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Full Length Article

Interaction of molecular mechanisms of plant-derived metabolites in Type 2 diabetes mellitus: A network pharmacology, docking and molecular

dynamics approach on AKT1 kinase

Ekambaram Gayathiri

^a,1

, Palanisamy Prakash

^b,1

, Somdatta Y. Chaudhari

^c

,

Sarvesh Sabarathinam

^d

, Subramanian Deepika Priyadharshini

^e

, Mohammad K. Al-Sadoon

^f

, Jithendra Panneerselvam

^g

, Soon Woong Chang

^h

, Balasubramani Ravindran

^h,i,*

,

Ravishankar Ram Mani

^j,*

aDepartment of Plant Biology and Plant Biotechnology, Guru Nanak College (Autonomous), Chennai 600042, Tamil Nadu, India

bDepartment of Botany, Periyar University, Periyar Palkalai Nagar, Salem 636011, Tamil Nadu, India

cDepartment of Pharmaceutical Chemistry, Modern College of Pharmacy, Nigdi, Pune. India

dPharmaco-Netinformatics Lab, Center for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai 602105, Tamil Nadu, India

eDepartment of Chemistry, Vellalar College for Women, Thindal, Erode 638012, Tamil Nadu, India

fDepartment of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia

gDepartment of Pharmaceutical Technology, IMU University, 57000, Kuala Lumpur, Malaysia

hDepartment of Civil&Energy System Engineering, Kyonggi University Yeongtong-Gu, Suwon, Gyeonggi-Do 16227, Republic of Korea

iCentre for Herbal Pharmacology and Environmental Sustainability, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, 603103, Tamil Nadu, India

jDepartment of Pharmaceutical Biology, Faculty of Pharmaceutical Sciences, UCSI University, Cheras, 56000, Kuala Lumpur, Malaysia

A R T I C L E I N F O Keywords:

Molecular screening Docking

Plants Diabetes mellitus

A B S T R A C T

Background:T2DM is a common metabolic disease with enormous effects on health worldwide; moreover, the use of phytochemicals as therapeutic compounds has drawn increasing attention. Therefore, the objective of this study was to assess the effectiveness of these phytochemicals in combating diabetes through a comprehensive evaluation of their interactions with biological networks through network pharmacology, molecular docking, and molecular dynamics simulations.

Objectives:The first goal of this study was to search and screen potential phytochemicals for binding with key proteins involved in T2DM, with special emphasis on AKT1 kinase, an integral component of the insulin signaling pathway.

Methods:Network pharmacology analysis was carried out, and the interaction network of targets associated with T2DM was generated using KEGG, STRING and Cytoscape 3.9.1 software’s. To determine the specific metabolic processes, cellular compartments, and molecular functions involved in T2DM, we performed Gene Ontology and KEGG analyses. An initial and short molecular docking study was conducted to analyze the binding modes, while the molecular dynamics simulations provided insights into the binding energy and stability of phytochemicals at target sites, with emphasis on rutin engaged with AKT1.

Results:In total, 10 hub genes were proposed to be involved in T2DM and can be considered candidate therapeutic targets, namely MTOR, CASP3, CCND1, TNF, MMP9, ALB, MDM2, AKT1, and HSP90AA1. Rutin was found to have the highest binding score for AKT1 in docking studies, while MD simulations identified the structural stability and persistence of the compound’s activity at the target enzyme loci.

* Corresponding authors.

E-mail addresses:[email protected](P. Prakash),[email protected](B. Ravindran),[email protected](R.R. Mani).

1Equally contributed.

Contents lists available atScienceDirect

Energy Nexus

journal homepage:www.elsevier.com/locate/nexus

https://doi.org/10.1016/j.nexus.2024.100351

Received 5 September 2024; Received in revised form 6 November 2024; Accepted 24 November 2024 Energy Nexus 17 (2025) 100351

Available online 27 November 2024

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T2DM in the USA is approximately 8.5 %. The high incidence of obesity in children is a general factor that leads to T2DM among teenagers and young adults in young adulthood. After 20 years, the incidence of T2DM increased from to 117-183 % per 100,000 people between 1990 and 2019. T2DM is a complicated disease that involves individual and genetic factors, lifestyle, and ecological components. Pharmacotherapy for diabetes is still characterized by certain limitations [1–2]. Modern studies have shown that phytochemicals extracted from plants, such as fruits, vegetables, and spices, could be the best alternative for treating diabetes. Naturally derived compounds are widely known for their effectiveness in activating many biochemical signalling pathways and are easily related to the newest therapeutic treatment plans, which use a single medicine to get effects on a number of targets in the body. Fla- vonoles and steroid saponins also exhibit antioxidant and anti-inflammatory, mediate glycemia, and facilitate insulin sensitivity and processes are key of the management of disorders, on diabetes.

These complex molecules, also known as ’biologically active compounds,’have a dual effect and can interact via various molecular routes involved in diabetes [3]. The phytoconstituents involved in network pharmacology as a new approach that has revolutionized traditional methods of drug investigation and the process of discovering compounds for medicinal use. This was achieved by applying computational approaches. Scientists can design a variety of molecular networks by assembling data from genetic, proteomic, and metabolomic studies.

These networks can track proteins and RNA at the level of interaction with plant chemicals or their targets within the human system. This systems biology approach will provide a more thorough and detailed understanding of how these chemicals function and develop associated therapeutic benefits. Finally, advanced informatics approaches, such as molecular docking and dynamic simulations, are beneficial for the ac- curate prediction of the strength of association as well as stability factors between plant-derived components and target proteins. The rapid and effective identification of lead drug candidates with the highest therapeutic efficiency is a demonstrative advantage of this technology [4].

This is, to a large extent, due to this research on the ongoing concern regarding caring for diabetes. The complexity of T2DM has been the focus of recent years, prompting researchers to search for multitargeting treatment approaches beyond single-drug conventional modalities.

T2DM is well established as a metabolic disorder but now it has been recognized that T2DM also represents an multi-factorial event characterized with chronic inflammation, oxidative stress and cellular signaling malfunctions. More recently, it has become apparent that these pathways are interlinked; hence, they cannot be affected individually, but require a holistic approach targeting multi-molecular targets simultaneously [4]. Phytochemicals, especially those from medicinal plants, have shown remarkable potential for modulating these multifaceted pathways. Recent research has suggested that certain compounds from natural products, such as flavonoids, alkaloids, terpenoids, and saponins, possess anti-inflammatory properties, as well as antioxidant and insulin-sensitizing actions, by directly affecting the protein signalling pathways implicated in glucose metabolism and insulin sensitivity. In addition, the use of modern computational tools, including systems biology and network pharmacology, with AI-driven

rapidly advancing research environment provides a platform for phytochemicals to serve as more than mere complementary therapies within current treatment strategies; instead, they should be considered holis- tically in the context of paving the way towards novel T2DM-specific cutting-edge patient-centric therapeutic approaches. This study recom- mends therapeutic amendments from the known target drug category to the holistic herbalary drug category. This argument is supported by evidence that plant-based compounds contribute to primal search.

Therefore, the integration of traditional medicinal plant knowledge and innovative scientific diagnostic techniques can lead to a more inclusive treatment strategy for the management of diabetes. This strategy entails minimizing the need for synthetic drugs that, in turn, disrupt life and ultimately develop more sustainable and patient-centered treatment programs.

2. Material and methods

2.1. Screening and selection of compounds

Screening of phytochemical library and drug-likeness profiling: A set of potential anti-diabetic compounds was generated from the literature survey. We downloaded the Canonical SMILES notation for all three compounds from PubChem (https://pubchem.ncbi.nlm.nih.gov/). The compounds were also confirmed with molsoft and swissADME databases for drug-likeness (DL), bioavailability (F) in silico, and molecular weight (MW). For a compound to be considered an eligible therapeutic candidate, it had to fulfil the following conditions:

Druganism (DL): More than 0.18 Bioavailability (F) %:>30 % MW: Below 500 Da

These were chosen to be sensitive hepatic biomarkers, so they would eliminate any candidates that could, in therapeutic doses necessary for T2DM management, have liver toxicities or induction of specific targets/

pathways known from the literature and interpretation by the PK/PD model as associated with unwanted effects.

2.2. Identifying compound and disease targets studies

For each screened phytochemical, we obtained the canonical SMILES notation and inputted it into the Swiss Target Prediction database (htt p://www.swisstargetprediction.ch/). This tool uses chemical similarity to determine the likelihood of a phytochemical interacting with a human protein using the bioactivity data gathered; thus, we were able to determine the probable targets of each phytochemical. We further refined the results by applying a probability filter with a minimum threshold greater than zero such that we could only obtain targets that could potentially interact with our chosen protein. This resulted in a refined list of possible protein targets for each phytochemical, which formed the basis for subsequent disease-specific target prediction.

To determine which of these protein targets was relevant to Diabetes Mellitus (DM), we utilized the DisGeNET database (https://www.disg enet.org/). It is a comprehensive human phenotypic database comprising manually annotated genes and diseases, hypothesis testing,

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and model organism data from scientific literature and experiments.

Through the application of the term“Diabetes Mellitus“(C0011849), we were able to obtain an extensive list of proteins implicated in DM pathophysiology. We then used InteractiVenn (https://www.interacti venn.net/), which allows dataset intersection and selection of the targets common to both datasets, that is, DM-related proteins from Dis- GeNET and phytochemical targets from Swiss Target Prediction. Based on the Venn diagram analysis, we identified proteins that may interact with the screened phytochemicals and are associated with DM. These goals are the common proteins between phytochemicals and DM datasets, which are considered the major targets for a more elaborate analysis because of the potential for multiple target interventions in treating diabetes mellitus.

2.3. Protein-Protein interaction network analysis

To analyze the molecular interactions associated with Diabetes Mellitus (DM), we constructed a Protein-Protein Interaction (PPI) network. The overlapping genes identified in the earlier screening stages were input into the STRING database (https://string-db.org/), including experimental, predicted data, and pathway databases. Frequent filtering of the confidence scores maintained the correctness and biological significance of the connections examined in our analysis. In addition to the analysis above, this PPI network enabled us to understand the dynamic interactions of the proteins that may be implicated in DM and related phytochemicals in the present study, which constitutes an essential aspect of determining the nature of the underlying interactions, disease processes, and potential treatment outcomes.

The resulting interaction network was imported into Cytoscape 3.10.1 (http://www.cytoscape.org/), a software application that focuses on the visualization of biological networks. To understand the structural organization of the PPI network in Cytoscape, we focused on the“hub” proteins; the proteins that usually have the most connections to other proteins in connection to cellular functions and diseases. Hub proteins have been identified and recommended as promising targets for drug intervention because of their ability to affect numerous downstream effects implicated in T2DM signaling pathways. Using Cytoscape to view these connections allowed for the consideration of the overall network architecture, target selection, and functional relevance of these protein interactions with respect to T2DM. This network analysis thus offered invaluable information concerning the potential ability of the selected phytochemicals to modulate T2DM pathways involving multiple proteins, towards the development of multi-targeted therapeutic strategies [5].

2.4. GO and KEGG pathway enrichment analysis

Therefore, we performed Gene Ontology (GO) and KEGG pathway enrichment analyses to understand the biological importance of the overlapping genes. These analyses were designed to determine the molecular roles that refute the mentioned targets, biological processes, and cellular components to which they belong, and metabolic and signaling pathways that include the identified targets. For this purpose, we utilized the DAVID Functional Annotation Tool (https://david.ncifc rf.gov/tools.jsp), a bioinformatics tool that utilizes bioknowledge databases to develop functional classifications for comprehensive gene lists.

In DAVID, the overlapping genes were classified based on their Molecular Function and Biological Process and Cellular Component, so that the functional analysis of the aforementioned genes in diabetes- related pathway expression and function in diabetic cellular activity could be elucidated. Furthermore, metabolic and disease pathway analysis using the KEGG pathway was performed to ascertain the metabolic and disease pathways that were abnormally expressed with regard to the target genes, especially those genes associated with diabetes mellitus. Enrichment p-values and numbers of genes are displayed as histograms and bubble charts with an emphasis on pathways and

functions with the lowest p-values and highest gene numbers. This approach allowed us to gain knowledge on how the selected phytochemicals might affect diabetes pathophysiology through the regulation of certain biological targets and networks [5].

2.5. Molecular docking using for targeted active molecules

Molecular docking studies were performed for 18 compounds, with a special focus on the most promising bioactivity profile of rutin, to investigate the plausible anti-diabetic mechanisms of the selected phytochemicals. The three-dimensional structures of crucial target proteins involved in diabetes, namely MTOR (PDB ID: 7PED), CASP3 (1RE1), CCND1 (6P8E), TNF (8HXQ), MMP9 (1ITV), ALB (1AO6), MDM2 (5VK1), AKT1 (5WBL), and HSP90AA1 (5NJX) were downloaded from the Protein Data Bank. These proteins were selected based on their involvement in the pathophysiology of diabetes, including insulin signaling, inflammation, and metabolic regulation. Each of these protein structures was prepared for docking with the Biovia Discovery Studio 2020 Client by determining active binding sites, removing water molecules and any co-crystal ligands from the structures that might interfere with interactions by the ligands, and adding polar hydrogens to stabilize their structures for proper hydrogen bonding during docking.

The charges were adjusted for the prepared proteins, making them dock-ready by converting them into PDBQT format compatible with the docking software. Docking studies were performed using AutoDock 4.2.6 and AutoDock Vina by setting the parameters to have the maximum accuracy of binding for each target protein. Grid boxes were carefully defined to cover the active binding regions. The dimensions included X: 45.67, Y: 36.30, and Z: 420.30 for some proteins, while other configurations, such as X: 15.54 and Y: 17.22 were used as alternatives to refine visualization and capture interactions. Therefore, this grid configuration allows the docking process to focus on high-potential binding regions and optimize the alignment of each phytochemical within the active site of the protein.

The docking results were analyzed using Discovery Studio 2020 Client through the display of binding affinities and interaction patterns, such as hydrogen bonding and hydrophobic interactions. Three- dimensional and two-dimensional representations of each docking pose were included in the analysis, showing the amino acid residues that interact with the binding of rutin to the target proteins. The use of these visuals was instrumental in allowing a look in great detail at how rutin and other compounds could influence the functionality of diabetes- related proteins, and hence their potential efficacy as anti-diabetic agents. This recourse thus established some of the important molecular interactions through such an elaborate approach that could lead to the development of phytochemical-based multitarget treatments for diabetes [6].

2.6. MD studies on ligand complex

Molecular dynamics (MD) simulations of AKTI+5280805 docking complexes were performed using the Desmond 2020 software platform [7]. The system was developed utilizing force fields 14-16 of the OPLS-2005. The study employed an explicit solvent model, which involved the incorporation of SPC water molecules into a periodic boundary barrier box with dimensions of 10×10×10Π. To simulate the conditions seen in biological entities, 0.15 M NaCl solutions were introduced into the system to counterbalance the charge [8] After the system was equilibrated using an NVT ensemble for 10 ns, the AKTI+ 5280805 complexes were retrained. In 12 ns) Equilibration and Mini- mization runs were conducted using an NPT ensemble to continue the prior phase. The Nose-Hoover chain (NHC) coupling method was used to create the NPT ensemble. The simulations were conducted at a constant temperature of 27^◦C, with a relaxation time of 1.0 ps and a pressure of 1 bar, which remained consistent throughout all simulations. The time interval was set as 2 fs. To regulate the pressure, we used a barostat

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approach with a relaxation time of 2 ps, following the (MTKK) Martyna-Tuckerman-Klein chain coupling system [9]. The particle mesh Ewald technique was used to compute long-range electrostatic interactions, with a Coulomb interaction radius of 9 Å. The RESPA inte- grator was used to calculate the bonded forces for each trajectory with a time step of 2 fs. AKTI+5280805 underwent a final production procedure, with a temporal span of 100 ns. The production procedure was conducted at a rate of 100 ns per unit. For the reliability of the MD simulations, a range of parameters were calculated, including the number of hydrogen bonds (H-bonds), root mean square deviation (RMSD), and radius of gyration (Rg). A stability evaluation was conducted using computational simulations.

3. Result and discussion

The results of our study were obtained from 431 different targets and contained 18 phytoconstituents that might affect 212 common genes associated with DM. These genes are included in a 2803 genes connected with DM (Fig. 1a-b) An in-depth analysis of the strategic significance of DM genes was implemented using a PPI network made using the STRING database and visualized in Cytoscape using the cyto Hubba plugin. The study nodes included MTOR, CASP3, CCND1, TNF, MMP9, ALB, MDM2, AKT1, and HSP90AA1. The mTOR enzyme affects DM through its relationship with insulin signaling and nutrient sensing, the disruption of which probably causes insulin resistance [10]. CASP3, which partici- pates in beta-cell apoptosis and is thus associated with apoptosis in patients with DM, showed the most significant decrease in this group

[11]. The CCND1, TNF, and MMP9 genes, which are the prime regula- tors of cell cycle development, inflammatory responses, and matrix remodeling, undergo exaggerated alterations under diabetic conditions and hence play significant roles in the susceptibility to insulin resistance and complications of diabetes [12]. ALB, MDM2, AKT1, and HSP90AA1 are represented by different cellular metabolic activities, from the action of amino acids through the degradation of proteins to signaling pathways that directly affect glucose metabolism and cell stress responses.

For example, AKT1 is crucial for insulin-mediated glucose absorption in muscle and fat tissues [13]. Phytochemicals and their main hub genes have a multifaceted impact on the pathophysiology of DM. For instance, phytochemical modulation of AKT1 activity can augment insulin sensitivity and enhance glucose uptake, which in turn is pertinent to diabetes therapy [14]. Similarly, MTOR and CASP3 shift cellular metabolism and apoptosis. Consequently, by living these conditions and simultaneously making the necessary alterations, it means controlling the levels of glucose from getting to a higher point and protecting pancreatic beta cells from being damaged.

The PPI network indicates that these phytochemicals not only regulate systemic effects but may also be useful in linking the pathway of insulin resistance, one of the fatalities after beta-cell apoptosis through inflammation and metabolic dysregulation–different facets of diabetes pathophysiology [15]. A holistic outlook on the management of patients with T2DM could result in more effective management strategies, transforming from single inherent medications to multitargeted phytochemicals [16]. The studies reported herein are intended to explain more clearly the phytoconstituents obtained through human proteins, Fig. 1. Overlapping compound related targets and disease related targets b) Top-ranked tanked targets.

Table 1

Molecular docking binding energy affinity of target ligand and complex.

MOLECULE PUB CHEM ID MTOR CASP3 CCND1 TNF MMP9 ALB MDM2 AKT1 HSP90AA1

7PED 1RE1 6P8E 8HXQ 1ITV 1AO6 5VK1 5WBL 5NJX

Rutin 5280805 -9.88 -9.73 -9.61 -9.52 -10.54 -9.2 -9.33 -11.06 -9.18

Apocynin 643302 -7.18 -5.79 -6.16 -6.18 -6.65 -6.51 -5.83 -6.06 -7.21

Thymol 6989 -5.26 -4.86 -6.38 -5.19 -6.71 -5.14 -4.17 -5.39 -4.77

Betulin 64971 -7.34 -6.87 -8.05 -7.8 -9.54 -9.09 -7.28 -8.42 -7.27

Paeonol 440285 -5.26 -4.92 -5.96 -5.24 -7.4 -5.38 -5.31 -5.34 -5.39

Indirubin 5284645 -6.72 -5.74 -6.86 -5.78 -6.81 -7.1 -6.23 -6.6 -6.51

Cryptolepine 227918 -6.52 -4.85 -5.95 -5.15 -5.97 -6.69 -5.09 -4.92 -5.2

Tannic Acid 162110 Average mass1701.198 Da

Sulforaphane 5350 -4.28 -3.71 -5.58 -3.76 -5.66 -4.24 -3.57 -4.11 -4.5

Echinacoside 441444 -5.47 -5.25 -5.89 -5.52 -6.49 -6.54 -5.56 -5.07 -4.97

Thymoquinone 10204 -7.84 -6.38 -7.7 -7.03 -9.44 -8.77 -6.87 -7.79 -7.4

Icariin 5318997 -8.84 -9.46 -10.23 -8.44 -9.78 -9.54 -9.74 -8.16 -8.55

Chlorogenic Acid 1405788 -8.09 -8.46 -9.42 -7.44 -9.36 -8.67 -7.98 -7.48 -7.28

Diosgenin 89870 -5.26 -7.12 -8.35 -7.15 -8.18 -9.87 -7.97 -9.96 -7.59

Chrysin 4444926 -6.78 -5.69 -8.69 -5.75 -7.43 -6.56 -5.73 -6.82 -7

Oleanolic Acid 10062 -6.94 -7.65 -8.07 -6.96 -7.9 -10.35 -8.02 -8.36 -6.66

Ellagic Acid 4445149 -7.94 -6.98 -7.32 -8.09 -7.47 -7.77 -6.5 -7.11 -6.98

Quinine 84989 -6.82 -5.35 -7.72 -5.78 -6.93 -7.71 -5.99 -6.14

Neohesperidin 5280637 Average mass 610.561 Da

Glaucarubinone 390388 -8.9 -7.21 -9.98 -9.39 -9.37 -8.76 -9.06 -8.18 -8.94

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which act as crucial players in the development of T2DM. The values in Table 1constitute an exhaustive list of the molecular binding energy levels for the hub genes and phytoconstituents, as per the docking

model. These data are significant because they indicate the receptivity level of every phytoconstituent that interacts with the target protein.

Rutin shows high affinity for AKT1 and MMP9. For AKT1, the value was -11.06, and for MMP9, it was -10.54. These are important as they imply rutin’s ability to influence financial regulatory networks, which has been proven to be essential in addressing the issue of diabetes. AKT1 is directly involved in the insulin signalling pathway, which regulates glucose uptake and metabolism at the cellular level. In contrast, MMP9 is active in the cleavage of extracellular matrices in nephropathy and retinopathy, and rutin may alleviate some complications (Fig. 2).

Thus, we demonstrated the dynamic relationship between flavonoids and the body’s responses to diabetes, with rutin demonstrating promising efficacy in both glycemic control and prevention of systemic complications [17]. The GO full analysis, which resulted in the recog- nition of 1025 genes with overrepresentation among the categories of Biological Processes, provides a new understanding of the genetic relationships underlying T2DM. This process is highly associated with redox regulation, which is central to the metabolic functions of sugar homeostasis and fat metabolism. Going from an initial more general comprehension to a more precise issue by which the genes play a role, this approach not only strengthens the information on the genetics of the disease, but also offers specific targets that could be used to fill these gaps for the treatment of clinically applicable diseases [18].

3.1. KEGG pathway analysis

The interconnecting nature of diseases such as cancer and neuro- logical disorders through KEGG pathway analysis using diabetes as a Fig. 2. KEGG pathway of the hub-genes.

Fig. 3. Biological process, cellular component and molecular function pathways of hug-genes.

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platform shows the complexity of systemic diseases and the efficacy of certain therapeutic agents in targeting biochemical processes [19]. This crossroads suggests the possibility that phytoconstituents play a role in modulating mechanisms in various types of disorders, thereby providing them as convenient tools in more general medical clinical settings where there is an abundance of inflammation and oxidative stress mechanisms, which are the most frequent mechanisms in chronic diseases [20]. Rutin is well known for its anti-inflammatory and hypoglycemic effects, and its role in regulating carbohydrate metabolism and oxidative stress. It sustains glucose metabolism and has indicated effects on systemic in- juries in diabetes, making it a favorable alternative for comprehensive diabetes intervention [21]. Apocynin has been recognized to improve glucose metabolism by hindering Nrf-2, GLUT4 and reducing inflammation [22]. Consequently, apocynin is a key agent that protects muscle functions that are significantly affected by metabolic diseases. Thymol,

an antioxidant, is thought to have a major effect on (OS), and is known to contribute to the development of diabetes. Consequently, it is likely to aid in disease progression and reduce complications. Along with its glucose-lowering effect, it may also affect insulin sensitivity, which is a key component of diabetes [23]. In an investigation to determine the ideal dosage and treatment period, which, by all means, should maximize the therapeutic value but simultaneously decrease the antagonistic effects [24].

Studies have shown that chrysine activates insulin signaling, which is a key feature controlling normal blood sugar levels. Interference in the next phase regulates the diminishing of insulin resistance, which is a major problem in T2DM. In addition, chrysin protects cells from oxidative stress (anti-inflammatory and antioxidant), which is strongly induced by high glucose levels, and mainly triggers the development of major diabetic complications such as nerve paralysis, diabetic Fig. 4. Schematic representation of the biomolecules for the active site of 1ITV5280805 b) 1RE15280805.

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retinopathy, and other microvascular complications. These effects will not only improve the treatment for primary diabetes but also reduce the occurrence of secondary complications that develop from the disease, making it an option for treatment [25,26]. Among catechins, ellagic acid is an active phytochemical that has reappeared as an agent in some experimental studies of diabetic animals. In these studies, glucose intolerance improved and insulin secretion increased among groups of animals compared to those treated with placebo. These attributes demonstrate that ellagic acid has a significant advantage in improving

pancreatic function, especially as a primary organ for the natural regulation of glucose levels in the body. The dosage and durational factors of ellagic acid are of paramount importance to accentuate its benefits; hence, it is necessary for clinical practitioners to embrace personalized treatment methods to achieve the best outcomes. Apoc- ynin, which is known for its effects on glucose metabolism, provides a clear picture of the dual effects of phytoconstituents as manageable products for diabetes. Through the higher expression of Nrf-2 and GLT4 mRNA, apocynin bolsters cellular antioxidant defense and improves Fig. 5.Schematic representation of the biomolecules for the active site2UZT5280805 b) 5NJX5280805.

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insulin sensitivity via cAMP. Furthermore, it is inversely related to the transcription of NOX2, NOX4, and NF-kB inflammatory markers, which indicates its suppression of inflammation, a common underlying theme in metabolic disorders. The accomplishment of this objective not only aids in glucose regulation but also defends against muscle atrophy, which is a vital factor in maintaining physical activity and quality of life in diabetic patients [27]. Network pharmacology and molecular docking

work together to provide detailed information about these phytonu- trients. Through these experiments, scientists can gain a better understanding of how the created organism would be able to interfere with and manipulate the environment. (Fig. 3.)

Such an approach is aimed at clarifying that the simultaneous entailing of the diversity of metabolic pathways and molecular targets is an appropriate way to fight the multifaceted nature of T2DM. By Fig. 6. Schematic representation of the biomolecules for the active site5VK15318997 b) 6P8E390388.

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addressing disease mechanisms at a broader level, such as inflammation, oxidative stress, and metabolic dysfunction, these compounds can be developed as effective therapeutic drugs that provide multiple benefits to patients more willingly [28]. Nevertheless, the use of multiple databases for active biochemical data and existing flaws with processed data sources remains a difficult task that should be addressed by the further development of high-throughput screening methods and better experimental validation. However, the positive outcomes yielded in the present study provide an excellent basis for future research. However, intensive and comprehensive clinical trials are required to ascertain the efficacy, safety, and optimal use of these phytoconstituents in diabetes.

Consequently, chrysin, ellagic acid, and apocynin play important roles in type 2 diabetes research. Not only do drugs have mechanisms that target the primary aspects of the disease but also secondary complications, which are of great concern to patients. Ultimately, research and clinical trials would further translate these initial discoveries into useful treatments that compete with existing therapies in more fields and will likely lead to more reliable, coherent, and personalized approaches to T2DM treatment. These methods of managing diabetes could be the breakthrough in treatment methods, and outcomes for diabetes patients would improve multi-nationally.

3.2. Docking and molecular s simulations of implications targeted molecules

Molecular docking is considered the most efficient technique for drug discovery. It provides an inexpensive and time-saving approach for finding new drugs and improving therapeutic agents that are already in use [29]. Thus, this computational observation indicates that molecules or ligands behave in specific ways when they bind to target proteins.

One of the important steps in molecular docking is to score the intermolecular interactions of effectiveness and stability. These scores are significant because they are also used to predict the potential binding affinity of the protein to the ligand, which is the same as that of a compound with the highest drug efficacy [30]. Molecular docking can represent and dynamically reproduce the interactions between ligands and receptors at the required level of the physical and chemical behavior of molecules [31]. For example, the adaptability of the display of ligands and receptors is of key importance in attaining ideal complementation, according to Cob-Calan et al. [32], and the intrinsic dynamic character of peptide or oligonucleotide molecules enhances effective binding. This flexibility enables molecules to adapt their shapes such that their interaction energy approaches the minimum value, consequently Fig. 7. Schematic representation of the biomolecules for the active site7PED5280805 b) AKT15280805.

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boosting the binding strength. Furthermore, hydrogen bonding, as mentioned by [33] Barker (1984) the specificity of the hydrogen bond ensures that the ligand is placed at the right amino acids, main

components for the activity of the drug. Another one is This scoring function is widely used to determine the binding free energy in docking studies and its ligan3d. Such complexes are likely to manifest their Fig. 8. Schematic representation of the biomolecules for the active site8HXQ5280805.

Fig. 9. Interaction fraction histograms of a) RMSD and b) RMSF&c - d) Ligand contract for AKT15280805 complex dynamics 100ns.

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desired pharmacological effects [34]. When scoring such functions, these energy components should always be considered, including electrostatic and van der Waals forces, which are crucial for understanding the details of the molecular interactions [35]. These parameters are critical because they are the signature of ligand specificity and reveal the strength of the ligand-RNA interaction [36,37]. This approach is mainly a scoring function for predicted binding affinities (Figs. 4–8) On the other hand, whereas docking simulations that are mostly static are not able to fully reflect what happens within a live cell, as the movements can be a bit dynamic, which can interfere with real drug-target interactions.

Fig. 9a-d. Nonetheless, despite these obstacles, the docking approach has always improved owing to the growth of computational power and algorithms, and the technique has become increasingly reliable and widely used at different stages of drug development. It is always a valid method that adds up to bioassays, providing a provisional model of molecular interactions that enables deeper learning.

Identification of strong binding affinities among phytochemicals and T2DM-related proteins will hopefully draw parallel use of similar compounds as rutin for effective adjuvant therapies while reducing higher dosages of synthetic drugs to minimize drug-related side effects. This multitarget capability of phytochemicals opens the door to the development of precision medicine in the management of diabetes, where treatments are personalized based on specific patient profiles, genetic factors, and individual responses to phytochemical interventions. These computational findings were further validated using molecular docking and network pharmacology approaches. Future in vitro and in vivo studies will provide a basis for the translation of these computational predictions to clinical practice. In summary, this will not only help in understanding the various mechanistic roles of phytochemicals in T2DM, but also support a paradigm shift toward more holistic and integrative strategies in diabetes care, combining phytochemical therapies with conventional treatments to achieve more effective and sustainable disease management.

3.3. Limitations of using multiple databases for biochemical data The use of multiple databases, such as Swiss Target Prediction, Dis- GeNET, and STRING, has enabled a broad and multifaceted approach to identify potential therapeutic targets and interactions for T2DM. How- ever, this approach has certain limitations. Each database applies different curation methodologies and weighting of evidence, which can result in inconsistencies across the datasets [38,39]. For example, Swiss Target Prediction relies on chemical similarity and known bioactivity data to predict potential targets [40], which may overlook less-studied targets that lack extensive bioactivity information. In contrast, Dis- GeNET aggregates gene-disease associations from diverse sources, including curated repositories and experimental studies, which can in- crease data redundancy and lead to variability in target reliability [41]

STRING focuses on both predicted and experimentally validated protein-protein interactions, but the inclusion of various evidence levels can introduce biases depending on the strength and type of data supporting each interaction [42]

These differences in data sources and curation methods can lead to variability in the generated target lists, potentially influencing downstream analyses and interpretations. Integrating targets from multiple databases may also risk overestimating target significance due to redundant or overlapping entries or omitting novel targets not thor- oughly represented in the data [43]. To address these challenges, targets were cross-referenced between databases and emphasis was placed on those corroborated by multiple sources to enhance reliability. Never- theless, we acknowledge that future studies could further mitigate these limitations by incorporating high-throughput experimental validation to confirm computational predictions, thus minimizing potential database bias and enhancing the robustness of our findings.

4. Conclusion

This study demonstrates the therapeutic potential of 18 phytochemicals in the management of T2DM using bio-computational tools, such as molecular docking, network pharmacology, and MD simulations, to explore interactions with key biological targets. Most impor- tantly, significant interactions in our study included those of Rutin with the AKT1 protein, a central regulator in T2DM pathophysiology and other aging-related disorders. The high usefulness of binding to AKT1 indicates that rutin has the potential to modulate T2DM. Predicted molecular mechanisms and metabolic pathways influenced by these phytochemicals were also reflected by a PPI network of 1,165 potential targets and GO and KEGG pathway enrichment analyses.

These findings are promising for practical application. Phytochemi- cals such as rutin may serve as adjuncts to traditional treatments for T2DM by improving glycemic control by reducing the high doses necessary with synthetic drugs, thus minimizing adverse side effects.

Because multiple targets are reached, these compounds may help in holistic treatment approaches, not only for managing glucose metabolism, but also for related conditions, including inflammation and oxidative stress, which are commonly found in diabetic complications.

Such multi-target interventions have the potential to improve patient outcomes and reduce the healthcare burdens associated with T2DM- related comorbidities.

Evidence for these findings through computational studies, especially regarding the pharmacokinetics, bioavailability, and safety profiles of the identified phytochemicals, should be prioritized in in vitro and in vivo studies. Of course, for any compound to be integrated into clinical practice, its efficacy and safety must be established through clinical trials. In addition, phytochemicals hold promise for precision medicine in that treatment plans can be designed to match individual genetic and metabolic profiles to maximize therapeutic efficacy with minimal adverse effects. This review adds to the increasing number of studies supporting the application of plant-based compounds in the management of T2DM and opens new avenues for innovative, multitarget therapeutic approaches that may revolutionize clinical practices in the care of diabetes.

Funding

Project No. GNC/GNCR/SMG_2023-24/01.

Lack of appropriate permission Nil.

CRediT authorship contribution statement

Ekambaram Gayathiri:Conceptualization.Palanisamy Prakash:

Conceptualization.Somdatta Y. Chaudhari:Software, Methodology.

Sarvesh Sabarathinam: Formal analysis. Subramanian Deepika Priyadharshini:Formal analysis.Mohammad K. Al-Sadoon:Formal analysis. Jithendra Panneerselvam: Funding acquisition. Soon Woong Chang:Data curation. Balasubramani Ravindran:Supervi- sion.Ravishankar Ram Mani:Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.

Acknowledgments

The authors acknowledge Guru Nanak College (Autonomous), Chennai, for providing the Resources and assistance required to conduct

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