Exploring the potential mechanisms of Mahuang Fuzi Xixin decoction in treating elderly bronchial asthma through network pharmacology, molecular docking, and molecular dynamics simulations

Abstract

Modern medical practice has confirmed the efficacy of Mahuang Fuzi Xixin Decoction (MHFZXXD) in treating elderly bronchial asthma, but its specific mechanisms of action remain to be clarified. Therefore, this study utilizes network pharmacology, molecular docking techniques, and molecular dynamics simulations to explore the key active components, core target genes, and potential mechanisms of MHFZXXD in the treatment of elderly bronchial asthma. Active components and related targets of MHFZXXD were identified through the retrieval and screening of the TCMSP, Swiss Targets Prediction, and Uniprot databases. Relevant targets for elderly bronchial asthma were searched using the GeneCards, OMIM, and Pharm GKB databases, followed by the selection of intersecting targets between the drug’s active components and the disease. A PPI network diagram was created using String and Cytoscape software, and the intersecting targets of the disease and the active components of traditional Chinese medicine were imported into the DAVID database for GO and KEGG enrichment analysis to further explore their potential mechanisms of action. Subsequently, molecular docking and molecular dynamics simulations were performed using AutoDock Vina and Gromacs to verify the binding capacity and stability of the core genes with the key active components. The study results indicate that the active components of MHFZXXD, such as quercetin, luteolin, and kaempferol, target multiple genes including AKT1, EGF, MYC, TGFB1, PTEN, and CCND1. They exert effects through signaling pathways such as TNF, PI3K-Akt, and HIF-1. Molecular docking and dynamics simulations show that the core targets bind stably with the key active components. Overall, MHFZXXD may reduce inflammatory responses and improve hypoxic conditions and apoptosis during the progression of elderly bronchial asthma through multiple active components, targets, and signaling pathways, thereby delaying the malignant progression of the disease. This provides relevant evidence and experimental data for clinical treatment and further research.

1. Introduction

Bronchial asthma is a heterogeneous disease characterized by chronic hyperreactive airway inflammation. Typical symptoms include rapid breathing, coughing, expectoration, chest tightness, and episodic nocturnal dyspnea.^[1] This illness can occur at any age but is more prevalent among children and the elderly. In recent years, the incidence of elderly bronchial asthma has been rising due to factors such as air pollution, respiratory viral infections, and the global increase in the aging population.^[2] While the clinical symptoms in the elderly are similar to those in younger individuals, the physiological state of older adults is distinct. Their bodily functions gradually deteriorate, and their immune system function declines. Many elderly patients also suffer from multiple underlying diseases, which can prolong the duration of the illness. If not controlled timely, an episode can rapidly worsen, leading to severe respiratory distress and potentially life-threatening conditions.^[3] Consequently, the proportion of severe cases among elderly bronchial asthma patients is relatively high, further increasing their mortality risk and gradually becoming a significant public health issue.^[4] Clinically, Western medicine often employs corticosteroids, antibiotics, and bronchodilators to manage respiratory symptoms. However, the effectiveness of these treatments is limited, leading to recurrent coughing after discontinuation of the medication and even long-term coughing and wheezing.^[5]

In traditional Chinese medicine (TCM), bronchial asthma is categorized as “Xiao Disease” or “Chuan Syndrome,” primarily affecting the lungs and associated with the spleen and kidney. The fundamental pathogenesis involves phlegm obstructing the airways and the lungs failing to distribute and descend Qi properly. The onset of bronchial asthma is attributed to phlegm latent in the lungs, serving as the “intrinsic root” of the disease. Exacerbations are triggered by external pathogenic factors or internal disturbances such as emotional stress or physical exhaustion, leading to phlegm obstructing the airways, causing the lung Qi to ascend abnormally. This results in constricted airways with audible wheezing, breathing difficulties, and in severe cases, an inability to lie flat due to severe dyspnea.^[6] Zhu Danxi first coined the term “asthma” and identified phlegm as the primary pathological factor, proposing treatment principles that focus on supporting the righteous Qi before the onset and attacking the pathogenic Qi once the disease manifests. Mahuang Fuzi Xixin Decoction (MHFZXXD), derived from the “Annotations on the Treatise on Cold Pathogenic Diseases,” consists of ephedra, aconite, and asarum. Its effects include dispersing lung Qi, transforming phlegm, stopping cough, and relieving asthma. Modern medical practice has confirmed the efficacy of MHFZXXD in treating elderly bronchial asthma,^[7] though its specific mechanisms of action remain to be clarified.

In recent years, network pharmacology has been increasingly applied in both clinical and experimental research. By leveraging the advantages of systems biology, it constructs comprehensive network relationships among drugs, targets, and diseases. This approach regulates gene expression based on the micro-pharmacology of traditional Chinese medicine, elucidating the mechanisms of drug actions through multiple pathways and targets, aligning with the holistic theoretical concepts of traditional Chinese medicine.^[8] Molecular docking is a theoretical simulation method used to study the interactions between small molecule ligands and protein receptors and to predict their binding modes and affinities. It explores the conformations of ligands within the binding sites of macromolecular targets. Through docking, it can predict the interactions between ligands and targets at the molecular level, or determine structure-activity relationships (SAR).^[9,10] Molecular dynamics simulation is a powerful tool for studying the dynamic behaviors of biomacromolecules such as proteins and DNA. Based on a single conformation, it explores the dynamic processes of interactions between targets and small molecules, better revealing the mechanisms behind the actions of small molecules and providing evidence for the identification of small molecules with potential targets.^[11] Therefore, this study will employ network pharmacology, molecular docking, and molecular dynamics simulations to explore the potential mechanisms of MHFZXXD, providing a foundation and insights for further research. The flowchart of this study is shown in Figure 1.

Figure 1.

The flowchart of the present study.

2. Materials and methods

2.1. Collection of main components and related targets of MHFZXXD

Active components of MHFZXXD were screened through the TCMSP database,^[12] using oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 as selection criteria.^[13] Based on these criteria, the main active components of ephedra, aconite, and asarum were identified. Subsequently, the corresponding targets of these active components were determined using the TCMSP database. For some active components whose targets were not found in the database, the Pubchem database^[14] was used to obtain the Canonical SMILES of the active components. These were then imported into the Swiss Targets Prediction database^[15] (which predicts therapeutic targets of small molecules based on the principle of similarity, with accuracy based on prediction scores, offering certain advantages), selecting “human” as the species and using “Probability > 0” as the filtering criterion. This step was followed by the supplementation of all potential targets for the active components not previously identified. Next, the targets predicted by the TCMSP database and Swiss Targets Prediction were integrated and deduplicated. The Uniprot database^[16] was then used to filter for human-related and validated targets, obtaining standardized names.

2.2. Collection of disease targets

In the GeneCards, PharmGKB, and OMIM databases, “Elderly bronchial asthma” was used as the search keyword to identify disease-related targets.^[17–19] Subsequently, these disease targets were merged and deduplicated to determine the target disease targets (The database urls referred to in 2.1 and 2.2 are shown in Table 1).

Table 1.

Databases and platforms used to collect data about drugs and diseases.

Databases	Website
TCMSP	https://tcmsp-e.com/tcmsp.php
Pubchem	https://pubchem.ncbi.nlm.nih.gov/
Swiss targets prediction	http://www.swisstargetprediction.ch/
Uniprot	https://www.uniprot.org/
GeneCards	https://www.genecards.org/
Pharm GKB	https://www.pharmgkb.org/
OMIM	https://www.omim.org/

2.3. Construction of the “MHFZXXD-elderly bronchial asthma” protein–protein interaction network

The drug targets and disease targets from sections 2.1 and 2.2 were imported into VENNY 2.1.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) to identify intersecting targets. These intersecting targets were then entered into the STRING database^[20] (https://string-db.org) to generate a protein–protein interaction (PPI) network. Genes not attached to the main body of the PPI network were removed, and the results were exported as a TSV file. This TSV file was then imported into Cytoscape 3.10.0,^[21] where the CytoHubba plugin was used to filter the top 150 genes based on maximum clique centrality (MCC), maximum neighborhood component (DMNC), and neighborhood component centrality (MNC).^[22] These genes were then re-imported into VENNY 2.1.0 to intersect and identify the core targets for treating elderly bronchial asthma with MHFZXXD. Subsequently, these core targets were re-imported into the STRING database to construct a PPI network diagram, which was further imported into Cytoscape 3.10.0 for visualization. The network analyzer plugin was used for topological analysis to determine the core therapeutic targets.

2.4. Construction of the “active component-disease target” network and identification of key active components

The interactions between the active components of traditional Chinese medicine identified in section 2.1 and the core targets identified in section 2.3 were organized to obtain network and type files. These files were imported into Cytoscape 3.10.0 software to construct the “Traditional Chinese Medicine-Active Component-Target-Disease” network for the treatment of elderly bronchial asthma with MHFZXXD. Subsequently, the network analyzer plugin was used to perform network topological analysis, and key parameters such as the degree value were utilized to assess the importance of the connections between active components and core targets.

2.5. GO and KEGG analysis

The intersection targets of traditional Chinese medicine active components and disease obtained in section 2.3 were imported into the DAVID database^[23] (https://david.ncifcrf.gov/) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results were then visualized using the Weishengxin online platform (https://www.bioinformatics.com.cn).

2.6. Molecular docking validation

To further analyze the interaction strength between core targets and key active components, the 3D structures of target core proteins and the molecular structures of key active components were obtained from the PDB database^[24] (http://www.rcsb.org/) and the TCMSP database, respectively. The structures of proteins and molecules were preprocessed using PyMOL and AutoDock Tools software. Molecular docking was then performed using AutoDock Vina software to validate their binding activity.^[25] Finally, the docking results were imported into Discovery Studio software for visualization and analysis of the docking conformations.

2.7. Molecular dynamics simulation

After molecular docking, the analysis revealed that Neokadsuranic acid B binds most stably with AKT1, with a binding energy of −9.32 kcal/mol. Subsequently, a 150 ns molecular dynamics simulation of this complex was performed using Gromacs 2023.^[26] Initially, a hexahedral simulation box was constructed, and the ligand-protein complex was placed at the center of the box. Water molecules (TIP3P) were added to fill the remaining volume of the box, followed by the addition of chloride/sodium atoms to neutralize the system. Energy minimization for each system was performed using the steepest descent method. To equilibrate the systems, a 2-step equilibration using the leap-frog algorithm was conducted for 100 ps each under NVT and NPT ensembles, maintaining the pressure at 1 bar and the temperature at 300K. The final simulation files were used to calculate root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (Rg), and the results were visualized and analyzed.

3. Results

3.1. Active components of traditional chinese medicine and related targets

After preliminary screening, a total of 52 active components of traditional Chinese medicine met the criteria. Using the TCMSP and Swiss Target Prediction databases, 466 targets were identified for these components. The target names were standardized using the UniProt database, and targets and active components without corresponding genes were removed. Subsequently, the active components of MHFZXXD were identified as 23 from ephedra, 21 from aconite, and 8 from asarum, with related targets identified as 461 for ephedra, 436 for aconite, and 155 for asarum. After merging and removing duplicate components and targets, a total of 44 active components and 466 targets were obtained.

3.2. Potential target prediction for the treatment of elderly bronchial asthma with MHFZXXD

Following the method described in section 2.2, searches were conducted in the GeneCards, OMIM, and PharmGKB databases, yielding 1363, 39, and 11 disease targets, respectively. After deduplication, a total of 1399 disease targets were identified. Subsequently, the targets of the active components from section 3.1 and the disease targets were imported into VENNY 2.1.0 to obtain 217 intersecting targets (Fig. 2). Using the core target selection method described in section 2.3, 69 core targets were ultimately identified. These 69 core targets were used to construct a PPI network diagram (Fig. 3A and B), consisting of 69 nodes and 1318 edges. Topological analysis was performed using the Network Analyzer plugin, and the core targets were ranked based on degree values. The top-ranked core targets are listed in Table 2, suggesting that these targets play a significant role in the treatment of elderly bronchial asthma with MHFZXXD.

Figure 2.

Venn diagram of common targets between MHFZXXD and elderly bronchial asthma. Note: EBA = elderly bronchial asthma.

Figure 3.

(A) PPI network of intersection targets between MHFZXXD and elderly bronchial asthma. (B) PPI network visualization of genes at the intersection of MHFZXXD and elderly bronchial.

Table 2.

Related parameters of core target network topology.

Target	Degree	Closeness centrality	Betweenness centrality
AKT1	67	0.985507246	0.027167684
EGF	62	0.918918919	0.020154539
MYC	61	0.906666667	0.016782883
TGFB1	61	0.906666667	0.020047243
PTEN	59	0.883116883	0.015566292
CCND1	58	0.871794872	0.014596983
MMP2	57	0.860759494	0.015942506
FOS	56	0.85	0.014438256

3.3. Construction of the “traditional Chinese medicine-active ingredient-disease-target” network and screening of key active ingredients

The network and type files obtained in section 2.4 were imported into Cytoscape 3.10.0 to construct the “Traditional Chinese Medicine-Active Ingredient-Target-Disease” network for the treatment of elderly bronchial asthma with MHFZXXD (Fig. 4). The network analyzer plugin was used for topological analysis. The analysis results showed that the active ingredients with the highest degree values were quercetin, luteolin, kaempferol, benzoylnapelline, Neokadsuranic acid B, ignavine, beta-sitosterol, and naringenin, indicating that these active ingredients play a significant role in the treatment of elderly bronchial asthma by MHFZXXD (Table 3).

Figure 4.

“TCM – active ingredient – target – disease” network diagram of MHFZXXD for the treatment of elderly bronchial asthma. Note: EBA = elderly bronchial asthma; MH = ephedra; FZ = aconite; XX = asarum.

Table 3.

Data related to network topology of key active ingredients.

Active	Belonging TCM	Degree	Betweenness	Closeness
Quercetin	MH	45	1650.7231	0.52105266
Luteolin	MH	22	434.6011	0.41949153
Kaempferol	MH, XX	14	188.89256	0.396
Benzoylnapelline	FZ	14	200.8062	0.37218046
Neokadsuranic acid B	FZ	14	165.51791	0.36666667
Ignavine	FZ	13	275.51422	0.38976377
Beta-sitosterol	MH	5	42.53849	0.36131388
Naringenin	MH	5	34.10841	0.35869566

FZ = aconite, MH = ephedra, XX = asarum.

3.4. Gene ontology function and Kyoto encyclopedia of genes and genomes pathway analysis

Using the DAVID database, GO and KEGG analyses were performed on the 217 intersecting targets between the active components of traditional Chinese medicine and the disease obtained in section 3.2. A total of 923 biological processes (BP), 94 cellular components (CC), and 186 molecular functions (MF) were identified. These were sorted based on P-value, and the top 10 BPs, CCs, and MFs along with their related parameters are visually analyzed in Figure 5A.

Figure 5.

(A) GO enrichment analysis. (B) KEGG enrichment analysis. Note: The number of differential genes in each pathway (count) is represented by the size of the dots, and the significance of the pathway P-value is indicated by the depth of color. (C) Primary classification of the 25 significant pathways in KEGG enrichment analysis. Note: The left vertical axis represents the names of the pathways, and the right vertical axis represents the names of the primary categories. (D) Secondary Classification of the 25 significant pathways in KEGG enrichment analysis. Note: The vertical axis represents the names of the secondary categories.

For the KEGG pathway analysis, 182 related pathways were identified. These were sorted by P-value, and the top 25 are shown in Figure 5B. Subsequently, the most significant 25 pathways were categorized into primary and secondary classifications (Fig. 5C and D).

3.5. Molecular docking results

The top 5 key active components identified in section 3.3 were docked with the top 6 core targets from section 3.2. The binding affinity between the active components and the targets was assessed based on the binding energy values (a binding energy < −4.25 kcal/mol indicates a certain level of binding activity; < −5.0 kcal/mol indicates a higher level of binding activity; and < −7.0 kcal/mol indicates a very strong binding activity^[27]). According to the heatmap in Figure 6, out of 30 docking results, 28 had a binding energy < −4.25 kcal/mol, accounting for 93.3%; 27 had a binding energy < −5.0 kcal/mol, accounting for 90.0%; and 12 had a binding energy < −7.0 kcal/mol, accounting for 40.0%. The 6 best binding results were further visualized in Figure 7.

Figure 6.

Molecular docking heatmap. Note: The horizontal axis represents the core targets, the vertical axis represents the key active components, and the darker the color, the stronger the binding activity.

Figure 7.

Molecular docking visualization.

3.6. Molecular dynamics simulation results

Using the Gromacs 2023 software package, a 150 ns molecular dynamics simulation was conducted on the complex Neokadsuranic acid B-AKT1. The dynamic stability and structural changes of the complex were analyzed using RMSD (of the protein backbone), RMSF, and Rg. The results showed that the RMSD values fluctuated around 0.310 nm, indicating that the system is relatively stable (Fig. 8A). Additionally, the RMSF values for most of the protein residues were <0.5 nm, suggesting that the protein maintains a relatively stable structure (Fig. 8B). Moreover, during the 150 ns MD simulation, the RMSD values of the small molecule stabilized at 0.22 nm, indicating minor fluctuations and stable binding with the protein (Fig. 8C). Figure 8D presents the Rg values of the protein, with the radius of gyration around 2.20 nm, indicating a stable protein structure. Figure 8E shows the hydrogen bond interactions between the protein and the compound, with most of the simulation time exhibiting 2 or more hydrogen bonds shorter than 0.35 nm. Overall, the ligand binds relatively stably with the protein, further suggesting that the ligand has high potential as a prospective inhibitor of the protein.

Figure 8.

Molecular docking display.

4. Discussion

Bronchial asthma is a chronic inflammatory airway disease characterized by airway inflammation, increased mucus secretion, and hyperreactivity of the airways. Currently, the diagnosis and treatment of bronchial asthma do not differentiate between young and elderly patients. However, elderly bronchial asthma may exhibit unique characteristics that complicate its management. Therefore, scholars suggest that a multidisciplinary and multidimensional approach should be adopted for the treatment of elderly bronchial asthma patients.^[28] Studies have shown that the clinical treatment with modified MHFZXXD can effectively improve the traditional Chinese medicine syndrome scores, bronchial asthma control scores, lung function, and other indicators in patients with elderly bronchial asthma, playing an advantageous role in enhancing the control of bronchial asthma and restoring lung function.^[29]

This study, through network pharmacology analysis, found that the main active components of MHFZXXD are quercetin, luteolin, kaempferol, and naringenin, among others. Quercetin, a flavonol compound found in plants, possesses anti-inflammatory, antioxidant, and antibacterial properties.^[30–32] It can alleviate iron drooping during neutrophilic inflammation in the airways, reduce M1 macrophage polarization,^[33] and inhibit particulate matter-induced damage to the airway epithelial cell barrier,^[34] showing potential in improving airway conditions.^[35] Quercetin is also an effective antioxidant, playing a crucial role in age-related diseases.^[36] Studies have shown that quercetin can significantly reduce histamine and protein content in BALF, PLA2 activity, and leukocyte recruitment. Histopathological examinations reveal that quercetin can improve mild infiltration of eosinophils and neutrophils, and its mechanism of action against bronchial asthma is similar to that of sodium cromoglycate and dexamethasone.^[37] Luteolin, a flavonoid compound, regulates airway inflammation in asthmatic rats by modulating PPARγ expression and the p38MAPK signaling pathway,^[38] and can also inhibit IL-36γ secretion-mediated MAPK pathway to alleviate neutrophilic asthma, providing a theoretical basis for the use of luteolin in the treatment of bronchial asthma.^[39] Kaempferol, as a dietary anti-inflammatory agent, plays a role in various diseases, with functions in regulating endoplasmic reticulum stress and autophagy, protecting cells from dysfunction by regulating autophagy in noncancerous cells.^[40,41] Additionally, studies have shown that kaempferol can alleviate airway inflammation in airway epithelial cells exposed to endotoxins and in asthmatic mice by regulating the Tyk2-STAT1/3 signaling pathway and modulating IL-8, making it a potential therapeutic agent for bronchial asthma.^[42] Naringenin, a dihydroflavonoid compound, has been demonstrated in cell experiments to potentially affect the biological functions of TGF-β1 in bronchial epithelial cells by inhibiting the phosphorylation of Smad2 and reducing the nuclear content of Smad2, thereby effectively improving airway remodeling, reducing inflammation levels, and alleviating inflammatory infiltration.^[43] These studies indicate that compounds such as quercetin, luteolin, kaempferol, and naringenin play roles in treating bronchial asthma primarily by protecting the barrier function of airway epithelial cells and regulating airway inflammation.

The core target genes identified in this study include AKT1, EGF, MYC, TGFβ1, PTEN, CCND1, MMP2, and FOS. AKT1, through the regulation of SIRT1, controls the levels of interleukin-6 and interleukin-1β, which are crucial pathways affecting lung function in patients with bronchial asthma.^[44,45] Furthermore, studies have shown that disrupting AKT1 signaling can block the entry of HIF-1α, STAT3, and NF-κB into the nucleus, thereby inhibiting the release of cytokines and immunoglobulin E. This can reduce airway hyperresponsiveness and potentially reverse airway remodeling.^[46] ERα is involved in the pathogenesis and progression of asthma, with its expression related to airway inflammation and remodeling. EGF can stimulate the increase in ERα expression and activate its phosphorylation through extracellular signal-regulated kinase and c-Jun N-terminal kinase pathways, thereby alleviating epithelial-mesenchymal transition and mucus production in asthmatic airway epithelial cells.^[47] The MYC family primarily includes members such as C-myc, N-myc, and L-myc, with C-myc playing a significant role in controlling cell growth and vitality. Reductions in its expression or inappropriate expression may be associated with cell apoptosis.^[48] Research indicates that TGFβ1 is associated with epithelial fibrosis and epithelial-mesenchymal transition. Inhibiting the activation of the TGFβ1/p-STAT3/CTGF signaling pathway can alleviate airway inflammation and remodeling, thereby suppressing the proliferation, migration, and inflammatory infiltration of vascular smooth muscle cells. PTEN plays a crucial role in maintaining cellular structure and signal transduction, primarily through the negative regulation of the PI3K/Akt pathway, thus participating in cell survival, proliferation, and migration.^[49] Studies have shown that the expression of PTEN is reduced in the respiratory epithelial cells of antigen-sensitized mice, and adenoviruses carrying PTEN complementary DNA can significantly reduce levels of eosinophils and inflammation.^[50] These findings suggest that PTEN may exert its role in asthma pathogenesis through epithelial cells and could play a critical role in the pathogenesis of airway inflammation in asthma. CCND1 is a positive regulator in the cell cycle process of mammalian cells, and the division and proliferation of asthmatic airway smooth muscle cells are closely related to high expression of CCND1. Additionally, high expression of CCND1 promoting the proliferation of airway smooth muscle cells can accelerate airway remodeling in asthma.^[51] Studies have shown that the level of MMP2 in the lung tissue of asthmatic rats correlates positively with the area of airway smooth muscle, suggesting that MMP2 may be involved in smooth muscle hyperplasia. Moreover, MMP2 can degrade type IV collagen in the basement membrane, leading to inflammation, and an increase in serum MMP2 concentration is associated with a higher percentage of eosinophils in the blood, further indicating that the pathophysiology and severity of asthma are closely related to MMP2.^[52] c-Fos, a product of the FOS gene, has been shown in experiments to have significantly increased expression in the bronchiolar epithelium of rats during acute asthma attacks, suggesting that asthma attacks are associated with the involvement of c-fos. c-Fos targets airway epithelial cells, leading to the activation and shedding of epithelial cells. Furthermore, the activation and shedding of damaged epithelial cells can release various inflammatory mediators and cytokines, exacerbating the inflammatory response.^[53]

GO and KEGG enrichment analyses suggest that the mechanism of action of MHFZXXD in treating elderly bronchial asthma involves various biological processes. According to the GO results, its cellular components include macromolecular complexes, the nucleus and cytoplasm, extracellular spaces, extracellular regions, and membrane rafts. Molecular functions range from intermolecular interactions to overall cellular functions, playing a crucial role in responding to external stimuli, regulating cell proliferation, inhibiting apoptosis, and reacting to hypoxia. These processes involve the activation of signaling pathways and changes in gene expression to adapt to environmental changes. The main biological processes include enzyme binding, homotypic protein binding, protein homodimerization activity, and steroid binding, all of which are closely linked to disease progression. KEGG pathway results indicate that pathways such as TNF, PI3K-Akt, and HIF-1 play significant roles in the therapeutic effects of MHFZXXD on elderly bronchial asthma. TNF-α, as a key cytokine of Th2 cells, plays a crucial regulatory role in the proliferation of goblet cells (GC) in the bronchial epithelium of asthma, overexpression of mucin 5AC, and the process of high mucus secretion.^[54] The PI3K-Akt pathway and Akt pathway regulate protein synthesis, proliferation, and survival. Studies have shown that the PI3K/Akt signaling pathway is involved in inhibiting allergic airway inflammation in asthmatic mice, reversing glucocorticoid resistance, alleviating airway remodeling, and reducing airway hyperresponsiveness (AHR), thus protecting patients from allergic asthma.^[55] HIF-1α is a major regulatory factor in the inflammatory activity of myeloid cells, including neutrophils and macrophages. Using HIF-1α inhibitors during the induction of bronchial asthma can reduce AHR and eosinophil proliferation, and both HIF-1α and HIF-2α can regulate the migration of eosinophils in opposite ways.^[56]

Molecular docking of 5 key active components with 6 core targets showed that among 30 binding complexes, 28 had binding energies < −4.25 kcal/mol, accounting for 93.3%. This indicates that the key active components have good docking activity with the core targets, further suggesting a close relationship between these active components and target proteins in the treatment of elderly bronchial asthma with MHFZXXD. The binding site of Neokadsuranic acid B with AKT1 is relatively stable, with favorable binding energy. This result was further validated through MD simulation, which showed high binding affinity. The ligand can bind relatively stably with the protein, indicating its high intrinsic biological activity. The validation results are consistent with the molecular docking outcomes.

5. Conclusion

In summary, this study explored the potential mechanisms of MHFZXXD in treating elderly bronchial asthma using a combination of network pharmacology analysis, molecular docking techniques, and molecular dynamics simulations. The analysis results indicate that active pharmaceutical ingredients such as quercetin, luteolin, kaempferol, and naringenin interact with core targets including AKT1, EGF, MYC, and TGFβ1, and exert effects through signaling pathways such as TNF, PI3K-Akt, and HIF-1. This suggests that MHFZXXD may reduce inflammatory responses and improve hypoxic conditions and apoptosis in the progression of elderly bronchial asthma through multiple active components, multiple targets, and multiple signaling pathways, thereby delaying the malignant progression of the disease. This provides new insights for further experimental research and expanding the scope of clinical applications. The limitation of this study lies in the lack of experimental validation of the conclusions drawn. Although preliminary validation was conducted through molecular docking and molecular dynamics simulations, which to some extent supported the conclusions, these methods cannot fully replace experimental validation. Therefore, conducting further experimental verification of the accuracy of these conclusions is necessary and will be the core of future research efforts.

Acknowledgments

The authors gratefully acknowledge the support by the Science and Technology Department of Jilin Province (Nos. 20230203189SF and YDZJ202202CXJD049).

Author contributions

Conceptualization: Li Shi.

Data curation: Huan Ding, Shaodan Hu.

Writing – original draft: Hongpeng Yu, Xiaotong Wei.

Writing – review & editing: Feng Sun, Zhenghua Cao.