Eclet. Quim. 50 | e-1600, 2025 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 1/23 1Tribhuvan University, Department of Chemistry, Kathmandu, Nepal. 2Ministry of Health and Population, Nepal Health Research Council, Kathmandu, Nepal. 3Bioinformatics and Cheminformatics Division, Scientific Research and Training Nepal P. Ltd., Bhaktapur, Nepal. 4Institute of Natural Resources Innovation, Kathmandu, Nepal. +Corresponding author: Ram Lal Swagat Shrestha, Phone: +9851038789, Email address: swagatstha@gmail.com Original Article Phytochemical, antioxidant, and enzyme inhibition potential exploration of Nyctanthes arbor-tristis via in vitro and in silico methods Nirmal Parajuli1 , Prabhat Neupane1 , Sujan Dhital1 , Samjhana Bharati1 , Timila Shrestha1 , Binita Maharjan1 , Bishnu Prasad Marasini2 , Jhashanath Adhikari Subin3 , Ram Lal Swagat Shrestha1,4+ Abstract Secondary metabolites in medicinal plants have been found to possess a broad spectrum of therapeutic properties. This study investigates the sequential extraction, quantitative phytochemicals, and bioactivity evaluations of Nyctanthes arbor-tristis leaf growing in Nepal. Methanolic extract contains the highest phenolics and resulted in the lowest IC50 values of 56±3 µg/mL and 157±3 µg/mL, in antioxidant and α-amylase inhibition assays, respectively. Hexane extract was found to contain abundant flavonoids and to be the most lethal to brine shrimp napuili with LC50 of 87±5 µg/mL. Phytochemicals arborside-C (ASC) and arborside-D (ASD) were found to be the most potent ligands to bind with α-amylase (PDB ID: 4GQR), resulting from docking and molecular dynamics simulation outcomes. The free energy changes calculated by the MMPBSA method and ADMET profiling of hit candidates supported by the spontaneity of complex formation reactions and their pharmacokinetic efficacy, respectively. This study proposes two compounds as hit candidates for the α-amylase target. Biological characterization using an in vivo approach is further recommended to assess their precise pharmacological validation. Article History Received September 19, 2024 Accepted January 15, 2025 Published September 22, 2025 Keywords 1. Nyctanthes arbor-tristis; 2. antioxidant; 3. α-amylase inhibition; 4. arborside-C; 5. arborside-D. Section Editors Assis Vicente Benedetti Rogéria Rocha Gonçalves Highlights Ultrasonic sequential extraction of phytochemicals from N. arbor-tristis leaf. Quantitative phytochemical study (TPC and TFC). Assessment of in vitro antioxidant, toxicity, and α- amylase inhibition bioactivities. Molecular docking and molecular dynamics simulations (MDS) of phytocompounds. Identification of hit compounds against pancreatic amylase through in silico methods. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://ror.org/02rg1r889 https://ror.org/01kk81m15 mailto:swagatstha@gmail.com mailto:parajulinirmal1999@gmail.com mailto:neupaneprabhat1998@gmail.com mailto:sujandhital07@gmail.com mailto:bharati.samjhana@gmail.com mailto:timilastha@gmail.com mailto:binitamhrjan@gmail.com mailto:bishnu.marasini@gmail.com mailto:subinadhikari2018@gmail.com mailto:swagatstha@gmail.com https://orcid.org/0009-0008-7068-6374 https://orcid.org/0009-0001-8256-7072 https://orcid.org/0009-0000-2384-1687 https://orcid.org/0009-0002-4301-7251 https://orcid.org/0009-0008-2686-6378 https://orcid.org/0009-0002-5606-3257 https://orcid.org/0000-0001-6153-5234 https://orcid.org/0000-0001-8515-9843 https://orcid.org/0000-0002-7939-2830 mailto:assis.v.benedetti@unesp.br mailto:assis.v.benedetti@unesp.br mailto:rrgoncalves@ffclrp.usp.br mailto:rrgoncalves@ffclrp.usp.br https://orcid.org/0000-0002-0243-6639 https://orcid.org/0000-0001-5540-7690 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 2/23 1. Introduction Nepal is home to a wide array of plants, with an estimated 9,000 different species of flowering plants (Khakurel et al., 2022; Kunwar et al., 2016). Plants have medicinal values due to secondary metabolites and are a good source of therapeutics (Kumar et al., 2022). Biosynthesis, extraction, identification, structural elucidation, quantification, and physical and chemical properties of phytochemicals are the key to the drug discovery process (Butler, 2004; Choo and Chai, 2023). Nyctanthes arbor-tristis L., a plant of the Oleaceae family, commonly called Night jasmine, is a typical shrub with bright, highly scented blooms that bloom at night and fall off before daybreak (Dewi et al., 2022). The plant is 10 to 30 m in height, and its leaves are 2–6 cm broad, 6–12 cm long, simple, petiolate, exstipulate, and reticulate venation (Solanki et al., 2021). N. arbor- tristis is distributed worldwide and found in tropical and subtropical regions, ranging in height up to 1500 m geographically in the Himalayan region of India, Nepal, and Pakistan (Jain and Pandey, 2016). N. arbor-tristis leaves have a variety of chemical components, including alkaloids, glycosides, flavonoids, terpenoids, and tannins (Bb et al., 2015; Meshram et al., 2012). The therapeutic benefit is related to the presence of possible phytochemicals such as nyctantic acid, β-sitosterol, oleanolic acid, friedelin, arborside-A, arborside-B, arborside-C, arborside-D, arbortristoside-A, arbortristoside-C, benzoic esters of loganin, 6-β- hydroxyloganin, mannitol, astragalin, ascorbic acid, and methyl salicylate found in the leaves (Agrawal and Pal, 2013; Sah and Verma, 2012; Meshram et al., 2012). The pharmacological studies showed their potential as antibacterial, anti-inflammatory, analgesic, antidiabetic, cough-suppressant, antioxidant, antimalarial, anti-arthritic, antispasmodic, antipyretic, immunostimulant, anthelminthic, antileishmanial, hepatoprotective, anti-allergic, antiviral, and CNS depressive (Sah and Verma, 2012; Laware and Shirole, 2023; Rawat et al., 2021). Each component of this plant has been used to treat various ailments in Ayurveda, including arthritis, digestive issues, tonics, laxatives, diuretics, asthma, cough, discomfort, hemorrhoids, and irregular menstrual periods (Dewi et al., 2022; Kushwah et al., 2023). Diabetes Mellitus (DM), type I and II are chronic hyperglycemia disorders. Type I diabetes is caused mainly by β- cell death of the pancreas that results in a reduction in insulin secretion, in contrast, type II diabetes (T2D) is characterized by insulin resistance in the cells (Mohamed et al., 2023). The ingestion and absorption of dietary carbohydrates substantially increase postprandial blood glucose levels (Sugandh et al., 2023). The enzyme α-amylase breaks down starch into glucose fragments by breaking down the glycosidic linkages (Proença et al., 2019). Human pancreatic α-amylase enzyme (HPAE) inhibition is a conventional method utilized to treat T2D (Ogunyemi et al., 2022). An in silico methodology has effectively decreased the expenditure associated with experimental procedures and the temporal requirements for determining complicated structures (Stănciuc et al., 2020). It aims to ascertain the potential method of binding orientation, binding affinity, and stability of the molecules to enzymes (Vasanthkumar et al., 2021). Molecular docking calculations and molecular dynamics simulations are used to test millions of possible binding orientations at receptor binding sites based on protein structures to propose the pharmacological significance of various chemicals in plant metabolites (Hollingsworth and Dror, 2018; Zhao et al., 2021). The structure- based drug design (SBDD) utilizes docking and simulation programs for virtual estimation of drug-likeness, and further ADMET prediction anchors to drug-like molecules through its pharmacokinetic evaluation that binds with a particular receptor protein to manipulate its function (Lolok et al., 2022). In this study, an assessment of the in vitro antioxidant, α- amylase inhibition, brine shrimp lethality assay (BSLA), and estimation of TPC and TFC of the different extract fractions of N. arbor-tristis leaf were carried out. Molecular docking virtual screening, molecular dynamics simulations, and ADMET predictions of the compounds found in the plant leaf were further used to understand their mechanism and pharmaceutical aptitude towards HPAE. The result of this study can be used to justify and validate the potential of the N. arbor-tristis in antioxidation, cytotoxicity, and, importantly, α-amylase inhibition. 2. Experimental 2.1. Chemicals Solvents hexane, chloroform, ethyl acetate, acetone, methanol (Qualigens Fine Chemicals), sulfuric acid, hydrochloric acid, aluminum chloride, sodium carbonate, dimethyl sulphoxide (DMSO), and sodium dihydrogen phosphate (Thermo-Fisher Scientific India) were used. Gallic acid (Hi-media Laboratories), 2,2-diphenyl-1-picrylhydrazyl (DPPH), quercetin (Wako Pure Chemicals, Osaka, Japan), Folin–Ciocalteu’s phenol reagent (FCR), ascorbic acid, acarbose, and α-amylase (Hi-media Laboratories) were used, which were imported from India. 2.2. Plant collection and identification The N. arbor-tristis leaves were collected in Sindhupalchok district, Nepal (altitude: 1350 m, latitude: 27°46'14" N, longitude: 85°48'59" E). The plant (voucher code 01KATH160201) was identified and verified at the National Herbarium & Plant Laboratories (KATH) in Lalitpur, Nepal. 2.3. Preparation of plant extracts The collected and dried 1 kg leaves of N. arbor-tristis were powdered using an electric grinder. Through an ultrasonic extraction process, different leaf extracts, hexane extract (HE), chloroform extract (CE), ethyl acetate extract (EAE), acetone extract (AE), methanol extract (ME), and distilled water extract (DWE), were prepared in six different solvents, hexane, chloroform, ethyl acetate, acetone, methanol, and distilled water, respectively through a sequential extraction (solid-liquid fractionation) in increasing polarity order of the solvents. 2.4. Preliminary phytochemical profiling A phytochemical study of N. arbor-tristis leaf was conducted to profile the various natural constituents in the extracts using a standard protocol (Banu and Cathrine, 2015). 2.5. Phenolic content (TPC) determination Folin-Ciocalteu colorimetric analysis based on an oxidation-reduction reaction was used to TPC with minor modifications (Gautam et al., 2022). From the serially diluted concentration of standard gallic acid stock (500 to 25 µg/mL), 20 µL of each was dispensed in a 96-well plate containing 100 µL of Folin-ciocalteu reagent and incubated for 5 min at room temperature in the dark. 80 µL of 7% Na2CO3 was added to the https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 3/23 reaction mixture and further incubated for another 2 h at 23 °C. The resulting blue-colored mixture was subjected to measuring absorbance using a spectrophotometer at 765 nm in triplicate, and a calibration curve was plotted. The exact process was repeated for all plant extract fractions, and the TPC of each extract was calculated as gallic acid equivalent per gram (GAE/g) using a calibration curve. 2.6. Flavonoid content (TFC) determination The aluminum chloride colorimetric assay was used to measure the TFC of the extracts described in previous work (Chandra et al., 2014). Briefly, the stock solution of standard quercetin in methanol was serially diluted (250 μg/mL to 25 μg/mL) and added to the microplate well. 2% aluminum chloride in methanol (100μL) was added to it and incubated for 10 min in the dark. The absorbance of the reaction mixture was measured at 425nm through a spectrophotometer. The calibration curve was plotted, the exact process was repeated for all plant extract fractions, and TFC was calculated as quercetin equivalent per gram (QE/g). 2.7. Antioxidant activity assay Using 2,2-diphenyl-1-picrylhydrazyl-hydrate (DPPH) (Blois, 1958), an antioxidant activity assay was done on the protocol described in previous literature, with some modifications (Sabudak et al., 2013). The stock solution of standard ascorbic acid was resolved into concentrations of 30 μg/mL to 2.5 μg/mL through serial dilution. 2 mL of each concentration of ascorbic acid was mixed with 2 mL 0.2 mmol/L DPPH solution in triplicate and kept in the dark for 30 min. The absorbance was measured at 517 nm against methanol and DPPH as a blank. The exact process was repeated for each extract fraction (triplicate) at different concentrations, and absorbance was measured. Using a graph plot between the percentage scavenging activity of extracts vs concentrations in GraphPad Prism, the half-maximum inhibitory concentration (IC50) of each extract was calculated. Equation 1 was applied to evaluate the %DPPH radical scavenging. % DPPH Scavenging = (Ablank−Asample) (Ablank) × 100% (1) 2.8. α-Amylase inhibition assay The α-amylase inhibition activity was performed using the 3,5-dinitrosalicylic acid (DNSA) method, with some modifications (Mustafa et al., 2021). 10% dimethyl sulfoxide (DMSO) was used to dilute the N. arbor-tristis leaf extracts and generate various concentrations. The diluted solutions in different test tubes were mixed with DMSO, buffer, and NaCl at a pH of 6.9. This mixture was added with α-amylase (1,4-α-D-glucano-glucanohydrolase) solution (200 μL) and incubated for 10 min at a temperature of 30 °C. The starch solution (0.1%) was added to each tube in a volume of 200 μL, and the tubes were incubated for 3 min. The process was stopped by adding DNSA reagent (200 μL) and warmed in a water bath for 10 min at 85-90 °C. After reaching room temperature, the reaction mixture was diluted by adding distilled water (5 mL), and the absorbance of the reaction mixture was measured at 540 nm. The blank with 100% enzyme activity was prepared by replacing the plant extract with 200 μL of buffer. The standard acarbose solution was taken as a positive control. The percentage of amylase inhibition was estimated using Eq. 2. % inhibition = 𝐴𝑏𝑠.𝑏𝑙𝑎𝑛𝑘−𝐴𝑏𝑠. 𝑠𝑎𝑚𝑝𝑙𝑒 𝐴𝑏𝑠.𝑏𝑙𝑎𝑛𝑘 × 100% (2) By plotting the extract concentrations against the percentage of α-amylase inhibition in the dose-inhibition curve using GraphPad Prism, the IC50 value was estimated for each extract. 2.9. Brine shrimp toxicity assay The brine shrimp toxicity assay is a valuable introductory screening tool to determine the potential toxicity of various compounds (Niksic et al., 2021). It involves assessing their potential to induce mortality in laboratory-cultured brine shrimp (Artemia salina) nauplii, and the protocol is based on previous work (Majumder et al., 2019). Artificial seawater was prepared, and brine shrimp eggs were hatched for 48 h. A stock solution of each extract was successfully diluted (1000 μg/mL to 62.5 μg/mL) using the serial dilution method. Varying amounts of plant extract were applied to ten nauplii and left for 24 h. DMSO was used as a blank, and potassium dichromate as a positive control. The mortality endpoint was observed for each extract fraction after application to the prepared solution in a triplicate format. The lethality percentage for each concentration was determined by counting the number of dead and live nauplii. Equation 3 was used to calculate the percentage mortality of the nauplii. % Mortality = No. of dead shrimps Total No. of shrimps × 100% (3) The LC50 represents the concentration at which the tested extract kills 50% of the brine shrimp nauplii. It was calculated using GraphPad Prism. 2.10. Computational tools 2.10.1. Ligand selection The previously isolated compounds (iridoids, flavonoids, and phenolic compounds) from different extracts of the leaves of N. arbor-tristis were taken as candidate ligands as HPAE inhibitors (Table 1). Molecular structures of some of the selected ligands are presented in Fig. 1. The Supplementary Information (Table 1S and Fig. 1S) lists other detailed information and structures of all selected ligands. Table 1. Some of the top candidate ligands selected from N. arbor-tristis leaf. Ligands Ligand ID Molecular weight (g/mol) PubChem CID Reference Arborside-A ASA 614.6 182902 Dewi et al. (2022) Arborside-B ASB 494.5 182903 Dewi et al. (2022) Arborside-C ASC 510.5 182904 Agrawal and Pal (2013) Arborside-D ASD 556.5 101685135 Agrawal and Pal (2013) Arbortristoside-A ATSA 566.5 6442162 Vishwakarma et al. (2022); Rathore et al. (1989) Arbortristoside-C ATSC 552.5 23955893 Dewi et al. (2022) Astragalin AG 448.4 5282102 Sah and Verma (2012) Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 4/23 Figure 1. Molecular structures of some of the top selected ligands, ASA (Arborside-A), ASB (Arborside-B), ASC (Arborside-C), and ASD (Arborside-D). Source: Elaborated by the authors. 2.10.2. Ligand preparation The 3D structures and coordinates of ligands were retrieved from the PubChem database in sdf format with the respective PubChem CID mentioned in Table 1. The bond order and molecular formula of compounds were verified and converted into pdb format using the PyMOL software (version 2.5.5) (Yuan et al., 2017). Using the conjugate gradient algorithm, the Universal Force Field (UFF) for 5000 cycles at 10–8 units of energy convergence was chosen after adding the hydrogen atoms in the Avogadro program (version 1.2.0) for molecular structure optimization (Hanwell et al., 2012). AutoDock Tools converted pdb into pdbqt, which is required for molecular docking (Trott and Olson, 2009). Root mean square deviation (RMSD) between the docked ligand (myricetin) and native ligand (myricetin) in the crystal structure of the protein (PDB ID: 4GQR) was calculated to be less than 2 Å for docking protocol validation (Shrestha et al., 2024) (presented in Fig. 2S in Supplementary Information). 2.10.3. Target selection and preparation HPAE (PDB ID: 4GQR) with a resolution of 1.20 Å, with an X-ray crystallographic structure, was restored from the RCSB database (https://www.rcsb.org/) (Berman et al., 2000). The protein structure visualization and processing were done using the PyMOL program. The active sites (catalytic triad, ASP197, GLU233, and ASP300) of the enzyme were determined using a co- crystallized receptor-ligand complex structure (Liu et al., 2017a). The protein was cleaned in PyMOL software by removing water molecules, ions, and non-standard residues, and the apo structure was stored as a pdb file. The protein was processed by adding polar hydrogen atoms and Kollman charges in AutoDock Tools and converted into pdbqt format, which was required for molecular docking. The grid box was set to cover all the active site residues. The grid box center (16.731, 17.235, and 42.467) and the grid box size (x = 44, y = 46, z = 44 in Å with spacing of 0.375 Å) in the receptor protein were selected. 2.10.4. Molecular docking calculations The binding mechanism (pose, orientation, and location) between the ligand and the receptor was investigated using molecular docking studies. The AutoDock Vina software (version 1.5.7) was used to conduct rigid molecular docking calculations (Trott and Olson, 2009). The chosen molecules were docked and examined based on the possible protein-ligand interactions and the lowest binding affinity (docking score). During molecular docking, the ligand remained flexible in the active site pocket of the protein despite the protein’s rigidity. Control parameters such as the number of modes, energy range, and exhaustiveness were 20, 4, and 64, respectively, for all docking computations. The stable protein-ligand complex with the highest binding affinity was ultimately determined using a scoring function. Biovia Discovery Studio Visualizer (version 21.1.0.20298) was used for the visualization of protein-ligand interactions (Shaweta et al., 2021). For the stability assessment in terms of geometrical and thermodynamic parameters, a molecular dynamics simulation of the complex with the pose with the best binding energies was selected. 2.10.5. Molecular dynamics simulation (MDS) The MDS of the ligand-protein adducts were performed using the GROMACS software (version 2021.2) (Abraham et al., 2015). The Charmm27 force field from the swissparam server (https://www.swissparam.ch/) (assessed on January 10, 2024) was used for both the ligand and the receptor (Zoete et al., 2011). Utilizing the TIP3P water model, a triclinic box system was solvated. 12 Å spacing was chosen to minimize erroneous interactions between the periodic images. The system was https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://www.rcsb.org/ https://www.swissparam.ch/ Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 5/23 neutralized, and an isotonic solution of NaCl was employed. At a physiological temperature (310 K), the system was equilibrated in four stages, each lasting 200 ps. For NVT equilibrium, the initial two stages were completed, and for NPT equilibrium, the final two phases were chained. The final production run was conducted for 100 ns without any constraints, and several parameters, including RMSF, SASA, RMSD, and Rg, were retrieved from the MDS trajectory using the built-in modules of the GROMACS program. 2.10.6. Binding free energy changes (ΔGBFE) estimation The MMPBSA method was used to calculate the change in the binding free energy (ΔGBFE) of the adduct (Onufriev and Case, 2019). The viability and spontaneity of the forward reaction were evaluated based on the assessment of free energy changes. The binding free energies of the complex, protein, and ligand were determined using the gmx-MMPBSA module on an equilibrated trajectory segment of 200 frames for 20 ns. Equations 4 and 5 were used to calculate the binding free energy change during complex formation (Wang et al., 2019). ΔGBFE = Gcomplex − Greceptor − Gligand (4) ΔGBFE= ΔH − TΔS = ΔEMM + ΔGSOLV –TΔS (5) where, ΔEMM = ΔEIN + ΔEvdw + ΔEELE ΔGSOLV = ΔGPB + ΔGSA ΔGBFE = Binding Free Energy changes ΔEMM =Energy change in gas phase molecular mechanics ΔEIN = Internal energy of the system ΔEvdw = van der Waals energy ΔGSOLV = Electrostatic solvation energy ΔGPB = Polar contributions in solute-solvent system ΔEELE = Electrostatic energy ΔS = Entropy changes of the system ΔGSA = Nonpolar contributions in the system The entropy term (TΔS) was not considered in the binding free energy calculation because of significant technical costs and errors raised during computational calculations (Wang et al., 2019). 2.11. ADMET profiling Swiss ADME (http://www.swissadme.ch/), ProTox-II (https://tox-new.charite.de/protox_II/), and pkCSM (https://biosig.lab.uq.edu.au/pkcsm/) servers (accessed on January 19, 2024) were used for calculating absorption, distribution, metabolism, excretion, and toxicity parameters of hit compounds and reference drugs (Acarbose and Miglitol) (Banerjee et al., 2018; Pires et al., 2015). 2.12. Statistical evaluation All the in vitro experimental results were taken in triplicate (n = 3). TPC, TFC, and binding free energy results were presented as mean ± SD (standard deviation), and quantitative biological activity tests (DPPH assay, amylase inhibition, and BSLA) were calculated in terms of mean ± SEM (standard error of mean) for a more reliable IC50 calculation. GraphPad Prism (version 9.4.1) was used to calculate IC50 values of bioactivities. TPC and TFC were calculated using Microsoft Excel 2021. 2.13. Computational resources Molecular docking calculations, data interpretation, and visualization were done using Windows 11 (8 GB RAM, 8-core CPU processor). Molecular dynamics simulation and binding free energy calculations were performed in Ubuntu 20.04.06, an LTS operating system, a 24-core processor machine with a 24 GB GPU accelerator. 3. Results and discussion 3.1. Qualitative estimation of phytochemicals Qualitative analysis of the phytochemicals gives a preliminary idea of constituents present in the extracts and helps to quantify and further characterize (Olayinka et al., 2010). The FT-IR analysis of the extract is presented in the Supplementary Information in Fig. 3S. Different extracts showed distinct results in screening following polarity and phytoconstituents present in the leaf of N. arbor-tristis. Alkaloids, flavonoids, terpenoids, glycosides, phenolic compounds, steroids, carbohydrates, and quinones were identified (Table 2) as the primary ingredients. Table 2. Phytochemical screening of the various extracts. Class of phytochemicals HE CE EAE AE ME DWE Alkaloids – + + + + + Phenolic Compounds – + + + + + Flavonoids – + + + + + Terpenoids + + + + + – Cardiac Glycosides – – + + + + Carbohydrates – + + + + + Proteins – – + + + + Triterpenoids + + + + – – Tannins – + + + + + Resins – – – + + + Steroids – + + + + – Quinones + + + + + – Saponins – – – – – – Note: + refers presence; – refers absence. HE (Hexane extract); CE (Chloroform extract); EAE (Ethyl acetate extract); AE (Acetone extract); ME (Methanol extract); DWE (Distilled water extract). Source: Elaborated by the authors. 3.2. Quantitative estimation of phytochemicals The yield percentage was found to be the highest for the extract CE (7.3%) among all extract fractions. Phenolic compounds and flavonoids are natural products that have the potential for pharmacological activity, like antioxidant, antidiabetic, anti-inflammatory, and Anticarcinogen (Zain and Omar, 2018). TPC of different fractions was determined using Folin-Ciocalteu reagent with slight modification with the help of a standard gallic acid calibration curve (Y = 0.0039X + 0.0568, and R2 = 0.9957), likewise, TFC was calculated through spectrophotometry of the colored solution of aluminum chloride reagent with extract with the help of a standard quercetin calibration curve (Y = 0.0068X + 0.00704, and R2 = 0.9997). The standard calibration curves are included in the Supplementary Information (Fig. 4S). The % yield, TPC, and TFC of all fractions are listed in Table 3. Extracts of AE and ME fractions of N. arbor- tristis were found to have a high content of the phenolic compound of 137±4 mg GAE/g and 139±4 mg GAE/g, respectively. TFC was found high in extract CE (369 ± 4 mg GAE/g) and HE (286 ± 10 mg GAE/g) fractions. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 http://www.swissadme.ch/ https://tox-new.charite.de/protox_II/ https://biosig.lab.uq.edu.au/pkcsm/ Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 6/23 Table 3. The yield, TPC, and TFC of different extract fractions. Extracts Chemical contents Yield% TPC (mg GAE/g) TFC (mg QE/g) HE 0.45 18 ± 4 286 ± 10 CE 7.3 46 ± 4 369 ± 4 EAE 1.55 78 ± 5 92 ± 4 AE 2.3 137 ± 4 62 ± 3 ME 3.15 139 ± 4 68 ± 4 DWE 2.97 56 ± 4 17 ± 2 Note: TPC and TFC = triplicate average ± SD. HE (Hexane extract); CE (Chloroform extract); EAE (Ethyl acetate extract); AE (Acetone extract); ME (Methanol extract); DWE (Distilled water extract). Source: Elaborated by the authors. 3.3. DPPH scavenging assay The DPPH radical scavenging assay gives the in vitro quantitative figure of the antioxidant potential of metabolites found in phytochemicals (Sethi et al., 2020). N. arbor-tristis extract fractions displayed active antioxidant potentials towards DPPH free radicals. Extracts ME and AE were found to be the most potent antioxidants with IC50 values of 56 ± 3 g/mL and 79 ± 3 µg/mL, respectively, which were found to be marginally higher IC50 values than standard ascorbic acid (Table 4). Other extracts (IC50 > 100 µg/mL) showed mild antioxidizing phenomena. The order of the DPPH scavenging capacity of the extracts can be illustrated as ME>AE>DWE>EAE>HE>CE. Table 4. Comparative antioxidant, amylase inhibition, and toxicity results containing values of various extracts with respective positive control. Extracts and positive controls Evaluated bioactivity DPPH scavenging (IC50 in µg/mL) α-Amylase inhibition (IC50 in µg/mL) Brine shrimp lethality assay (LC50 in µg/mL) HE 120 ± 5 547 ± 9 97 ± 2 CE 410 ± 6 386 ± 15 295 ± 7 EAE 126 ± 2 1656 ± 8 161 ± 5 AE 79 ± 3 919 ± 9 240 ± 5 ME 56 ± 3 157 ± 3 175 ± 3 DWE 104 ± 6 1799 ± 7 998 ± 10 *Ascorbic acid 17 ± 3 – – *Acarbose – 52 ± 1 – *K2Cr2O7 – – 152 ± 2 Source: Elaborated by the authors. 3.4. Alpha-amylase inhibition assay The α-amylase inhibition activity of different extracts is listed in Table 4. Among the fractions, ME of IC50 157 ± 3 µg/mL was found to be a significant α-amylase inhibitor compared to other fractions. Extracts HE and CE exhibited moderate inhibition, whereas EAE, AE, and DWE showed weak inhibitory activity to the amylase enzyme. The extract ME showed a good amylase inhibition activity compared with standard acarbose (52 ± 1 µg/mL), signifying the antidiabetic potential of N. arbor- tristis. The amylase enzyme inhibition potential of the extracts was found to be in the following order: ME > CE > HE > AE > EAE > DWE. 3.5. Brine shrimp toxicity evaluation Brine shrimp cytotoxicity assay portrayed a moderate toxicity in all extracts. LC50 of HE (97 ± 2 µg/mL) was found to be the least toxic to brine shrimp larvae. LC50 values of extracts CE, EAE, AE, and ME were found to be moderate, and comparable to each other and the positive control potassium dichromate (LC50 < 300 µg/mL). The comparative illustration of all the extracts with lethal concentration is mentioned in Table 4. The trending fitting curve of the observed data of different bioactivity is presented in Figures 5S, 6S, and 7S in Supplementary Information. 3.6. Computational virtual screening 3.6.1. Binding affinities from molecular docking calculation The most conventional method to inhibit HPAE is to bind it with a suitable ligand/drug at its orthosteric site (Cele et al., 2022). Molecular docking is an easy, preliminary, virtual, and rapid computational method to compute and analyze the compatibility of any molecule (guest) and its possible therapeutic activity with the active macromolecular protein (host) through Host-Guest interaction (Das et al., 2024). Further viability and stability of the docked complex were assessed using MDS. The compounds found in the leaves of N. arbor-tristis were examined to determine their HPAE binding capacity through computation. Most of the candidate ligands scored better in molecular docking than the native ligand myricetin (–33.1 kJ/mol) with the amylase receptor (PDB ID: 4GQR) (Bitew et al., 2021), and the calculations are shown in Table 5. Conventional hydrogen bonds, Pi-alkyl, Pi- Pi stacked, other hydrophobic interactions, and van der Waals interactions were the noticeable non-covalent interactions in the protein-ligand complexes. Among all ligands, ASC and ASD scored the same affinity of –33.5 kJ/mol, and it was found that these ligands exhibited a stable trajectory in MDS, which might be a consequence of the strong interactions in the adduct with a larger hydrogen bond count and proper orientation of the ligand with the receptor. By conventional hydrogen bond, ligand ASC interacted with amino acid residues, HIS305, GLU233, ASP197, and ASP300 (<3 Å). On the other hand, ligand ASD interacted with TRP59, TYR151, THR163, and HIS 201 through hydrogen bonding (<2.6 Å) along with other possible interactions. Such strong interactions (between ligands and active site triad) might provide stability to the complexes, which were further supported by the MDS results of both ligands, which are discussed later. Although ASB showed the highest docking score (–34.7 kJ/mol), it was found to be unstable in the amylase binding pocket (in MDS). The docking score and its validity through MDS signified the stability of the adduct at physiological temperature, which could result in the inhibition of the target enzyme (Omar et al., 2022). Most of the ligands were found to interact with the catalytic triad of amylase (APS197, GLU233, and ASP300), along with ASP356, HIS305, ILE235, HIS201, TRP59, ALA106, and ALA198 (Chothani et al., 2024; Renganathan et al., 2021; Zahra et al., 2024). https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 7/23 Table 5. Interactions between the compounds (top ligands, drug, native ligand) and the amino acid residues in ligand-protein complexes from molecular docking calculations. Candidate Ligands Docking Score (kJ/mol) Interactions Active site residues (Distance Å) ASB –34.7 Conventional Hydrogen Bond GLU233 (2.47, 2.66), ASP300 (2.12) Pi-Alkyl TRP59 (4.38, 5.10), HIS305 (4.60) Carbon Hydrogen bond HIS305 (3.77) Alkyl LEU165 (4.74) van der Waals TRP58, ASP197, TYR62, HIS201, GLN63, VAL107, LEU162, THR163, ARG195, ILE235, ASC –33.5 Conventional Hydrogen Bond ASP197 (2.45), GLU233 (2.07), HIS305 (2.79), ASP300 (2.73) Alkyl LEU162 (3.90), LEU165 (4.94) Pi-Alkyl TRP59 (4.20), HIS305 (2.79) Pi-Pi Stacked TRP59 (4.06) van der Waals TRY62, TYR151, THR163, ARG195, GLN63, ALA198, TRP58, LYS200, ILE235 ASD –33.5 Conventional Hydrogen Bond TRP59 (2.14), TYR151 (2.57), THR163 (2.21), HIS201 (2.13, 2.58) Pi-Alkyl HIS299 (4.38) Carbon Hydrogen bond GLU233 (3.34), ASP300 (3.38) Pi-Pi Stacked TYR151(3.93) van der Waals TRP58, GLN63, LEU162, ARG195, ASP197, ALA198, GLU60, LYS200, TYR62, ILE235 ASTA –33.5 Conventional Hydrogen Bond GLU233 (1.84), ASP300 (2.23) Pi-Alkyl HIS201 (5.16) Carbon Hydrogen bond TRP59 (3.80), HIS299 (3.60) Pi-Pi Stacked TRP59 (4.98) Pi-sigma TRP59 (3.86) Alkyl LEU162 (5.44), LEU165 (4.42), GLU233 (5.80) van der Waals TRP58, TYR62, GLN63, TYR151, THR163, ARG195, ASP197, ALA198, HIS305, GLY306 AG –33.1 Conventional H-Bond GLN63 (2.82), ASP197 (4.43) Pi-Pi Stacked TYR62 (5.08), TRP59 (4.03, 4.66) van der Waals TRP58, GLN63, HIS101, LEU162, LEU165, ALA198, GLU233, ILE235, GLY306 Pi-Alkyl ALA307 (5.05) Carbon Hydrogen bond TRP59 (3.24) Pi-Pi Stacked TRP59 (5.16) Pi-sigma ILE235 (3.93) Pi-Donor Hydrogen bond HIS299 (3.28) van der Waals TRP58, TYR62, GLN63, TYR151, LEU162, ARG195, ASP197, ALA198, LYS200, HIS305, GLY306, GLY308 #Myricetin –33.1 Conventional H-bond GLN63 (2.13), ARG195 (2.33), GLU233 (2.31) Pi-Pi staked TRP59 (4.36, 5.39), TYR62 (4.83) Van der Waals HIS101, LEU162, LEU162, ASP197, ALA198, HIS299, ASP300 *Acarbose Yi et al. (2022) –32.2 Conventional H-bond GLN63 (2.33, 2.36, 2.62), ARG195 (3.04), GLU233 (1.98, 2.33), ASP300 (2.35) Pi-donor hydrogen bond TRP59 (3.78, 3.88) van der Waals VAL49, ILE51, TRP58, TYR62, LEU162, THR163, ASP197, ALA198, HIS299, PHE256, GLY306, GLY306, ARG303, HIS305, TRP357 Note: #Native ligand; *Antidiabetic reference drug. ASB (Arborside-B); ASC (Arborside-C); ASD (Arborside-D); ATSA (Arbortristoside-A); ATSC (Arbortristoside-C); Bold residues (catalytic triad residues in the orthosteric side of Human pancreatic α-amylase). Source: Elaborated by the authors. The observations indicated that the conventional hydrogen bond between electronegative acceptor and hydrogen, Pi-Pi stacked link between two aromatic rings, Pi-alkyl interaction between the alkyl group and aromatic ring or unsaturation, van der Waals’ interaction, and other noncovalent interactions were found to be present between ligand and protein complexes. Ligand ASC formed H-bonds with active site residues GLU233 (H-acceptor), ASP197 (H-acceptor), and ASP300 (H-acceptor) by accepting the hydrogen from the H-donor (–OH) site of the ligand, and residues HIS201 (H-donor) and HIS305 (H-donor) donated the hydrogen to the acceptor oxygen site of the ligand. LEU162, LEU165, TYR59, and HIS305 interacted with ASC to bind by hydrophobic interactions containing Pi-Pi stacking, Pi-alkyl, and alkyl-alkyl interactions. Similarly, in ligand ASD, residues TRP59 and THR163 acted as hydrogen acceptors. HIS201 and TYR153 played a role as hydrogen donors in forming hydrogen bonds, and residues HIS299 and TYR153 showed hydrophobic interaction with the ligand. Docking scores and interactions of the ligands displayed the potential binding capability of the ligands towards HPAE, which could eventually be the subsequent inhibitory action of compounds in physiological reactions. The comparison (docking score and interactions) of selected compounds with native ligand (myricetin) and the drug acarbose (–32.2 kJ/mol) further supported the effective interactions and stability of the complexes formed with amylase enzyme. Figurative (3D interaction with the hydrophobicity of protein and solvent accessibility surface (SAS) in 2D) illustrations of molecular docking calculations of major active compounds are presented in Fig. 2. Other calculated data and figures are included in the supplementary information (Tables 1S, 2S, and Fig. 8S). https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 8/23 Figure 2. Interactive presentations of the protein-ligand complex of {(ASC (a, a’), ASD (b, b’) and Myricetin (c, c’)} 3D with the hydrophobicity of protein and 2D with solvent accessibility surface from molecular docking including bond length (Å), the color of atoms in the ligand 2D structure, red, black, and grey are for O, H, and C atoms, respectively. Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 9/23 3.6.2. Molecular dynamics simulations (MDS) 3.6.2.1. Root mean square deviation (RMSD) MDS trajectory was used to extract the RMSD of ligand and protein for a duration of 100 ns, and it was considered to compute the dynamics and stability of the simulated complex (Adcock and McCammon, 2006). The stability of the simulated complex is assessed by the ligand and protein backbone RMSD and the smoothness of curves (Aier et al., 2016). Among the selected compounds, ASC and ASD displayed better simulation results as they showed a stable trajectory (Fig. 3a) with a value below 6 Å. The protein backbone curves were lower than those of the ligand, indicating that the receptor's geometry remained stable throughout the MDS. The ligand binding to the receptor did not change the 3D structure of the adduct throughout 100 ns of the simulation time. The compound ASC depicted a smooth trajectory with an RMSD lower than 6 Å, but the RMSD for ASD was stable below 4 Å. The stability of the trajectory of the protein backbone in complexes (RMSD 1.8 nm) and apo structure (RMSD 1.5 nm) was found to support posture conservation, and the stability of the ligands during simulation at the binding site of the protein suggests their good inhibition potential towards the HPAE. A comparative and figurative presentation of the RMSD of ligand and protein backbone relative to the protein backbone in the protein-ligand complexes is given in Fig. 3. 3.6.2.2. Root mean square fluctuation (RMSF) RMSF measures the fluctuation of alpha carbon atoms and conformational changes of the protein backbone during the MDS (Martínez, 2015). Fig. 3c presents the RMSF of alpha carbon atoms of the protein backbone in the ASC and ASD complexes. A larger RMSF may account for the RMS deviation (Martínez, 2015). Small RMSF peaks and fluctuations (helix and sheet structure of protein) on most of the catalytic sites and binding catalytic triad (ASP197, GLU233, and ASP300) signified the binding of the ligands ASC and ASD to the protein, which indicated the effective interaction of ligands at the active site and provided rigidity to the fluctuation of the protein backbone. The unusual rise of the curve at residue numbers up to 0.6 nm around 56, 310, 350, and 460 might be caused due to the presence of fluctuating loops at a larger distance. 3.6.2.3. Radius of gyration (Rg) The radius of gyration (Rg) focuses on the conformational change in the simulated protein-ligand complex from MD simulation, as shown in Fig. 3d. Rg provides the average separation between all dispersed elements from the molecule’s central axis (Liu et al., 2017b). The correlation between RMSD, RMSF, and Rg provides insight into the relationship between complex compressibility, delocalization of the ligand, and residual fluctuations. Low RMSD, the rigidity of protein residues at binding sites, and the unchanging Rg support the stability of the complex formed. The Rg was nearly constant at about 2.34 nm for both complexes and the apo structure (Fig. 9S), roughly equal to the Rg before complexes formed (2.35 nm). The minimal variation in Rg suggested no appreciable deformation of the receptor’s geometry and compactness upon the binding of ligands (Ahmed et al., 2022). Therefore, the adducts of ASC and ASD with HPAE remained stable in terms of compactness during the MDS. Figure 3. (a) Comparative MDS trajectory with RMSD of ASC (red) with protein backbone (maroon) and for ASD (blue) with protein backbone (orange), (b) SASA of protein in complex with ASC (maroon) and ASD (orange), (c) RMSF plot of protein backbones (maroon-colored curve for ASC and orange-colored curve for ASD complex) and (d) Radius of gyration of protein complexes with ligand ASC (maroon) and for ASD (orange). Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 10/23 3.6.2.4. Solvent accessible surface area (SASA) The SASA measures the accessible surface of the protein to the solvents quantitatively and the change in compactness of the protein surface on adduct formation with a ligand. The trajectory observed in the SASA plot for each protein complex was regular up to 100 ns with minor fluctuations, as shown in Fig. 3b. The value of SASA, the steady areas about 197 and 204 nm2, was observed for protein-ligand complexes with compounds ASD and ASC, respectively, suggesting some exposure in protein morphology after adduct formation. The comparison of the SASA of the apo structure (about 200 nm2) with the complexes of compounds, ASD and ASD, signified no appreciable change in the SASA, and the value was found even lower for ASD complex after adduct formation which was strongly supporting result to interpret the negligible change in the surface area of the enzyme on complex formation and geometrical change in enzyme. The result implied that the change in accessibility of the solvent to the hydrophobic surface of the receptor after binding with the ligand was minimal (Zhang and Lazim, 2017). The apo protein structure MD simulation results are included in the supplementary information (Fig. 9S). 3.6.2.5. Hydrogen bond count The hydrogen bond is a non-covalent interaction that has a significant role in providing stability to the protein-ligand complex in various biological processes (Chikalov et al., 2011). A higher hydrogen bond count would enhance the stability of the complex. Figure 4 illustrates the variation in hydrogen bond count on the course of MDS between the ligand and protein for 100 ns. Both the ligands (ASC and ASD) interacted with a high number of hydrogen bonds; ASC and ASD possess up to 4 or 5 hydrogen bonds most of the time and reach up to 8 hydrogen bonds in some transitions (Fig. 4). The result might suggest slightly more stability of the ASD complex due to the interaction with protein than the ASC complex in different time frames based on hydrogen bond counts. 3.6.2.6. Thermodynamic stability calculations The binding free energy change (ΔGBFE) from the MMPBSA calculation (Eqs. 4 and 5) showed the spontaneity of the complex formation reaction. The total change in binding free energy (ΔGBFE) of the protein-ligand complexes was –138 ± 15 kJ/mol and –91 ± 15 kJ/mol for ASC and ASD complexes, respectively, as shown in Table 6. The equilibrated part of the trajectory of the last 10 ns was taken to calculate the ΔGBFE in complexes. The van der Waals interaction, polar solvation energy, electrostatic energy, and energy contributions of phases were calculated as energy components using the MMPBSA module. ΔGBFE<0 suggested the spontaneous nature of the complex formation reaction, which could eventually support the active amylase inhibition potential of the ligands (Olsson et al., 2008). The ΔGBFE for the ASC complex was found to be quite lower than the ΔGBFE of the ASD complex, which signified the more stable complex formation of the former than the latter. So, the binding affinities, relatively lower RMSD, minimal RMSF, relatively higher hydrogen bond count, and negative ΔGBFE (< –91 kJ/mol) of the protein-ligand complexes could strongly support the HPAE inhibition capability of ligands ASC and ASD. A figurative presentation of the moving average value of the MMPBSA calculation of free energy change is included in supplementary information (Fig. 10S). Figure 4. H-bond counts in (a) red for the ASC-protein complex and (b) blue for the ASD-protein complex. Source: Elaborated by the authors. Table 6. Thermodynamic parameters and their contributions to the total free energy change of complexes. Binding free energy components Energy change (kJ/mol) of the receptor with ASC ASD ΔVDWAALS –172 ± 14 –161 ± 14 ΔEPB 252 ± 20 258 ± 23 ΔEEL –199 ± 22 –170 ± 19 ΔEMPOLAR –18.7 ± 0.6 –18.5 ± 0.6 ΔGGAS –371 ± 25 –331 ± 15 ΔGSOLV 234 ± 19 241 ± 11 ΔGBFE –137 ± 15 –91 ± 15 Note: ASC (arborside C); ASD (arborside D); value = Energy change ± SD; ΔGBFE = Binding free energy; ΔEGAS = Energy change in gas phase molecular mechanics; ΔEELE = Electrostatic energy; ΔVDWAALS = van der Waals energy; ΔGSOLV = Electrostatic solvation energy summation; ΔGPB = Polar contributions in solute-solvent system. Source: Elaborated by the authors. 3.7. Pharmacokinetics and pharmacodynamics of hit candidates ADMET prediction helps to recognize the significance of ligands towards pharmaceutical efficacy, therapeutic aptitude, pharmacokinetics, and pharmacodynamics (Pires et al., 2015). Table 7 summarizes the possible results of the ADMET predictions of compounds and drugs. The ligands (ASC and ASD) https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 11/23 were found in toxicity class 4, indicating a slightly toxic nature (Banerjee et al., 2018). The lethal dose of 50% (LD50) for ASC and ASD was predicted at 2000 mg/kg, which was lower than that of acarbose, which predicted the toxic nature of the ligand compared to the drug acarbose and less harmful than the drug miglitol (1190 mg/kg). Consensus LogP (<0.6) showed the lipophilicity of both compounds as reference drugs and was comparatively smaller than that of the drugs. None of them followed the Ro5 for drug-likeness except miglitol. Ligand ASC was found to be immunotoxic like acarbose, which might indicate the cause of interruption to the immune system (Zerdan et al., 2021). All ligands were inactive regarding hepatotoxicity, carcinogenicity, mutagenicity, cytotoxicity, and phosphoprotein p53. The LD50 for ASC (2.544 mol/kg) and ASD (2.819 mol/kg) were found to be comparable to that of acarbose on oral rat acute toxicity. The logBB value of compounds (< –1) signified poor permeability to the blood-brain barrier (BBB), and the LogSP value (< –4) indicated the impermeability of compounds to the central nervous system (Banerjee et al., 2018; Carpenter et al., 2014). Metabolic properties of both ligands were found, as shown by the drugs. The gastrointestinal absorption factor for the compounds was predicted to be as low as that for drugs, which might depend on the molecular structure and solubility of the candidate. The total clearance measures mainly the hepatic, biliary, and renal clearance, which determines the steady-state dose concentration in the body, and was found to be higher for the compounds than for acarbose and miglitol, which signified that the body could easily release the compound residues through excretion than drugs. Table 7. ADMET profile of best candidate ligands compared with standard drugs. ADMET Parameters Compounds (units) ASC ASD *Acarbose *Miglitol Toxicity class 4 4 6 4 LD50 (mg/kg) 2000 2000 24000 1190 Lipinski rule (RO5) No No No yes Consensus LogP –0.31 –1.12 –6.22 –3.26 Immunotoxicity Active Inactive Active Active Hepatotoxicity Inactive Inactive Inactive Active Carcinogenicity Inactive Inactive Inactive Inactive Mutagenicity Inactive Inactive Inactive Inactive Cytotoxicity Inactive Inactive Inactive Inactive Phosphoprotein p53 Inactive Inactive Inactive Inactive CYP2D6 substrate No No No No CYP3A4 substrate No No No No CYP2C9 inhibitor No No No No CYP2C19 inhibitor No No No No CYP2D6 inhibitor No No No No CYP3A4 inhibitor No No No No Oral rat acute toxicity (mol/kg) 2.544 2.819 2.447 2.257 BBB permeability (logBB) –1.456 –1.496 –1.717 –1.501 CNS permeability (logPS) –4.27 –4.517 –6.438 –4.842 GI absorption Low Low Low Low Total clearance log (mL/min/kg) 1.009 1.031 0.428 0.815 Note: ASC (Arborside C), ASD (Arborside D), and *Reference drugs. Source: Elaborated by the authors. The comparison of top candidates with the standard drugs acarbose and miglitol (Basnet et al., 2023) implied the positive therapeutic behavior of compounds. Hence, the ADMET analysis of the compounds ASC and ASD revealed comparative therapeutic significance to the standard drugs. Plant N. arbor-tristis has been reported on various biological activities like antioxidant, antidiabetic, and cytotoxicity in different methodologies to expose its medicinal significance. However, six different solvent extracts of leaves of N. arbor-tristis through sequential extraction in ascending polarity and their ethnomedicinal studies through in vitro and computational antidiabetic analysis using HPAE in detail have not been carried out yet. This study showed the presence of different phytochemicals containing alkaloids, flavonoids, phenolic compounds, glycosides, and reducing sugars in the leaves of the selected plant. Quantitative phytochemical analysis revealed the presence of high phenolic contents in the acetone extract (AE) and methanolic extract (ME) of 137 ± 4 mg GAE/g and 139 ± 4 mg GAE/g, respectively (Table 3). Chloroform extract (CE) and hexane extract (HE) were found to have high TFC compared to other extract fractions. The result portrayed the significant antioxidant nature of the leaf extract of N. arbor-tristis, as extract ME showed the best antioxidant activity with an IC50 of 56 ± 3 µg/mL among all leaf extracts, which was comparable to the antioxidant activity of the control ascorbic acid. Other extracts showed moderate antioxidizing potential toward DPPH free radicals. Akki et al. (2009) studied the DPPH scavenging assay of plant leaves in different solvents (pet ether, butanol, ethyl acetate, and butylated hydroxytoluene), and butanol extract showed the best scavenging against DPPH radical. Formerly, the medicinal properties of this plant have been explored in the flowers and seeds of the plant and found to have significant antioxidant properties of different extracts (Mishra et al., 2016; Mishra et al., 2022). The therapeutic potential of natural products is due to the presence of phytoconstituents containing iridoids, flavonoids, alkaloids, and others (Chauhan and Banerjee, 2024; Naseem et al., 2024). The quantitative phytochemical assessment showed the presence of high phenolic content in extracts AE and ME, and similarly high flavonoid content in all extracts except DWE. A better antioxidant nature of ME might be due to the presence of functional components than in other extracts. These could act upon harmful reactive oxygen, reactive nitrogen, and free radical species in the human body to minimize alternation in cellular functioning, https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 12/23 metabolism, and could prevent the formation of diseases (Yang et al., 2016). A high content of metabolites containing flavonoids, phenolics, and iridoids in extracts is the backbone of their pharmaceutical significance, which has supported the observations of this study (Phuyal et al., 2020). In this study, extract HE was found to be most lethal to Artemia salina nauplii, and the lethal concentration of 97 ± 2 µg/mL in BSLA. The toxicity of the extracts, ME and CE, was found to be comparable to that of HE, and other extracts were found to be less toxic than the control potassium dichromate (Table 4). The results showed that HE, ME, and EAE were found (LC50 < 200 µg/mL) moderately toxic, other extracts (LC50 < 500 µg/mL) were found weakly toxic, and DWE (LC50 > 500 µg/mL) was found nontoxic (Niksic et al., 2021). The BSLA assay helps to evaluate the potential of herbal plants towards anticancer activity through cytotoxicity screening (Meyer et al., 1982). The α-amylase inhibition assay helped estimate the potential antidiabetic activity of the different leaf extract fractions. A computational approach using human pancreatic amylase enzyme was carried out to analyze the possible effective interactions, the molecular mechanism of complex formation, and to support the in vitro antidiabetic activity. Methanolic extract fraction (ME) was the most potent amylase inhibitor with IC50, 157 ± 3 µg/mL compared to other fractions and standard acarbose (Table 4). Extract HE and CE showed mild inhibition activity, and the rest of the fractions showed weak inhibition activity against α- amylase. A computational examination of compounds found in leaves of N. arbor-tristis (Table 1) was carried out. The in vitro experimental outcome and computational results helped to better understand the molecular mechanism and possible effective interaction of the ligand with the target protein. To predict the possible compound responsible for amylase inhibition, previously isolated and reported compounds of N. arbor-tristis characterized by GC-MS, LC-MS, 1H NMR, and 13C NMR were selected. Such structures were optimized, energy minimized, and screened through molecular docking and MDS. Among all the selected compounds, ASC and ASD were found to be bound most effectively to the amylase enzyme, which could be proposed as potential HPAE inhibitors, as interpreted by molecular docking calculation with a high binding affinity (–33.5 kJ/mol for both), reasonable geometric configuration, and thermodynamic parameters. Negative binding free energy (ΔGBFE < 0), comparative pharmacokinetic and pharmacodynamic properties of the compounds compared with the drugs Acarbose and Miglitol (Table 7), shown by ADMET profiling, provided strong evidence of spontaneity of the complexes along with binding efficacy to HPAE and drug likeness through computer-based assessments. The amylase inhibition potential of the extracts and stability of the enzyme-ligand complexes on computational screening portrayed the extracts’ HPAE inhibition capability and the plant’s antidiabetic potential. Overall, the results of this study illustrated and supported the ethnomedicinal importance of the leaf extract of N. arbor-tristis. The high content of potential bioactive metabolites, in vitro experiments, and computational virtual screening outputs supported the good antioxidant and HPAE inhibition potential of the plant. 4. Conclusions The study showed that the methanolic extract (ME) was found to be the most significant extract in all in vitro antioxidants, α-amylase inhibition, and brine shrimp lethality assays; however, other fractions showed moderate responses to the bioactivity evaluations. Among the selected ligands, arborside-C (ASC) and arborside-D (ASD) showed significant affinity to the human pancreatic α-amylase enzyme, as determined through molecular docking, molecular dynamics simulation, and ADMET profiling. Polar regions of the ligands (mainly the hydroxyl group) were found to be effective binding sites to the target protein amylase in molecular docking analysis. The integration of in vitro and in silico analysis in this study demonstrated the potential amylase inhibitory capability of plant phytoconstituents. As in silico analysis gives the possible therapeutic estimation through virtual screening at a molecular level, the phytochemicals isolated from this plant (hit candidates arborside-C, and arborside-D) were proposed to be used for in vivo bio-characterization, validation, and optimization to estimate their medicinal significance to treat hyperglycemia through the amylase enzyme inhibition mechanism. Authors’ contribution Conceptualization: Ram Lal Swagat Shrestha; Jhashanath Adhikari Subin; Bishnu Prasad Marasini; Data curation: Jhashanath Adhikari Subin; Nirmal Parajuli; Prabhat Neupane; Formal Analysis: Jhashanath Adhikari Subin; Bishnu Prasad Marasini; Nirmal Parajuli; Funding acquisition: Ram Lal Swagat Shrestha; Nirmal Parajuli; Timila Shrestha; Investigation: Jhashanath Adhikari Subin; Bishnu Prasad Marasini; Methodology: Ram Lal Swagat Shrestha; Jhashanath Adhikari Subin; Bishnu Prasad Marasini; Project administration: Ram Lal Swagat Shrestha; Timila Shrestha; Resources: Ram Lal Swagat Shrestha; Binita Maharjan; Samjhana Bharati; Software: Ram Lal Swagat Shrestha; Jhashanath Adhikari Subin; Supervision: Ram Lal Swagat Shrestha; Jhashanath Adhikari Subin; Bishnu Prasad Marasini; Validation: Jhashanath Adhikari Subin; Bishnu Prasad Marasini; Nirmal Parajuli; Visualization: Nirmal Parajuli; Prabhat Neupane; Sujan Dhital; Samjhana Bharati; Writing – original draft: Nirmal Parajuli; Writing – review & editing: Jhashanath Adhikari Subin; Bishnu Prasad Marasini; Binita Maharjan; Nirmal Parajuli. 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Chem. 2011, 32 (11), 2359–2368. https://doi.org/10.1002/jcc.21816 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.48317/IMIST.PRSM/morjchem-v12i2.46845 https://doi.org/10.20959/wjpps20213-18575 https://doi.org/10.3390/biom10081096 https://doi.org/10.7759/cureus.43697 https://doi.org/10.1002/jcc.21334 https://doi.org/10.1007/s13205-020-02547-0 https://doi.org/10.21203/rs.3.rs-1345800/v2 https://doi.org/10.1021/acs.chemrev.9b00055 https://doi.org/10.1002/jcp.25349 https://doi.org/10.1186/s12906-022-03649-3 https://doi.org/10.1002/wcms.1298 https://doi.org/10.1016/j.ijbiomac.2024.129241 https://doi.org/10.5530/pj.2018.4.111 https://doi.org/10.3390/ijms22158242 https://doi.org/10.1038/srep44651 https://doi.org/10.1016/j.jff.2021.104739 https://doi.org/10.1002/jcc.21816 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 16/23 Supplementary Information FT-IR Analysis of the extract FTIR spectroscopic analysis provides significant insights into the chemical composition and structural characteristics of the analyzed compounds. FT-IR (PerkinElmer Spectrum IR; Version 10.6.2) analysis of all the extracts was conducted at the Amrit Campus in Kathmandu. The spectroscopic analysis of FTIR enables the identification of functional groups found in the extracts (Grasel et al., 2016). The comparative spectral peaks of extracts are presented (Fig. 3S). Alcohols, alkyl groups, and carbonyl groups were found most abundant in the extract fractions. Ligand structures Figure 1S. Molecular structures of selected ligands for molecular docking and standard drugs (acarbose and miglitol) drawn in ChemDraw 16. Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 17/23 Figure 2S. (RMSD <2 Å), Superimposition of the native ligand myricetin (green) with docked ligand (yellow) myricetin, visualized in PyMOL software. Source: Elaborated by the authors. Figure 3S. FTIR analysis of extract fractions. Source: Elaborated by the authors. Standard calibration curves for TPC and TFC Figure 4S. Standard calibration curve of (a) Gallic acid and (b) Quercetin solution. Source: Elaborated by the authors. Comparative and figurative presentation of antioxidant potential of extract fractions (a) (b) Figure 5S. The comparative trending curve of DPPH assay of extracts in dose-inhibition response with (a) Log(concentration) vs % scavenging of DPPH and (b) concentration vs % scavenging with positive control ascorbic acid. Source: Elaborated by the authors. y = 0.0039x + 0.0568 R² = 0.9957 0,0 0,5 1,0 1,5 2,0 2,5 0 100 200 300 400 500 600 A b so rb a n ce ( 7 6 5 n m ) Concentration (µg/mL) Gallic Acid Calibration Curve(a) y = 0.0068x + 0.0704 R² = 0.9997 0,0 0,5 1,0 1,5 2,0 2,5 0 50 100 150 200 250 300 A b so rb a n ce ( 4 2 0 n m ) Concentration (µg/mL ) Quercetin Standard Curve(b) https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 18/23 Alpha amylase inhibition assay (a) (b) Figure 6S. Nonlinear regression graphic presentation of (a) log(concentration) vs percentage inhibition of enzyme (b) Concentration vs percentage inhibition of enzyme with positive control acarbose. Source: Elaborated by the authors. Brine shrimp lethality assay (a) (b) Figure 7S. Nonlinear regression comparative curves of (a) log (concentration) vs % mortality and (b) concentration vs % mortality of triplicate with control, the plot is drawn from GraphPad Prism in dose-inhibition response plot. Note: HE (Hexane extract); CE (Chloroform extract); EAE (Ethyl acetate extract); AE (Acetone extract); ME (Methanol extract); DWE (Distilled water extract). Source: Elaborated by the authors. Protein-ligand Interactions https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 19/23 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 20/23 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 21/23 Figure 8S. 3D and 2D interactive presentations of {ASA (a, a’), ASB (b, b’), ASTA (c, c’), ASTC (d, d’), BHL (e, e’), CLSA (f, f’), AG (g, g’), and Acarbose (h, h’)} with hydrophobicity in receptor protein in molecular docking. Source: Elaborated by the authors. Apo structure Dynamics and characterization Figure 9S. Presentation of MDS of the apo structure of amylase protein (4GQR) of (a) RMSD (b) RMSF (c) Rg (d) SASA. Source: Elaborated by the authors. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 22/23 Figure 10S. Binding free energy change with respect to the time frame of the complex with ligand (a) ASC (b) ASD. Source: Elaborated by the authors. Table 1S. Ligands with their docking scores. Ligands Ligand ID Molecular weight (g/mol) PubChem ID Docking score (kJ/mol) References 6-β-Hydroxyloganin BHL 406.4 341846 –29.7 Rathore et al., (1989) Calceolarioside-A CLSA 478.4 5273566 –32.6 Agrawal and Pal (2013); Dewi et al. (2022) Ascorbic acid ABA 176.12 54670067 –22.6 Agrawal and Pal (2013); Sah and Verma (2012) Methyl salicylate MSC 152.15 4133 –23.0 Arbortristoside D ATSD 584.5 14632886 –32.6 Agrawal and Pal (2013) Vanillic acid VA 168.15 8468 –33.0 Agrawal and Pal (2013); Sah and Verma (2012) Syringic acid SA 198.17 10742 –24.3 Source: Elaborated by the authors. Table 2S. Detailed information about some ligands containing docking scores, type of interactions and active site residues involved. Candidates Docking score (kJ/mol) Interactions Active site residues (Distance Å) ASA –32.6 Conventional H-bond ASP356 (3.04) Pi-Pi Stacked TYR62 (4.41) Pi-Alkyl TRP59 (4.57) Carbon-Hydrogen Bond HIS305 (3.54) van der Waals PRO54, TRP58, GLN63, LEU162, THR163, LEU165, ASP197, GLU233, ILE235, HIS299 CLSA –32.6 Conventional Hydrogen Bond HIS201 (1.98), GLU233 (2.88), ASP300 (2.83) Pi-Alkyl ALA307 (5.05) Carbon Hydrogen bond TRP59 (3.24) Pi-Pi Stacked TRP59 (5.16) Pi-sigma ILE235 (3.93) Pi-Donor Hydrogen bond HIS299 (3.28) van der Waals TRP58, TYR62, GLN63, TYR151, LEU162, ARG195, ASP197, ALA198, LYS200, HIS305, GLY306, GLY308 ATSC –31.4 Conventional Hydrogen Bond ALA106 (2.42), VAL107 (2.88) Pi-Alkyl TRP59 (5.23) Pi-Anion ASP197 (4.04), ASP300 (4.66) Pi-Pi Stacked TYR62 (4.55) Pi-sigma TRP59 (3.65) Alkyl LEU165 (3.90) van der Waals ILE51, PRO54, TRP58, GLN63, LEU162, THR163, LEU165, ARG195, GLU233, HIS299, ASN298, HIS305 Continue… https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 Original Article https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 ISSN 1678-4618 page 23/23 Note: ASA (arborside-A); CLSA (Calceolarioside-A); ATSC (arbortristoside-C); BHL (6-β-Hydroxyloganin). Source: Elaborated by the authors. References Agrawal, J.; Pal, A. Nyctanthes arbor-tristis Linn - A Critical Ethnopharmacological Review. J. Ethnopharmacol. 2013, 19, 645–658. https://doi.org/10.1016/j.jep.2013.01.024 Dewi, N. K. S. M.; Fakhrudin, N.; Wahyuono, S. A Comprehensive Review on the Phytoconstituents and Biological Activities of Nyctanthes Arbor Tristis L. J. Appl. Pharm. Sci. 2022, 12 (8), 9–17. https://doi.org/10.7324/JAPS.2022.120802 Grasel, F. D. S.; Ferrão, M. F.; Wolf, C. R. Development of Methodology for Identification the Nature of the Polyphenolic Extracts by FTIR Associated with Multivariate Analysis. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2016, 153 (2016), 94–101. https://doi.org/10.1016/j.saa.2015.08.020 Rathore, A.; Juneja, R. K.; Tandon, J. S. An Iridoid Glucoside Form Nyctanthes arbor-tristis. Phytochemistry. 1989, 28 (7), 1913–1917. https://doi.org/10.1016/S0031-9422(00)97886-5 Sah, A. K.; Verma, V. K. Review Paper Phytochemicals and Pharmacological Potential of Nyctanthes arbor-tristis: A Comprehensive Review. Int. J. Res. Pharm. Biomed. Sci. 2012, 3 (1), 420–427. BHL –29.7 Conventional H-bond ASP197 (2.02, 2.52), ALA198 (2.66) Pi-Alkyl TRP58 (5.20, 5.78), TRP59 (4.35, 5.37), HIS305 (4.51) Carbon-Hydrogen Bond ASP300 (3.79) van der Waals TYR62, HIS101, LEU162, THR163, LEU165, ARG195, HIS201, GLU233 ILE235, ASP300, TRP356, TRP357 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1600 https://doi.org/10.1016/j.jep.2013.01.024 https://doi.org/10.7324/JAPS.2022.120802 https://doi.org/10.1016/j.saa.2015.08.020 https://doi.org/10.1016/S0031-9422(00)97886-5