Skip to main content
NCBI home page
In Silico Pharmacology logoLink to In Silico Pharmacology
. 2024 May 14;12(1):43. doi: 10.1007/s40203-024-00219-y

Anti-diabetic, anti-pancreatic lipase, and anti-protein glycation potential of Irvingia gabonensis stem bark extracts: in vitro and in silico studies

Akhere A Omonkhua 1,✉, Catherine Otitolaiye 1,2, Bobby Aguebor-Ogie 1, Olusola Elekofehinti 3, Edward Okello 4, Iyere Onoagbe 5, Friday Okonofua 6
PMCID: PMC11091014  PMID: 38751710

Abstract

Diabetes mellitus is a chronic metabolic disorder that affects glucose, lipid, and protein metabolism. Targeting these metabolic derangements can optimize the therapeutic strategies for this disease. Utilizing in vitro and in silico models, this study investigated the ability of aqueous and ethanol extracts of Irvingia gabonensis to inhibit α-amylase, α-glucosidase, pancreatic lipase, and protein glycation. High-performance liquid chromatography (HPLC) was used to identify the compounds found in the stem bark of I. gabonensis. In silico analysis determined the binding mode and mechanism of interactions between the enzymes and phytochemicals. With an IC50 value of 11.47 µg/ml, the aqueous extract demonstrated higher inhibitory efficacy against α-amylase compared to the ethanol extract (IC50 19.88 µg/ml). However, the ethanol extract had stronger inhibitory activities against α-glucosidase, pancreatic lipase, and protein glycation compared to the aqueous extract (IC50 values of 3.05, 32.85, 0.0014 versus 25.72, 332.42, 0.018 µg/ml respectively). Quercetin ranked highest in binding energy with α-amylase (-6.6 kcal/mol), α-glucosidase (-6.6 kcal/mol), and pancreatic lipase (-5.6 kcal/mol). This was followed by rhamnetin (6.5, 6.5, and 6.1 kcal/mol respectively). Hydrogen bonding, hydrophobic interactions, and pi-pi stacking are forces responsible for the binding of quercetin and rhamnetin to these enzymes. Molecular dynamics simulation showed that the lead phytochemicals formed stable and energetically stabilized complexes with the target proteins. This study showed that the extracts of I. gabonensis stem bark had significant in vitro anti-diabetic, anti-pancreatic lipase, and anti-protein glycation activities. The strong binding affinities of some of the identified compounds could be responsible for the inhibitory potential of the extracts. I. gabonensis stem bark could be further explored as a natural remedy for the treatment of diabetes mellitus and its complications.

Keywords: Anti-diabetic, Anti-pancreatic lipase, Anti-protein glycation, Irvingia gabonensis, In vitro, In silico

Introduction

Apart from the high blood glucose levels (hyperglycemia) which is the hallmark of diabetes mellitus; the disease adversely affects the metabolism of carbohydrates, proteins, and lipids (Dhodi et al. 2014). Chronic hyperglycemia can lead to the non-enzymatic glycation of proteins and lipids through the Maillard reaction (Singh et al. 2014; Siahbalaei et al. 2020). These glycations could result in structural and functional degradation thereby compromising the biological functions of the proteins involved (Alavi et al. 2013; Anguizola et al. 2013; Lee and Wu 2015; Perera et al. 2016). The most common examples of glycated proteins are glycated albumin, glycated haemoglobin, fructosamine, and advanced glycation end products (AGEs) (Welsh et al. 2016; Fishman et al. 2018). Inhibition of protein glycation (for example, advanced glycation end products) is a major therapeutic approach which can delay the progression of diabetes-related complications (Olaokun et al. 2013; Sadowska-Bartosz and Bartosz 2015).

Also, in both type 1 and type 2 diabetes, chronic hyperglycemia is linked to hyperlipidemia (elevated chylomicrons and very low-density lipoproteins) (Champe et al. 2008). The increased formation of these products in diabetes is associated with complications such as stroke, heart disease, nephropathy, retinopathy, neuropathy, and eventual morbidity and mortality (Kitada et al. 2010; Arora and Singh 2013; Singh et al. 2014; Chandra et al. 2019). This implies that different pathways and enzymes are involved in the pathogenesis of diabetes which may be targeted in the treatment of the disease.

The use of pharmaceuticals or herbal preparations that target multiple aspects of diabetes pathogenesis may be a more effective treatment strategy than using monotherapies, even though monotherapies have had some success in treating diabetes. As such, using drugs that lower glucose and lipid levels, and inhibit the glycation of proteins, may be a better strategy for treating diabetes (Alam et al. 2019). Over the years, studies have found that the treatment of diabetes in Nigeria involves the combination of two or more different hypoglycemic drugs (polytherapy) to rigorously address the different aspects of the disease (Yusuff et al. 2008; Ogbera and Ekpebegh 2014; Amao et al. 2018). Most of these drugs are expensive with side effects (Enwere et al. 2006). As such, many patients use medicinal plants and other natural products which are easily accessible and cheap (Mahomoodally 2013; WHO 2013). Apart from being inexpensive, many antidiabetic medicinal plants show little or no side effects and can address multiple aspects of the pathogenesis of the disease (Yusuff et al. 2008; Alam et al. 2019). Antidiabetic herbal preparations or other natural products that also inhibit hyperlipidemia and protein glycation, would greatly improve the treatment outcomes for diabetes.

Studies have shown that different parts of Irvingia gabonensis have glucose and lipid lowering effects in experimental settings. I. gabonensis is a medium-sized tree found in the tropical rainforests of West Africa. It is a member of the Irvingiaceae family (order: Rutales). The tree is also known as Dikanut tree, African mango tree, or bush mango tree because its fruit looks like a small mango (White et al., 1996; Matos et al. 2009). The first mention of the ability of I. gabonensis nuts (kernels) to lower elevated blood sugar in type 2 diabetic patients was by Adamson et al. in 1990. The methanol and hexane extracts of the seeds of this plant showed hypoglycaemic effects in experimental diabetes (Ngondi et al. 2006; Hossain et al. 2012). Furthermore, the increased glucose and lipid levels of streptozotocin-diabetic rats were reduced, over the course of 24 weeks, by the aqueous extract of I. gabonensis stem bark (Omonkhua et al. 2014). Similarly, Omonkhua and Onoagbe (2012) reported that the aqueous extract of I. gabonensis stem bark had hypoglycaemic and anti-obesity effects on normal rabbits. Previous studies have revealed that the leaf and stem bark of I. gabonensis have antimicrobial activities (Fadare and Ajaiyeoba 2008; Nworie et al. 2016). I. gabonensis stem bark also contains an excellent reservoir of bioactive compounds (phytochemicals) with the ability to scavenge deleterious oxidants (Otitolaiye et al. 2023).

The binding interaction of phytochemicals isolated from therapeutic plants with enzymes/proteins associated with diabetes plays a crucial role in the development of antidiabetic drugs (Klebe 2013). Therefore, this investigation was designed to determine the ability of the aqueous and ethanol extracts of I. gabonensis to inhibit α-amylase, α-glucosidase, pancreatic lipase, and protein glycation activities utilizing in vitro techniques. This study also determined the molecular interaction of compounds identified from I. gabonensis stem bark with α-amylase, α-glucosidase, and pancreatic lipase using a computational approach to assess the extracts’ ability to ameliorate multiple aspects of diabetes pathogenesis.

Materials and methods

Sample collection and extract preparation

Fresh stem bark of Irvingia gabonensis was collected in October from a farm in Akungba-Akoko, Ondo-State, Nigeria (Coordinates: 7°28’10"N, 5°44’10"E). The plant was identified at the Forest Research Institute, Ibadan, Oyo-State with a sample deposited at the herbarium (ID No, FHI 112,492). The stem bark was washed, shade dried, and pulverized. The air-dried powdered sample was divided into two portions. The first part was exhaustively extracted, via maceration, with absolute ethanol while the second portion was exhaustively extracted with distilled water. The extracts obtained were then separately freeze-dried and quantified.

Inhibitory assays

α-Amylase inhibitory assay

This assay was done according to the method of Sigma-Aldrich (2014) but with slight modification. The α-amylase solution was prepared in cold sodium phosphate buffer − 20 mM, pH 6.9 (1 Unit/ml of α-amylase). Acarbose standard solution, at a concentration of 1 mg/ml, was then serially diluted to concentrations of 2.5, 5, 10, 20, 40, 80, 160, 320, and 640 µg/ml. The aqueous and ethanol extracts were also prepared using the same concentrations. To the extracts or standard (250 µl) was added 1% starch solution (250 µl) and 1U/ml amylase (250 µl). These were mixed and allowed to incubate for 3 min at room temperature. Afterwards, 250 µl of 3,5-dinitrosalicylic acid (96 mM) was added, and the solution was capped. These were boiled for 15 min in a water bath. To obtain a total volume of 4 ml, 3 ml of distilled water was added when the mixture had reached room temperature. After mixing by inversion, the solution’s absorbance was taken at 540 nm. The same technique was used to prepare a reference control, but distilled water was used in place of the extract. Each assay was carried out three times.

The percentage inhibition was calculated using the formula:

graphic file with name M1.gif

α-Glucosidase inhibitory assay

The inhibitory effect of α-glucosidase was determined according to the method of Elya et al. (2012). The extracts’ stock solutions (1 mg/ml) were serially diluted to 12.5, 25, 50, 100, 200, 400, 800, and 1000 µg/ml. The standard i.e. positive control was acarbose and it was prepared in the same concentration as the extracts. One hundred and twenty ml of 10 mM paranitrophenyl glucoside and 200 µl of 67 mM sodium phosphate buffer, pH 6.8, were added to 40 µl of the extract/standard. These were mixed and incubated at 37oC for 15 min. Then, 40 µl of α-glucosidase enzyme (0.1 U/ml) was added, and the mixture was once more incubated for 15 min at 37oC. The hydrolysis of α-D-glucopyranoside to p-nitrophenol was measured at 405 nm after the addition of 800 µl of 200 mM sodium carbonate was used to stop the entire reaction. The same procedure was used to prepare a reference control, but distilled water was used in place of the extract. Each assay was carried out three times; the IC50 and the percentage of α-glucosidase inhibition were calculated.

Pancreatic lipase inhibition assay

For this assay, the procedure described by Chedda et al. (2016) was employed with a few modifications. To prepare the standard solution, 120 mg of orlistat was dissolved in 120 ml of methanol (1 mg/ml). The ethanol extract was made by dissolving 0.02 g of extract in 20 ml of methanol (1 mg/ml), whilst the aqueous extract was made by dissolving 0.02 g in 20 ml of distilled water (1 mg/ml). The standard and extracts were then successively diluted to various concentrations of 12.5, 25, 50, 100, 200, 300, 400, and 500 µg/ml. To prepare the porcine pancreatic lipase enzyme solution, 6 mg of the enzyme was dissolved in 10 ml of Tris buffer solution (100 mM, pH 7.4). Para-nitrophenyl butyrate (pNPB) was employed as the substrate, 8.403 µl of pNPB stock solution in a vial was made up to 10 ml with acetonitrile. All reagents were freshly prepared. A total of 500 µl of porcine pancreatic lipase enzyme solution, 1000 µl of Tris buffer solution, and 250 µl of pNPB solution were added to 250 µl of standard or extracts at various concentrations. The absorbance was measured at 400 nm after 30 min of incubation at 37oC. Each assay was carried out three times. Both the IC50 and the percentage of pancreatic lipase inhibition were calculated.

Protein glycation inhibition assay

For this analysis, aqueous and ethanol extract solutions (1 mg/ml) were prepared and serially diluted into concentrations of 0.78, 1.56, 3.125, 6.25, 12.5, 25, 50 and 100 µg/ml. One ml of protein solution (containing 1 mg/ml of gelatin) and 1 ml of glyceraldehyde (20 mg/ml) were added to 60 µl of extracts and standard (Siahbalaei et al. 2020). These were sealed and kept at room temperature for 24 h in the dark. The fluorescence of the various reaction solutions was measured with a spectrofluorimeter at 370 nm (excitation) and 440 nm (emission). As a benchmark, aminoguanidine (10 mg/ml) was employed. Each assay was carried out three times. The IC50 and the percentage of protein glycation inhibition were subsequently calculated.

graphic file with name M2.gif
graphic file with name M4.gif

High-performance liquid chromatography (HPLC)

Using a reversed-phase technique as previously described by Elekofehinti et al. (2013), phytochemicals in the extracts were detected using Shimadzu Series HPLC coupled with a diode array detector (DAD). In this case, the mobile phase used was acetonitrile and water. A standard form of the analytes’ profile was initially injected into the HPLC, and this produced a chromatogram window with a specific peak region and peak profile. Then 5 µl of the sample was injected at 2 ml/min flow rate into the HPLC column to obtain a corresponding peak area and peak profile in a chromatogram. After that, the peak area of the sample and the standard were analyzed in relation to the standard’s concentration, to determine the sample’s concentration.

Molecular docking studies

Schrödinger suite software, version 2018-1 (Schrödinger, LLC, New York, NY) algorithm was used for computational analysis. Following the procedure outlined above, compounds found from I. gabonensis stem bark extracts using HPLC-DAD, were downloaded from the PubMed compound database in 2D format, and optimized into 3D using the Marvinsketch suite and MMFF94 force field (Elekofehinti et al. 2020a). The X-ray crystal structures of α-glucosidase (PDB ID: 2QMJ) and α-amylase (PDB ID: 2QV4) were obtained from the Protein Data Bank of the Research Collaboratory for Structural Bioinformatics (http://www.rcsb.org). The sequence with accession number AAA99053.1 was utilized in homology modeling to obtain the 3D structure of human pancreatic lipase on the SWISmodel server (Elekofehinti et al. 2019).

Molecular docking calculations using glide

Ligand preparation

For docking, compounds derived from I. gabonensis stem bark extracts were prepared using the LigPrep module in the Maestro and Schrödinger suites. The appropriate chiralities in low-energy 3D structures were generated. At a physiological pH of 7.2 ± 0.2, the probable ionization states for each ligand structure were computed. The ligands were minimized using an OPLS3 force field, and all other options were left at their default settings.

Protein preparation

The Protein Preparation Wizard in Maestro-v11.2 was used to process the crystal structures of α-amylase, α-glucosidase, and the homology model of lipase, as described by Iwaloye et al. (2020). Bond orders were assigned in the pre-process tab, and any hydrogen atoms that might have been missing from the PBD structures were added. The RMSD of heavy atoms was fixed at 0.3Å, and full energetic optimization was carried out using the OPLS3 force field in the final refinement stage.

Molecular docking study

Using the “receptor grid generation” option in the glidev7.5 program of Maestro-v11.5, the receptor grid was generated around the binding site of α-amylase, α-glucosidase, and lipase. The extra precision (XP) workflow module of the Schrödinger suite was then used with default parameters to dock the prepared ligands into the receptor grid (Elekofehinti et al. 2020b). The advanced scoring algorithm used by XP docking eliminates false positives and penalizes ligands that could not match the receptor well. Using the docking scores, Glide Gscores, Glide energies, and Glide emodels, the docking calculation’s outcomes were quantified.

Docking pose analysis

The pose was analyzed using the pose-viewing mode of the Maestrov11.5. The H-bond interactions, as well as the bad and ugly Van der Waals contacts to the receptors, were visualized using default settings.

Molecular dynamics (MD) simulation

Schrodinger LLC’s Desmond package was utilized to perform all-atom classical MD simulations with the hit molecules in top-docking poses with α-amylase, α-glucosidase, and lipase. The simulations ran for a total of one hundred nanoseconds. Using static views of the molecular docking results, the position of ligands within the protein binding pocket was seen. The Protein Preparation Wizard tool in Maestro was used for the first preprocessing of the protein-ligand complex. Complex optimization and minimization using default settings were required for this. After that, Maestro’s System Builder tool was used to get every system ready. The solvent model selected was Transferable Intermolecular Interaction Potential 3 Points (TIP3P), and an orthorhombic box measuring 10 × 10 × 10 Å was used. The models were supplemented with counter ions (Na+ or Cl−) to attain electrical neutrality. Protein-ligand complexes were simulated using the OPLS_2005 force field and RESPA integrator parameters. Previous to the simulation, the models underwent a relaxing procedure. The NPT ensemble was utilized at a temperature of 300 K and a pressure of 1 atm for all molecular dynamic simulations. To replicate physiological circumstances, 1.661 mM NaCl was added. The root mean square deviation (RMSD) of the protein and ligand over time was used to gauge the stability of the simulations, and trajectories were stored for use in further analysis at intervals of 100 ps. Furthermore, the protein-ligand interactions, radius of gyration, intramolecular hydrogen bond, protein RMSF, protein secondary structure elements, ligand RMSF, molecular surface area, solvent accessible surface area, and polar surface area were identified.

Statistical analysis

All tests were done in triplicates and results were expressed as means ± standard error of mean. The mean was calculated using GraphPad 7.0 software while the IC50 was calculated using the logarithmic or linear graph on Microsoft Excel software where applicable.

Results and discussion

The available drugs for the treatment of diabetes (e.g. insulin, metformin, and acarbose) have side effects such as weight gain, diarrhoea, hypoglycemia, headache, and respiratory distress; and they are expensive (Ezuruike and Prieto 2014; Miller et al. 2014; Chaudhury et al. 2017). This has increased the interest in exploring local medicinal plants which are believed to have little or no side effects. The ability of medicinal plants to treat different targets in the pathology of diabetes presents a strong basis for their assessment as viable therapeutic alternatives. This study investigated the ability of aqueous and ethanol extract of I. gabonensis stem bark to target three aspects of diabetes dysfunction i.e. hyperglycaemia, dyslipidaemia, and protein glycation; all of which lead to long-term diabetic complications.

In vitro inhibitory activities of Irvingia gabonensis stem bark extracts

In this study, the ethanol and aqueous extract of I. gabonensis stem bark gave a total yield of 2.89% and 1.78% respectively. The antidiabetic potential of the extracts was further investigated by assessing the α-amylase and α-glucosidase inhibitory activities. From Fig. 1A, the α-amylase inhibitory activity of the aqueous extract was higher than that of the ethanol extract. In addition, the inhibitory activities of the aqueous extracts compete favourably with acarbose against α-amylase based on the IC50 values as shown in Table 1. As seen in Fig. 1B, both aqueous and ethanol extracts of I. gabonensis had higher inhibitory activities against α-glucosidase compared to acarbose which was used as the standard. Their respective IC50 values were – acarbose (1364.53 µg/ml), aqueous extract (25.72 µg/ml), and ethanol extract (3.05 µg/ml) (Table 1).

Fig. 1.

Fig. 1

Open in a new tab

The inhibitory effects of the aqueous and ethanol extracts of Irvingia gabonensis stem bark extracts and standards on the activity of [A] α-amylase, [B] α-glucosidase, [C] pancreatic lipase, [D] protein glycation. All tests were done in triplicates

Table 1.

The IC50 values of aqueous and ethanol extracts of Irvingia gabonensis stem bark compared with the respective standards

Inhibitory assay Standard Standard IC50 (µg/ml) Aqueous extract IC50 (µg/ml) Ethanol extract IC50 (µg/ml)
α-Amylase Acarbose 2.93 11.47 19.88
α-Glucosidase Acarbose 1364.53 25.72 3.05
Pancreatic lipase Orlistat 12.03 332.42 32.85
Protein glycation Aminoguanidine 8.24 0.018 0.0014
Open in a new tab

Also, the inhibitory activities of the extracts on pancreatic lipase was determined and compared with orlistat, a standard inhibitor of pancreatic lipase (Fig. 1C). Orlistat (12.03 µg/ml) and the ethanol extract (32.85 µg/ml) had higher inhibitory potential (lower IC50 values) against pancreatic lipase compared to the aqueous extract (332.42 µg/ml) (Table 1). The percentage inhibition increased with increasing concentration.

The aqueous and ethanol extracts of I. gabonensis stem bark showed significantly higher inhibitory activities against protein glycation with IC50 values of 0.018 µg/ml and 0.0014 µg/ml respectively compared with the aminoguanidine standard (8.24 µg/ml) (Fig. 1D; Table 1).

Indeed, the inhibitory effect of the aqueous extract of I. gabonensis stem bark (IC50 value of 11.47 µg/ml) against α-amylase activity is similar to the inhibitory effect of pure ursolic acid (IC50 value of 6.7 µg/ml) isolated from Syzygium cumini leaves (Poongunran et al. 2017). Compared to the standard acarbose, the extracts, especially the ethanol extract, had a superior inhibitory effect against α-glucosidase activity. This supports previous studies that acarbose has little or no inhibitory activity against α-glucosidase (Oki et al. 1999; Shai et al. 2010; Kim et al. 2004; Schafer and Hogger 2007). The results from this study also showed that the ethanol extract of I. gabonensis stem bark (IC50 3.05 µg/ml) had similar inhibition activity against α-glucosidase, with the ethanol extracts of Garcinia daedalanthera leaf, Amaracarpus pubescens leaf, Antidesma celebicum leaf, and Willughbeia tenuiflora leaf with IC50 values of 2.33 µg/ml, 3.64 µg/ml, 2.34 µg/ml, and 8.16 µg/ml respectively (Elya et al. 2012). α-Amylase and α-glucosidase are involved in the breakdown of the glycosidic bonds, α1–4 and α1–6 linkages respectively, resulting in the release of glucose and maltose before being absorbed into the bloodstream. The action of these enzymes allows a rapid increase in blood glucose after a meal (postprandial hyperglycemia) which limits overall glycemic control in diabetes. The inhibition of these enzymes would result in delayed carbohydrate digestion and subsequently reduce glucose absorption which will eventually reduce postprandial hyperglycemia (Elya et al. 2015; Shimabukuro et al. 2006). This study showed that both aqueous and ethanol extracts of I. gabonensis stem bark possess glucose-lowering effects by controlling the influx of glucose into the bloodstream through their inhibitory potential on α-amylase and α-glucosidase activities. Studies have shown that continuous elevation of blood glucose (hyperglycemia) can result in diabetes complications such as nephropathy, neuropathy, retinopathy, and stroke (Kitada et al. 2010).

The ethanol extract of I. gabonensis stem bark had higher pancreatic lipase inhibitory potential with IC50 of 32.85 µg/ml compared with the aqueous extract with IC50 of 332.42 µg/ml (Table 1) while the standard drug, orlistat, had the highest inhibitory activity with IC50 of 12.03 µg/ml. Pancreatic lipase is involved in the hydrolysis of triglycerides into fatty acids and glycerol for absorption. Elevated levels of fatty acids and triglycerides in the blood contribute to obesity and diabetes (Champe et al. 2008). This study showed that the aqueous and ethanol extracts of I. gabonensis stem bark can help in slowing down the digestion and absorption of triacylglycerol by inhibiting the action of pancreatic lipase and subsequently lowering elevated lipid levels, characteristics of diabetes mellitus. The inhibitory potential of the ethanol extracts (32.85 µg/ml) of I. gabonensis stem bark against pancreatic lipase compares favourably with the aqueous extracts of Vitis vinifera and Rhus coriaria with IC50 values of 14.13 µg/ml and 19.95 µg/ml respectively (Jaradat et al. 2017).

This study has also shown that aqueous and ethanol extracts of I. gabonensis stem bark are very effective in inhibiting protein glycation with IC50 values of 0.018 µg/ml and 0.0014 µg/ml respectively. Other studies on protein glycation inhibition report alcohol extracts of Lawsonia inermis leaf with an IC50 value of 82.06 µg/ml (Sultana et al. 2008), and Petalostigma banksii root with an IC50 value of 34.49 µg/ml (Deo et al. 2016). Prior studies have revealed that protein glycation is involved in the progression of diseased conditions such as diabetes, arteriosclerosis, and kidney diseases (Siahbalaei et al. 2020). Studies have also shown that prolonged accumulation of advanced glycation end products (AGEs) can cause damage to the kidneys and promote inflammation thereby increasing the patient’s risk of diabetes complications (Alam et al. 2019; Singh et al. 2014). The results of this study show that I. gabonensis stem bark extracts have great potential of ameliorating diabetes complications via inhibition of protein glycation.

HPLC results of Irvingia gabonensis stem bark

The use of high-performance liquid chromatography technique to identify the chemical constituents of I. gabonensis stem bark extracts revealed the presence of bioactive chemicals which are presented in Tables 2 and 3. The results of the aqueous extract showed that kaempferol, quercetin, and cinnamic acid were the most abundant compounds (Fig. 2A), with peak area of 7534.34%, 1137.56%, and 1134.88% respectively (Table 2). Likewise, the results for the ethanol extract of this plant revealed kaempferol (7887.52%), cinnamic acid (1635.72%) and quercetin (1563.76%) as the most abundant compounds (Table 3; Fig. 3B).

Table 2.

Bioactive compounds identified from the aqueous extract of Irvingia gabonensis stem bark using High Performance Liquid Chromatography (HPLC)

Component Retention Area Height Conc (mg/g)
Ellagic acid 1.27 739.18 25.95 1.2625
Methyl ellagic acid 2.48 501.22 17.32 0.8561
Cinnamic acid 2.75 1134.88 18.38 1.9383
Gallic acid 4.45 353.66 6.94 0.604
3-Fredelanone 5.47 65.88 2.57 0.1125
Rhamnetin 6.48 99.26 2.47 0.1695
Lupeol 7.95 193.99 9.69 0.3313
Kaempferol 11.05 7534.34 145.71 12.868
Quercetin 12.17 1137.56 37.73 1.9429
Quercetin 12.80 63.15 11.68 0.1079
Alpha-curcumene 17.62 367.75 6.59 0.6281
Zingiberene 19.69 193.28 7.63 0.3301
Dodecanal 20.23 98.73 3.68 0.1686
Open in a new tab

Table 3.

Bioactive compounds identified from the ethanol extract of Irvingia gabonensis stem bark using High Performance Liquid Chromatography (HPLC)

Component Retention Area Height Conc (mg/g)
Ellagic acid 1.27 109.71 32.23 0.1874
Methyl ellagic acid 2.48 749.22 24.05 1.2796
Cinnamic acid 2.75 1635.72 24.581 2.7938
Gallic acid 4.45 517.28 9.65 0.8835
3-Fredelanone 5.47 123.04 4.134 0.2101
Rhamnetin 6.48 141.88 3.58 0.2423
Lupeol 7.95 212.08 11.19 0.3622
Kaempferol 11.05 7887.52 148.45 13.472
Quercetin 12.17 1563.76 43.87 2.6708
Alpha-curcumene 17.62 547.94 9.23 0.9359
Zingiberene 19.688 238.19 10.88 0.4068
Dodecanal 20.233 177.51 9.69 0.3032
Open in a new tab

Fig. 2.

Fig. 2

Open in a new tab

[A] Chromatogram of Aqueous Extract of Irvingia gabonensis Stem Bark by HPLC. [B] Chromatogram of Ethanol Extract of Irvingia gabonensis Stem Bark by HPLC

Fig. 3.

Fig. 3

Open in a new tab

The 2D and 3D ligand interaction of [A] quercetin, [B] ellagic acid, [C] rhamnetin with amino acid residues within the binding pocket of α-amylase

Kaempferol is a flavonoid with the formular C15H10O6 and molar mass 286.23 g/mol. It plays an important role in protecting the body against physical, chemical, and mechanical injury; lipid peroxidation; and reactive oxygen and nitrogen species (Abo-salem 2014; Alam et al. 2020). In addition, kaempferol has cardiovascular and neuroprotective effects, and anti-microbial and anti-cancer effects (Abo-salem 2014; Silva-dos-Santos et al., 2021; Periferakis et al. 2022; Singh et al. 2022). The presence of quercetin, also a flavonoid, shows the therapeutic importance of this plant. Quercetin (molecular formular, C15H10O7 and molar mass 302.24 g/mol) is a potent radical scavenger and anti-tumor agent. Quercetin is effective against lipid peroxidation, platelet aggregation, inflammatory cascade, aldose reductase, polyol accumulation, and cardiovascular disorder (Williams et al. 2004; Cornish et al. 2007; Kim et al. 2011; Dabeek and Marra 2019). Previous studies have shown that quercetin is effective in lowering blood glucose level by enhancing insulin sensitivity and insulin signaling (Kim et al. 2011); improving renal function in case of diabetic nephropathy (Lai et al. 2012), and reducing oxidative stress (Anjaneyulu and Chopra 2004; Cornish et al. 2007). Cinnamic acid exhibits antioxidant, anticancer, and antibacterial activities (Das et al. 2019; Wille and Berhow 2019). It has a molecular formula of C9H8O2 and a molar mass of 148.16 g/mol, with a honey-like odour (Adedeji et al. 1991). The abundance of these compounds in I. gabonensis stem bark could account for the wide spectrum of biological activities of this plant. Ellagic acid is also one of the compounds identified by HPLC. It is an effective antioxidant against diabetic nephropathy and neuropathy (Baluchnejadmojarad and Roghani 2012) and prevents lipid peroxidation (Sakthivel et al. 2008).

All the compounds identified in the stem bark of I. gabonensis in this study were also found in the fruit pulp and peel extract of the plant (Adeseko et al., 2019). Similarly, previous findings showed that I. gabonensis seeds contain compounds such as kaempferol, ellagic acid, quercetin and rhamnetin (Sun and Chen 2012).

The ability of phytochemicals found in I. gabonensis to bind effectively with α-amylase, α-glucosidase, and pancreatic lipase, may underscore their ability to ameliorate hyperglycaemia and hyperlipidaemia. As such, all the identified compounds were docked for their binding affinities and molecular interactions with α-amylase, α-glucosidase, and pancreatic lipase.

Molecular docking and binding interactions

A specialized software (Shrodinger™) was used to determine the binding interaction of bioactive compounds identified from I. gabonensis stem bark with α-amylase, α-glucosidase, and pancreatic lipase. The docking of these compounds revealed 5 compounds (quercetin, rhamnetin, ellagic acid, kaempferol, and gallic acid) that showed good binding energy with α-amylase (Table 4). Quercetin had the highest binding interaction of -6.638 kcal/mol, followed by rhamnetin with − 6.556 kcal/mol. Although, kaempferol was the most abundant compound in I. gabonensis stem bark; quercetin had a better binding affinity for the enzymes when docked. As such, the molecular interactions of quercetin with α-amylase, α-glucosidase, and pancreatic lipase were investigated.

Table 4.

Binding energy of compounds from Irvingia gabonensis stem bark with α-amylase

S/N Compound name PubChem ID Binding affinity
(Kcal/mol)
1 Quercetin 5,280,343 -6.638
2 Rhamnetin 5,281,691 -6.556
3 Ellagic acid 5,281,855 -5.979
4 Kaempferol 5,280,863 -5.757
5 Gallic acid 370 -5.536
6 Lupeol 259,846 -3.918
7 Cinnamic acid 444,539 -3.673
8 Methyl ellagic acid 5,281,860 -3.202
9 Alpha-curcumene 92,139 -3.093
10 Zingiberene 92,776 -3.074
11 Dodecanal 8194 -0.545
Open in a new tab

The molecular interaction of the compounds identified from I. gabonensis stem bark and the binding pose with α-amylase are shown in Fig. 3. It was observed that the interaction of the compounds with α-amylase is stabilized by hydrogen bonding, pi-pi stacking, and hydrophobic interactions. For instance, the results in Fig. 3 show that quercetin and ellagic acid formed 2 hydrogen bonds each with the α-amylase residue, Asp-197; while the interacting residues, Asp 197 and Asp 300 formed 2 hydrogen bonds with rhamnetin. Figure 3a show the interacting residues of α-amylase with quercetin as Leu-162, Asp-300, Gln-63, Asp-197, and Trp-59. Studies have shown that the binding pose of any compound at the binding site (pocket) of a protein determines the amino acid residues for interaction as well as the binding affinity (Elekofehinti et al. 2021). The interaction the compounds with the amino acids are crucial to the inhibition of α-amylase (Baez-Santos et al. 2014; Rampogu et al. 2018). The compounds showed two intermolecular hydrogen bonds (Fig. 3) with the amino acids at the binding pocket of α-amylase.

The X-ray crystal structure of α-glucosidase was downloaded from the protein data bank and docked with the identified phytochemicals to determine the binding affinity (Table 5). The result show that the binding affinities ranged from − 0.499 to -6.646 kcal/mol, with quercetin having the highest binding affinity value of -6.646 kcal/mol. The interactions of some of the ligands (ellagic acid, quercetin, and rhamnetin) with α-glucosidase in addition to their binding pose at the binding socket of α-glucosidase are shown in Fig. 4. Rhamnetin formed the highest number of hydrogen bonds (5) with α-glucosidase residues; Tyr 605, Asp 543, Asp 443 (2), Asn 327. Ellagic acid on the other hand formed 3 hydrogen bonds (Asp 203, Arg 526, Tyr 605), while quercetin formed 4 hydrogen bonds (Asp 542, Asp 443 (2), Asn 327) with α-glucosidase as seen in Fig. 4 (indicated by the purple arrow).

Table 5.

Binding energy of compounds from Irvingia gabonensis with α-glucosidase

S/N Compound name PubChem ID Binding affinity
(Kcal/mol)
1 Quercetin 5,280,343 -6.646
2 Rhamnetin 5,281,691 -6.527
3 Ellagic acid 5,281,855 -6.164
4 Gallic acid 370 -5.891
5 Kaempferol 5,280,863 -5.627
6 Cinnamic acid 444,539 -5.396
7 Alpha-curcumene 92,139 -3.944
8 Methyl ellagic acid 5,281,860 -3.307
9 Zingiberene 92,776 -2.418
10 Lupeol 259,846 -1.598
11 Dodecanal 8194 -0.499
Open in a new tab

Fig. 4.

Fig. 4

Open in a new tab

The 2D and 3D ligand interaction of [A] ellagic acid, [B] quercetin, [C] rhamnetin with amino acid residues within the binding pocket of α-glucosidase

The result of the binding free energy of compounds derived from I. gabonensis stem bark with pancreatic lipase (Table 6) revealed that 3 compounds showed good binding energy. Quercetin exhibited the highest binding interaction with the value of -5.685 kcal/mol compared to the energy values of rhamnetin − 5.149 kcal/mol, and gallic acid − 5.114 kcal/mol. The 2D diagram of the compounds showing the interactions with amino acid residues such as Gly-410, Asp-408, Tyr-357, Lys-444, and Val-442 of pancreatic lipase can be seen in Fig. 5. The docking studies revealed that a lot of hydrogen bonds, van der Waals, and hydrophobic bonds play a key role in the binding of these compounds with pancreatic lipase. Figure 5 illustrates that Asp 411, Val 409, Asp 408, Cyx 321, Asp-295, Ala-298, Gly-410, Lys-444, Asn 441, Val 442 Tyr-357, and Gln-323 are the interacting residues of pancreatic lipase with the compounds quercetin, gallic acid, methyl ellagic acid, and rhamnetin. Out of the 4 compounds, gallic acid formed 5 hydrogen bonds (Tyr 357, Asp 408, Gly 410, Lys 444), methyl ellagic acid, 3 hydrogen bonds (Tyr 357, Gly 410, Lys 444), and quercetin, 4 hydrogen bonds (Tyr 357, Gly 410, Lys 444) with pancreatic lipase amino acid residues. A binding interaction between the compounds and pancreatic lipase in the body is needed to exert its inhibitory and other pharmacological actions (Klebe 2013; Elekofehinti et al. 2020b).

Table 6.

Binding energy of compounds from Irvingia gabonensis stem bark extracts with pancreatic lipase

S/N Compound name Pubchem ID Binding affinity
(Kcal/mol)
1 Quercetin 5,280,343 -5.685
2 Rhamnetin 5,281,691 -5.149
3 Gallic acid 370 -5.114
4 Methyl ellagic acid 5,281,860 -4.792
5 Kaempferol 5,280,863 -4.173
6 Ellagic acid 5,281,855 -4.069
7 Alpha-curcumene 92,139 -3.352
8 Quercetin 5,280,343 -3.333
9 Rhamnetin 5,281,691 -2.715
10 Zingiberenes 92,776 -2.684
11 Lupeol 259,846 -2.668
12 Gallic acid 370 -2.532
13 3-Friedelanone 16,072,330 -2.280
14 Ellagic acid 5,281,855 -2.220
15 Cinnamic acid 444,539 -1.740
16 Ellagic acid 5,281,855 -1.631
17 Kaempferol 5,280,863 -1.563
18 Dodecanal 8194 2.178
Open in a new tab

Fig. 5.

Fig. 5

Open in a new tab

The 2D ligand interaction of [A] gallic acid, [B] methyl ellagic acid, [C] quercetin, [D] rhamnetin with amino acid residues involved in the inhibition of pancreatic lipase

Molecular dynamics simulation

The Desmond package, created by Schrodinger LLC, was utilized in all-atom classical molecular dynamics (MD) simulations with the top-docking pose of quercetin, the hit molecule, with α-amylase, α-glucosidase, and lipase. The MD simulation was used to examine the dynamic behavior of the identified biomolecular systems across time. It entailed applying physical laws and equations of motion to represent the interactions between individual atoms and molecules. In computer-aided drug design, calculating the root mean square deviation (RMSD) is a conventional method for evaluating structural alterations of a macromolecule during molecular dynamics simulations (Sargsyan et al. 2017). In this study RMSD was used to check the mobility and stability of quercetin, the compound with the highest affinity and good physicochemical properties inside the hydrated active pocket of α-glucosidase (Fig. 6a panel A), α-amylase (Fig. 6a panel B), and lipase (Fig. 6a panel C) during a 100 ns simulation.

Fig. 6a.

Fig. 6a

Open in a new tab

Molecular Dynamics (MD) simulation trajectory plot of α-glucosidase (A), α-amylase (B) and lipase (C) in unbound and quercetin bound complex 100 ns MD simulation. The RMSD of the studied proteins and quercetin, the lead compound complex during 100 ns MD simulation

The RMSD of the protein-ligand interaction (Fig. 6b) was within the acceptable range. The range is between 1 and 4 and from the results, the difference between the apo protein and the one bounded with quercetin is 0.4 Å. To examine the conformational changes of the target proteins combination with quercetin, a 100 ns MD simulation was performed, and the related RMSD values were determined. Structural changes and deviations from the original structure in the three proteins were observed when complexed with quercetin. Quercetin had fewer fluctuations with lipase followed by α-amylase and α-glucosidase. The relative decrease in RMSD value of the complex with respect to apo protein (α-amylase, α-glucosidase, and lipase) indicates the conformational change and increased rigidity and stability of the protein upon binding with quercetin (Kolawole et al. 2020).

Fig. 6b.

Fig. 6b

Open in a new tab

Molecular Dynamics (MD) simulation trajectory plot of α-glucosidase (A), α-amylase (B) and lipase (C) in unbound and quercetin bound complex 100 ns MD simulation. The RMSF of the studied proteins and quercetin, the lead compound complex during 100 ns MD simulation

Root square means fluctuation (RMSF) was deployed in this study to assess the flexibility of the protein structure. In the RMSF plot, an average was made on the total time per residue. A decrease in the flexibility of α-glucosidase when complexed with quercetin was observed as shown (Fig. 6b panel A). Fluctuation was observed in atoms 380 and 830 in α-glucosidase-quercetin complex with respect to apo α-glucosidase. The highest fluctuation was observed with atom 370 in α-amylase-quercetin complex with respect to apo α-amylase (Fig. 6b panel B) while atom 410 had the highest fluctuation in lipase-quercetin complex with respect to apo lipase (Fig. 6b panel C). The fluctuations observed in this study could be due to the presence of loops within the protein molecule.

Conclusion

This study has shown that the aqueous and ethanol extracts of I. gabonensis stem bark have inhibitory effects against α-amylase, α-glucosidase, pancreatic lipase, and protein glycation. The two extracts produced much better α-glucosidase inhibitory activity compared to acarbose (the IC50 for the ethanol extract was lower); while the anti-protein glycation activities of the extracts were significantly higher than standard aminoguanidine. The anti-diabetic, and anti-pancreatic lipase activities observed in this study could be due to the high binding affinities of the identified compounds (quercetin, rhamnetin, ellagic acid, and gallic acid). Therefore, more research is needed to determine the therapeutic effects of I. gabonensis stem bark, as it shows significant promise in the treatment of diabetes and its complications.

Acknowledgements

The study was funded by the National Research Fund (NRF) of the Nigeria Tertiary Education Trust Fund (TETFund) awarded to AO with reference code TETFund/DR&D/CE/NRF/STI/28/VOL1.

Author contributions

A.O. – funding, conception, experimentation (supervised all experiments), data analysis, manuscript writing, and manuscript revision. C.O. – experimentation (in vitro analysis), data analysis, manuscript writing, and manuscript revision. B.A. – experimentation (in vitro analysis), data analysis, and manuscript revision. O.E. – experimentation (in silico analysis), data analysis, and manuscript revision. E.O. - funding, conception, experimentation (supervised in vitro experiments), data analysis, and manuscript revision. I.O. - funding, conception, reviewed the experimental protocol, and manuscript revision. F.O. - funding, conception, reviewed the experimental protocol, and manuscript revision.

Declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

All authors declare that there is no conflict of interest.

Ethical approval

Ethical approval to conduct this study was obtained from the Ethics Committee, Faculty of Pharmacy, University of Benin, Nigeria with the reference number - EC/FP/019/19.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Abo-Salem OM. Kaempferol attenuates the development of diabetic neuropathic pain in mice: possible anti-inflammatory and anti-oxidant mechanisms. Maced J Med Sci. 2014;2:424–430. doi: 10.3889/oamjms.2014.073. [DOI] [Google Scholar]
  2. Adamson I, Okafor C, Abu-Bakare A. A supplement of Dikanut (Irvingia gabonesis) improves treatment of type II diabetics. West Afr J Med. 1990;9(2):108–115. [PubMed] [Google Scholar]
  3. Adedeji J, Hartman TG, Rosen RT, Ho CT. Free and glycosidically bound aroma compounds in hogplum (Spondias Mombias L. J Agric Food Chem. 1991;39(8):1494–1497. doi: 10.1021/jf00008a025. [DOI] [Google Scholar]
  4. Adeseko CJ, Sanni DM, Salawu SO, Kade IJ, Fatoki TH. HPLC-UV standard phenolic constituents of African bush mango (Irvingia gabonensis) and molecular docking on polyphenol oxidase. J Appl Life Sci Int. 2019;22(1):1–11. doi: 10.9734/JALSI/2019/v22i130119. [DOI] [Google Scholar]
  5. Alam F, Shafique Z, Amjad ST, Bin-Asad MHH. Enzymes inhibitors from natural sources with antidiabetic activity: a review. Phytother Res. 2019;33:41–54. doi: 10.1002/ptr.6211. [DOI] [PubMed] [Google Scholar]
  6. Alam W, Khan H, Shal MA, Cauli O, Saso L. Kaempferol as a dietary anti-inflammatory agent. Curr Therapeutic Standing Molecules. 2020;25(18):4073. doi: 10.3390/molecules25184073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Alavi P, Yousefi R, Amirghofran S, Karbalaei-Heidari HR, Yousefi R. Structural analysis and aggregation propensity of reduced and non-reduced glycated insulin adducts. Appl Biochem Biotechnol. 2013;170:623–638. doi: 10.1007/s12010-013-0207-1. [DOI] [PubMed] [Google Scholar]
  8. Amao OS, Nwaiwu OO, Olusanya AW, Arikawe AP. Prescription pattern of antidiabetic drugs in a private medical facility in Lagos. Afr J Online. 2018;73:5–6. [Google Scholar]
  9. Anguizola J, Matsuda R, Barnaby OS, Hoy K, Wa C, DeBolt E. Glycation of human serum albumin. Clin Chim Act. 2013;425:64–76. doi: 10.1016/j.cca.2013.07.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Anjaneyulu M, Chopra K. Quercetin, an antioxidant bioflavonoid, attenuates diabetic nephropathy in rats. Clin Exp Pharmacol Physiol. 2004;31:244–248. doi: 10.1111/j.1440-1681.2004.03982.x. [DOI] [PubMed] [Google Scholar]
  11. Arora M, Singh U. Molecular mechanisms in the pathogenesis of diabetic nephropathy: an update. Vascul Pharmacol. 2013;58:259–271. doi: 10.1016/j.vph.2013.01.001. [DOI] [PubMed] [Google Scholar]
  12. Baez-Santos YM, Barraza SJ, Wilson MW, Agius MP, Mielech AM, et al. X-ray structural and biological evaluation of a series of potent and highly selective inhibitors of human coronavirus papain-like proteases. J Med Chem. 2014;57:2393–2412. doi: 10.1021/jm401712t. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Baluchnejadmojarad T, Roghani M. Chronic oral epigallocatechin gallate alleviates streptozotocin-induced diabetic neuropathic hyperalgesia in rat: involvement of oxidative stress. Iran J Pharm Res. 2012;11(4):1243–1253. [PMC free article] [PubMed] [Google Scholar]
  14. Champe PC, Harvey RA, Ferrier DR (2008) Lippincott’s illustrated reviews: biochemistry. Fourth Edition. Wolters Kluwer Publisher, New Delhi, India. Pp 337–348
  15. Chandra K, Singh P, Dwivedi S, Jain SK. Diabetes mellitus and oxidative stress: a co-relative and therapeutic approach. J Clin Diagn Res. 2019;13:BE07–BE12. doi: 10.7860/JCDR/2019/40628.12878. [DOI] [Google Scholar]
  16. Chaudhury A, Duvoor C, Dendi VSR, Kraleti S, Chada A, Ravilla R, Marco A, Shekhawat NS, Montales MT, Kuriakose K, Sasapu A, Beebe A, Patil N, Musham CK, Lohani GP, Mirza W. Clinical review of antidiabetic drugs: implications for type 2 diabetes mellitus management. Front Endocrinol (Lausanne) 2017;8:6. doi: 10.3389/fendo.2017.00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chedda U, Kaikini A, Bagle S, Seervi M, Sathaye S. In-vitro pancreatic lipase inhibition potential of commonly used Indian spices. IOSR J Pharm. 2016;6(10):10–13. [Google Scholar]
  18. Cornish KM, Williamson G, Sanderson J. Quercetin metabolism in the lens: role in inhibition of hydrogen peroxide induced cataract. Free Radic Biol Med. 2007;33:63–70. doi: 10.1016/S0891-5849(02)00843-2. [DOI] [PubMed] [Google Scholar]
  19. Dabeek WM, Marra MV. Dietary quercetin and kaempferol: bioavailability and potential cardiovascular-related bioactivity in humans. Nutrients. 2019;11(10):2288. doi: 10.3390/nu11102288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Das B, Goud VV, Das C. Phenolic compounds as functional ingredients in beverages. Sci Beverages. 2019;14:285–323. [Google Scholar]
  21. Deo P, Hewawasan E, Karakoulakis A, Claudie DJ, Nelson R, Simpson BS, Smith NM, Semple SJ. Invitro inhibitory activities of selected Australian medicinal plant extracts against protein glycation, angiotensin converting enzyme (ACE) and digestive enzymes linked to type II diabetes. BMC Complement Altern Med. 2016;16(1):435. doi: 10.1186/s12906-016-1421-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Dhodi JB, Mestry SN, Juvekar AR. Diabetic nephropathy- genesis prevention and treatment. Ind J Pharm Sci. 2014;6(9):42–47. [Google Scholar]
  23. Elekofehinti OO, Kamdem JP, Boligon AA, Athayde ML, Lopes SR, Waczyk EP, Kade IJ, Adanlawo IG, Rocha TR. African eggplant (Solanum anguivi Lam.) Fruit with bioactive polyphenolic compounds exerts in vitro antioxidant properties and inhibits Ca2+-induced mitochondrial swelling. Asian Pac J Trop Biomed. 2013;3(10):757–766. doi: 10.1016/S2221-1691(13)60152-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Elekofehinti OO, Lawal AO, Ejelonu OC, Jeff-Agboola YA, Famusiwa CD, Adanlowo IG. In silico antimicrobial properties of saponins against food pathogens and spoilage microorganisms. Nig J Pure Appl Sci. 2019;32(2):3436–3446. [Google Scholar]
  25. Elekofehinti OO, Onunkun AT, Olaleye TM. Cymbopogon citratus (DC) stapf mitigates ER-stress induced by streptozotocin in rats via down regulation of GRP78 and up-regulation of Nrf2 signaling. J Ethnopharm. 2020 doi: 10.1016/j.jep.2020.113130. [DOI] [PubMed] [Google Scholar]
  26. Elekofehinti OO, Iwaloye O, Josiah SS, Lawal AO, Akinjiyan MO, Ariyo EO. Molecular docking studies, molecular dynamics and ADME/tox reveal therapeutic potentials of STOCK1N-69160 against papain-like protease of SARS-CoV-2. Mol Diversity. 2020 doi: 10.1007/s11030-020-10151-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Elekofehinti OO, Iwaloye O, Molehin OR, Famusiwa CD. Identification of lead compounds from large natural product library targeting 3 C-like protease of SARS-CoV-2 using E-pharmacophore modeling, QSAR and molecular dynamics simulation. Insilico Pharmacol. 2021;9:49. doi: 10.1007/s40203-021-00109-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Elya B, Basah K, Munim A, Yuliastuti W, Bangun A, Septiana EK (2012) Screening of alpha glucosidase inhibitory activity from some plants of Apocynaceae, Clusiaceae, Euphorbiaceae, and Rubiaceae. J Biomed Biotechnol Article ID 281078. 10.1155/2012/281078 [DOI] [PMC free article] [PubMed]
  29. Elya B, Handayani R, Sauriasari R, Azizahwati, Hasyyati US, Permana IT, Permatasan YI. Antidiabetic activity and phytochemical screening of extracts from Indonesian plants by inhibition of alpha amylase, alpha glucosidase and dipeptidyl peptidase IV. Pak J Biol Sci. 2015;18(6):279–284. doi: 10.3923/pjbs.2015.279.284. [DOI] [Google Scholar]
  30. Enwere OO, Salako BL, Falade CO. Prescription and cost consideration at a Diabetic Clinic in Ibadan, Nigeria: a Report. Annals Ib Postgrad Med. 2006;4:35–39. [Google Scholar]
  31. Ezuruike UF, Prieto JM. The use of plants in the traditional management of diabetes in Nigeria: pharmacological and toxicological considerations. J Ethnopharm. 2014;155:857–924. doi: 10.1016/j.jep.2014.05.055. [DOI] [PubMed] [Google Scholar]
  32. Fadare DA, Ajaiyeoba EO. Phytochemical and antimicrobial activities of the wild mango-irvingia gabonensis extracts and fractions. Afr J Med Med Sci. 2008;37(4):119–124. [PubMed] [Google Scholar]
  33. Fishman SL, Sonmez H, Basman C, Singh V, Poretsky L. The role of advanced glycated endproducts in the development of coronary artery disease in patients with and without diabetes mellitus: a review. Mol Med. 2018;24(1):59. doi: 10.1186/s10020-018-0060-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hossain MS, Sokeng S, Shoeb M, Hassan K, Mosihuzzaman M, Nahar N, Ali L, Rokeya B. Hypoglycemic effect of Irvingia gabonensis in type 2 Diabetic Long-Evans rat. Dhaka Univ J Pharm Sci. 2012;11(1):19–24. doi: 10.3329/dujps.v11i1.12482. [DOI] [Google Scholar]
  35. Iwaloye O, Elekofehinti OO, Babatomiwa K, Fadipe TM. In-silico molecular studies of selected compounds as novel inhibitors for phosphodiesterase-5 (PDE5) in the management of erectile dysfunction. J Comput Sci Syst Biol. 2020;13:307. doi: 10.37421/jcsb.2020.13.307. [DOI] [Google Scholar]
  36. Jaradat N, Zaid AN, Hussein F, Zaqzouq M, Aljammal H, Ayesh O. Anti-lipase potential of the organic and aqueous extracts of ten traditional edible and medicinal plants in Palestine; a comparison study with orlistat. Medicines. 2017;4(89):1–13. doi: 10.3390/medicines4040089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kim YM, Wang MH, Rhee HI. A novel alpha-glucosidase inhibitor from pine bark. Carbohydr Res. 2004;339(3):715–717. doi: 10.1016/j.carres.2003.11.005. [DOI] [PubMed] [Google Scholar]
  38. Kim JH, Kang MJ, Choi HN, Jeong SM. Quercetin attenuates fasting and postprandial hyperglycemia in animal models of diabetic mellitus. Nutr Res Pract. 2011;5:107–111. doi: 10.4162/nrp.2011.5.2.107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kitada M, Zhang Z, Mima A, King G. Molecular mechanisms of diabetic vascular complications. J Diabetes Invest. 2010;1(3):77–89. doi: 10.1111/j.2040-1124.2010.00018.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Klebe G. Protein-ligand interactions as the basis for drug action. In: Klebe G, editor. Drug Design. Berlin, Heidelberg: Springer; 2013. pp. 61–88. [Google Scholar]
  41. Kolawole AO, Kolawole AN, Olofinsan KA, Elekofehinti OO. Kolaflavonone of Kolaviron selectively binds to subdomain 1B of human serum albumin: Spectroscopic and molecular docking evidences. Comput Toxicol. 2020;13:100118. doi: 10.1016/j.comtox.2020.100118. [DOI] [Google Scholar]
  42. Lai PB, Zhang L, Yang LY. Quercetin ameliorates diabetic nephropathy by reducing expressions of transforming growth factor-β1 and connective tissue growth factor in streptozotocin induced diabetic rats. Ren Fail. 2012;34(1):83–87. doi: 10.3109/0886022X.2011.623564. [DOI] [PubMed] [Google Scholar]
  43. Lee P, Wu X. Modifications of human serum albumin and their binding effect. Curr Pharm Design. 2015;21(14):1862–1865. doi: 10.2174/1381612821666150302115025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mahomoodally MF. Traditional medicine in Africa: an appraisal of ten potent African medicinal plants. Evid Based Complement Altern Med Article ID. 2013;–617459:14. doi: 10.1155/2013/617459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Matos L, Nzikou JM, Matouba E, Pandzou-Yembe VN, Mapepoulou TG, Linder M, Desobry S. Studies of Irvingia gabonensis seeds kernels: oil technological applications. Pak J Nutr. 2009;8:151–157. doi: 10.3923/pjn.2009.151.157. [DOI] [Google Scholar]
  46. Miller BR, Nguyen H, Hu CJ, Lin C, Nguyen QT. New and emerging drugs and targets for type 2 diabetes: reviewing the evidence. Am Health Drug Benefits. 2014;7(8):452–463. [PMC free article] [PubMed] [Google Scholar]
  47. Ngondi YL, Djiotsa EJ, Fossouo Z, Oben Y. Hypoglycaemic effect of the methanol extract of Irvingia gabonensis seeds on streptozotocin diabetic rats. Afr J Tradit Complement Altern Med. 2006;3(4):74–77. doi: 10.4314/ajtcam.v3i4.31179. [DOI] [Google Scholar]
  48. Nworie O, Orji JO, Ekuma UO, Agah MV, Okoli CS, Nweke MC. Antibacterial activity of the leaf and stem bark of Irvingia gabonensis (Bush mango) against Escherichia coli and Staphylococcus aureus. Global J Pharmacol. 2016;10(1):13–18. [Google Scholar]
  49. Ogbera AO, Ekpebegh C. Diabetes mellitus in Nigeria: the past, present and future. World J Diabetes. 2014;5(6):905–911. doi: 10.4239/wjd.v5.i6.905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Oki T, Matsui O, Osajima Y. Inhibitory effect of alpha-glucosidase inhibitors varies according to its origin. J Agric Food Chem. 1999;47(2):550–553. doi: 10.1021/jf980788t. [DOI] [PubMed] [Google Scholar]
  51. Olaokun OO, McGaw LJ, Eloff JN, Naidoo V. Evaluation of the inhibition of carbohydrate hydrolysing enzymes, antioxidant activity and polyphenolic content of extracts of ten African Ficus species (Moraceae) used traditionally to treat diabetes. BMC Complement Altern Med. 2013;13(1):94–103. doi: 10.1186/1472-6882-13-94. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Omonkhua AA, Onoagbe IO. Effects of long-term oral administration of aqueous extracts of Irvingia gabonensis bark on blood glucose and liver profile of normal rabbits. J Med Plants Res. 2012;6(13):2581–2589. doi: 10.5897/JMPR11.561. [DOI] [Google Scholar]
  53. Omonkhua AA, Onoagbe IO, Fajimeye IA, Adekola MB, Imoru ZA. Long term antidiabetic and antihyperlipidaemic effects of aqueous stem bark extract of Irvingia gabonensis in streptozotocin-induced diabetic rats. Biokemstri. 2014;26(1):1–8. [Google Scholar]
  54. Otitolaiye C, Omonkhua A, Oriakhi K, Okello E, Onoagbe I, Okonofua F. Phytochemical analysis and in vitro antioxidant potential of aqueous and ethanol extracts of Irvingia gabonensis stem bark. Pharmacog Res. 2023;15(2):363–372. doi: 10.5530/pres.15.2.039. [DOI] [Google Scholar]
  55. Perera HKI, Premadasa WKVK, Poongunran J. α-glucosidase and glycation inhibitory effects of Costus speciosus leaves. BMC Complement Altern Med. 2016;16(2):1–9. doi: 10.1186/s12906-015-0982-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Periferakis A, Periferakis K, Badarau IA, Petran EM, Popa DC, Caruntu A, Costache RS, Sheau C, Caruntu C, Costache DO. Kaempferol: antimicrobial properties, sources, clinical and traditional applications. Int J Mol Sci. 2022;23(23):15054. doi: 10.3390/ijms232315054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Poongunran J, Perera HKI, Jayasinghe L, Fernando IT, Sivakanesan R, Araya H, Fujimoto Y. Bioassay-guided fractionation and identification of α-amylase inhibitors from Syzygium cumini leaves. Pharm Biol. 2017;55(1):206–211. doi: 10.1080/13880209.2016.1257031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Rampogu S, Baek A, Zeb A, Lee KW. Exploration for novel inhibitors showing back-to-front approach against VEGFR-2 kinase domain (4AG8) employing molecular docking mechanism and molecular dynamics simulations. BMC Cancer. 2018;18(1):264. doi: 10.1186/s12885-018-4050-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sadowska-Bartosz I, Bartosz G. Prevention of protein glycation by natural compounds. Molecules. 2015;20(2):3309–3334. doi: 10.3390/molecules20023309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sakthivel M, Elanchezhian R, Ramesh E, Isah M, Jesudason CN, Thomas PA. Prevention of selenite-induced catarctogenesis in Wistar rats by the polyphenol, ellagic acid. Exp Eye Res. 2008;86(2):251–259. doi: 10.1016/j.exer.2007.10.016. [DOI] [PubMed] [Google Scholar]
  61. Sargsyan K, Grauffei C, Lim C. How molecular size impacts RMSD applications in molecular dynamics simulations. J Chem Theory. 2017;13(4):1518–1524. doi: 10.1021/acs.jctc.7b00028. [DOI] [PubMed] [Google Scholar]
  62. Schafer A, Hogger P. Oligomeric procyanidins of French maritime pine bark extract (Pycnogenol®) effectively inhibit α-glucosidase. Diabetes Res Clin Pract. 2007;77(1):41–46. doi: 10.1016/j.diabres.2006.10.011. [DOI] [PubMed] [Google Scholar]
  63. Shai LJ, Masoko P, Mokgotho MP, Magano SR, Mogale AM. Yeast alpha glucosidase inhibitory and antioxidant activities of six medicinal plants collected in Phalaborwa, South Africa. South Afr J Bot. 2010;76(3):465–470. doi: 10.1016/j.sajb.2010.03.002. [DOI] [Google Scholar]
  64. Shimabukuro M, Higa N, Chinen I, Yamakawa K, Takasu N. Effects of a single administration of acarbose on postprandial glucose excursion and endothelial dysfunction in type 2 diabetic patients: a randomized crossover study. J Clin Endocrinol Metab. 2006;91(3):837–842. doi: 10.1210/jc.2005-1566. [DOI] [PubMed] [Google Scholar]
  65. Siahbalaei R, Kavoosi G, Shakeri R. In vitro antioxidant and antidiabetic activity of essential oils encapsulated in gelatin-pectin particles against sugar, lipid and protein oxidation and amylase and glucosidase activity. Food Sci Nutr. 2020;8(12):6457–6466. doi: 10.1002/fsn3.1935. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Sigma-Aldrich (2014) Enzymatic assay of α-amylase (EC 3.2.1.1). Sigma-Aldrich Co. LLC. http://www.sigmaaldrich.com/technical-documents/protocols/biology/enzymatic-assay-of-a-amylase.html
  67. Silva-dos-santos J, Goncalves CJP, de-oliveira CP, Ortega MM (2021) The pharmacological action of kaempferol in central nervous system diseases: a review. Front Pharmacol 11.565700 [DOI] [PMC free article] [PubMed]
  68. Singh VP, Bali A, Singh N, Jaggi AS. Advanced glycation end products and diabetic complications. Korean J Physiol Pharmacol. 2014;18(1):1–14. doi: 10.4196/kjpp.2014.18.1.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Singh D, Kumar K, Ahmed S (2022) Natural herbal products for cancer therapy. In Understanding cancer. Academic press. Pp 257–268 ISBN 9780323998833 10.1016/B978-0-323-99883.3.00010-x
  70. Sultana N, Choudhary MI, Khan A. Protein glycation inhibitory activities of Lawsonia inermis and its active principles. J Enzyme Inhib Med Chem. 2008;24(1):257–261. doi: 10.1080/14756360802057500. [DOI] [PubMed] [Google Scholar]
  71. Sun J, Chen P. Ultra high-performance liquid chromatography with highresolution mass spectrometry analysis of African mango (Irvingia gabonensis) seeds, extract and related dietary supplements. J Agric Food Chem. 2012;60:8703–8709. doi: 10.1021/jf302703u. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Welsh KJ, Kirkman MS, Sacks DB. Role of glycated proteins in the diagnosis and management of diabetes: research gaps and future directions. Diabetes Care. 2016;39(8):1299–1306. doi: 10.2337/dc15-2727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. White L, Albernethy K (1996) Guide de la végétation de la réserve de la Lopé. Gabon; ECOFAC Gabon
  74. WHO . World Health Organisation (Ed) WHO traditional medicine strategy 2014–2023. Geneva, Switzerland: WHO; 2013. [Google Scholar]
  75. Wille JJ, Berhow MA. Isolation and elucidation of Antiirritant and Antimicrobial Bioactives Derived from Plant sources and from human sebum. Stud Nat Prod Chem. 2019;62:411–432. doi: 10.1016/B978-0-444-64185-4.00011-3. [DOI] [Google Scholar]
  76. Williams RJ, Spencer JP, Rice-Evans C. Flavonoids: antioxidants or signalling molecules? A review. Free Radic Biol Med. 2004;36(7):838–849. doi: 10.1016/j.freeradbiomed.2004.01.001. [DOI] [PubMed] [Google Scholar]
  77. Yusuff K, Obe O, Joseph B. Adherence to anti-diabetic drug therapy and self-management practices among type-2 diabetics in Nigeria. Pharm World Sci. 2008;30(6):876–883. doi: 10.1007/s11096-008-9243-2. [DOI] [PubMed] [Google Scholar]

Articles from In Silico Pharmacology are provided here courtesy of Springer

RESOURCES