Mechanism of action of Huangbaichen Sanwei formulation in treating T2DM based on network pharmacology and molecular docking

Abstract

Huangbaichen Sanwei formulation (HBCS) has been reported to have a good hypoglycemic effect, but its pharmacological mechanism of action remains unclear. We used network pharmacology and molecular docking to explore the potential mechanism of action of HBCS against type-2 diabetes mellitus (T2DM). Fifty-five active components from HBCS interfered with T2DM. Twenty-five core targets, such as AKT1, INS, INSR, MAPK1 were identified. Enrichment analyses showed that HBCS was involved mainly including insulin receptor signaling pathway, extracellular region, and insulin-like growth factor receptor binding and other biological processes; common targets had roles in treating T2DM by regulating diabetic cardiomyopathy and insulin resistance. Molecular docking verified that components combined with core targets. HBCS play a part in treating T2DM through multiple components and targets at the molecular level, which lays a theoretical foundation for research using HBCS to treat T2DM. The components, predicted targets, and T2DM targets of HBCS were searched through databases, and common targets were determined. Further screening of the core targets was conducted through the establishment of a protein -protein interaction network. The core targets were analyzed by Gene Ontology (GO) annotation utilizing the DAVID platform. And the enrichment of signaling pathways was explored by employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Cytoscape 3.9.1 was employed to construct a “TCM-components-core target-pathway” network. Autodock Vina was used to dock molecules to compare the binding activity of active molecules with targets.

1. Introduction

Diabetes mellitus (DM) is a metabolic disorder characterized by dysregulation of sugar, protein, and fat metabolism, primarily resulting in hyperglycemia. DM is divided mainly into insulin-dependent (or type-1) diabetes mellitus and non-insulin-dependent (or type-2) diabetes mellitus (T2DM). In recent years, the prevalence of T2DM has been increasing year by year. The burden caused by the multiple complications of DM on families and society has become increasingly serious.^[1] Notably, the prevalence of T2DM in China is particularly high. A study has projected that by the year 2050, the age-standardized prevalence of DM worldwide would increase by > 2%.^[2]

Traditional Chinese Medicine (TCM) has been used for centuries in Asia. TCM formulations have been used widely in China to treat DM.^[3] The meticulous documentation by physicians in earlier centuries, has ensured the preservation of numerous TCM formulations exhibiting definite curative effects, facilitating contemporary research and development. In comparison to Western medicine, the utilization of TCM formulations for treatment is characterized by safety, non-toxicity, and enduring efficacy.^[4]

Huangbaichen Sanwei formulation (HBCS) is used to treat DM in China. Based on years of clinical experience by professor Tong Xiaolin (an expert in DM in China) tested Huangqi (HQ), Baizhu (BZ), and Chenpi (CP) for their compatibility of use together. Huangqi is used to treat T2DM and is found in the dry roots of Astragalus membranaceus. Huangqi is present in many TCM formulations used to treat DM.^[5] Its extract can relieve DM, DM-related complications, and nephropathy.^[6,7] Baizhu and Chenpi are also compatible for use in formulations. Huangqi, Baizhu, and Chenpi and their formulations have a 2-way regulatory effect on blood sugar.^[8] However, due to the multiple components of TCM formulations, the mechanism of action of HBCS against DM is not known.

Network pharmacology is a commonly employed approach to elucidate the mechanism of action of Traditional Chinese Medicine (TCM) formulations in the treatment of diseases, achieved through the construction of a network comprising “compounds” and “targets.”^[9,10] It coincides with the “integrity” of TCM. Therefore, we used network pharmacology combined with molecular docking to explore the efficacy and mechanism of action of HBCS in the treatment of T2DM, so as to provide reference for clinical promotion and subsequent basic research.

2. Materials and Methods

2.1. Screening of the chemical components in HBCS

All the chemical constituents of HQ, BZ, and CP were examined in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP; http://tcmspw.com/tcmsp.php) and the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (Batman-TCM; http://bionet.ncpsb.org.cn/batman-tcm/index.php). TCMSP was subjected to ADME (absorption, distribution, metabolism, and excretion) screening, with the criteria of oral bioavailability ≥30% and druglikeness ≥0.18. BATMAN-TCM was screened by scores and P values, with the threshold values set at ≥30 and ≤.05, respectively.

2.2. Prediction of chemical components selected from HBCS

In order to examine the chemical components screened in section 2.1, we conducted a verification of the Simplified Molecular Input Line Entry System (SMILE) number of chemical components in PubChem (https://pubchem.ncbi.nlm.nih.gov). Subsequently, the target proteins associated with the efficacious components were identified through the utilization of SwissTargetPrediction (http://swisstargetprediction.ch/) and Drug Bank (https://go.drugbank.com/). Then, the target proteins projected by the chemical components in HQ, BZ, and CP were determined by combining the targets in TCMSP and BATMAN-TCM.

2.3. Collection of disease-related targets

The databases utilized for the identification of disease targets, with “T2DM” as the designated keyword, included Therapeutic Target Database (TTD; http://db.idrblab.net/ttd), DrugBank, Online Mendelian Inheritance in Man (OMIM; https://omim.org), GeneCards (www.genecards.org), and other relevant databases. In the GeneCards database, after keyword searching, we searched for disease target proteins with a relevance score ≥30 as the screening condition.

2.4. Acquisition of potential targets of HBCS for T2DM treatment

The prediction targets of chemical components and target proteins associated with the disease were entered into the UniProt database (www.uniprot.org). The limited species was “Human,” which was transformed into the corresponding gene name. Results were merged, de-duplicated, and imported into Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny) to draw Venn diagrams. Finally, common targets were obtained.

2.5. Building a protein–protein interaction (PPI) network and screening core targets

The potential target proteins identified through the utilization of Venn diagrams were subsequently inputted into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string-db.org). The scope of investigation was “Human.” The PPI network of A. membranaceus, A. macrocephala, and P. citri Tangerinae for treating T2DM was obtained. Analytical results were imported into Cytoscape 3.9.1. The selection of core targets was based on their topological parameters.

2.6. Enrichment analysis of GO and KEGG

Using Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.ncifcrf.gov), core targets were analyzed by annotation using the Gene Ontology (GO) database. The resulting data was visualized using a micro-information platform (www.bioinformatics.com.cn). The core target was introduced into Metascape (http://metascape.org). The species was limited to Homo sapiens. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was employed to ascertain which signaling pathways had been enriched using a threshold value of P ≤ .01.

2.7. Construction of a “TCM–components–core targets–pathways” network

A network analysis was conducted in Excel to examine the relationship between “traditional Chinese medicine-active ingredient,” “ingredient-core target” and “core target-pathway.” These 4 tables were imported into Cytoscape3.9.1 as nodes. The interaction among them was expressed by connecting lines. Hence, a network of “TCM–components–core targets–pathways” was established, and then extended.

2.8. Molecular docking to verify targets

The 3-dimensional structure (Protein Database format) of target proteins with a high value was obtained from the UniProt database. Next, we drew the chemical composition with ChemDraw 2020 (Thermo Scientific, Waltham, MA, USA). Then, we clicked “MM2 minimize” to minimize its energy and saved it in mol2 format. Subsequently, the target was subjected to dehydration and hydrogenation using AutoDock Tools 1.5.7 (https://autodock.scripps.edu). The ligand and receptor were converted into pdbqt format. Finally, the core targets with a high degree value were subjected to molecular-docking with chemical components using AutoDock Vina. Subsequently, ligands and receptors with low binding energy and strong affinity were screened out and visualized by PyMOL (https://pymol.org).

3. Results

3.1. Collection of chemical components

After data screening, 267 chemical constituents were found: 113 for HQ, 62 for BZ, and 92 for CP.

3.2. Target prediction of chemical components

There were 3074 predicted targets corresponding to the chemical composition: 1110 for HQ, 760 for BZ and 1204 for CP. After weight removal, 697 predicted targets were obtained.

3.3. Collection of disease-related targets

After searching in Drugbank, OMIM, GeneCards and TTD, 897 targets related to T2DM were obtained. Finally, after merging and de-duplication, 444 disease-related targets were obtained.

3.4. Acquisition of potential targets of HBCS for T2DM

The predicted targets of HBCS components intersected with disease targets, and 124 common targets were obtained (Fig. 1).

Figure 1.

Predicted target and disease target Wayne diagram.

3.5. Construction of a PPI network and screening of core targets

The potential target proteins obtained were inputted into the STRING database. Then, we imported the PPI network processed by the STRING database was imported into Cytoscape 3.9.1 to generate a network diagram (Fig. 2). The color and size of nodes changed according to the degree value (Fig. 3). It can be seen that the Dgree values of AKT1, ALB, IL6 and other targets are higher, and the target colors are darker. After analyses of topological parameters, 24 core targets were screened (Table 1), and 53 active components of HBCS when treating T2DM were identified (Table 2).

Figure 2.

PPI target network diagram. PPI = protein–protein interaction.

Figure 3.

Gene ontology (GO) enrichment analysis.

Table 1.

Network topology parameters of core targets in PPI network.

Name	Degree	Betweenness centrality	Average shortest path length	Closeness centrality
INS	79	0.122	1.397	0.716
ALB	77	0.114	1.463	0.684
AKT1	72	0.039	1.521	0.658
IL6	68	0.024	1.545	0.647
PPARG	65	0.032	1.579	0.634
TNF	65	0.018	1.587	0.630
TP53	62	0.019	1.620	0.617
CTNNB1	59	0.026	1.628	0.614
IL1B	58	0.012	1.645	0.608
IGF1	58	0.013	1.653	0.605
ESR1	53	0.012	1.694	0.590
PPARA	52	0.016	1.711	0.585
MMP9	49	0.041	1.727	0.579
CAT	48	0.033	1.744	0.573
MAPK1	43	0.010	1.785	0.560
CAV1	42	0.028	1.777	0.563
ADIPOQ	42	0.004	1.843	0.054
STAT1	40	0.037	1.826	0.548
CYP3A4	33	0.045	1.826	0.548
APOB	33	0.012	1.909	0.524
PRKCD	27	0.037	2.008	0.498
F2	26	0.014	1.926	0.519
CACNA1A	13	0.047	2.116	0.473
CACNA1C	11	0.009	2.116	0.473

Table 2.

Basic information of main components of HBZS in the treatment of T2DM.

NO.	MOL ID	Compound name	TCM
1	MOL000371	3,9-di-O-methylnissolin	HQ
2	MOL000378	7-O-methylisomucronulatol	HQ
3	MOL000430	betaine	HQ
4	MOL000429	Crystal VI	HQ
5	MOL000390	daidzein	HQ
6	MOL000389	FERULIC ACID (CIS)	HQ
7	MOL000392	formononetin	HQ
8	MOL000388	gamma-aminobutyric acid	HQ
9	MOL000437	Hirsutrin	HQ
10	MOL005928	isoferulic acid	HQ
11	MOL000354	isorhamnetin	HQ
12	MOL000422	kaempferol	HQ
13	MOL000421	nicotinic acid	HQ
14	MOL000069	palmitic acid	HQ, BZ
15	MOL000098	quercetin	HQ
16	MOL000415	rutin	HQ
17	MOL011455	20-Hexadecanoylingenol	HQ
18	MOL011456	3,5-Dimethoxystilbene	HQ
19	MOL001788	Adenine	HQ
20	MOL000395	Canavanine	HQ
21	MOL000842	Sucrose	HQ
22	MOL000059	Uridine	HQ, BZ
23	MOL000114	vanillic acid	HQ
24	MOL000635	vanillin	CP
25	MOL000264	Terpinolene	CP
26	MOL002110	Ocimene	CP
27	MOL002111	N-Methyltyramine22	CP
28	MOL004328	naringenin	CP
29	MOL000023	Limonene	CP
30	MOL000305	lauric acid	CP
31	MOL000202	Gamma-Terpinene	CP
32	MOL000197	Beta-Myrcene	CP
33	MOL000125	Alpha-Pinene	CP
34	MOL000126	Beta-Pinene	CP
35	MOL000036	Caryophyllene	CP
36	MOL005812	naringin	CP
37	MOL005828	nobiletin	CP
38	MOL005814	tangeretin	CP
39	MOL000204	Elemene	CP
40	MOL000771	p-coumaric acid	CP
41	MOL000270	3-Carene	CP
42	MOL000056	DTY	BZ
43	MOL000050	GLY	BZ
44	MOL000052	Gulutamine	BZ
45	MOL000053	Jurubine	BZ
46	MOL000068	L-Ile	BZ
47	MOL000067	L-Valin	BZ
48	MOL000046	atractylone	BZ
49	MOL000041	PHA	BZ
50	MOL000036	beta-caryophyllene	BZ
51	MOL000049	3β-acetoxyatractylone	BZ
52	MOL000024	alpha-humulene	BZ
53	MOL000065	ASI	BZ

BZ = Baizhu, CP = Chenpi, HQ = Huangqi, TCM = Traditional Chinese medicine.

3.6. Enrichment analysis of GO and KEGG pathways

In order to gain a deeper comprehension of the involvement of core targets in the pathogenesis and biological functions of T2DM, the utilization of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.

GO were classified into 3 categories (Fig. 3): biological process (BP), cellular component (CC), and molecular function. It was known that these targets were related to biological processes such as insulin receptor signaling pathway, positive regulation of glucose import, extracellular region, plasma membrane, insulin-like growth factor receptor binding, and insulin receptor substrate binding.

Analyses of signaling-pathway enrichment using the KEGG database revealed the “diabetic cardiomyopathy pathway,” “insulin resistance pathway,” “type II diabetes pathway” “nonalcoholic fatty liver pathway” and “AGE-RAGE signaling pathway of diabetic syndrome” to be enriched (Fig. 4).

Figure 4.

KEGG pathway enrichment results. KEGG = Kyoto Encyclopedia of Genes and Genomes.

3.7. “TCM–components–core target-pathway” network

The network diagram was constructed using Cytoscape 3.9.1 (Fig. 5). The topology parameters of this network are shown in Table 3. The network contained 216 nodes and 672 edges, with an average degree value of 7.639, and 40 nodes were ≥ 7.639. The findings indicate that nodes with a higher number of connections were more inclined to assume an interventionist role in diseases. The results showed that quercetin, daidzein, kaempferol, and other active ingredients had a large role. AKT1, MAPK1, PIK3R1 and other targets had high degree vlaues. The core target had a great influence on the “AGE-RAGE signaling pathway in diabetic complications,” “pathway in diabetic complications,” “diabetic cardiomyopathy” and “insulin resistance.”

Figure 5.

Chinese medicine-component-core target-pathway network diagram. Triangles represent components; pentagon represent targets; squares represent TCM and V represents pathways. TCM = Traditional Chinese medicine.

Table 3.

Network topology parameters.

NO.	Name	Degree	Closeness centrality	Betweenness centrality	Node property
1	MAPK1	109	0.523	0.241	Protein target
2	AKT1	101	0.504	0.179	Protein target
3	PIK3R1	98	0.501	0.156	Protein target
4	TNF	62	0.434	0.119	Protein target
5	IL6	48	0.410	0.059	Protein target
6	NOS3	37	0.394	0.138	Protein target
7	TGFB1	34	0.374	0.025	Protein target
8	INS	28	0.366	0.015	Protein target
9	PTEN	27	0.366	0.015	Protein target
10	CACNA1C	25	0.358	0.023	Protein target
11	INSR	24	0.361	0.011	Protein target
12	quercetin	13	0.456	0.021	Compound
13	kaempferol	6	0.422	0.007	Compound
14	daidzein	6	0.374	0.008	Compound
15	naringenin	5	0.402	0.007	Compound
16	Lauric Acid	5	0.402	0.003	Compound
17	GLY	5	0.284	0.020	Compound
18	gamma-aminobutyric acid	5	0.410	0.023	Compound
19	N-Methyltyramine	4	0.417	0.025	Compound
20	Jurubine	3	0.325	0.004	Compound
21	alpha-humulene	3	0.316	0.005	Compound
22	AGE-RAGE signaling pathway in diabetic complications	23	0.488	0.055	KEGG
23	Diabetic cardiomyopathy	15	0.443	0.033	KEGG
24	Insulin resistance	14	0.456	0.023	KEGG
25	Alzheimer disease	14	0.427	0.013	KEGG
26	Type II diabetes mellitus	13	0.449	0.016	KEGG
27	nonalcoholic fatty liver disease	11	0.407	0.007	KEGG
28	FoxO signaling pathway	10	0.405	0.004	KEGG
29	Adipocytokine signaling pathway	9	0.340	0.006	KEGG
30	MAPK signaling pathway	8	0.406	0.004	KEGG
31	AMPK signaling pathway	8	0.371	0.003	KEGG
32	Insulin signaling pathway	8	0.405	0.008	KEGG
33	PI3K-Akt signaling pathway	8	0.427	0.008	KEGG
34	Pathways in cancer	8	0.405	0.003	KEGG
35	HQ	22	0.366	0.043	TCM
36	CP	18	0.347	0.096	TCM
37	BZ	15	0.294	0.019	TCM

BZ = Baizhu, CP = Chenpi, HQ = Huangqi, KEGG = Kyoto Encyclopedia of Genes and Genomes, TCM = Traditional Chinese medicine.

3.8. Molecular docking analysis

The 5 core target proteins (AKT1, IL6, MAPK1, PIK3R1, and tumor necrosis factor) were subjected to molecular docking with the top-10 chemical components of Huangqi, Baizhu, and Chenpi. The statistical results of binding energy are shown in Table 4. In the docking results of 50 core targets–chemical components, 45 groups had a binding energy less than −4.25 kcal/mol (accounting for 90%) and 35 groups had a binding energy less than −5.0 kcal/mol (accounting for 70%). The data indicated that the main active components in the compound had strong binding activity with core targets. Visualization of the molecular docking of quercetin, jurubine, kaempferol, and other chemical components with MAPK1, PIK3R1, AKT1, and other proteins is shown in Figure 6. These findings provide preliminary confirmation that the chemical constituents in the compound from strong interactions such as hydrogen bonds, which preliminarily confirmed the results stated above.

Table 4.

Statistical results of molecular docking binding energy (binding energy/kcal mol⁻¹).

Compound	AKT1	MAPK1	PIK3R1	IL6	TNF
quercetin	−6.1	−7.2	−8.9	−7.6	−5.2
kaempferol	−6.0	−7.4	−8.8	−7.6	−6.2
Jurubine	−7.6	−8.5	−9.3	−8.1	−6.4
Lauric acid	−3.9	−4.4	−5.6	−4.1	−3.4
N-Methyltyramine	−4.8	−5.3	−5.9	−5.3	−5.0
daidzein	−6.4	−7.0	−9.3	−6.9	−5.4
Alpha-humulene	−5.9	−5.9	−7.5	−5.7	−5.1
Gamma-aminobutyric acid	−3.7	−4.3	−4.6	−4.2	−3.6
naringenin	−6.0	−6.9	−8.7	−7.4	−6.0
GLY	−3.4	−4.6	−4.4	−3.9	−2.9

Figure 6.

Visualization of compound-core target protein molecular docking with strong activity.

4. Discussion

DM is a chronic metabolic disorder characterized by elevated blood glucose levels (hyperglycemia).^[11] With the rapid development of science and technology society, the number of T2DM patients induced by living habits has increased significantly. Hyperglycemia and long-term metabolic disorders in T2DM can lead to serious complications, which imparts burdens on families and society.^[12] Facing the complex diseases of T2DM, increasing numbers of researchers have turned their attention to TCM when treating T2DM.^[13] TCM formulations are safe, have few side-effects, and influence multiple targets. Clinically, T2DM formulations often show a deficiency of middle qi. HBCS prescribed by Professor Xiaolin Tong has been shown to invigorate “qi” and nourish blood and at the same time can regulate the blood sugar of patients.

In 2007, Hopkin defined the concept of “network pharmacology” for the first time.^[14] Based on network pharmacology, the mechanism by which several TCM formulations treated complex diseases (e.g., metabolic syndrome, diabetic nephropathy) was revealed,^[15,16] which was a key step in the promotion of TCM theory. In particular, the combined application of network pharmacology and modern technologies such as molecular docking can clearly analyze the action targets and regulatory pathways of some complex Chinese medicine prescriptions, and provide directions for further experimental research.^[17,18]

Utilizing network pharmacology and molecular docking, techniques, this study elucidates the multifaceted mechanisms by which HBCS effectively addresses T2DM through the modulation of multiple targets and pathways. Furthermore, the investigation delves into the identification of key active components, potential targets and pathways associated with the therapeutic efficacy of HBCs in the treatment of T2DM.

Through database retrieval, a PPI-network screening yielded a total of 53 chemical components and 24 core targets associated with HBCS for T2DM. Subsequent network construction and analysis revealed that the prominent chemical components encompassed quercetin, naringenin, kaempferol, daidzein, and lauric acid. Notably, we found that quercetin and naringenin could reduce expression of the proinflammatory gene semaphorin 3E and and reduce the activity of dipeptidyl peptidase-4 in insulin-resistant HepG2 cells, thus having a role in T2DM treatment.^[19] In addition, Prawej Ansari^[20] found that quercetin could improve oral glucose tolerance and the insulin secretion of pancreatic β-cells, while inhibiting the release of proinflammatory markers and delaying the continuous deterioration of DM. The flavonoid kaempferol is a potential therapeutic agent for nonalcoholic fatty liver disease associated with T2DM. Kaempferol regulates hepatic fat accumulation by activating Sirt1/AMPK signal transduction.^[21] Kaempferol can also prevent STZ-induced diabetic nephropathy through the antioxidant potential mediated by Nrf-2/HO-1 axis up-regulation,^[22] and it can delay the failure and death of pancreatic β-cells by regulating endoplasmic reticulum stress, thus improving T2DM significantly.^[23] Daidzein can improve lipid metabolism and activate peroxisome proliferator-activated receptors in Zucker rats and RAW 264.7 cells, and be used to treat hyperlipidemia-related T2DM.^[24] Tham^[25] reported that lauric acid may alleviate insulin resistance by regulating the insulin sensitivity and mitochondrial disorders of macrophages, which improves insulin resistance.

The core targets of HBCS (AKT1, MAPK1, PIK3R1, and IL-6) were screened out by creating a PPI network and analyzing of the topological parameters of the network diagram. AKT1 is the main downstream target of PI3K-AKT signaling. AKT phosphorylation can induce insulin transport to the liver, pancreas, skeletal muscle, and adipose tissue to maintain glucose homeostasis, promote insulin secretion, and regulate the balance of lipid metabolism.^[26,27] Research has demonstrated that diabetic rats exhibit insulin resistance and a decrease in PI3K expression, indicating the significant involvement of PI3K in insulin metabolism and blood-sugar regulation.^[28] MAPK1 and IL-6 are related to inflammatory pathways, and are cytokines involved in the growth, differentiation, and functional regulation of various cells. They have key roles in the immune response and inflammatory reactions, and inhibit insulin resistance in T2DM.^[29] In addition, in the network diagram, insulin receptors and insulin proteins had high degree values, showing that insulin receptors have key roles in sugar metabolism.^[30] The advanced glycation end products–receptor for advanced glycation end products (AGE–RAGE) signaling pathway was the most enriched signaling pathway according to the KEGG database, indicating that core targets can directly affect this pathway and play a therapeutic role in T2DM.^[31–33]

Further, the molecular docking results showed that quercetin, kaempferol, naringin, α-germacrene, and daidzein had low binding energy to the core targets AKT1, MAPK1, PIK3R1, and IL-6, which suggested that they had strong binding activity to target proteins. Molecular docking simulation reveals how ligands interact with important amino groups and acid residues on active sites through hydrogen bonding and hydrophobic interaction.^[34] Generally, when the binding energy is less than zero, it indicates that the component and target can be spontaneously combined. A lower binding energy, signifies a stronger binding ability and a more stable conformation. Consequently, HBCS may exert its effects on AKT1, MAPK1, and other targets by means of quercetin, jurubine, kaempferol and other active components, and then modulat the related pathways, and potentially contributing to the treatment of T2DM.

Although this study has provided a relatively clear analysis of the mechanism of action of HBCS in treating T2DM, there are still some limitations. For example, our research lacks in vivo and in vitro experimental validation. Additionally, network pharmacology studies can be conducted using serum drug chemistry. In the future, our research team will conduct in vivo and in vitro experiments to further investigate the therapeutic effects of HBCS on T2DM. We will validate the signaling pathways and functional proteins regulated by HBCS based on the results of network pharmacology analysis.

5. Conclusion

In this study, network pharmacology and molecular docking techniques were employed to identify the active components of HBCS and explain the underlying molecular mechanisms involved in the treatment of type 2 diabetes mellitus (T2DM). The results suggest that HBCS may exert synergistic effects on key targets such as AKT1, MAPK1, and PIK3R1, which are modulated by the components present in HQ, BZ, and CP. HBCS also has a role in treating T2DM through the AGE–RAGE signaling pathway with regard to DM complications, diabetic cardiopathy, and insulin resistance. Consequently, the clinical application of HBCS may represent a rational and promising approach for the treatment of T2DM.

Author contributions

Conceptualization: Chunnan Li, Hui Zhang.

Data curation: Jiaming Shen, Xiaolong Jing.

Formal analysis: Chunnan Li.

Funding acquisition: Chunnan Li, Jiaming Sun.

Methodology: Jiaming Shen, Yuelong Wang.

Resources: Hui Zhang, Jiaming Sun.

Software: Jiaming Shen, Lu Liu.

Writing – original draft: Chunnan Li, Lu Liu.

Writing – review & editing: Chunnan Li, Kaiyue Zhang.