Antioxidant and antidiabetic effects of Flemingia macrophylla leaf extract and fractions: In vitro, molecular docking, dynamic simulation, pharmacokinetics, and biological activity studies

Fatema, K., Sharmin, A. A., Fatema Sharna, J., Haque, M. A., Rahman, M. M., Sarker, S., Kazi, M., Rahman, M. R., Namakka, M., Uzzaman, M., and Patwary, M. A. M. (2024). “Antioxidant and antidiabetic effects of Flemingia macrophylla leaf extract and fractions: In vitro, molecular docking, dynamic simulation, pharmacokinetics, and biological activity studies,” BioResources 19(3), 4960-4983.

Abstract

Flemingia macrophylla has traditionally been applied to relieve inflammation, diabetes, and circulatory complications. The leaf extract of F. macrophylla and its fractions were investigated for their in-vitro antioxidant and anti-diabetic properties. The phytochemical screening showed valuable phytochemicals, including glycosides, flavonoids, saponins, etc. GC‒MS analysis of the phytochemicals in the methanol extract detected 19 bioactive compounds. Among the diverse fractions, the ethyl acetate fraction (EFM) exhibited the highest phenol and flavonoid contents of 557 mg GAE/g and 326 mg QCE/g, respectively. The total antioxidant content of EFM was found to be 292.41±19.16 mg AAE/g, while its antidiabetic study showed the greatest level of α -glucosidase (IC₅₀: 11.27±1.25 µg/mL) and α -amylase (IC₅₀: 10.04±0.63 µg/mL) inhibitory effects. The docking results showed that C6 had the highest binding scores of -9.0, -7.4, and -7.6 kcal/mol against antioxidant (6NGJ), α-glucosidase (5NN5), and α-amylase (4GQR) proteins, respectively. The dynamics simulation disclosed that C6-receptor protein complexes remained stable at the binding pocket under human body conditions and retained their stiff morphology for 100 nanoseconds (ns). ADMET results demonstrated their noncarcinogenic and well-absorbed properties, where PASS prediction data confirmed their efficacy as an antioxidant, antiulcerative, thrombolytic, and antidiabetic. Therefore, F. macrophylla has potential health benefits.

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Antioxidant and Antidiabetic Effects of Flemingia macrophylla Leaf Extract and Fractions: In vitro, Molecular Docking, Dynamic Simulation, Pharmacokinetics, and Biological Activity Studies

Kaniz Fatema,^a,c Ayesha Akter Sharmin,^a Jinat Fatema Sharna,^a,c Md. Anamul Haque,^a,c,* Mst. Mahfuza Rahman,^a Shahin Sarker,^b Mohsin Kazi,^d Md. Rezaur Rahman,^e,* Murtala Namakka,^e Monir Uzzaman,^c,f and Md Abdul Majed Patwary ^g,*

DOI: 10.15376/biores.19.3.4960-4983

Keywords: Flemingia macrophylla; Antioxidant and antidiabetic; Molecular docking and dynamics; ADMET and PASS prediction

Contact information: a: Department of Pharmacy, Comilla University, Cumilla-3506, Bangladesh; b: Department of Pharmacy, Jessore Science and Technology University, Jessore, Bangladesh; c: Department of Drug Design, Computer in Chemistry and Medicine Laboratory, Dhaka, Bangladesh; d: Department of Pharmaceutics, College of Pharmacy, PO BOX 2457, King Saud University, Riyadh, 11451, Saudi Arabia; e: Department of Chemical Engineering, Universiti Malaysia Sarawak, Jln Datuk Mohammad Musa, 94300 Kota Samarahan, Sarawak; f: Department of Applied Chemistry, Graduate School of Engineering, Mie University, Tsu 514-8507, Mie, Japan; g: Department of Chemistry, Comilla University, Cumilla-3506, Bangladesh; *Corresponding authors: rmrezaur@unimas.my (M.R.R); anamul@cou.ac.bd (M.A.H.); mamajedp@gmail.com (M.A.M.P.)

GRAPHICAL ABSTRACT

INTRODUCTION

Natural products are usually secondary metabolites produced by organisms in response to environmental contexts such as illness, competition, and dietary changes. Approximately 35% of medicines are made using natural ingredients (Calixto 2019). Free radicals are necessary for mitochondrial respiration and metabolism (Kumar et al. 2014). By causing redox reactions, these free radicals, which are much more sensitive than oxygen molecules, can cause damage to biomacromolecules such as nucleic acids, proteins, and lipids (Iriti and Faoro 2008). When normal antioxidant protection and repair systems of the human body cannot eliminate the state of peroxidation, it can lead to bad outcomes such as DNA damage, cell death, cancer, and aging (Bakri et al. 2024). Antioxidants protect biomolecules, proteins, sugars, and triglycerides (Umesha et al. 2013). They reduce cell and tissue damage by avoiding or delaying oxidation (Fındık et al. 2011).

Diabetes mellitus is one of the most serious diseases that affect most of the world’s population (Alsolami et al. 2023). It is linked to hyperglycemia due to a full or relative lack of insulin result or action. Long-term hyperglycemia damages valuable organs, including the retina, kidney, heart, and blood vessels (Alam et al. 2014). Traditional medicine praises herbal antidiabetic medications, but they have not been marketed. Enzymes that break down starch tend to be reduced in a body that is suffering from diabetes. Dietary glucose release causes postprandial hyperglycemia. In addition, α-amylase and α-glucosidase break down polysaccharides into monosaccharides. To reduce postprandial hyperglycemia, one promising technique is to slow glucose uptake by blocking α-glucosidase and α-amylase, which are important carbohydrate metabolizing enzymes (Arumugam et al. 2013).

Charchara is the local name for the woody shrub F. macrophylla, which belongs to the Fabaceae family. This plant has been used historically for a variety of purposes, including the relieving of inflammation and diabetes, as well as the improvement of blood circulation (Begum et al. 2013). In the northeastern region of India it is traditionally used as a folk remedy by practitioners for its hypoglycemic and antidiabetic activity (Syiem and Khup 2007). Kabir et al. (2015) conducted a study on the computational prediction of the ability of isolated chemicals from F. macrophylla to dissolve blood clots, as well as their interaction with target molecules. Shahadat et al. (2015) investigated the antipyretic and in vivo analgesic properties of F. macrophylla in Swiss albino mice, as well as its in vitro anti-inflammatory activity. Hsieh et al. (2011) also investigated the hepatoprotective influence of the aqueous extract of F. macrophylla in rats. The present article reports a comprehensive study of F. macrophylla leaf extract and four fractions using both computer-simulated and experimental analyses to recognize the properties of the key active constituents to examine the potential health benefits and evaluate the antioxidant, and in-vitro antidiabetic potential.

EXPERIMENTAL

Chemical Reagents

In this investigation, methanol, 2,2-diphenyl-1-picrylhydrazyl (DPPH), ascorbic acid, and Mayer’s reagents were purchased from Merck (Darmstadt, Germany). Ferric ammonium molybdate, potassium ferricyanide, trichloroacetic acid, DNSA reagent, α-glucosidase, and α-amylase, were obtained from Sigma Chemicals Co. (St. MO, USA). All the reagents as well as chemicals were of the highest chemical and analytical grade.

Leaf Extraction and Fractionation

In two amber-colored containers, 2.5 L of methanol was poured into 500 g of powdered leaves in each with 14 days of occasional shaking (50 rpm) followed by filtration. To remove the solvent, a rotary evaporator was utilized. After removing the solvent, F. macrophylla generates a greenish-black gummy concentration. The Kupchan method was applied to the methanol extract (MFM) to obtain petroleum ether (PFM), dichloromethane (DFM), ethyl acetate (EFM), and water (WFM) fractions (Kupchan and Tsou 1973). Finally, the extract and fractions were dried and stored for further applications.

Preliminary Phytochemical Screening

Earlier developed methods of phytochemical assessment were applied for freshly prepared extract and fractions to screen steroids, resins, anthraquinones, tannins, phlobatanins, saponins, glycosides, alkaloids, and flavonoids (Tripathi and Mishra 2015; Odeja et al. 2017; Bayero et al. 2019).

Quantitative Analysis of Leaf Extract and Fractions

Total phenol content (TPC)

The TPC was determined using a modified Folin-Ciocalteu technique (Wolfe et al. 2003). An aliquot of the standard/extract/fractions (50, 100, 200, and 400 µg/mL) was mixed with 4 mL of Na₂CO₃ solution and 2 mL of Folin-Ciocalteu reagent. To achieve the appropriate color, the tubes were vortexed vigorously for 20 min at 25 °C. A UV spectrophotometer was employed to identify absorbance at 756 nm. Gallic acid (standard, GA), equivalents (GAE), or mg of GA/g of dry extract, was employed to calculate the phenol content (Rahman et al. 2015).

Total flavonoid content (TFC)

The approach developed by Brighente et al. (2007) was utilized to assess the TFC. Two mL of the extract, fractions, and standard were added with an equivalent volume of 2% w/v AlCl₃.6H₂O solution. The mixture was stirred quickly, and after one hour of incubation at 20 °C, the absorbance at 415 nm was recorded by using a UV spectrophotometer. The results were calculated using a quercetin (QC) calibration curve and are expressed as mg QCE/gram of dry extract (Kasangana et al. 2015).

In-vitro Antioxidant Effect

Total antioxidant content (TAC)

TAC was measured with a UV-spectrophotometer and the phosphomolybdenum assay, as per reference (Prieto et al. 1999). Briefly, 2.7 mL of phosphomolybdenum reagent and 0.3 mL of a 1 mg/mL extract/fraction/ascorbic acid (AA) solution were mixed in a test tube with 28 mM Na₃PO₄ and 4 mM ammonium molybdate in 0.6 M H₂SO₄ acid. After a 90-min incubation period at 95 °C in a water bath, absorbance at 695 nm was measured against a blank (0.3 mL methanol) (Kasangana et al. 2015).

DPPH radical scavenging effect

The DPPH free radical-scavenging test was applied to evaluate the free radical-scavenging capacity identified by Blois (1958) and Demarchelier et al. (1997). Plant extracts decolorize DPPH methanol mixtures. Antioxidants cause DPPH to yellow, which appears violet or purple in methanol solution. Here, 1.6 mL of extract or fraction solution was combined with 2.4 mL of DPPH solution in methanol at various concentrations (ranging from 6.25 to 400 µg/mL). The samples were vortexed at room temperature (RT) (25-26 °C) with a 30-min break in the dark before the absorbance at 517 nm was recorded (Rahman et al. 2015). The DPPH radical scavenging activity was determined by Eq. 1 (Rahman et al. 2022).

(1)

Ferric reducing capacity test (FRCT)

The ferric-reducing capacity of the sample was adjusted using Oyaizu’s technique (Oyaizu 1986). The Perl technique was used to calculate the amount of Fe²⁺ in the H₂O while taking Prussian blue concentration into account. First, 12.5 mL of sample/standard solutions (12.5 to 400 g/mL) was added to 0.2 mL of potassium buffer and 12.5 mL of [K₃Fe(CN)₆] solution. The next step was to incubate the reaction mixtures at 50 °C for 20 min. Then, 2.5 mL of a 10% trichloroacetic acid solution was added to each test tube. Two milliliters of the supernatant were extracted by adding 2.5 mL of distilled H₂O and 0.5 mL of a 0.1% ferric chloride solution after the tubes had been centrifuged at 3000 rpm for 10 min. A UV spectrophotometer was employed to assess the absorbance of the solution against a blank at 700 nm. After that, the 50% effective concentration (EC₅₀) of each extract and standard component was determined (Rahman et al. 2015).

In-vitro Antidiabetic Test

In-vitro α-glucosidase inhibition assay

The glucosidase inhibition test was carried out using the method depicted by Elya et al. (2012). Plant extract and fraction solutions (10 to 100 µg/mL) were prepared in 5% dimethyl sulfoxide. Then, 80 µL of the sample or standard acarbose solution was combined with 20 µL of α-glucosidase solution (0.01 mg/mL). After 10 min of incubation at 37 °C, 50 µL of 5 mM p-nitrophenyl-D-glucopyranoside (p-NPG) was added to start the reaction. Then, 2.5 mL of 0.1 M Na₂CO₃ solution was added after 60 min of incubation at 37 °C. The absorbance at 400 nm was used to measure α-glucosidase activity (Alqahtani et al. 2019). The percentage of inhibition employed to define α-glucosidase inhibitory activity was calculated using a similar sort of Eq. 2, as follows:

(2)

In-vitro α-amylase inhibition assay

The technique depicted by Kwon et al. (2008) was utilized to determine the α-amylase inhibitory activity. Twenty mL of α-amylase solution (0.5 mg/mL) was added to 500 µL of extract/fractions or acarbose (standard), then combined with 500 µL of 20 mM sodium phosphate buffer at pH 6.8. The mixture was further incubated at 25 °C for 10 min. Each test tube was therefore loaded with 500 µL of a 1% starch solution in a 0.02 M sodium phosphate buffer at pH 6.9 and was again incubated at 25 °C for 15 min. Di-nitro salicylic acid (0.5 mL) was applied to stop the reaction. The test tubes were then cooled to room temperature after spending 5 min submerged in boiling water. The absorbance at 540 nm was measured after diluting the reaction mixture with 10 mL of distilled H₂O. Following Eq. 2, the percentage of inhibition employed to express the α-amylase inhibitory activity was determined. The crude extract, solvent fractions, and acarbose IC₅₀values were determined using the dose-response curve, which was interpolated using the linear regression analysis (Alqahtani et al. 2019).

GC‒MS Analysis

GC‒MS analysis was employed on the methanol extract using a GCMS-QP2020 (Shimadzu, Japan). The analysis was conducted using an RTX-5 MS capillary column (30 m x 0.25 mm x 0.25 µm) that had a cross band of 5% diphenyl-95% dimethylpolysiloxane. Herein, 1.72 mL/min of helium (99.99%) was employed as the carrier gas in this experiment. The temperature of the oven was initially set at 80 °C (isothermal for 2 min) and raised to 150 °C at a rate of 5 °C (kept for 3 min). After raising the temperature by 5 °C per min and holding it for 5 min, the oven’s final temperature was 280 °C. The splitless injection of the sample (1 µL) was performed at 50:0 with an injection temperature of 220 °C. The whole duration of the run was 50 min. An ionizing potential of 70 eV was utilized to guarantee electron-impact ionization, and the ion source was adjusted to 280 °C. The mass spectra were found in the 45-350 (m/z) scan range, and their components were identified using a probability-based approach by comparing them to the spectra of known compounds stored in the NIST08.LIB database (Gomathi et al. 2015).

Computational Analysis

Preparation of ligands and proteins, docking, and nonbonding interactions

The structures of six significant compounds identified in leaf extract were determined employing GC‒MS and denoted as C1, C2, C3, C4, C5, and C6, as shown in Fig. 1.

Fig. 1. Chemical structures of AA, AC, and six particular compounds with PubChem ID

Besides, the standard drugs ascorbic acid (AA) and acarbose (AC) were used to relate the binding affinity as well as nonbonding interactions. By operating the Gaussian 09 W software package using density functional theory (DFT), the geometry optimization was accomplished in the presence of B3LYP (Rupa et al. 2022) hybrid functionals using Pople’s 6-31G (d, p) basis set (Sure et al. 2014). The Protein Data Bank (PDB) format was used to acquire the three-dimensional structure of the protein from the RCSB (Rose et al. 2016). These structures contain antioxidants (PDB ID: 6NGJ) (Do et al. 2019), antidiabetic α-glucosidase (PDB ID: 5NN5) (Roig-Zamboni et al. 2017), and α-amylase (PDB ID: 4GQR) (Williams et al. 2012), receptor proteins, respectively. To eliminate unwanted heteroatoms, chains, H₂O molecules, and co-crystallized ligands, PyMOL (Version 1.7.4) was utilized. In order to decrease the chain energy and remove superfluous protein interactions, the Swiss PDB reader (Version 4.1.0) was employed. Lastly, the 6NGJ, 5NN5, and 4GQR proteins were docked in a flexible fashion using the PyRx (Version 0.8) program. The medication is considered as the ligand, while protein works as macromolecules. The center grid boxes along the x, y, and z axes were preserved at 53.87, 70.63, and 58.28 Å for 6NGJ, 82.95, 79.57, 80.19 Å for 5NN5, and 58.99, 78.20, 58.54 Å for 4GQR protein, respectively. For nonbonding interactions, the drug and protein were saved as a single PDB file after docking and placed into BIOVIA Discovery Studio 2021.

Molecular Dynamics (MD) Simulation

Using the AMBER14 force field, the MD simulation of the docked complexes was carried out in YASARA dynamics (Uzzaman et al. 2021). The docked complexes underwent cleaning, and the mechanism that was responsible for the H-bond network was orientated. A cubic simulation cell was employed, utilizing a TIP3P solvation mechanism. The physiological form of the complex was set as 298 K, 0.9% NaCl, and pH 7.4. The energy was minimized with the steepest gradient algorithm by the simulated annealing method. The time stage of the simulation was adjusted to 2 fs. The simulation trajectory was set to save after every 100 ps and extended to 100 ns. The radius of gyration, solvent accessible surface area (SASA), root mean square deviation (RMSD), and H-bond were all computed using the simulated trajectories (Krieger and Vriend 2015).

Pharmacokinetics, Biological Activity, and Drug-likeness Prediction

The ADMET (absorption, distribution, metabolism, excretion, toxicity) profile is crucial in medication design and analysis, since it considers both pharmacokinetic and pharmacodynamic factors. To have desirable and/or safe therapeutic impacts, a medicine must first be readily absorbed, then distributed uniformly throughout the body, and finally digested properly. The drug should leave the body within the predicted time range, whether through urine, feces, or other means. The AdmetSAR (Guan et al. 2019), and way2drug (Filimonov et al. 2014), and Swiss ADME web server were utilized for the pharmacokinetics and biological predictions.

Statistical Analysis

The three sets of data we obtained were presented along with their respective means, standard deviations, and interquartile ranges. The ANOVA was performed in SPSS version 15.0, and all charting was done in Graph Pad Prism version 6.0. The IC₅₀ and EC₅₀ values were computed using MS Excel-10.

RESULTS AND DISCUSSION

Preliminary Phytochemical Analysis

F. macrophylla leaf extracts and fractions showed saponins, steroids, flavonoids, alkaloids, resins, glycosides, and tannins in MFM, PFM, DFM, EFM, and WFM respectively. Table S1 shows that all extracts and fractions contained saponins, steroids, flavonoids, and anthraquinone. Alkaloids were absent in the MFM extract and PFM fraction, whereas phlobotannins were absent in the WFM, and the EFM fractions lacked glycosides. It is believed that plants contain a large number of phytochemicals; hence, a plant-based diet can aid in the prevention of many diseases (Mujeeb et al. 2014) agents, anti-inflammatories, anticoagulants, cardioprotective, sedatives, and hypotensive agents (Dey et al. 2020). However, plant-derived steroids are widely known for their cardiotonic and insecticidal activities. They are often used in medicine due to their well-known biological roles (Patel and Savjani 2015). As a resource of steroids and alkaloids, F. macrophylla can be used to heal various inflammatory conditions and cardiotonic diseases.

Total Phenol and Flavonoid Content

Phenols and flavonoids are the two most important secondary plant metabolites, which have unique biological activity as natural antibacterial agents and are superior to many other manufactured antibacterial agents (Bouyahya et al. 2016). The bulk of naturally occurring phenolic compounds, or flavonoids, are present in different plant sections both as glycosides and in free form. Numerous biological effects, including the prevention of angiogenesis, the suppression of mitochondrial adhesion, the antiulcer and antiarthritic properties, and the inhibition of protein kinase, have been found (John et al. 2014). The total phenolic compound in the MFM extract and fractions (PFM, DFM, EFM, and WFM) was raised in a concentration-dependent way, and gallic acid (GA) was utilized as a control. Fig. S3 depicts the extract and fractions, which were qualified in mg/gm of GAE. The TPCs for MFM, PFM, DFM, EFM, and WFM were in the following order at 400 µg/mL EFM > DFM > WFM > MFM > PFM, with 557.42±67.11, 443.55±26.82, 217.27±14.07, 86.31±40.74, and 41.87±26.67 mg/g (Table 1) [reported 43.8 ± 0.22 mg/g by ref (Begum et al. 2013). On the other hand, the EFM fraction contained a significantly higher flavonoid content (326.36±7.80 mg/g) than the DFM (214.11±12.84 mg/g), WFM (156.29±15.58 mg/g), MFM (84.87±17.52 mg/g) (Fig. S6) [reported 64.4 ± 0.56 mg/g by ref (Begum et al. 2 2013) fractions and PFM (47.45±5.10 mg/g) extract.

In-Vitro Antioxidant Effect

Total antioxidant content

The TAC was computed using the reduction of Mo(iv) and Mo(v) by the fractions and extract as well as the growth of a green phosphate/Mo(v) complex when the pH was acidic. It evaluates the antioxidants that are both H₂O and fat-soluble to determine their effectiveness (Jan et al. 2013). Table 1 depicts the highest value of TAC found from the EFM (292.41±19.16 mg/g), following DFM (274.84±2.34 mg/g), then WFM (203.21±4.68 mg/g), MFM (177.54±13.04 mg/g) extract, and the lowest value found from PFM (126.12±6.19 mg/g) fraction.

Table 1. TPC, TFC, TAC, IC₅₀ (DPPH), EC₅₀ (FRPT), and α-Glucosidase and α-Amylase Inhibitory Effects of Leaf Extracts and Fractions

Note: Data are means ± SD, n=3, IC: inhibition concentration, EC: effective concentration

DPPH radical scavenging effect

The antioxidant activity can also be determined by the DPPH scavenging activity and ferric reducing activity because the antioxidant activity cannot be determined by a single procedure. DPPH is a strong free radical and is commonly employed for the assessment of the antioxidant activity of plant extracts (Ali et al. 2010). Herein, AA, EFM, DFM, WFM, MFM, and PFM each had IC₅₀ values of 5.07±0.06, 15.62±0.74, 21.78±0.53, 25.52±0.89, 64.58±1.48, and 81.67±3.78 μg/mL, respectively (Table 1).

Ferric reducing capacity test (FRCT)

The FRCT assay of all the fractions, extract, and AA increased with a gradual rise in concentration. Reducing power is frequently used to assess a plant’s ability to combat free radicals (Rahman et al. 2015). AA, a standard reducing agent, showed the highest absorbance (12.20±0.15 μg/mL) at concentrations ranging from 12.5 to 400 µg/mL. Among the extracts, EFM showed the maximum effect with an EC₅₀value of 18.37±1.49 μg/mL, and PFM showed the lowest effect with an EC₅₀ value of 75.95±4.69 μg/mL (Table 1).

In Vitro Antidiabetic Test

In vitro α-glucosidase and α-amylase inhibition activity

Postprandial hyperglycemia can be reduced by inhibiting the activity of α-glucosidase and α-amylase in the intestinal and pancreatic tracts. This occurs as a direct result of the presence of additional carbs, which causes a delay in the digestion of absorbable monosaccharides (Kifle et al. 2021). The α-glucosidase and α-amylase inhibition activities are presented in Table 1. In this study, the EFM fraction showed the highest effect with IC₅₀ values of 11.27±1.25 and 10.04±0.63 µg/mL for both α-glucosidase and α-amylase, respectively.

GC‒MS Analysis

The GC‒MS chromatogram of the methanol extract of F. macrophylla (Fig. 2) displayed 19 peaks, which indicates the presence of 19 phytochemicals. From the GC‒MS data, benzene,1,3-dimethyl- had the shortest retention time (3.6 min), and 7-octylidenebicyclo [4.1.0] heptane had the longest (39.01 min) retention time. Likewise, (C1, 13.31%) and (C2, 9.04%) were the most common chemicals (Table 2). In this study, more bioactive components, such as C3, phytol, and tetradecanoic acid may have contributed to the enhanced antioxidant, anti-inflammatory, antiulcerative, and thrombolytic activity (Bodoprost and Rosemeyer 2007; Abirami and Rajendran 2011; Kala et al. 2011). Additionally, C4 has been linked to anticancer, antioxidant, chemo-preventive, gastroprotective, hepatoprotective qualities, pesticide, anti-tumor, and sunscreen capabilities. Previous studies have shown that octadecadienoic acid possesses anti-inflammatory, hypocholesterolemic, and antiarthritic effects (Gomathi et al. 2015) (Ponnamma and Manjunath 2012).

Fig. 2. GC‒MS chromatogram of the methanolic extract

Table 2. GC‒MS Analysis of the Leaf Extract of F. macrophylla in Methanol

Molecular Docking Simulation

The structure-based drug design approach known as molecular docking can predict how well a medication will bind to a receptor (Uzzaman et al. 2023). It is an important tool for hit recognition, lead improvement, and biological remediation, since the binding score and kind of drug-receptor protein interaction can be certainly assessed (Jannat et al. 2024), higher negative value indicates more tight binding with the receptor. Figure 3 presents a comprehensive overview of the binding affinity values with the antioxidant and antidiabetic potentials.

Fig. 3. Binding affinity of the selected compounds and standard drugs with (a) antioxidant (6NGJ), and (b) antidiabetic (5NN5) proteins, respectively

Docking and Non-bonding Interaction (NBI) against 6NGJ

In this study, the binding affinity of the considered standard drug (AA) was -6.4 kcal/mol, with the 6NGJ receptor, where C6 owed the highest binding score (-9.0 kcal/mol) with the same protein compared to other studied compounds. The C5 had a low binding score (-5.1 kcal/mol), indicating a weak binding with the respective protein (Fig. 3).

Fig. 4. (a) Surface outlook of the docked conformation of C1 (clay), C2 (magenta), C3 (yellow), C4 (red), and (b) C5 (blue), C6 (orange) at the binding site of the 6NGJ

Fig. 5. Hydrogen bond-surface area of the studied compounds with the 6NGJ protein

Noncovalent interactions are essential for medication stability, changes in binding affinity, and therapeutic efficacy (Patil et al. 2010). To promote protein-ligand interactions, the H-bond must be strong, and the distance should be equal to or smaller than 2.3 Å (Uzzaman et al. 2021). In this investigation, all the molecules contain H-bonds except for C2, C4, and C5. There are three C-H bonds present in AA, C1, and C6 with PHE704 (3.06146 Å), ALA412 (2.49876 Å), and SER422 (2.58701 Å) amino acids, respectively. Herein, the C2 compound is interlinked with HEM801 amino acids via a π-σ bond at a distance of 2.55031 Å. In addition, all the molecules have alkyl (except AA) and π-alkyl interactions (except AA, C1) with various amino acids (Table S9).

Docking and NBI Against 5NN5

The C6 demonstrated the highest binding score (-7.4 kcal/mol) of all the studied compounds, whereas AC displayed a -7.5 kcal/mol binding score. However, C3 has a poor link to the protein due to its low binding score (-4.4 kcal/mol) (Fig. 3). Hence, C3 has two conventional H-bonds with the ARG594 amino acid, and C1 has a C-H bond with HIS717 (2.72409 Å). The standard drug AC has four conventional H-bonds, two C-H bonds, and a π-alkyl bond with numerous amino acids. Furthermore, all the compounds have alkyl and π-alkyl interactions with numerous amino acid residues (Fig. 7).

Fig. 6. (a) Surface outlook of the docked conformation of C1 (green), C2 (cyan), C3 (magenta), C4 (yellow), C5 (orange), and C6 (red), and (b) cartoon view of the docked conformation at the binding site of the 5NN5

Fig. 7. Hydrogen bond surface area of studied compounds with the 5NN5 protein

Docking and NBI against 4GQR

For antidiabetic action with the α-amylase protein, the C6 chemical had the highest binding score (-7.6 kcal/mol), and C1 binds poorly to the protein (-4.9 kcal/mol) (Fig. 3). The binding score order was AC > C6 > C4 > C2 > C5 > C3 > C1. According to the findings, C2 has a conventional H-bond with ASP300 (2.34645 Å) amino acids, and C1 has two carbon H-bonds with GLU233 (3.07608 Å) and HIS299 (2.42742 Å). On the other hand, π-σ interactions have been discovered in C3 with the amino acid TRP59 at a distance of 2.67916 Å. In addition, all the compounds have alkyl and π-alkyl interactions with numerous amino acid residues, but C6 has no alkyl bond. However, the reference drug AC contains conventional H-bonds and C-H bonds (Fig. 8).

Fig. 8. (a) Surface outlook of the docked conformation of C1 (green), C2 (cyan), C3 (magenta), C4 (yellow), C5 (red), and C6 (blue), and (b) cartoon view of the docked conformation at the binding site of the 4GQR

Fig. 8b. Hydrogen bond-surface area of the studied compounds with the 4GQR protein

Molecular Dynamic (MD) Simulation

To understand the relative stability of the complex, the root means square deviations of the simulation trajectories were calculated. Figure 9 (a) specifies that the complex from C6 had a stable profile from the beginning and showed a flexible nature during the passage of 30 ns. The complexes were stable after that and maintained the rigid profile up to 100 ns. The average RMSD of the C4 and C6 complexes were below 2.5 Angstrom, which indicates the strong stable nature of the complex. Additionally, the solvent-accessible surface area of the complexes was evaluated to identify any surface area modifications. The SASA profile in Figs. 9(b) and 10(b) for complexes C4 and C6 indicate that all complexes maintained less deviation throughout the simulation trajectories, which indicates the stable nature of the complex. The radius of gyration of the simulated complexes defines the mobile nature of the complexes, whereas Fig. 9(c) and Fig. 10(c) indicate that C4 and C6 complexes exhibited less deviation. The H-bond from both complexes in Fig. 9(d) and Fig. 10(d) demonstrates the strong binding profile of the complex.

Fig. 9. MD simulation of 6NGJ, 5NN5, and 4GQR proteins in complex with C4 ligand (a) RMSD of docked complexes from α-C atoms. The structural exchanges of receptor protein using (b) SASA, (c) Radius of Gyration (d) number of H-bonds developed, respectively

Fig. 10. MD simulation of 6NGJ, 4GQR, and 5NN5 proteins in complex with C6 ligand (a) RMSD of docked complexes from α-C atoms. The structural variations in the receptor protein using (b) SASA, (c) Radius of Gyration (d) number of H-bonds developed respectively.

Pharmacokinetic Analysis

The pharmacokinetic profile analysis is represented in Table 3. According to the study, each chemical exhibits good HIA and CACO-2 permeability. Oral bioavailability is good without AA, C5, and C6 compounds. In addition, the BBB is good with all chemicals. AA, AC, and all discovered medications are noncarcinogenic and have oral toxicity categories III (C1, C2, C4, C5, C6) and IV (AA, AC, and C3) (Li et al. 2014). In this study, all medications were p-GP and CYP4502C9 non-inhibitors. On the other hand, AC and C4 have been shown to suppress hERG, which can lengthen the QT interval (Hasan et al. 2023).

Biological Activity Prediction

PASS’s web server can predict the biological characteristics of several substances (Lagunin et al. 2000). Utilizing PASS data, the AA, AC, and six compounds were investigated (Table 4), and their antioxidant activities ranged from 0.222 (C3) to 0.928 (AA). Here, these compounds have anti-inflammatory, α-glucosidase inhibitory, and α-amylase inhibitory effects (except AA). Furthermore, with C4 > C1 > C2 > C5 > C6 > C3 > AA, each molecule possesses anti-ulcerative effects. Moreover, all the compounds exhibited antipruritic (without AC) characteristics, with C2 showing the highest value (0.719). Finally, all the selected compounds demonstrated the aforementioned effects.

Table 3. Pharmacokinetic Analysis

HOB = Human oral bioavailability, HIA = Human intestinal absorption, BBB = Blood-brain barrier, C2P = CACO-2 permeability, P-GpS = P-glycoprotein substrate, P-GI = P-glycoprotein inhibitor, hERG = human ether a-go-go-gene, AOT = Acute oral toxicity.

Table 4. Biological Activity Prediction of the Selective Compounds

CONCLUSIONS

The leaf extract and fractions contained alkaloids, flavonoids, phenolic compounds, and other phytochemicals. The quantitative investigation revealed that the ethyl acetate fraction (EFM) had the highest total phenol content (TPC) and total flavonoid content (TFC). In addition, EFM and dichloromethane (DFM) fractions showed the highest total antioxidant content (TAC) with 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and ferric-reducing capacity.
During the antidiabetic evaluation, the EFM fraction showed a significant inhibitory influence on α-amylase enzymes and α-glucosidase.
Furthermore, gas chromatography – mass spectrometry (GC‒MS) analysis showed the presence of bioactive phytoconstituents with antipyretic, anti-inflammatory, antioxidant, and antidiabetic properties.
Based on molecular docking results, the C4 and C6 compounds would be a valuable source of potential drug candidate for their greatest binding score against antioxidant and antidiabetic proteins.
According to dynamic simulation, the complex from C6 exhibited a steady profile from the start and displayed flexibility up until 30 ns periods. After that, the complexes remained stable and kept their stiff profile for 100 ns. The average RMSD of the C4 and C6 complexes was under 2.5Å, indicating the complex’s high stability.
ADMET results demonstrated that the chosen compounds are noncarcinogenic and well absorbed. The PASS prediction results showed moderate to strong antioxidant, anti-inflammatory, and other actions.
These findings suggest that F. macrophylla leaf may be a potent herbal medicine source for drug development.

ACKNOWLEDGMENTS

The authors would like to acknowledge the assistance and scientific contribution from the Department of Pharmacy, Comilla University, Cumilla 3506, Bangladesh. The authors would like to acknowledge Universiti Malaysia Sarawak (UNIMAS) for the research support. The authors also would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2024R301), King Saud University, Riyadh, Saudi Arabia.

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Article submitted: April 1, 2024; Peer review completed: May 24, 2024; Revised version received and accepted: May 28, 2024; Published: June 5, 2024.

DOI: 10.15376/biores.19.3.4960-4983