Exploring the mechanisms of Shenmai injection against Chronic Obstructive Pulmonary Disease based on network pharmacology and molecular docking validation

doi:10.21203/rs.3.rs-4838357/v1

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Exploring the mechanisms of Shenmai injection against Chronic Obstructive Pulmonary Disease based on network pharmacology and molecular docking validation

https://doi.org/10.21203/rs.3.rs-4838357/v1

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Background Chronic obstructive pulmonary disease (COPD) is a common respiratory disease with high morbidity and mortality. The conventional therapies remain palliative and have various undesired effects. Shenmai injection (SMI) has shown positive effects on COPD, but its molecular mechanisms are still unclear. This study aims to investigate the bioactive ingredients and underlying mechanisms of SMI in treating COPD based on network pharmacology analysis and molecular docking validation.

Methods Active ingredients in SMI were sourced from the TCMSP, SymMap, and BATMAN-TCM databases, with their targets predicted using Swiss target prediction. Potential COPD targets were obtained from the GeneCards, OMIM and GEO databases. Cytoscape software was employed to construct the candidate component-target network for SMI in treating COPD. Furthermore, the functions and signaling pathways of shared targets between SMI and COPD were enriched by GO and KEGG analyses. Finally, molecular docking studies of key active ingredients and hub targets were performed using Autodock vina software.

Results A total of 28 active components were identified, corresponding to 606 targets, including 341 common targets shared by the active components and COPD. The top 10 hub targets were screened, namely STAT3, SRC, EGFR, HSP90AA1, AKT1, IL6, TNF, BCL2, JUN and CCND1. GO enrichment analysis yielded 127 entries for biological processes (BP), 32 for cellular components (CC), and 33 for molecular functions (MF). The significantly enriched iterms in BP, CC, and MF enrichment were associated with response to xenobiotic stimulus, plasma membrane, and protein serine/threonine/tyrosine kinase activity, respectively. KEGG enrichment analysis identified 189 significantly enriched pathways, primarily including pathways in cancer, PI3K-Akt signaling pathway and chemical carcinogenesis- receptor activation. These pathways play roles in the effect of SMI on treating COPD. Molecular docking results demonstrated the effective binding of the primary active ingredients (Ophiopogonanone A, Ruscogenin, Stigmasterol) to their respective targets (EGFR, AKT1, HSP90AA1).

Conclusion SMI treats COPD through a multi-component, multi-target, and multi-pathway synergistic network, providing a basis for further exploration of SMI's mechanisms in COPD treatment.

Shenmai injection

Chronic obstructive pulmonary disease

Network pharmacology

Molecular docking

PI3K

Chronic obstructive pulmonary disease (COPD) is a common clinical disease, defining features include persistent airflow obstruction and respiratory symptoms ^[1]. COPD is generally associated with an increased chronic inflammatory response in the airways and lung tissues to harmful particles or gases. Its airflow limitation is often not fully reversible, leading to a progressive decline in lung function. If not effectively controlled, COPD can further develop into pulmonary heart disease and respiratory failure. Consequently, COPD is associated with high morbidity and mortality rates, causing tremendous socioeconomic burdens. It has become an important public health issue and a leading cause of death and disability worldwide^[2]. According to the World Health Organization, COPD has become the third leading cause of death globally and is projected to affect nearly 400 million people until 2030^[3]. In China, the overall prevalence of COPD among adults aged 40 years or older was 13.6% in 2014–2015 ^[4]. COPD is also a major public health problem that severely impairs human health^[5].

Currently accessible therapeutic alternatives for COPD primarily focus on symptom control. The main pharmacotherapy involves inhaled long-acting beta-agonists (LABAs), long-acting muscarinic antagonists (LAMAs) and inhaled corticosteroids (ICS)^[6]. Although these drugs are generally considered safe for treating COPD, they can cause adverse events such as increased drug resistance and hormone-related side effects^{[7, 8]}. Moreover, these conventional therapies have limitations and are not always effective in controlling symptoms and exacerbations^{[6, 9]}. Therefore, novel strategies are required for better therapeutic response and mitigated side effects^[10].

Traditional Chinese Medicine (TCM) plays an increasingly important role in the treatment of COPD due to its high efficacy and fewer adverse reactions^{[11, 12]}. Recent studies have provided further evidence supporting the efficacy, safety, and underlying mechanisms of TCM in treating COPD^[13]. TCM can reduce the frequency and duration of acute exacerbation in COPD patients, thereby improving their quality of life by delaying airway remodeling, eliminating phlegm, inhibiting inflammation, alleviating oxidative stress, and improving mitochondrial function^[14–17]. TCM offers more extensive treatment options and significant potential for ongoing research in the prevention and treatment of COPD.

Shenmai injection (SMI) is a Chinese patent drug extracted from Panax ginseng (Panax ginseng C.A. Mey, steamed and dry) and Ophiopogon japonicus (Ophiopogon japonicus [L.f.] Ker-Gawl, root), derived from the prescription Shendong yin in the book "Zheng Yin Mai Zhi"^[18]. It nourishes yin and jin, improves pulmonary function, and is widely used in treating qi-negative shock, coronary heart disease, viral myocarditis, granulocytopenia, and other conditions. In China, it is widely used as an organ-protective agent for treating shock, cerebral infarction, coronary heart disease, chronic pulmonary disease, malignant diseases, and more^[19–22]. SMI is often prescribed COPD therapy and has shown favorable outcomes in relieving COPD symptoms in patients^{[23, 24]}. Additionally, several clinical studies have demonstrated that SMI can reduce the incidence of adverse events when combined with Western medicine^[25]. However, the main components and detailed molecular mechanism of SMI in treating COPD remain unclear.

Network pharmacology, a systems biology method that integrates numerous authoritative medical databases, is increasingly applied to study the mechanisms of TCM. Using network pharmacology, the drug-target and target-disease network can be constructed, and the active components of TCM, potential targets can be effectively predicted. Moreover, it can conduct enrichment analysis and molecular docking, etc^[26].

Herein, this research adopts the method of network pharmacology, exploring the pharmacodynamic material basis and potential molecular mechanism of SMI in treating COPD. And molecular docking technology was used to verify on the binding ability of the core target to COPD, so as to provide theoretical foundation for carrying out fundamental experiment study and reasonable clinical application of SMI.

The flowchart of study strategy was shown in Fig. 1

Figure 1 The flowchart of study strategy.

2.1. Collection of ingredients and targets of SMI

The potential ingredients of Red ginseng and Ophiopogon japonicus in SMI were obtained through the Traditional Chinese Medicine Systems Pharmacology (TCMSP, https://tcmspw.com/tcmsp.php) database, SymMap (www.symmap.org) database and BATMAN-TCM (HomePage-BATMAN (ncpsb.org.cn)) database^[27]. The potential active components in SMI were screened by Swiss ADME (http://www.swissadme.ch/ ) database. Based on Lipinski's principle, small molecule compounds that meet three or more of the following criteria possess drug-like properties: molecular weight (MW) ≤ 500, moriguchi logP (MlogP) ≤ 4.15, H-bond donors (HBD) ≤ 5, and H-bond acceptors (HBA) ≤ 10. Oral bioavailability prediction is based on the Veber filter, which considers the number of rotatable bonds (RoB) ≤ 10 and the topological polar surface area (TPSA) ≤ 140 as having better oral bioavailability^[28]. In this study, compounds that meet both Lipinski's principle and the Veber filter were included as active ingredients of SMI^[29]. The compound targets were identified by inputting them into the Swiss Target Prediction database (http://www.swisstargetprediction.ch/ )with the species limited to “Homo sapiens” and “probability 0.1”. The protein targets of SMI compounds were mined and integrated using the TCMSP and BATMAN-TCM databases.

2.2 Acquisition of COPD-related targets

The keyword “chronic obstructive pulmonary disease” was used to explore potential targets in the GeneCards (https://www.genecards.org) ^[30] and Online Mendelian Inheritance in Man (OMIM) (https://omim.org/) databases^[31]. In GeneCards, a higher score indicates a closer association of the target with the disease. Targets from GeneCards with a relevance score > 10 were selected^[22]. Additionally, to refine the disease target information, we downloaded expression profiling data from GSE76925 on the microarray platform GPL10558 from the GEO database (http://www.ncbi.nlm.nih.gov/geo). Multi-chip joint analysis was performed using the SVA and limma packages in R, and batch correction was conducted. The threshold for identifying differentially expressed genes (DEGs) was set at logFC > 1 and P < 0.05. Then, volcano and heatmap plots of DEGs were plotted in Hiplot Pro (https://hiplot.com.cn/), a comprehensive web service for biomedical data analysis and visualization. Finally, disease targets were obtained by combining the targets from the three databases and removing duplicates.

2.3 Identification of common targets and Construction of the Compound-Target Network

The intersection of SMI and COPD targets were the main focus of this study. Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) was used acquire the common targets of COPD and SMI. The information of the active ingredients and common targets was then imported into Cytoscape software (version 3.10.1) to construct a compound-target interaction network, showing the relationships between candidate active components and targets. The software was used to calculate network topology parameters, including degree and betweenness centrality (BC), to screen the main candidate targets and monomer components.

2.4 Protein-protein interaction (PPI) network and identification of core targets.

To further investigate the interaction between SMI and COPD, the common targets were uploaded to the STRING 11.0 (https://cn.string-db.org) database. The species was restricted to ‘Homo sapiens’ and protein interactions with a confidence score > 0.4 were selected. The PPI relationships were obtained and visualized using Cytoscape v3.10.1 software. Additionally, the CytoNCA plugin in Cytoscape was used for topological analysis of the targets in the PPI network. To identify crucial core genes, the screening process was performed using the Cytohubba plugin of Cytoscape software, and 10 core targets were obtained.

2.5 Functional enrichment analysis

To analyze the molecular biological functions of SMI-COPD common targets, Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database (https://david.ncifcrf.gov/). GO analysis includes three aspects: biological process (BP), cellular component (CC) and molecular function (MF). The top 10 GO functional enrichment items were selected in ascending order by P-value.KEGG enrichment analysis was performed to identify important signaling pathways with a threshold of p < 0.05. Dot plots were created using an online platform (https://www.bioinformatics.com.cn) for data analysis and visualization.

2.6 Molecular Docking

Molecular docking was performed to visually investigate the interactions between the major identified components and selected potential targets. First, the two-dimensional structures of core components were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov) and subsequently converted to three-dimensional structures in mol 2 format using ChemDraw 3D software. The protein structures were obtained from the PDB database. The docking components and major target proteins were pretreated using PyMOL software, including the addition of hydrogens, removal of water molecules, and hydrogenation calculations. Next, the ligand was docked to the receptor using Autodock Vina 1.1.2 software. The docking results were visualized using Discovery Studio software.

3.1. Active compounds and targets screening

By searching the TCMSP, BATMAN-TCM and SYMMAP databases, deleting the duplicate components, 184 candidate components of SMI were finally obtained, which including 80 chemical components of red ginseng and 104 chemical components of Ophiopogon japonicus. Compounds meeting both the Lipinski principle and Veber filter criteria were selected as active ingredients of SMI for further study. Ultimately, 28 active ingredients were identified: 7 from red ginseng and 21 from Ophiopogon japonicus (Table 1).

Table 1

Basic information of the active ingredients
Herb	Molecule name	Formula	MW	Rotatable bonds	H-bond acceptors	H-bond donors	TPSA	MLOGP
Ophiopogon japonicus	Jasmololone	C11H16O2	180.24	3	2	1	37.3	1.46
	Ophiopogonanone A	C18H16O6	328.32	2	6	2	85.22	1.3
	Ophiopogonanone B	C18H18O5	314.33	3	5	2	75.99	1.44
	Ophiopogonanone C	C19H16O7	356.33	3	7	2	102.29	0.66
	Ophiopogonanone D	C2 0H18O7	370.35	4	7	1	91.29	0.88
	Ophiopogonanone E	C19H20O7	360.36	4	7	3	105.45	0.59
	Ophiopogonanone F	C20H22O7	374.38	5	7	2	94.45	0.82
	Ophiopogonone A	C18H14O6	326.3	2	6	2	89.13	1.11
	Ophiopogonone B	C18H16O5	312.32	3	5	2	79.9	1.25
	Ophiopogonone C	C19H14O7	354.31	3	7	2	106.2	0.47
	Orchinol	C16H16O3	256.3	2	3	1	38.69	2.45
	Ruscogenin	C27H42O4	430.62	0	4	2	58.92	4.09
	Diosgenin	C27H42O3	414.62	0	3	1	38.69	4.94
	Oleanolic Acid	C30H48O3	456.7	1	3	2	57.53	5.82
	Stigmasterol	C29H48O	412.69	5	1	1	20.23	6.62
	Flavonoid	C15H10O5	270.24	1	5	3	90.9	0.52
	Stigmasterol-Beta-D-Glucoside	C35H58O6	574.83	8	6	4	99.38	3.85
	N-Trans-Feruloyltyramine	C18H19NO4	313.35	7	4	3	78.79	1.89
	Uridine	C9H12N2O6	244.2	2	6	4	124.78	-2.24
	Methyl Ophiopogonanone B	C19H20O5	328.36	3	5	2	75.99	1.67
	Methyl Ophiopogonanone A	C19H18O6	342.34	3	6	1	74.22	1.53
Red ginseng	beta-sitosterol	C29H50O	414.71	6	1	1	20.23	6.73
	20(R)Protopanaxdiol	C30H52O3	460.73	4	3	3	60.69	5.12
	(20S)-Protopanaxadiol	C30H52O3	460.73	4	3	3	60.69	5.12
	ginsenoside-Rh1_qt	C30H52O4	476.73	4	4	4	80.92	4.27
	(20S)-Protopanaxatriol	C30H52O4	476.73	4	4	4	80.92	4.27
	20(R)-Protopanaxatriol	C30H52O4	476.73	4	4	4	80.92	4.27
	Agidol 7	C25H36O2	368.55	6	2	2	40.46	5.47

Using the Swiss Target Prediction database with a species filter set to “Homo sapiens”and a probability threshold of > 0.1, we identified 1625 targets. After removing duplicates, 606 SMI-related targets remained. Cytoscape software (3.10.1) was used to construct the component-target network, resulting in a diagram with 339 nodes and 2192 edges (Fig. 2).

Figure 2 SMI component-target network diagram

Ten major active components of SMI were topologically analyzed by Cytoscape software topology as described in Table 2.

Table 2

Ten major active components
Number	Components	Degree	Betweenness	Closeness
1	Orchinol	137	72512.86	0.39
2	Jasmololone	119	64242.52	0.38
3	Ophiopogonanone B	112	47337.86	0.37
4	N-Trans-Feruloyltyramine	110	59663.43	0.38
5	Ophiopogonanone A	108	45407.05	0.38
6	Ruscogenin	101	53113.32	0.38
7	(20R)-Protopanaxatriol	90	27097.35	0.37
8	Oleanolic Acid	86	46300.62	0.37
9	(20S)-Protopanaxatriol	75	15985.47	0.36
10	Stigmasterol	74	34888.15	0.37

3.2. Acquisition potential targets of SMI in treating COPD.

This GSE76925 dataset contains a genome-wide analysis of lung tissue from 111 COPD cases and 40 control smokers, used to identify differential genes in COPD. Using the screening criteria, we identified 301 DEGs: 46 up-regulated and 255 down-regulated genes. The volcano and heat maps are shown in Fig. 3. We combined the 301 DEGs with 2387 genes from GeneCards and OMIM databases, deduplicating to obtain 3737 targets. Among these, 341 targets were common to both SMI and COPD, as shown in Fig. 4, and became the focus of the subsequent analysis.

Figure 3 Volcano and heat map plots of DEGs in COPD. (a) Volcano plot of COPD-related DEGs. Red represents significantly upregulated genes in COPD and blue represents significantly downregulated genes in COPD, and gray represent genes not significantly differentially expressed. (b) Heat map of top 20 COPD-related DEGs, high gene expression is indicated by red with low gene expression by blue.

Figure 4 Venn map of the drug SMI and COPD

3.3 Construction of PPI network diagram and identification of core genes

PPI network analysis was conducted using the STRING online database, with results visualized in Cytoscape software. The PPI network (Fig. 4a) contains 341 nodes and 2209 edges, with an average node degree of 13. Nodes represent proteins, and edges represent protein-protein associations. Darker and larger nodes indicate higher significance. The Cytohubba plugin of Cytoscape software was used to analyze the hub genes based on topological parameters. The top 10 genes were identified based on degree value: STAT3, SRC, EGFR, HSP90AA1, AKT1, IL6, TNF, BCL2, JUN, and CCND1 (Fig. 5a).

Figure 5 (a) PPI network diagram. (b) The core genes were selected.

3.4 Functional enrichment analysis

The GO enrichment analysis results for SMI in treating COPD targets were shown in Fig. 6 (a) and included three aspects: biological process (BP),cellular component (CC), and molecular function (MF). A total of 192 GO enriched terms were identified with p < 0.05 and q < 0.05. Among these, 127 terms were related to BP, primarily including response to xenobiotic stimulus, protein phosphorylation, and negative regulation of apoptotic process. There were 32 terms related to CC, including plasma membrane, membrane raft, and integral component of plasma membrane. In the MF category, enrichment was primarily observed in functions involving protein serine/threonine/tyrosine kinase activity, protein kinase activity and protein tyrosine kinase activity. The core targets were mainly enriched in multiple KEGG pathways, as shown in Fig. 6 (b). Pathways with higher enrichment significance included pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, chemical carcinogenesis-receptor activation, pancreatic cancer and PI3K-Akt signaling pathway. The highly ranked and COPD-related PI3K-Akt signaling pathway was selected for further study (Fig. 7).

Figure 6 GO and KEGG enrichment analysis. (a) The top ten BP, CC and MF terms and the related genes. (b) The bubble diagram of the top 10 KEGG pathways.

Figure 7 The PI3K-Akt signaling pathway of potential target genes of SMI in COPD. Arrows indicate upstream and downstream relationships between genes. The red blocks are SMI target genes in the network.

3.5 Molecular Docking

Molecular docking analysis was conducted between ten active core components (orchinol, jasmololone, ophiopogonanone B, N-Trans-Feruloyltyramine, ophiopogonanone A, ruscogenin, (20R)-protopanaxatriol, oleanolic acid, (20S)-protopanaxatriol, and stigmasterol), selected from the component-target network, and five core targets (STAT3, SRC, EGFR, HSP90AA1, and AKT1) screened from the PPI network. The docking scores are presented in Table 3. The stability of the ligand-receptor complex correlates with the binding energy. Larger absolute values of docking affinity indicate a more stable docking complex^[32]. Studies have shown that the docking fraction value less than − 5.0 kcal·mol^− 1 indicates best binding activity, and less than − 7.0 kcal·mol^− 1 indicates high binding activity^[33]. The docking results, shown in Fig. 8, revealed that the docking fraction values were all less than − 5.0 kcal·mol^− 1, confirming that the core components of the drug had good binding activity to the core target.

Table 3

Top 10 core targets.
Number	Targets	Degree	Betweenness:	Closeness
1	STAT3	78	4983.67	0.24
2	SRC	77	9122.92	0.25
3	EGFR	76	6314.76	0.25
4	HSP90AA1	73	7962.47	0.25
5	AKT1	73	4774.85	0.25
6	IL6	67	6799.42	0.24
7	TNF	60	4848.79	0.24
8	BCL2	59	3763.65	0.24
9	JUN	53	1588.95	0.24
10	CCND1	51	1499.27	0.24

The lowest docking fraction value for each target was visualized using Discovery Studio, as shown in Fig. 10. STAT3, SRC, and HSP90AA demonstrated strong binding to oleanolic acid, ruscogenin and ophiopogonanone A, respectively. Similarly, EGFR and AKT1 demonstrated strong binding to stigmasterol. The visual explanation of docking results analyzed the interaction between SMI-active ingredients and the potential targets of COPD.

Figure 8 the heat map of molecule docking scores. the color indicates the binding activity, the darker of the color, the better to the binding activity.

Table 4 The binding energy between core substances and key targets of COPD.

Table 4

The binding energy between core substances and key targets of COPD.
Core component	Binding energy/(kcal·mol^− 1)
Core component	STAT3	SRC	EGFR	HSP90AA1	AKT1
Orchinol	-6.4	-5.6	-7.9	-9.0	-9.1
Jasmololone	-5.1	-5.2	-6.6	-6.8	-6.3
Ophiopogonanone B	-7.1	-6.5	-8.8	-8.8	-9.3
N-Trans-Feruloyltyramine	-6.8	-6.2	-8.9	-8.9	-9.0
Ophiopogonanone A	-7.8	-7.0	-9.5	-10.1	-10.5
Ruscogenin	-8.1	-8.2	-9.7	-7.7	-10.3
(20R)-Protopanaxatriol	-8.0	-6.2	-9.2	-6.4	-7.7
Oleanolic Acid	-8.7	-7.1	-9.7	-7.3	-8.6
(20S)-Protopanaxatriol	-7.0	-6.2	-9.3	-7.6	-6.8
Stigmasterol	-7.9	-6.2	-9.9	-6.7	-10.8

Figure 9 Visualiation of molecular docking results (docking score <-8.0 kcal·mol^− 1).

In recent years, great progress has been made in treating COPD with TCM. The application of TCM can enhance clinical symptoms of COPD patients, decrease acute exacerbations, improve lung function, and reduce inflammatory responses^[34]. According to TCM, the main pathogenesis of COPD is the deficiency of lung, spleen and kidney and the mixture of deficiency and excess^[35]. SMI is a herbal injection extracted from Red ginseng and Ophiopogon japonicus, commonly used for treating qi-yin deficiency. Studies have shown that SMI possesses anti-inflammatory and antioxidant properties, which can alleviate pulmonary inflammation and oxidative stress^{[36] [37, 38]}. However, the effective components of SMI in treating COPD, as well as its specific targets and signaling pathways, remain unclear. Therefore, it is essential to elucidate the pharmacological mechanisms of SMI in COPD using network pharmacology.

In this study, screening and topological analysis of relevant databases revealed 28 potential components of SMI that may play crucial roles in COPD treatment. These include orchinol, jasmololone, ophiopogonanone B, N-trans-feruloyltyramine, ophiopogonanone A, ruscogenin, (20R)-protopanaxatriol, oleanolic acid, (20S)-protopanaxatriol and stigmasterol, among others. While the pathogenesis of COPD remains incompletely understood, it is generally associated with inflammation, oxidative stress, protease/anti-protease imbalance, and decreased immunity^[39]. Ophiopogonanone A and Ophiopogonanone B belong to a class of homoisoflavonoids, an uncommon flavonoid group primarily sourced from Ophiopogon japonicus. These compounds possess oxygen free radical scavenging, anti-oxidative, anti-inflammatory, and myocardial protective properties^{[40, 41]}. 20(S)-protopanaxatriol has been shown to inhibit extracellular matrix deposition and reduce the levels of proinflammatory cytokine^[42]. Ruscogenin exerts a protective effect on lung injury due to its anti-inflammatory and anti-thrombotic activities^[43]. Furthermore, n-trans-feruloyltyramine demonstrates potent antioxidant capabilities, significantly protecting against ROS-induced oxidative damage by improving cell viability, restoring cell morphology, and maintaining mitochondrial integrity^[44]. Additionally, stigmasterol has been documented to have immunomodulatory effects with substantial therapeutic potential in murine experiments^[45]. These findings suggest that SMI may employ multiple effective molecular compounds acting through diverse mechanisms in the treatment of COPD.

A total of 341 common targets between SMI and COPD were predicted, leading to the construction of the PPI network. The hub genes of SMI in treating COPD were identified as STAT3, SRC, EGFR, HSP90AA1, AKT1, IL6, TNF, BCL2, JUN, and CCND1. The expression and activation of STAT3 are involved in the inflammatory and fibrotic responses associated with COPD development^{[46, 47]}. Additionally, the SRC/MAPK pathway has been studied for its crucial role in COPD progression^[48]. EGFR plays a pivotal role in airway inflammation, showing higher expression levels in COPD patients compared to smokers with normal lung function. Activated EGFR contributes to the proliferation of airway epithelial goblet cells and increased mucus production. Studies have reported that EGFR levels in the small airways of COPD patients are associated with decrease in airway functionality^[49]. Literature reports indicate that the pathogenesis of squamous cell lung carcinoma in COPD patients is regulated by HSP90AA1^[50]. Moreover, Akt1 regulates cell proliferation and apoptosis, as well as the release of inflammatory factors and activation of inflammatory cells, playing a crucial role in the development of COPD^{[51, 52]}.

As previously mentioned, the targets mainly focus on inflammation and cancer. The KEGG enrichment analysis identified significant pathways such as pathway in cancer, PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications and EGFR tyrosine kinase inhibitor resistance. Airway wall remodeling and mucus hypersecretion are key pathological features of COPD, marked by structural changes in the airway wall, luminal stenosis, and restricted airflow^[53]. The PI3K/Akt signaling pathway is significantly involved in the pathogenesis of COPD, influencing the activation of inflammatory cells, secretion of inflammatory mediators, and airway remodeling^[54]. Studies have shown heightened activation of the PI3K/Akt pathway in airway epithelial cells of COPD patients compared to those without the condition, suggesting a potential role in airway remodeling and fibrosis via NF-κB or mTOR signaling pathways^{[55, 56]}. Furthermore, abnormal PI3K signaling adversely affects the function of airway epithelial cells and impairs alveolar immune cells, leading to excessive immune responses^[57]. This aberrant immune response induces chronic inflammation, contributing to COPD development^[58]. Therefore, SMI may regulate downstream inflammatory cytokines by attenuating PI3K/AKT signaling. Chronic inflammation elevates levels of EGFR and its ligands. A positive correlation exists between the expression and activation of EGFR and the proliferation of airway epithelial goblet cells and mucus production^[59]. Studies have indicated that COPD patients exhibit higher levels of EGFR expression compared to smokers with normal lung function, suggesting a link between COPD and EGFR overexpression. Research suggests that EGFR levels in the small airways of patients with COPD are associated with decreased airway function^[39]. COPD is reported to be associated with cancer development and can increase the risk of lung cancer^[60].

Molecular docking is a key method for screening active ingredients and calculating their binding affinity to target proteins, which determines binding stability^[61]. Typically, affinity is represented by the binding score in molecular docking; the lower the affinity, the more stable the binding to the target protein. In the study, molecular docking results indicated that EGFR, AKT1 and HSP90AA1 showed excellent docking scores with various components, all below − 6.0 kcal·mol^− 1. Notably, AKT1 exhibited the strongest interaction with stigmasterol, evidenced by its lowest docking score of − 10.8 kcal·mol^− 1. These molecular docking findings demonstrate favorable binding activity between core components of SMI and key target proteins. Consequently, these core components may act as pharmacodynamic agents within SMI, modulating key targets for COPD treatment.

Comorbidities of COPD significantly impact the quality of life, exacerbation frequency, and survival of COPD patients, presenting greater management challenges^[62]. Additionally, addressing comorbidities has long been recognized as essential to COPD management. Based on the study findings, the mechanism of SMI in treating COPD includes anti-cancer and anti-inflammatory effects, as well as modulation of various disease pathways such as atherosclerosis and diabetic complications. These findings suggest that SMI could offer extensive and pleiotropic pharmacological activities against COPD comorbidities, aligning with the holistic concept of TCM and the principle of “homotherapy for heteropathy”. Furthermore, this highlights an advantage of TCM in managing COPD.

This present study preliminarily elucidated the molecular mechanism of SMI in treating COPD using network pharmacology and molecular docking. However, certain limitations exist in this study. Firstly, the collection of bioactive components and targets from currently available resources may not be comprehensive. Secondly, confidence is limited due to a lack of further experimental verification. In the future, efforts will be made to validate the therapeutic mechanism of SMI at both cellular and animal model levels.

In conclusion, this comprehensive bioinformatic analysis utilizing network pharmacology and molecular docking technology provided numerous testable hypotheses regarding the underlying molecular mechanisms of SMI in treating COPD. The findings offer preliminary insights into the complex mechanisms through which SMI exerts its therapeutic effects on COPD, involving multiple ingredients, targets, and signaling pathways. Furthermore, the efficacy of core active ingredients and targets was confirmed through molecular docking simulations. Overall, the results offer valuable guidance for clinical applications and serve as a basis for further experimental validation of SMI in COPD treatment.

Acknowledgements

We thank all authors for their contributions and support.

Authors’ contributions

Conception and design: Xiaolin LIU, Jingru GONG, Huiping LU; Administrative support: Huiping LU; Provision of study materials: Yang Chen and Lu Yi; Collection and assembly of data: Xiaolin LIU and Qu Dan; Data analysis and interpretation: all authors; Manuscript writing: all authors; Final approval of the manuscript: all authors.

Funding

Key Discipline Construction Project of Pudong Health Bureau of Shanghai：Clinical Pharmacy (Grant No.PWZxk2022-27)

Clinical Pharmacy Key Specialized subject Construction Project of Pudong Hospital affiliated to Fudan University (Grant No. Tszk2020-05)

Availability of data and materials

The data generated in this study are available from the corresponding author upon request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors agree to publish this paper.

Competing interests

The authors declare that there is no conflict of interest.

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Exploring the mechanisms of Shenmai injection against Chronic Obstructive Pulmonary Disease based on network pharmacology and molecular docking validation

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Abstract

Figures

1. Background

2. Materials and Methods

2.1. Collection of ingredients and targets of SMI

2.2 Acquisition of COPD-related targets

2.3 Identification of common targets and Construction of the Compound-Target Network

2.4 Protein-protein interaction (PPI) network and identification of core targets.

2.5 Functional enrichment analysis

2.6 Molecular Docking

3. Results

3.1. Active compounds and targets screening

3.2. Acquisition potential targets of SMI in treating COPD.

3.3 Construction of PPI network diagram and identification of core genes

3.4 Functional enrichment analysis

3.5 Molecular Docking

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

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Version 1