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Exploring Artocarpus heterophyllus phytochemicals as novel mineralocorticoid receptor inhibitors: A computational approach to hypertension therapy
* Corresponding author E-mail address: subinadhikari2018@gmail.com (J Adhikari Subin)
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Received: ,
Accepted: ,
Abstract
This research intends to explore the potential of phytochemicals derived from Artocarpus heterophyllus (jackfruit) as novel mineralocorticoid receptor (MR) inhibitors for hypertension therapy. The primary objective is to identify natural compounds with superior binding affinities and stability compared to the native ligand. This offers a safer alternative to synthetic antihypertensive drugs, which often cause adverse effects. A computational approach was employed, combining molecular docking, molecular dynamics simulations (MDS), and absorption, digestion, metabolism, excretion, and toxicity (ADMET) analysis. A database of 60 phytochemicals from A. heterophyllus was prepared, and their binding affinities to the MR protein (PDB ID: 5L7E) were evaluated using the DockThor platform. The top six ligands with the highest binding affinities were further analyzed through 200-ns MDS to assess structural and thermodynamic stability. Key descriptors such as radius of gyration (Rg), root mean square fluctuation (RMSF), root mean square deviation (RMSD), solvent-accessible surface area (SASA), and binding free energy change (ΔG) were calculated to evaluate the stability and interactions of the protein-ligand complexes. Six phytochemicals exhibited stronger binding affinities than the native ligand (-9.619 kcal/mol), with heterophylol (ligand01) showing the highest binding affinity (-10.378 kcal/mol). MDS revealed that heterophylol and artoindonesianin Q (ligand02) formed the most stable complexes, with heterophylol demonstrating the lowest binding free energy changes (ΔG = -39.91 kcal/mol). Structural analyses confirmed the stability of these complexes over time, with minimal fluctuations in RMSD, RMSF, and Rg. Hydrogen bond interactions with key residues (e.g., CYS849, ARG817) and hydrophobic interactions (e.g., LEU766, MET852) further validated these ligands’ stability and binding efficacy. The findings highlight the potential of A. heterophyllus phytochemicals, particularly heterophylol and artoindonesianin Q, as natural MR inhibitors for hypertension treatment. These compounds demonstrated superior binding affinities, structural stability, and thermodynamic favorability compared to the native ligand, positioning them as promising candidates for further experimental and clinical validation. This study provides a computational foundation for developing safer, plant-based antihypertensive therapies, contributing to the advancement of natural product-based drug discovery.
Keywords
Hypertension
Natural therapeutics
Protein-ligand interaction
Steroid receptor
1. Introduction
Hypertension is a key contributing factor to several diseases, such as congestive heart failure, cirrhosis, nephrotic syndrome, edema, and stroke (Fitrya et al., 2023). Finerenone (Kerendia), approved by the FDA in 2021, is a mineralocorticoid receptor antagonist used to lower the risk of cardiovascular death and heart attacks. However, this can cause side effects such as irregular heart rhythms, elevated potassium levels, and kidney problems (Singh et al., 2022) (Ravid & Laffin, 2022). Exploring alternative treatments with natural ingredients is crucial for minimizing side effects. The mineralocorticoid receptor (MR), a member of the steroid receptor family, functions as a transcription factor and is linked to heart disease upon activation (Buonafine et al., 2018). MR activation is crucial in the onset of hypertension (Yang & Young, 2009). With aging, elevated MR expression in the heart and blood vessels leads to a greater incidence of cardiovascular diseases (Kuster et al., 2005). A potential strategy for managing cardiovascular diseases is modulating the MR with phytochemicals from medicinal plants, providing a natural and safer alternative. Aldosterone binding to the MR induces a structural change, initiating gene transcription that elevates blood volume and pressure. Inhibiting this interaction with phytocompounds could block receptor activation, decreasing sodium and water retention, enhancing diuresis, and reducing the risk of hypertension-related heart diseases (Yang & Young, 2009).
Natural products represent a valuable reservoir of bioactive compounds, refined over millions of years to help plants defend against environmental challenges and pathogens (Rosa & Nengi, 2024). Artocarpus heterophyllus, known as jackfruit, is a species of tree in the mulberry family, Moraceae (Biworo et al., 2015). It is useful for its antibacterial, antidiabetic, anti-inflammatory, antioxidant, cardiovascular protective, and anti-helminthic properties (Shanmugapriya et al., 2011). Therefore, it is imperative to explore its phytochemicals to identify an effective bioactive component and for a molecular-level understanding of the inherent mechanism.
Understanding the mechanisms of ligand binding to a protein is a fundamental step in designing more selective and potent drugs targeting a specific protein related to a disease (Lu et al., 2010). Molecular docking is a widely used technique in drug discovery, designed to predict how and where a ligand binds to its target protein. This method evaluates the 3D arrangement of ligands within the binding sites of target proteins, a process known as ligand pose prediction (Vittorio et al., 2024). It estimates binding affinity through sampling and scoring. Sampling explores ligand conformations while scoring evaluates interaction strength and stability (Lin et al., 2020). Molecular dynamics simulation (MDS) complements molecular docking by providing deeper insights into protein-ligand interactions, offering both structural and time-dependent perspectives based on a molecular interaction potential model (Singh et al., 2022). Since biomolecules are inherently dynamic, their functional and interactive behaviors rely on the constant motion of their atoms. MDS predicts the movements of individual atoms in a protein or molecular system over time, enabling the observation of dynamic behaviors, atomic interactions, and conformational changes (Singh et al., 2022). These simulations yield detailed insights into biomolecular structures, functions, and the fate of protein-ligand interactions. The primary objective of this study is to identify a molecule capable of binding to the orthosteric site of the target protein (MR) with greater strength than that of the native ligand. The stability is evaluated through spatial parameters, such as root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), radial pair distribution function (RPDF), root-mean-square deviation (RMSD), and gyration radius (Rg), along with thermodynamic parameters like binding free energy changes, providing critical data on the molecule’s potential as a modulator.
2. Materials and Methods
2.1 Ligand structural preparation
A database containing 60 phytochemicals derived from Artocarpus heterophyllus (Agu et al., 2024; Noor et al., 2024; Sreeja Devi et al., 2021; Thapa et al., 2016) was constructed in PDB format (Table S1). The structural data files (SDF) with 3D coordinates of these ligands were extracted from the PubChem server (https://pubchem.ncbi.nlm.nih.gov/) (Kim et al., 2023). Polar hydrogen atoms were adjusted, and the molecular formulas were confirmed using the Avogadro software (Hanwell et al., 2012). The geometries of the ligands were optimized, and the steric hindrance was monitored, as well as the accuracy of bond orders was confirmed using the methodology outlined by Shrestha et al. (2024). Here, an energy convergence criterion of 10-8 units, a maximum number of structure generations of 5000, conjugate gradient method of search, and the force field of UFF were employed (R. L. S. Shrestha et al., 2024).
2.2 Protein target search and preparation
The mineralocorticoid receptor (PDB ID: 5L7E) (https://doi.org/10.2210/pdb5L7E/pdb) (Fig. 1) was chosen as the target protein, featuring a resolution of 1.86 Å and a native ligand (6Q0). According to the SuperPred web server (https://prediction.charite.de/) (Nickel et al., 2014), the receptor showed a 61.69% probability, reflecting the likelihood of ligand interaction with the protein target, along with a model accuracy exceeding 90%. The protein’s structural data was retrieved from the RCSB Protein Data Bank (https://www.rcsb.org/) in PDB format. Any additional chains, water molecules, and ions in the target were deleted, polar hydrogens were added, and the structure was saved in PDB format for molecular docking calculations.
- Mineralocorticoid receptor structure in holo form in ribbon representation (cyan) with the native ligand (brown) in a ball-and-stick model at the orthosteric pocket.
2.3 Molecular docking calculations
DockThor (https://www.dockthor.lncc.br/), a web-based platform, was employed for molecular docking calculations in a hydrated environment (Guedes et al., 2024). This tool predicts the optimal pose of a ligand with the lowest binding energy in the receptor’s active site, providing insights into the binding geometry (Yusuf et al., 2008). The docking box was centered at coordinates (x = 9, y = 14, z = 11) with dimensions of (16 × 16 × 16) Å3 and a discretization value of 0.17, covering the orthosteric site. For the genetic algorithm-based search, a total grid points of 857,375 and 1,000,000 evaluations of the scoring function were performed, using a population of 750 and 24 runs.
2.4 Protein-ligand interactions
The PDB files of the top six receptor-ligand adducts based on the docking scores were uploaded to LigPlot+ (v.2.2.9), which was used to generate 2D visualizations of the protein-ligand interactions (Laskowski & Swindells, 2011).
2.5 MDS
MDS of the adducts with the top binding values was conducted using the GROMACS code (ver. 2021.2) following the details reported in (Adhikari Subin & Shrestha, 2024). The CHARMM27 force field was applied to the protein, while for the small organic molecules were generated using the SwissParam tool (http://www.swissparam.ch/). A simulation box with a 1.0 nm spacing was created, filled with TIP3P water molecules, and an isotonic NaCl solution (0.15 M) was added. The system was neutralized by introducing the required number of counter ions (Cl⁻/Na⁺). Equilibration was conducted for 1 ns each in both NVT and NPT ensembles, repeated twice, at a biological temperature of 310 K and a normal pressure of 1 bar. The movement constraints on the protein-ligand complex were released throughout the 200-nanosecond production run, which employed a time step of 2 fs. Various geometrical and thermodynamic parameters, including Rg, RMSF, SASA, RPDF, and RMSD, were calculated from the MDS trajectory using GROMACS’s built-in modules. Snapshots of the ligand-bound region at distinct time intervals were captured using the dump command from the command-line interface, adhering to the previously established protocol (Shrestha et al., 2024).
2.6 Binding free energy change estimation
The equilibrated portion of the MDS trajectories (20 ns) was used to evaluate the end-state binding free energy change, based on the approach described in previous studies (Neupane et al., 2023; R. Shrestha et al., 2024). The change in binding free energy (ΔG) was approximated using the MM-PBSA module, as described by the following equation (Valdés-Tresanco et al., 2021).
2.7 Physicochemical and ADMET properties
To assess the drug-likeness properties of the compounds, the hit compounds were analyzed using the pkCSM server (https://biosig.lab.uq.edu.au/pkcsm/prediction), which provided insights into their physicochemical and ADMET properties. The SMILES data from PubChem were used for this analysis.
2.8 Computational resources
Molecular docking calculations, output analysis, and interpretation were conducted on a PC with 8 GB of memory and an Intel processor. MDS and thermodynamic derivation were carried out on a system running Ubuntu 20.04 LTS, featuring a 12th-generation Intel processor, 64 GB of RAM, 2 TB of storage, and an NVIDIA RTX 4090 GPU accelerator.
3. Results and Discussion
All the values presented in the results and discussion sections are expressed as mean ± standard deviation.
3.1 Molecular docking protocol validation
The heavy atom RMSD of 1.79 Å, calculated by aligning the co-crystallized native ligand with the docked ligand, confirmed the accuracy of the docking protocol (Fig. S1). An RMSD below 2.0 Å is considered a benchmark for successful binding mode reproduction (Ramírez & Caballero, 2018), indicating that the algorithm accurately captured the global minima of the protein-ligand complex and ensured reliable results.
3.2 Docking scores and comparative analysis
Table S2 presents the docking scores of the best six ligands and the native ligand, along with their PubChem CIDs. All six ligands exhibited binding affinities stronger than that of the native ligand (-9.619 kcal/mol) (Fig. 2). Ligand01 exhibited a binding affinity of -10.378 kcal/mol, whereas ligand06 had a binding affinity of -9.726 kcal/mol. Since a higher (more negative) binding affinity signifies a stronger interaction (Du et al., 2016), ligand01 was held more strongly to the protein compared to ligand06. This suggests that these six ligands are more potent than the native ligand, making them potential candidates as stronger competitive inhibitors for regulating the target protein’s function.
- Chemical structures of the top six phytocomponents from Artocarpus heterophyllus, ranked by their binding scores (kcal/mol).
3.3 Ligand and backbone dynamics in adducts at various times stages during the production run
The ligand dynamics at different stages of the production run were examined using snapshots captured at corresponding time points, which act as visual descriptors (Table 1). The ligand dynamics across the complexes demonstrated significant variations in the level of conservation of its pose and stability. In complex01, ligand01 was positioned within the protein’s catalytic site throughout the 200 ns simulation, maintaining a stable position without significant changes. However, it exhibited slight rotational movements till 50 ns. Similarly, ligand02 in complex02 exhibited no noticeable changes throughout the 200 ns production run, reflecting conservation of the pose and strong stability. In contrast, ligand03 and ligand05 in complexe03 and complex05, respectively, showed minor rotational and translational movements between 100 ns and 150 ns, indicating moderate flexibility. Ligand04 in complex04 exhibited minimal rotational adjustments from 1 ns to 150 ns, while ligand06 in complex06 displayed negligible delocalization and some rotation over the entire 200 ns simulation. These findings underscore the variation in behaviors of ligands within the active site, with ligands like ligand01 and ligand02 showing stable interactions, while others, such as ligand03 and ligand05, demonstrated some movements. This diversity suggested differences in binding of the ligand by the protein across the complexes under near-physiological conditions. Regarding the backbone dynamics, it was observed to be quite stable across all the complexes. It showed that the receptor is druggable and maintains its geometrical integrity. The visual description has been explained mathematically by various parameters in the next section.
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3.4 Geometrical descriptions of the complexes
3.4.1 Root mean square deviation (RMSD) of ligand relative to the protein backbone
RMSD analysis helps to evaluate the stability of the ligand and protein within the complex by monitoring the changes in the positions of the ligand and protein backbone over time compared to their initial configuration (Chhetri et al., 2024). Fig. 3 illustrates the RMSD profiles of the top six ligands relative to the protein backbone. The conservation of pose or geometry over time is assessed by this parameter and is one of the prime evaluators of geometrical stability.
- RMSD profile of the top six ligands- ligand01 (magenta), ligand02 (red), ligand03 (green), ligand04 (blue), ligand05 (yellow), and ligand06 (brown)- compared to the protein backbone in their corresponding adducts extracted from MDS trajectories.
The RMSD plot for ligand01 reached equilibrium after 50 ns and remained almost flat for the rest of the production run, with an average RMSD of 0.154 ± 0.026 nm. The RMSD curves for ligand02 and ligand04 remained consistently flat throughout the production run, indicating their continued stability, with an average RMSD of 0.295 ± 0.027 nm and 0.146 ± 0.023 nm, respectively. This suggested that the ligands maintained a stable position at the protein’s catalytic site with minimal motion. Snapshots of complex02 further support this observation, where the ligand02 remained nearly with the same orientation from 1 ns to 200 ns (Table 1). Similarly, ligand04 preserved its configuration with minimal delocalization from the catalytic site. Ligand03 achieved a stable trajectory after approximately 30 ns and remained consistent until the end of the simulation, with an average RMSD of 0.194 ± 0.030 nm. The RMSD plot for ligand05 revealed that the ligand was stabilized after 10 ns, remained steady up to 75 ns, displayed fluctuations between 75 and 100 ns, and regained stability thereafter until 200 ns. This is also evident in the snapshots of these complexes, where rotational and translational motion were observed between 100 and 150 ns. Ligand06 attained equilibration after 20 ns and remained stable until 75 ns. It exhibited fluctuations between 75 and 100 ns, followed by a smooth and stable trajectory from 100 to 200 ns, with an average RMSD of 0.265 ± 0.048 nm. Overall, the ligand RMSD plots relative to the protein backbone indicated that complexes with ligand01, ligand02, ligand03, and ligand04 displayed the highest stability, whereas complexes with ligand05 and ligand06 showed moderate stability. These results highlight the potential of the phytochemicals to modulate the target protein effectively, supporting the research’s desired objective.
3.4.2 RPDF
The RPDF, g(r), describes the probability of the ligand’s center of mass (COM) being located at a distance (r) from the protein’s COM, averaged across the simulation. This distribution highlights the spatial arrangement of the ligand around the protein or its relative movement (Kopera & Retsch, 2018).
The RPDF curve for complex02 and complex03 showed a single, narrow peak at 0.9 nm (Fig. 4), which aligned with the observed stability of the ligand and protein backbone in snapshots of these complexes (Table 1). These peaks reflect the localization of the protein’s COM relative to the ligand’s COM, indicating that the ligand remained consistently confined to its initial position over time. Complex04 also displayed a single peak at 0.9 nm, but it was less sharp compared to the peaks for complex02 and complex03. In the case of complex01, two peaks were observed: a smaller peak at 0.85 nm and a narrow peak at 0.9 nm. This suggests that the ligand alternated between two preferred positions, with a brief disruption in its conserved pose. For complex05, two peaks were identified: one at 1.1 nm and a broader one at 1.25 nm. Similarly, complex06 showed two peaks: a tall peak at 0.9 nm and a broader, shorter peak at 1.15 nm, which resembled the broader peak in the case of complex05. Of the six complexes, complex02, complex03, and complex04 indicated that the ligand remained confined to the catalytic site, potentially capable of influencing protein function. The ligand01 of complex01, although observed to occupy two positions briefly, predominantly remained in the second position for most of the simulation period. This suggested that, similar to ligand02, ligand03, and ligand04, it has the potential to influence protein function. In contrast, ligand05 and ligand06 were found to shift away from their COM for some duration, as reflected in their RMSD curves also (Fig. 3), which showed significant fluctuations caused by their transient occupancy at the active site (Table 1).
- RPDF between the COM of the ligand and the COM of the protein in different complexes obtained from the MDS trajectories: complex01 (magenta), complex02 (red), complex03 (green), complex04 (blue), complex05 (yellow), and complex06 (brown); a single sharp peak indicates the ligand’s localization relative to the protein.
3.4.3 Hydrogen bond modulation
The variation in the donor-acceptor (D-A) distance within the hydrogen bonds formed between the ligand and receptor protein in the complexes was monitored during the simulation. In complex01, SER811 formed a moderately stable and nearly strong hydrogen bond after approximately 20 ns, with an average D-A distance of 0.313 ± 0.048 nm, which persisted until the end of the production phase, though intermittent spikes indicated fluctuations in bond length (Fig. 5). Similarly, in complex02, the oxygen donor of ASN770 established a strong and stable hydrogen bond with the oxygen acceptor of ligand02, reaching equilibrium at around 5 ns and maintaining stability throughout the production run, with an average D-A distance of 0.207 ± 0.037 nm and occasional spikes. For other complexes, hydrogen bonds formed and broke repeatedly over the 200 ns period. Notably, SER811 and ASN770 were absent initially but were formed later in complex01 and complex02, respectively. These observations allowed for the monitoring of hydrogen bond formation, breakage, and distances during the MDS to assess the stability of the complexes.
- Variation in donor-acceptor (D-A) distances of hydrogen bonds in two complexes during MDS: hydrogen bond between SER811 and ligand01 (magenta) and hydrogen bond between ASN770 and ligand02 (red).
3.5 Thermodynamical analysis
3.5.1 Binding free energy changes (∆GBFE)
The ∆GBFE variations for the six adducts were computed from the 20 ns equilibrated segment of the MDS trajectory (Table 2). This analysis was conducted to determine the spontaneity and feasibility of the adduct-formation processes.
| Complex | ∆EVDW | ∆EEL | ∆EPB | ∆ENPOLAR | ∆GBFE |
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| Complex01 | -58.72 ± 2.64 | -5.96 ± 2.56 | 29.67 ± 1.77 | -4.91 ± 0.10 | -39.91 ± 3.52 |
| Complex02 | -55.25 ± 2.42 | -21.98 ± 3.87 | 45.65 ± 2.74 | -4.71 ± 0.11 | -36.29 ± 3.29 |
| Complex03 | -56.50 ± 2.84 | -0.23 ± 3.68 | 28.73 ± 2.91 | -4.96 ± 0.10 | -32.49 ± 3.26 |
| Complex04 | -49.91 ± 2.14 | -0.19 ± 3.39 | 28.23 ± 3.50 | -4.36 ± 0.08 | -25.84 ± 3.12 |
| Complex05 | -54.42 ± 2.94 | -3.26 ± 3.65 | 28.99 ± 5.09 | -5.31 ± 0.13 | -34.00 ± 3.10 |
| Complex06 | -53.24 ± 2.97 | -16.86 ± 4.44 | 41.74 ± 4.71 | -4.69 ± 0.09 | -33.05 ± 4.25 |
The ∆GBFE values for all the complexes were negative, indicating that the complex formation reactions were spontaneous in nature. Complex01 exhibited the most favorable binding free energy change of -39.91 ± 3.52 kcal/mol, reflecting a high thermodynamic stability level. Similarly, the ∆GBFE values for the other complexes were recorded as -36.29 ± 3.29 kcal/mol for complex02, -32.49 ± 3.26 kcal/mol for complex03, -25.84 ± 3.12 kcal/mol for complex04, -34.00 ± 3.10 kcal/mol for complex05, and -33.05 ± 4.25 kcal/mol for complex06. These negative values suggest that all the complexes exhibit spontaneous binding and are thermodynamically favorable. Out of all the components, the van der Waals energy (∆EVDW) was the dominant contributor to the overall binding energy in all the complexes. The electrostatic energy (∆EEL) was negative in all complexes, except for complex03 and complex04. In complex03, the electrostatic energy was slightly positive at 0.23 ± 3.68 kcal/mol, while in complex04, it was 0.19 ± 3.39 kcal/mol, indicating weak repulsion or negligible interaction. Despite the significant positive contribution from the polar solvation energy (∆EPB), the overall binding free energy change (∆GBFE) was negative in all cases, demonstrating that the complex formation remained spontaneous. This suggests that while the polar solvation energy may be unfavorable, the other components, particularly van der Waals interactions, drive a sustained thermodynamically spontaneous process of complex formation.
3.5.2 Decomposition analysis per residue
The contribution of each residue to the binding free energy was analyzed using gmx_MMPBSA. The decomposition analysis showed that both ligand01 and ligand02 have strong binding affinity for the mineralocorticoid receptor (MR), interacting notably with residues in or near the ligand-binding domain (LBD) of the 5L7E structure (Table S5). In the ligand01-MR complex, LEU938 and MET852 contributed the lowest binding free energies (-2.20 and -1.51 kcal/mol, respectively), with MET852 located in the LBD (Fig. 6a). Although LEU938 is outside the LBD, it still played a key stabilizing role. For the ligand02-MR complex, LEU938 (-2.47 kcal/mol) and PHE942 (-1.59 kcal/mol) (Fig. 6b) were major contributors, with PHE942 belonging to the LBD.
- Binding free energy contributions of active residues and ligands in the (a) 5L7E-ligand01 and (b) 5L7E-ligand02 complexes.
Overall, at least one residue from the LBD was a key contributor in each complex, emphasizing the LBD’s central role in ligand binding and receptor function.
Considering all the geometrical and thermodynamic parameters extracted from MDS trajectories, it can be inferred that ligand01 (heterophylol) and ligand02 (artoindonesianin Q) demonstrated greater effectiveness than the native ligand in forming stable adducts. The RMSD analysis further reinforced their robust and consistent binding stability, supported by additional descriptors such as RPDF, hydrogen bond modulation, RMSF, SASA, and Rg. The findings suggest that ligand01 and ligand02 may potentially serve as competitive inhibitors against the native ligand, influencing the activity of the target protein (MR).
4. Conclusions
This study underscores the potential of phytochemicals from Artocarpus heterophyllus as novel mineralocorticoid receptor inhibitors for hypertension treatment. Using a comprehensive computational approach that included molecular docking and MDS, six phytochemicals were identified with superior binding affinities compared to the native ligand. Among them, heterophylol (ligand01) and artoindonesianin Q (ligand02) exhibited the strongest binding affinities, the highest thermodynamic stability, and drug likeness properties, positioning them as promising candidates for further research. The stability of these protein-ligand complexes was further validated through mathematical descriptors, such as RMSD, RMSF, Rg, SASA, and binding free energy calculations, confirming their structural integrity and robustness over time. These findings suggest that heterophylol and artoindonesianin Q may function as competitive inhibitors at the MR binding site. Their strong binding affinities, along with favorable thermodynamic properties, highlight their potential as natural antihypertensive agents. This study provides a robust computational foundation for further experimental and clinical validation, offering a pathway for developing safer, plant-based therapeutic alternatives for hypertension. Future research should focus on in vivo and in vitro studies to validate the efficacy, safety, and bioavailability of these compounds, ultimately advancing natural product-based drug discovery and contributing to the development of novel treatments for cardiovascular diseases.
CRediT authorship contribution statement
Ram Lal (Swagat) Shrestha: Conceptualization, Resources and Writing; Manila Poudel and Ashika Tamang: Methodology, Writing and Interpretation; Shiva M.C. and Nirmal Parajuli: Software, Methodology and Data curation; Aakar Shrestha, Timila Shrestha, Samjhana Bharati and Binita Maharjan: Data collection, Validation and Visualization; Bishnu P. Marasini: Supervision and Writing-Review and Editing; Jhashanath Adhikari Subin: Supervision, Formal analysis and Writing-Review and Editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Declaration of Generative AI and AI-assisted technologies in the writing process
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Appendix A. Supplementary data
Supplementary information are provided in a separate file “Supplementary” https://dx.doi.org/10.25259/JKSUS_370_2025.
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