; and scan range of m / z 200–1700. In step 3, the multivariate statistical analysis method includes: using GraphPad Prism 9.5.1 for analysis of variance (ANOVA) to perform statistical analysis on samples from multiple groups, and screening for metabolites with statistically significant differences (P < 0.05).

[0041] (e) Map the differential metabolites to the pathways obtained in step (c) to establish a dynamic association between "component-target-metabolite" and elucidate the causal mechanism by which the drug improves heart failure through target-regulated metabolic reprogramming; (f) Evaluate the binding affinity between the active ingredient in step (a) and the core target in step (b) through molecular docking, and verify the high-affinity component-target pair based on the binding energy threshold; Molecular docking analysis includes the following steps: using the Uniport database to find the tertiary structure of key proteins and saving it as a ".pdb" file; importing the potential active ingredient in step (a) into ChemDraw and saving it as a ".mol2" file, and uploading the component and protein structure to AutoDock for molecular docking. The receptor protein for molecular docking is the core target protein in step (b), and the ligand molecule is the ".mol2" structure of the potential active ingredient of the Trichosanthes kirilowii, Allium macrostemon, and Pinellia ternata decoction in step (a). (g) Integrate the results of steps (a)-(f) to cross-validate the causal association between network pharmacology targets and changes in metabolomics metabolites.

[0042] Figure 6 Metabolomics analysis of heart tissue samples was presented. Figure 6 A shows the FBMN analysis of cardiac tissue after GXBD treatment. This FBMN (functional metabolic network) diagram illustrates the metabolic changes after GXBD treatment. Each node represents a metabolite, and the color and size of the node are related to the change in the metabolite or the importance of the pathway. Different colored nodes and lines represent different metabolic pathways or metabolites, reflecting changes in different metabolic networks before and after treatment. Figure 6B represents the iPath analysis. The iPath analysis uses a metabolic pathway map, which displays detailed information about different metabolic pathways. The size and color of the circles in the map represent statistical significance (FDR-adjusted p-value) and the influence of each metabolic pathway. Darker colors indicate lower FDR-adjusted p-values, meaning these pathways are more significant in the analysis. This clearly shows which metabolic pathways underwent significant changes under GXBD treatment. Figure 6 C represents the pathway enrichment analysis. This section uses scatter plots to illustrate the enrichment of metabolic pathways. The X-axis represents the pathway impact, and the Y-axis represents the statistical significance (P-value). Red and yellow dots represent metabolic pathways significantly enriched in GXBD treatment, especially those with significant P-values ​​(FDR-adjusted P < 0.001), such as glycolysis and amino acid metabolism pathways. These plots demonstrate the impact of GXBD treatment on cardiac tissue metabolic pathways, revealing key changes in metabolic pathways and their potential impact on disease models. The color and size contrasts in the plots help illustrate differences in statistical significance and pathway impact.

[0043] Figure 7 The study illustrates the metabolic pathways associated with potential biomarkers and the differences between different experimental groups. The experimental groups included a healthy group, a model group, and groups receiving different doses of GXBD treatment (low, medium, and high doses). The diagrams clearly show the variations in different metabolites across these groups.

[0044] The metabolites shown in the figure include lactate, pyruvate, methionine, glutathione, serine, glycine, malic acid, fumarate, succinate, α-ketoglutarate, glutamine, and glutamate. These metabolites, along with related enzymes (such as lactate dehydrogenase LDH, pyruvate kinase PKM2, serine hydroxymethyltransferase SHMT1, and glutamate dehydrogenase GDH), participate in the regulation of various metabolic pathways. Specifically, the figure highlights several key metabolic pathways, including the conversion of acetyl-CoA to lactate, related metabolites of the citric acid cycle (such as malic acid, fumarate, succinate, and α-ketoglutarate), and the glutathione synthesis pathway. The bar chart shows the differences in metabolite levels between different groups, with each bar representing the mean for each group, and statistical significance indicated by an asterisk. Comparisons between the healthy group, the model group, and the GXBD treatment group reveal the regulatory effects of different doses of GXBD on metabolic pathways. For example, pyruvate was significantly upregulated in the model group sample, while lactate was significantly downregulated. Alterations in the pyruvate-lactate axis have been reported as a fundamental characteristic of heart failure and are used for early diagnosis. After intervention with Gualou Xiebai Banxia Decoction, both metabolites returned to levels similar to the control group, especially in the M-treated group. Meanwhile, serine, glycine, and methionine have been reported to be involved in one-carbon metabolism, which is significantly correlated with heart failure. Furthermore, serine is a component of glutathione, a cellular antioxidant that combats oxidative stress; the decrease in glutathione levels in the model group samples may be due to excessive consumption. Conversely, Gualou Xiebai Banxia Decoction can increase glutathione levels, protecting tissues from damage and thus preventing heart disease. Through the analysis of these differences, researchers were able to assess the impact of GXBD on these potential biomarkers, providing a scientific basis for treatment strategies. This figure, through a vivid depiction of metabolic pathways and bar chart analysis, helps us understand the effects of GXBD treatment at different doses on the metabolic system.

[0045] like Figure 8 As shown in this invention, the mechanism of action of GXBD in the treatment of heart failure in mice is revealed. GXBD improves the symptoms of heart failure in mice, reduces cardiac fibrosis, and repairs myocardial damage by remodeling energy metabolism. Specifically, GXBD regulates multiple metabolic pathways, including the TCA cycle, pyruvate-lactate axis, glutamate metabolism, and one-carbon metabolism. These changes help improve cellular energy supply and metabolic balance. Furthermore, the effects of GXBD may also be achieved by regulating the HIF-1 and TNF-α signaling pathways, promoting the suppression of inflammatory responses and the optimization of cellular metabolism, thereby alleviating the pathological damage of heart failure. These findings provide important theoretical basis for the potential application of GXBD in the treatment of heart failure.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for analyzing the mechanism of action of Gualou Xiebai Banxia Decoction in treating heart failure, characterized in that, Includes the following steps: (a) The potential active ingredients and their targets of action of Gualou Xiebai Banxia Decoction were obtained through network pharmacology analysis, and the intersection with the target of heart failure was obtained to obtain the target genes of component-disease intersection. (b) Construct a protein interaction network based on the intersection target genes, and screen core targets through topology analysis; (c) Perform pathway enrichment analysis on core targets to elucidate the drug action mechanism; (d) Detect metabolite changes in a heart failure model after drug administration using metabolomics technology, and screen for differentially expressed metabolites; (e) Map the differential metabolites to the pathway obtained in step (c) to establish a dynamic association between components, targets, and metabolites, and elucidate the causal mechanism by which drugs improve heart failure through target-regulated metabolic reprogramming. (f) The binding affinity between the active ingredient in step (a) and the core target in step (b) was evaluated by molecular docking, and the high affinity ingredient-target pair was verified based on the binding energy threshold. (g) Integrate the results of steps (a)-(f) to cross-validate the causal association between network pharmacology targets and changes in metabolomics metabolites.

2. The method according to claim 1, characterized in that, The screening threshold for potential active ingredients in step (a) is: Oral bioavailability (OB) > 30%, drug similarity (DL) > 0.18, and intestinal epithelial cell permeability (Caco-2) > -0.

4.

3. The method according to claim 1, characterized in that, The selection of core targets in step (b) is based on: The median of degree centrality, closeness centrality, and betweenness centrality is used in topological analysis, and the target points are selected by sorting them in descending order of degree centrality.

4. The method according to claim 1, characterized in that, The metabolomics analysis in step (d) includes: (d1) Preparation of Trichosanthes kirilowii, Allium macrostemon and Pinellia ternata decoction: Add rice wine to the medicinal materials, decoct and concentrate, then filter; (d2) The heart failure animal model was administered the drug by gavage, and heart tissue was collected and processed for derivatization. (d3) Metabolites were detected by liquid chromatography-mass spectrometry (LC-MS), and differentially expressed metabolites were screened by multivariate statistical analysis.

5. The method according to claim 4, characterized in that, The derivatization process in step (d2) uses the DIAAA method: Add 5 μL HOBt, 5 μL DIAAA-TEA and 5 μL HATU to the dry sample in sequence, shake the reaction at room temperature for 1 min and then terminate with 35 μL acetonitrile.

6. The method according to claim 4, characterized in that, The chromatographic conditions for LC-MS in step (d3) are as follows: Column: Octadecylsilane-bonded silica gel packing material; Mobile phase A: 0.1% formic acid aqueous solution; Mobile phase B: 0.1% formic acid acetonitrile solution; Gradient elution program: 0.0–0.5 min, mobile phase A from 98.0% → 95.0%, mobile phase B from 2.0% → 5.0%; 0.5–2.0 min, mobile phase A from 95.0% → 92.2%, mobile phase B from 5.0% → 7.8%; 2.0–4.0 min, mobile phase A from 92.2% → 91.0%, mobile phase B from 7.8% → 9.0%; 4.0–6.0 min, mobile phase A from 91.0% → 86.0%, mobile phase B from 9.0% → 14.0%; 6.0–10.0 min, mobile phase A from 86.0% → 77.0%, mobile phase B from 14.0% → 23.0%; 10.0–13.0 min, mobile phase A from 77.0% → 67.0%, mobile phase B from 23.0% → 33.0%; 13 From 18.0 to 18.0 min, mobile phase A decreased from 67.0% to 53.0%, and mobile phase B decreased from 33.0% to 47.0%; from 18.0 to 22.0 min, mobile phase A decreased from 53.0% to 40.0%, and mobile phase B decreased from 47.0% to 60.0%; from 22.0 to 24.0 min, mobile phase A decreased from 40.0% to 10.0%, and mobile phase B decreased from 60.0% to 90.0%; from 24.0 to 24.5 min, mobile phase A decreased from 10.0% to 5.0%, and mobile phase B decreased from 90.0% to 95.0%; from 24.5 to 26.9 min, mobile phase A decreased from 5.0% to 5.0%, and mobile phase B decreased from 95.0% to 95.0%; from 26.9 to 27.0 min, mobile phase A decreased from 5.0% to 98.0%, and mobile phase B decreased from 95.0% to 2.0%.

7. The method according to claim 4, characterized in that, The LC-MS mass spectrometry conditions in step (d3) are as follows: Ion source: ESI; Capillary voltage: 3500V; Nitrogen temperature: 300℃ (backflushing), 325℃ (auxiliary); Scan range: m / z 200~1700.

8. The method according to claim 4, characterized in that, The multivariate statistical analysis in step (d3) includes: Analysis of variance (ANOVA) was used to screen for differentially expressed metabolites between groups (P<0.05).

9. The method according to claim 1, characterized in that, Molecular docking in step (f) includes: (f1) Obtain the tertiary structure file of the core target and the molecular structure file of the active ingredient; (f2) Calculate the binding energy using molecular docking software and screen for component-target pairs with binding energy ≤ -5.0 kcal / mol; wherein, the target structure in step (f1) is derived from the Uniport database and the active ingredient structure is generated by ChemDraw; and docking is performed using AutoDock Vina software in step (f2).

10. The method according to claim 1, characterized in that, It also includes step (h): Animal models of heart failure were used to verify the improvement of pathological phenotypes, and the results were compared with those of integrated analysis to ensure that the mechanism predictions were consistent with the actual efficacy.

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