Insulin resistance, poor glucose homeostasis, oxidative stress, inflammation, and pancreatic β-cell dysfunction are the hallmarks of Type 2 Diabetes Mellitus (T2DM), a chronic metabolic disease. The search for safer multitarget therapeutic agents made from natural products is necessary due to the rising incidence of type 2 diabetes and the drawbacks of traditional antidiabetic treatments. Apigenin and catechin have anti-inflammatory, antihyperglycemic, insulin-sensitizing, and antioxidant qualities. The current study examined the therapeutic mechanisms of apigenin and catechin against type 2 diabetes using integrated network pharmacology and molecular docking techniques. Protein-protein interaction (PPI), Gene Ontology (GO), and KEGG pathway enrichment studies were performed after T2DM-associated genes and potential phytochemical targets were found using publically accessible databases. Important hub genes linked to insulin signaling and glucose metabolism were found by network analysis, including PIK3R1, ESR1, AKT1, EGFR, and PTK2. PI3K-Akt and endocrine resistance pathways were significantly enriched, according to KEGG analysis. Catechin has a strong binding affinity for PIK3R1 (-6.1 kcal/mol) and AKT1 (-6.4 kcal/mol) according to molecular docking, whereas Apigenin had docking scores of -5.7 and -6.3 kcal/mol, respectively, when compared to Metformin. These results offer a scientific foundation for additional experimental confirmation and imply that catechin and apigenin may have antidiabetic effects through multitarget regulation.
Keywords: Type 2 Diabetes Mellitus; Catechin; Apigenin; Insulin Resistance; Phytochemical Therapeutics; Target Identification.
Type 2 diabetes mellitus (T2DM), also known as noninsulin dependent diabetes mellitus (NIDDM), is a metabolic disorder characterized by persistent hyperglycemia caused by insulin resistance and progressive pancreatic β-cell dysfunction. It accounts for approximately 90-95% of all diabetes cases worldwide. The pathophysiology of T2DM involves impaired insulin responsiveness, altered glucose metabolism, and disturbances in carbohydrate, lipid, and protein metabolism.
Major risk factors include genetic predisposition, obesity, sedentary lifestyle, poor dietary habits, stress, and aging.
The disease is associated with severe complications such as cardiovascular disease, nephropathy, neuropathy, retinopathy, diabetic foot ulcers, and increased susceptibility to infections [1-3].
According to the International Diabetes Federation, approximately 537 million people were affected by diabetes globally in 2021, and this number is expected to rise significantly by 2045. India is considered one of the countries with the highest diabetic populations due to urbanization, obesity, reduced physical activity, and dietary changes [4-6]. Current treatment strategies for T2DM include lifestyle modifications and pharmacological interventions such as metformin, sulfonylureas, thiazolidinediones, DPP-4 inhibitors, SGLT-2 inhibitors, GLP-1 receptor agonists, and insulin therapy. Recent research has focused on phytoconstituents with antidiabetic potential due to their ability to regulate glucose metabolism, reduce oxidative stress, and improve insulin sensitivity [7- 8]. Catechin, a flavan-3-ol flavonoid found in medicinal plants such as Acacia catechu and Camellia sinensis, possesses antioxidant, anti-inflammatory, antidiabetic, cardioprotective, neuroprotective, and anticancer activities. Studies have demonstrated that catechin improves insulin sensitivity, regulates glucose metabolism, and reduces oxidative stress and inflammatory responses [9-11].
Apigenin, a naturally occurring flavone present in parsley, chamomile, celery, onions, and Matricaria chamomilla, exhibits antioxidant, anti-inflammatory, antidiabetic, neuroprotective, and cardioprotective properties. Apigenin has been reported to improve insulin sensitivity, reduce hyperglycemia, and protect pancreatic β-cells from oxidative damage through modulation of signaling pathways such as MAPK, PI3K/Akt, and NF-κB [12-14]. In recent years, network pharmacology has emerged as a strong systemslevel approach to studying the complex interactions between bioactive substances, target proteins, signaling pathways, and diseases. This computational method, when paired with molecular docking, enables the identification of new therapeutic targets as well as the assessment of ligandprotein binding affinities, therefore expediting drug development and mechanistic study. Although several studies have reported the antidiabetic potential of Catechin and Apigenin individually, limited research has explored their combined molecular mechanisms against T2DM using integrated computational approaches [15-20]. Therefore, the present study aimed to evaluate the therapeutic potential of Catechin and Apigenin against type 2 diabetes mellitus using network pharmacology and molecular docking.
The literature was used to aid the chemical selection process. The selected bioactive compounds were then searched in the PubChem database to obtain their chemical structures and SMILES notations. Potential target proteins linked with these chemicals, with potential scores higher than zero, were then predicted using the Swiss Target Prediction Database.
We searched the GeneCards database for possible targets related with breast cancer. This database combines data from several sources to provide complete information on human genes. The selected targets were collated for additional study and classified as type 2 diabetes mellitus-related targets [21].
We developed a Venn diagram to assess the overlap between type 2 diabetes mellitus-associated targets and possible targets for Catechin and Apigenin. (Figure 1)
The STRING database (https://string-db.org/) collects known and projected protein-protein interactions, including functional and physical correlations. For this study, we utilized STRING [21] to look at the connections between the chemicals in our analysis and the targets associated with type 2 diabetes mellitus, finding potential therapy targets. To build a reliable protein-protein interaction (PPI) network for these targets, we employed the "Homo sapiens" organism with a confidence level of 0.4. This confidence score is commonly employed because it strikes a good balance between sensitivity and specificity, resulting in a credible criteria for interaction prediction. As a consequence, only proteins with interaction scores of 0.4 or higher were included to the PPI network, increasing the accuracy of our potential target identification.
To identify key pathways and related Gene Ontology (GO) concepts, all potential therapeutic targets were analyzed using pathway and GO enrichment analyses in the String database [21] (https://string-db.org/). This investigation shed light on the biological processes (BP), molecular functions (MF), and cellular components (CC) connected with the targets. Pathways and GO keywords having a significance level of p < 0.05 were chosen for further study.
The compound-target interaction network was created by linking the targets associated with each chemical. Cytoscape 3.10.2 (Cytoscape Consortium, San Diego, CA, USA) was used to visualize the network [21]. Nodes in the network represent drugs and their targets, while edges show their interactions.
Molecular docking investigations were carried out in accordance with the previously published approach described by Ahmad et al. (2024). The three-dimensional structures of target proteins were obtained from the Protein Data Bank, while ligand structures were obtained from PubChem. AutoDock Vina was used to investigate binding affinity and protein-ligand interactions. The docked complexes with the lowest binding energy were chosen and visualized using PyMOL and Discovery Studio, following the previously defined computational process [21].
Applying type 2 diabetes mellitus as a keyword, data were extracted from the GeneCards database, providing 18223 target genes. Among these, 123 targets were identified as shared by the chemicals and type 2 diabetes mellitus-related genes, indicating prospective therapeutic targets for compounds used to treat type 2 diabetes. A Venn diagram (Figure 1) depicts these overlapping targets.
3.2 Construction of the Compound-Target network To investigate the signaling pathways and functional functions of the identified target genes, we used Cytoscape data analysis. This strategy helped create a thorough compound-target network, as shown in Figure 2. The
indicates a more important involvement for the protein in the network. Following a filtering procedure based on the defined criteria, numerous hub genes were found, including PIK3R1, ESR1, AKT1, EGFR, PTK2, ERBB2, IGF1R, KDR, and MMP2. These proteins, which include biological enzymes and cytokines, play critical roles in a variety of regulatory processes such as signal transduction and protein phosphorylation. Among these genes, the top five nodal targets were PIK3R1, ESR1, AKT1, EGFR, and PTK2. The substantial connections between these genes and other possible therapeutic targets highlight their significance in type 2 diabetes mellitus therapy.
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52 3.15E-20 .
92 5.82E-23 89 7.38E-18 32 9.83E-18 33 9.77E-17 33 3.33E-15 34 4.14E-14
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The pathways related with potential therapeutic targets for type 2 diabetic mellitus therapy were discovered using KEGG pathway enrichment analysis. Signalling pathways were obtained using the string database. The top ten signalling pathways were then shown in a bar graph (Table 4 and Figure 5), sorted by P value in ascending order. The results revealed that the major targets were significantly overrepresented in the Endocrine resistance pathway (Figure 5A).
Molecular docking (MD) studies of Catechin and Apigenin with key type-2-Diabetes mellitus-associated targets, including PIK3R1 (node degree = 15) and AKT1 (node degree = 9), were performed using AutoDock Vina to evaluate binding affinities and interaction profiles. The docking analysis assessed parameters include binding affinity (kcal/mol), interacting amino acid residues. The obtained docking results demonstrated favorable ligandprotein interactions and are summarized in Table 5 and illustrated in Figure 6.
Metformin was used as the reference standard in the molecular docking investigation to assess the binding affinities of Catechin and Apigenin against the type 2 diabetes mellitus-associated target proteins PIK3R1 (PDB ID: 1UNQ) and AKT1 (PDB ID: 1PBW). Docking scores were represented in kcal/mol, where higher binding stability and a stronger ligand-protein interaction are indicated by lower binding energy. With a docking score of -6.1 kcal/mol, catechin showed the highest binding affinity among the phytochemicals examined, followed by apigenin with -5.7 kcal/mol, according to docking analysis against
1 Metformin PIK3R1 2 Catechin PIK3R1 3 Apigenin PIK3R1 4 Metformin AKT1 5 Catechin AKT1 6 Apigenin AKT1 PIK3R1. Metformin, a common medication, had a docking score of -4.4 kcal/mol, indicating a reduced binding affinity. These findings suggest that Catechin and Apigenin may interact more effectively with the active binding pocket of PIK3R1, which is an important regulatory component of the PI3K signaling pathway involved in insulin signaling and glucose metabolism.
In a similar molecular docking against AKT1 revealed that Apigenin had a similar docking score of -6.3 kcal/mol while Catechin had the greatest interaction with a docking value of -6.4 kcal/mol. Metformin, on the other hand, showed a docking score of -4.3 kcal/mol, indicating a relatively lower binding affinity toward AKT1. The potential relevance of catechin and apigenin in regulating insulin signaling, glucose absorption, and cellular metabolic regulation linked to type 2 diabetes mellitus is suggested by their significant interaction with AKT1. Favorable hydrogen bonding, hydrophobic interactions, and stable molecule orientation within the target proteins' active sites may be responsible for the increased binding affinity of apigenin and catechin. Modulation of PIK3R1 and AKT1 may lead to better glycemic control and a decrease in diabetic complications since these proteins are essential for the PI3K/Akt signaling pathway, which controls glucose homeostasis, insulin sensitivity, and cellular survival. Compared to the common medication Metformin, the molecular docking results show that Catechin and Apigenin have greater binding affinities toward diabetes-associated targets, suggesting that they have promising antidiabetic potential. These results support the therapeutic significance of these phytochemicals as multitarget candidates for the management of type 2 diabetes mellitus.
1UNQ -4.4 1UNQ -6.1 1UNQ -5.7 1PBW -4.3 1PBW -6.4 1PBW -6.3
Metformin-(1PBW) 3D
Through multitarget and multipathway regulation, the current study showed that apigenin and catechin had remarkable therapeutic potential against Type 2 Diabetes Mellitus. Important hub genes and signaling pathways linked to insulin resistance, glucose metabolism, oxidative stress, and inflammation were found by network pharmacology analysis. When compared to metformin, molecular docking further demonstrated the great binding affinity of apigenin and catechin toward important target proteins PIK3R1 and AKT1. All things considered, the results point to these phytochemicals as possible multitarget antidiabetic drugs and offer a solid basis for further experimental and clinical research.
Shaikh Faizan contributed to data collection, network pharmacology analysis, and manuscript drafting. Shaikh Asif Ayyub performed molecular docking studies and interpretation of the results. Dr. Shaikh Mehmood Dawood conceptualized and supervised the study, reviewed the manuscript and approved the final version for publication.
The authors declare that no financial support or funding was received for this study.
The authors declare that there are no conflicts of interest regarding the publication of this manuscript.
This study was entirely based on computational and in silico methodologies including network pharmacology and molecular docking approaches. Therefore, ethical approval and informed consent were not required as no human participants or experimental animals were involved in the study.
All authors have read and approved the final version of the manuscript for publication.
The authors are sincerely thankful to Aurangabad Pharmacy College for providing the necessary academic support, research facilities, and computational resources to carry out this research work. The authors also express their gratitude to the Principal of Aurangabad Pharmacy College for continuous encouragement and valuable guidance throughout the study. Furthermore, the authors acknowledge the support and cooperation extended by the college management and trust in facilitating the successful completion of this research work.
During the preparation of this manuscript, generative artificial intelligence (AI)-assisted tools were utilized only for language refinement, grammatical correction, and improvement of scientific readability.