Abstract
Diabetes is a prevalent and serious metabolic disorder affecting millions globally, and it poses extensive health risks due to elevated blood glucose levels. One promising approach for managing diabetes is the inhibition of α-glucosidase, an enzyme that plays a crucial role in carbohydrate metabolism. Targeting α-glucosidase can help delay glucose absorption, thus controlling postprandial blood sugar spikes. Dihydropyrimidones, a core structural class present in various biologically active natural compounds, have been recognized for their diverse therapeutic potential, including anti-diabetic properties. In this study, we evaluated a library of previously synthesized 37 Dihydropyrimidone derivatives to assess their potential as α-glucosidase inhibitors. We identified 34 derivatives with significant inhibitory activity, exhibiting IC50 values in the range of 5.30–56.72 µM. Among these, compounds 2, 4–7, 9–11, 13–16, 31, 32, and 33 demonstrated high potency, with IC50 values below 20 µM; the most active compound, 5, achieved an IC50 of 5.30 µM. A detailed kinetic study on compound 5 revealed a competitive inhibition mode with a Ki value of 16.10 ± 0.0075 µM. Additionally, cytotoxicity assays confirmed that compound 5 is non-toxic to BJ cell lines, underscoring its safety for therapeutic use. The computational studies further supported the inhibitory potential by illustrating key interactions and binding affinities between the Dihydropyrimidone derivatives and the α-glucosidase, highlighting these compounds as promising candidates for diabetes management.
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Introduction
Diabetes is a well-known global health issue and the most prevalent metabolic illness in both developing and developed nations. By 2030, the WHO projects that there will be 366 million cases, up from 171 million in 20001. Type-II diabetes mellitus, which has multiple aetiologies and is caused by chronic hyperglycaemia, insulin resistance, and dyslipidaemia, is frequently linked to metabolic diseases. The primary causes of diabetes mellitus are excessive hepatic glucose production or glucose intolerance1. Additionally, it harms the micro-vascular tissues, leading to complex disorders like retinopathy, nephropathy, and neuropathy. Furthermore, one of the causes of atherosclerosis is postprandial hyperglycaemia2,3. Additionally, it contributes to shorter lifespans and produces certain diabetic complications (micro/macrovascular issues such as ischemic heart disease.
Orally administered antidiabetic drugs have been used either alone or in conjunction with insulin to treat hyperglycaemic individuals4,5,6,7. To reach a normal sugar level, numerous anti-diabetic medications were produced in the previous ten years. To prevent diabetes consequences such polyuria, polydipsia, polyphagia, recurrent weight loss, poor vision, nausea, and skin infections, such interventions are unable to lower plasma glucose levels and achieve normoglycemia. Due to this, there is an urgent need to create efficient oral medications that can restore normal glucose and insulin levels8,9,10,11.
The exo-type glycosidase enzyme α-glucosidase (EC 3.2.1.20), which is found on the brush borders of the small intestine, catalyses the hydrolysis of carbohydrates from the non-reducing end to produce glucose molecules12. One of the approved therapeutic targets for the development of medications to prevent problems brought on by hyperglycaemia is α-glucosidase13. The α-glucosidase inhibitors AGIs have received a lot of attention lately due to their potential clinical applications as antiviral and hyperglycaemia-controlling medications. α-Glucosidase controlled the conversion of polysaccharides to monosaccharides in the colon, which reduced the blood glucose level. Acarbose, voglibose, and miglitol, three commercially available -glucosidase inhibitory anti-diabetic medications, are claimed to produce several side effects, including diarrhoea, constipation, abdominal pain, nausea or vomiting, dizziness, pneumatosis intestinalis, and flatulence14. Therefore, safer α-glucosidase inhibitors are required for use as anti-diabetic medications.
The distinctive and varied motif for drug research, agriculture, and industry is heterocycles. Benzothiazoles are fused bicyclic organosulfur compound in which thiazole ring fused with the benzene ring leads to a class of aromatic compounds presenting an ample collection of biological applications endowed with potent antibacterial15, antiallergic16, antifungal17, anticonvulsant18, anti-inflammatory19, anti-helminthic20, antitubercular21, and anticancer22. In this reference, the benzothiazole skeleton extensively serves as a significant structural motif and part of various medicinally important compounds. Medicinal researchers paid attention towards the pharmacological aptitude of this chemically diverse skeleton when riluzole approved clinically as an anticonvulsant drug23. A few derivatives of benzothiazoles, including N-(6-substituted-1,3-benzothiazol-2-yl) benzenesulf-namide were found to have promising antidiabetic potential in vivo in a NIDDM (non-insulin-dependent diabetes mellitus) rat model24. The hypoglycemic activities of the structurally similar benzothiazoles have been reported in recent years25,26,27.
The literature show the pharmaceutical importance of these dihydropyrimidinone derivatives (Fig. 1). Moreover, Natural and synthetic compounds, alongside traditional medicines, play a crucial role in pharmaceutical development by offering diverse bioactive molecules, enhancing drug discovery, and providing valuable insights into new therapies for treating a wide range of diseases30,31,32,33,34. Therefore, we investigated these compounds for their anti-diabetic capability. The characteristics of pyrimidine derivatives may be helpful in the treatment of diabetes. In addition to playing a variety of roles in biological processes, pyrimidine derivative-containing heterocyclic compounds have important chemical and pharmacological significance in the business. Additionally, the use of pyrimidines conjugated with thiazole nuclei has grown in popularity in clinical and medical settings15. Dihydropyrimidinones (DHPM), heterocyclic derivatives containing a pyrimidine as a cyclic core moiety, have drawn more attention in medicinal chemistry for a long time because it occur as a key scaffold in natural products with significant biological activity35. Moreover, (DHPM) are also reported for their in vitro α-glucosidase and α-amylase activities [1]. Thiazolopyrimidines have become increasingly well-known in recent years due to their excellent performance in pharmaceutical substances as bioavailable CXCR2 antagonists and anti-diabetic medicines, antimicrobial agents, antioxidants, anti-inflammatory agents, antimalarials, and antitubercular activities, antiviral, and anticancer agents36,37,38. Moreover, dihydropyrimidone are also reported for their anti-diabetic potential via targeting they carbohydrates catabolic enzyme α-amylase and α-glucosidase28. In this context we design this study to evaluate these compounds against α-glucosidase.
Results and discussion
General chemistry
Three ingredients were used to create the substituted 4-aryl dihydropyrimidones in a single pot. Urea, methyl acetoacetate, and a variety of aldehydes are combined in a process at 80 to 90 ºC with continual stirring. The catalyst is copper nitrate trihydrate (Scheme 1). The process of heating and stirring was continued until the mixture solidified. Thin layer chromatography was used to assess reaction progress (TLC). After the reaction was finished, the solid product was thoroughly cleaned with distilled water before being recrystallized from ethanol to produce the pure products. 1 H-, 13 C-NMR, EIMS, and HREI-MS were used to characterize the structures of all synthetic compounds24. Moreover, the mechanism of synthesis is described in (Fig. 2).
Description of the synthesis of one-pot” three component dihydropyrimidone derivatives 1–37.
Description of the synthesis mechanism of dihydropyrimidones.
In vitro biological evaluation
As the dihydropyrimidone derivatives are already reported for their anti-diabetic and various other medicinal importance, thus we design this study to explore the therapeutic importance of compounds 1–37. Among them except compounds 19–21 all exhibited potent inhibitory capability with IC50 values ranging 5.30–56.72 µM (Table 1), while comparing with marketed drug acarbose (IC50 = 873.34 ± 1.67 µM). Our findings revealed potent inhibitory capability of these new identified inhibitors. We established a detailed structure activity relationship for all compounds to demonstrate the significance of the attached groups (Fig. 3). For instance, compound 1 with the aryl toluene group substituent resulted into potent inhibition against the key catabolic enzyme of carbohydrates, α-glucosidase with IC50 value 31.16 ± 0.42 µM as compared to acarbose. Compound 2 with 1-ethoxy-4-methylbenzene group exhibited significant increase in the inhibitory capability against α-glucosidase (IC50 = 9.20 ± 0.30 µM). While compound 3 with 1-methoxy-4-methylbenzene moiety displayed decline in the inhibitory capability (IC50 = 26.50 ± 0.63 µM). The effect of methoxy group exhibited an overwhelming inhibition. Like compound 4 with 1-methoxy-2-methylbenzene exhibited potent inhibitory capability (IC50 = 7.34 ± 0.28 µM).
The inhibitory effect was enhanced in compound 5 with 1,4-dimethoxy-2-methylbenzene (IC50 = 5.30 ± 0.29 µM), as compared to compound 4. Compound 6 with the substitution of 1,2-dimethoxy-3-methylbenzene exhibited potent inhibitory activity with IC50 value 8.42 ± 0.36 µM. Similarly compound 7 with 1,2-dimethoxy-4-methylbenzene exhibited potent activity against α-glucosidase 11.31 ± 0.36 µM. In contrast, compound 8 with 2-methoxy-4-methylphenyl acetate exhibited an abrupt decrease in the inhibitory activity with IC50 value 37.62 ± 0.70 µM. Compounds 9 and 10 with 2-fluoro-4-methoxy-1-methylbenzene and 1-fluoro-2-methoxy-4-methylbenzene exhibited almost similar inhibitory effect with IC50 values 15.36 ± 0.56 and 17.28 ± 0.46 µM respectively. Interestingly, compound 11 with 1-bromo-2,3-dimethoxy-5-methylbenzene also exhibited potent activity against α-glucosidase (IC50 = 13.14 ± 0.47 µM. On the other hand, compound 12 with 1-(benzyloxy)-2-methoxy-4-methylbenzene displayed decline in the inhibitory activity with IC50 value 34.69 ± 0.72 µM. Compound 13 with 1,2,3-trimethoxy-5-methylbenzene exhibited high potency with IC50 value 6.14 ± 0.27 µM.
The effect of hydroxyl along with halogens was also evaluated for their inhibitory against α-glucosidase. Compounds 14 and 15 with the addition p-cresol and m-cresol groups resulted into potent activity against α-glucosidase with IC50 values 18.26 ± 0.51 and 20.44 ± 0.38 µM. The inhibitory effect of 2-chloro-4-methylphenol was more favourable and exhibited potent inhibition (IC50 = 14.30 ± 0.27 µM).
Compounds 16 and 17 with the substitution of 1-methylnaphthalene and 2-methylnaphthalene groups displayed decrease in their inhibitory activity against α-glucosidase with IC50 values 44.64 ± 1.10 and 40.25 ± 0.78 µM. Compounds 22–24 with the addition of 2-methylfuran, 2,5-dimethylfuran and 3-methylthiophene exhibited very close inhibitory activity against α-glucosidase with IC50 values 47.30 ± 1.32, 50.33 ± 0.23 and 46.25 ± 0.65 µM. Similarly, compounds 24 and 25 with 2-methylthiophene and 2,5-dimethylthiophene groups displayed almost the same inhibition with IC50 values 42.90 ± 0.56 and 39.62 ± 1.11 µM. The inhibitory effect of halogens exhibited potent inhibitory activity. Compounds 27–29 with 1-chloro-2-methylbenzene, 1,3-dichloro-2-methylbenzene and 1-chloro-4-methylbenzene displayed potent inhibitory activity against α-glucosidase with IC50 values 25.40 ± 0.61, 22.61 ± 0.70 and 27.19 ± 0.46 µM respectively. The addition of 1-bromo-4-methylbenzene in compound 30 exhibited a slight increase in their inhibitory capability with IC50 value 20.17 ± 0.54 µM. The addition of 1-fluoro-2-methylbenzene in compound 31 further improved their inhibitory effect with IC50 value 18.33 ± 0.41 µM. In contrary, the addition of 1-methyl-4-(trifluoromethyl) benzene in compound 32 resulted decrease in their inhibitory capability with IC50 value 26.10 ± 0.56 µM. In compound 33 the addition of 1-methyl-4-nitrobenzene displayed potent inhibitory capability with IC50 value 16.10 ± 0.35 µM. The inhibitory effect of p-cymene in compound 34 displayed decline in the α-glucosidase inhibition with IC50 value 53.19 ± 1.42 µM. Compound 35 with the addition of N, N,4-trimethylaniline exhibited potent inhibitory capability (IC50 = 35.40 ± 0.82 µM). In compounds 36 and 37 the addition of 1-(benzyloxy)-3-methylbenzene and 1-(benzyloxy)-2-methylbenzene groups displayed decline in their inhibitory potential against α-glucosidase with IC50 values 49.36 ± 1.20 and 56.72 ± 1.40 µM respectively. Overall, the different substituents at various positions were involved in the inhibitory capability against α-glucosidase.
The structure-activity relationship (SAR) analysis of dihydropyrimidone derivatives highlights that methoxy substitutions, particularly in para and ortho positions, significantly enhance α-glucosidase inhibitory activity, with compounds like compound 5 with 1,4-dimethoxy-2-methylbenzene (IC50 = 5.30 ± 0.29 µM) showing notable potency. Multi-methoxy configurations, as seen in trimethoxy-substituted compounds, also improve inhibition. Halogenated derivatives (F, Cl, Br) combined with methoxy or hydroxyl groups further increase efficacy, like in compound 11 exemplified by 1-bromo-2,3-dimethoxy-5-methylbenzene (IC50 = 13.14 ± 0.47 µM). In contrast, bulky naphthalene groups decrease activity due to steric hindrance, while electron-withdrawing nitro groups, such as in 1-methyl-4-nitrobenzene, enhanced the inhibitory potential in compound 33 16.10 ± 0.35 µM. Acetate substitution markedly reduces activity, suggesting it disrupts effective enzyme interaction. Together, these findings underscore that methoxy and halogen/hydroxyl groups are favorable for inhibition, while bulkier or acetate groups are less effective.
Structure activity relationship of compounds 1–37.
Kinetic study
To reveal the inhibition mechanism of the investigated inhibitors, we selected the most potent inhibitors for their mechanistic study to insight into their inhibition pattern. The kinetic study of compound 4 exhibited mixed type of inhibition with ki value 5.00 ± 0.016 µM, while compounds 5 and 13 exhibited a competitive type of inhibition with Ki values, 8.37 ± 0.008 and 3.77 ± 0.002 µM respectively (Table 2; Fig. 4). In competitive type of inhibition, the compound binds with the active site residue of the enzyme, hence this type of inhibition increases the Km of enzyme while Vmax of the enzyme remains constant (Fig. 4D & G). On the other hand, in mixed type of inhibition the inhibitor can bind to the active site or allosteric site of the enzyme. Thus, increase or decrease Km, depending on the affinity of the inhibitor while Vmax always decreases due to the binding of inhibitor (Fig. 4A).
Mode of inhibition of α-glucosidase by compound 4, 5 and 13 (A,D,G) Lineweaver-Burk plot of reciprocal of rate of reaction (V) vs. reciprocal of substrate in the absence of (filled rectangle), and in the presence of 16.00 µM (open circle), 8.00 µM (filled circle), and 4.00 µM (open rectangle), of compound 4, 5 and 13. (B,E,H) Secondary replot of Line Weaver-Burk plot between the slopes of each line on Line Weaver-Burk plot vs. different concentrations of compound 4, 5 and 13. (C,F,I) Dixon plot of reciprocal of rate of reaction (V) vs. different concentrations of compound 4, 5 and 13.
Cytotoxicity evaluation against BJ cell line
The cytotoxicity evaluation results against the BJ cell line for all tested compounds are presented in Figs. 5, 6, 7, 8 and 9. Specifically, Fig. 5 illustrates the results for compounds 1–7, Fig. 6 for compounds 8–14, Fig. 7 for compounds 15–21, Fig. 8 for compounds 22–28, and Fig. 9 for compounds 29–34. Three different concentrations were used according to their inhibitory potency against α-glucosidase including, 60, 30 and 15 µM. All compounds exhibited non-cytotoxic effect as at the highest concentration the cell growth percent inhibition is below 25% while at 15 µM the percent inhibition is below 10%. These significant findings insight into the level of safety of these identified inhibitors. As the non-cytotoxic nature of enzyme inhibitors could be a drug candidate for the pharmaceutical applications. Therefore, these inhibitors could be a possible drug candidate for the treatment of diabetes.
Cytotoxicity evaluation results against BJ cell line of compounds 1–7.
Cytotoxicity evaluation results against BJ cell line of compounds 8–14.
Cytotoxicity evaluation results against BJ cell line of compounds 15–21.
Cytotoxicity evaluation results against BJ cell line of compounds 22–28.
Cytotoxicity evaluation results against BJ cell line of compounds 29–34.
Molecular docking
Herein, the molecular docking simulation was employed to rationalize the observed anti-glucosidase activity. The docking results reveal that the docked compounds accommodated well in the binding site of α-glucosidase located at the C-terminal ___domain (Fig. 10). Similarly, the compound 2 displayed the lowest docking score of -7.6 kcal/mol followed by 13, 5, 6, 3 and 4 with the docking scores of -7.3, -7.2, -6.7, -6.4 and − 5.7 kcal/mol, respectively. Insight into the docked pose of compound 2, reveal the significant hydrogen bond contacts and a network of hydrophobic interactions (Fig. 10A). The oxygen of dihydro pyrimidine scaffold mediates three hydrogen bonds with the side chain of Glu304, Asn347 and Asp349 with a bond distance of 3.2 Å, 2.5 Å and 2.2 Å, respectively. Further, the protein-ligand complex was stabilized a π-π interaction with Phe300 and several hydrophobic interactions with Tyr71, Phe177, Arg212, Thr215, Tyr344, His348 and Gln350. Interestingly, compound 3 exhibited a binding mode similar to that of 2, with the same types of interactions (Fig. 10B). Similarly, the oxygen of dihydro pyrimidine scaffold establishes three hydrogen bonds with the side chain of Glu304, Asn347 and Asp349 with a bond distance of 3.2 Å, 2.4 Å and 2.1 Å, respectively while the pyrimidine ring mediate π-π stacking with Phe300. Moreover, the Tyr71, Phe177, Arg212, Thr215, Tyr344, His348 and Gln350 of α-glucosidase were involved in hydrophobic interactions with the compound. In case of compound 4, slightly different binding was observed (Fig. 10C). The dihydro pyrimidine scaffold bend towards the Tyr71, Arg212 and His348 and moves away from the Phe177, Thr215, Phe300 and Glu304. Further, the oxygen of substitution mediates a hydrogen bond with Asn347 with a bond distance of 2.1 Å. The compound 5 also demonstrated a network of significant interactions with the binding site residues of α-glucosidase (Fig. 10D). The dihydro pyrimidine scaffold sandwich between the Phe177, Phe300 and Asp349 and by mediating hydrophobic interactions while a hydrogen bond was observed with the side chain of Tyr71. Other binding site residues including Arg212, Asn347 and His348 were involved in the hydrophobic interaction with the compound. In case of compound 6 and compound 13, the dihydro pyrimidine scaffold stacked between the Tyr71, Phe177, Thr215 and Phe300 and mediates the hydrophobic interactions (Fig. 10E,F). While the substitution on the dihydro pyrimidine scaffold exhibited hydrophobic interactions with the Tyr344, His348, Asn347 and Asp349 of α-glucosidase. In addition, the compound 6 also displayed a hydrogen bond with side chain of Asn347 with a bond distance of 3.3 Å. Overall the results indicated that the different substitutions alter the interactions within the active site, potentially impacting their inhibitory effectiveness.
Molecular interactions between the six most potent compounds and the α-glucosidase. The residues lining the binding site of α-glucosidase are displayed in lime green sticks. A–E) The compounds 3–6 and F) compound 13 are displayed in different colour sticks. The red dotted lines represented the hydrogen bond contacts.
Molecular dynamics simulation
The molecular dynamics (MD) simulation of three of the most potent compounds (4, 5 and 13) with α-glucosidase was carried out to evaluate the time dependant behaviour of the complexes. The compound 4 with the α-glucosidase enzyme revealed a stable interaction between the ligand and protein throughout the simulation period. The protein-ligand RMSD plot showed that the Cα atoms of the protein stabilize around 2.5 Å after an initial equilibration phase, which indicated that the enzyme reached a steady conformation (Fig. 11). The ligand RMSD remained fairly constant around 3.0 Å, which suggested that compound 4 maintain a stable binding position within the binding site of the enzyme. Analysis of protein RMSF highlighted the flexibility of individual residues, with most residues showing low fluctuations (approximately 1 Å), which also indicated stability (Fig. 11). However, certain regions, particularly between residues 250–300 and near the C-terminal end, exhibited higher flexibility, likely due to the presence of loops or solvent-exposed regions. The protein-ligand contact profile revealed significant interactions, with key residues like Asp68, Thr125, Glu214, Arg212, and Asp349 showing strong hydrogen bonding and hydrophobic interactions (Fig. 11). Principal Component Analysis (PCA) further demonstrated that the complex explores distinct conformational states, with clusters indicating stable conformations that the system adopts during the simulation (Fig. 11).
MD simulation results of compound 4 with α-glucosidase enzyme. Protein-Ligand RMSD and RMSF show the stability and flexibility of the complex, while Protein-Ligand Contacts identified the key residues involved in binding. PCA analysis indicated a stable conformational space for the complex.
The MD simulation of compound 5 with the α -glucosidase enzyme revealed a stable and well-defined interaction within the enzyme’s binding site. The protein-ligand RMSD plot showed that the Cα atoms of the protein stabilize around 2.8 Å after the initial equilibration phase, which indicated that the protein reached a consistent conformation (Fig. 12). The ligand RMSD remains relatively steady around 2.5–3.0 Å, which suggested that compound 5 maintains a stable position within the binding site throughout the simulation. The protein RMSF analysis revealed low flexibility across most residues, with slight fluctuations observed primarily in the region between residues 300 and 400 (Fig. 12). In terms of protein-ligand contacts, key residues such as Asp69, Arg212, Asp349, and Asp408 were frequently involved in hydrogen bonding, hydrophobic, and ionic interactions, which highlighted their importance in maintaining the binding stability of compound 5 within the binding site (Fig. 12). The PCA plot further demonstrated that the complex samples distinct conformational states, with clustering patterns which suggested the system transitions between specific substates while remaining within stable conformational regions (Fig. 12).
MD simulation results of compound 5 with α-glucosidase enzyme. RMSD and RMSF analysis revealed stable binding and moderate flexibility. Protein-Ligand Contacts highlighted essential interactions, and PCA showed the complex maintaining stability with minor conformational changes.
The MD simulation results for compound 13 with the α-glucosidase enzyme indicated a stable interaction pattern throughout the simulation. The protein-ligand RMSD plot showed that the Cα atoms of the protein reach a steady RMSD around 3.5 Å after an initial increase, which suggested that the protein’s conformation stabilizes over time (Fig. 13). The ligand RMSD rises slightly higher, stabilizing around 6–7 Å, which suggested that while compound 13 remains bound, it may exhibit some flexibility within the binding site. The protein RMSF analysis revealed notable fluctuations in certain residue regions, especially around residues 200–300 and 500–550, potentially indicating flexible regions (Fig. 13). The protein-ligand contacts plot highlighted that residues like Asp69, Met71, and Arg143 play key roles in stabilizing the ligand, and showed significant interactions such as hydrogen bonds, hydrophobic contacts, and water bridges (Fig. 13). Additionally, the PCA plot suggested that the system explores multiple conformational states but remains within a stable conformational space, with transitions indicated by color gradients across frames (Fig. 13).
MD simulation results of compound 13 with α-glucosidase enzyme. RMSD and RMSF plots indicated stable binding with slight ligand flexibility, while Protein-Ligand Contacts identified critical residues in binding stability. PCA revealed the complex exploring a stable conformational space.
Binding free energy
The binding free energy for compounds 4, 5, and 13 with α-glucosidase enzyme, was calculated using the Prime MM-GBSA module in Schrödinger (Fig. 14). Compound 4 showed a binding energy (ΔGBind) of -23.13 kcal/mol, with a substantial coulombic interaction (ΔGCoulomb = − 20.50 kcal/mol) and favourable van der Waals interactions (ΔGvdW = − 31.34 kcal/mol). The lipophilic (ΔGLipo = − 6.42 kcal/mol) and hydrogen bonding (ΔGHbond = − 2.76 kcal/mol) terms also contributed to the stability of binding. The solvent energy (ΔGSolv = 36.30 kcal/mol) opposes binding but was counterbalanced by other interactions. The ligand strain energy was 2.29 kcal/mol. Compound 5 had a binding energy of -20.67 kcal/mol, with a smaller coulombic contribution (ΔGCoulomb = − 4.58 kcal/mol) but a stronger lipophilic interaction (ΔGLipo = − 12.31 kcal/mol). The van der Waals interactions (ΔGvdW = − 34.94 kcal/mol) significantly supported binding stability. The solvent energy (ΔGSolv = 31.79 kcal/mol) partly opposes binding, while the ligand strain energy was 1.36 kcal/mol. Compound 13 exhibited the strongest binding affinity with a ΔGBind of -30.15 kcal/mol. Despite a minimal coulombic contribution (ΔGCoulomb = − 0.37 kcal/mol), the van der Waals interactions (ΔGvdW = − 37.87 kcal/mol) and lipophilic effect (ΔGLipo = − 12.00 kcal/mol) were highly favourable, which compensate the opposing solvent energy (ΔGSolv = 20.43 kcal/mol). The ligand strain energy was 2.01 kcal/mol.
Binding free energy results for compounds 4, 5, and 13 with the α-glucosidase enzyme, calculated using the Prime MM-GBSA module in Schrödinger. The contributions of various energy terms, including Coulombic, van der Waals, lipophilic, hydrogen bonding, and solvent energies, to the overall binding affinity are depicted.
Discussion
The dihydropyrimidone derivatives demonstrated promising α-glucosidase inhibitory activity, with compounds 4, 5, and 13 identified as potent competitive inhibitors based on kinetic studies. These compounds increased the Km without affecting Vmax, confirming competitive inhibition, where the inhibitor binds at the enzyme’s active site. Cytotoxicity evaluation on BJ cell lines indicated that all active compounds-maintained cell viability, with less than 25% inhibition even at 60 µM, confirming their non-cytotoxic nature and suitability as drug candidates. The results of MD simulations also indicate that all compounds maintain stable binding interactions throughout the simulations, with favourable contributions from key interactions that support their binding affinity. Notably, the binding free energies of compounds 13, 4, and 5 show a clear correlation with their inhibition constants values, demonstrating that compounds with more negative ΔGBind have stronger binding affinities, as reflected in lower Ki values. Compound 13, with the most favourable binding energy of -30.15 kcal/mol, exhibited the strongest binding affinity, correlating with its lowest Ki value of 3.77 ± 0.002 µM. Compound 4, with a binding energy of -23.13 kcal/mol, also showed a moderate binding affinity, reflected in a Ki of 5.00 ± 0.016 µM. In contrast, compound 5 had the least favourable binding energy of -20.67 kcal/mol, which corresponds with its highest Ki value of 8.37 ± 0.008 µM. These findings highlight the safety and efficacy of these competitive α-glucosidase inhibitors, underscoring their potential for therapeutic development in diabetes management.
Experimental
Chemistry
We bought reagents from Sigma-Aldrich in the USA. Analytical grade materials were used for all reagents and solvents throughout. On pre-coated silica gel, GF-254, thin layer chromatography was carried out (Merck, Germany). At 254 and 366 nm, ultraviolet light was used to see the spots. The compounds’ melting points were calculated using a BUCHI-M560 melting point equipment. Mass spectrometers MAT 312 and MAT 113D were used to record the spectra. On a Bruker AM spectrometer that operated at 300, 400, and 500 MHz, the39H12, C-NMR data were collected. The coupling constant (J) is given in Hz, and the chemical shift values are given in ppm (δ), relative to tetramethyl silane (TMS) as an internal reference.
General experimental procedures
In a 100 mL round-bottomed flask, the following ingredients were added: urea (1 mmol), ethyl acetoacetate (1.2 mmol), substituted aryl aldehyde (1 mmol), and copper nitrate trihydrate (10 mol percent) as catalyst. The mixture was heated at 80–90 ºC while being stirred continuously. The reaction’s success was periodically monitored using TLC. After the reaction was finished, the solid mixture was washed with extra distilled water and then recrystallized from ethanol to produce pure products in large yields24.
In vitro α-glucosidase inhibitory assay
Assay was carried out using our already available methodology40. The experiment was performed by employing 20 µL of each test compound in various concentrations were plated in triplicate, 135 µL of 0.5 mM Phosphate buffer was added followed by 20 µL of enzyme solution. Plate was incubated at 37 ºC for 15 min and readings were recorded at 400 nm. After 15 min of incubation, 25 µL of substrate solution was added and changes in absorbance was recorded for 30 min at 400 nm. Final absorbance was taken at the end of 30 min. Blank and standard inhibitors were also implemented in each experiment. (7% DMSO) was used as negative control, while acarbose was used as positive control.
Kinetic study assay
To assess the mechanism of action of the α-glucosidase the similar procedure was followed with a slight modification. Here, in the enzyme kinetic experiments we employed four different concentrations of the substrate after 15 min incubation at 37 ºC. By the same way we dissolved the substrate in the same 50 mM phosphate buffer and after 15 min incubation four different concentration 0.1, 0.2, 0.4 and 0.8 mM was added to initiate the reaction, and experiment was run for 30 min and changes in absorbance was recorded at 400 nm.
Cytotoxicity effect evaluation of compounds 1–37
We used the BJ cell line to conduct in-vitro research of all the tested compounds except non-active compounds 19–21 to examine the cytotoxic effect of these inhibitors. Here, we planned our experiment to use a precise number of cells 6 × 104 cells/well into the plate. We filled each well with 100 µL, and then we incubated it for 24 h at 37 °C in 5% CO2. Subsequently, the 96-well plate was incubated for 48 h under identical precise experimental conditions with 50 µL/well (60, 30 and 15 µM) of the investigated drugs plus 150 µL/well of fresh medium. Following the second incubation cycle, 200 µL of yellow MTT dye was added to each well in a 1:10 ratio to replace the old media, and the mixture was incubated for four hours. Subsequently, the dye was eliminated, and 100 µL/well of DMSO (dimethyl sulfoxide) cell culture grade was added to the 96-well plate to dissolve the formazan crystals of MTT dye that had turned purple. Using a spectrophotometer, the resulting variations in the (OD) optical density were tracked at 540 at the conclusion of the experiment.
Statistical analysis
The programs were employed to analyze the attained results for biological activity, the SoftMax Pro package and Excel were utilized.
The given formula below was used to calculate percent inhibition.
EZ-FIT (Perrella Scientific, Inc., USA) was used for IC50 calculations of all tested samples. To overcome on the expected errors all experiments were performed in triplicate, and variations in the results are reported in Standard Error of Mean values (SEM).
Molecular docking
Molecular docking is a computational technique used in drug design to predict the preferred binding orientation and interaction strength between a small molecule and a target protein, aiding in the identification of potential therapeutic compounds41,42. Herein, the docking studies were carried out to predict the molecular interactions of the synthesized compounds with α-glucosidase enzyme. The selected compounds with the most potent activity including 3–6 and 13 were sketched in MOE v.2019.01 software43. Afterwards, the compounds were subjected to structure correction and protonation followed by energy minimization using the MMFF94x force field. Since the crystal structure of α-glucosidase (Saccharomyces cerevisiae) is not reported yet, we generated the homology model using the isomaltase from Saccharomyces cerevisiae (PDB ID 3A4A) as a template44. The geometry of the generated model was corrected using the Structure Correction module of MOE. Subsequently, the protons were added, and partial charges were applied. The structure was then subjected to energy minimization using the Amber10:EHT force field. Previously established protocol was used for docking studies45,46,47. Briefly, Triangular Matcher was used as a placement method with Rigid protocol while London dG and GBVI/WSA dG were kept as scoring and rescoring functions. For each compound, thirty poses were generated and five best were retained for analysis. All the graphics were rendered by Chimera software48.
Molecular dynamics simulation
Molecular dynamics (MD) simulations were performed using Desmond on a Linux machine49. Protein structures were prepared using the Protein Preparation Wizard in Schrödinger. System preparation for MD simulations was carried out with the System Builder module in Desmond, employing the TIP3P water model to solvate each protein within an orthorhombic box with a 10 Å buffer. Na + ions were introduced to neutralize the system, and a NaCl concentration of 0.15 M was simulated to mimic physiological conditions. Before running the production MD simulations, each system was equilibrated using Desmond’s default relaxation protocol, which includes two stages of energy minimization (with and without restraints) followed by four stages of MD simulations with progressively decreasing restraints under the NVT/NPT ensembles. The production MD simulations were then conducted for 100 ns under the NPT ensemble using the OPLS3e force field. Finally, the resulting trajectories were analyzed using the Simulation Event Analysis tool in Desmond to extract key dynamic properties and interactions.
Binding free energy
To evaluate the binding free energy of each compound with the alpha-glucosidase enzyme, we employed Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) calculations using Schrödinger’s Prime module. MM-GBSA is a thermodynamic approach widely recognized for its effectiveness in assessing binding affinities50. This method calculates the binding free energy (ΔGbind) based on the optimized energies of the free ligand, free receptor, and ligand-receptor complex. For our analysis, the Prime MM-GBSA tool was used to predict the energy of optimized free ligands, free receptor (α-glucosidase), and the resulting ligand-receptor complex. These calculations were performed in an implicit solvent environment generated using the VSGB solvation model within the S-OPLS force field.
Conclusion
The present study was designed to investigate new inhibitors against the key metabolic enzyme of carbohydrate α-glucosidase to develop a therapeutic approach for the treatment of diabetes type 2. In conclusion, this study identifies thirty-four potent α-glucosidase inhibitors with IC50 values ranging from 20.47 to 86.36 µM, demonstrating superior inhibitory potency compared to the standard drug acarbose (IC50 = 875.75 ± 2.10 µM). The most potent compound, inhibitor 5, displayed a competitive inhibition mechanism, engaging directly with the active site of α-glucosidase, as confirmed by kinetic studies and molecular docking analysis. The docking and MD simulation results further support that these inhibitors effectively occupy the enzyme’s binding site, preventing substrate access. Additionally, cytotoxicity assays on BJ cell lines confirmed that all compounds were non-cytotoxic, reinforcing their potential safety profile. These findings suggest that the identified inhibitors are promising candidates for further development in diabetes type 2 treatment, warranting additional studies to fully explore their medicinal properties and therapeutic efficacy.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Change history
15 May 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41598-025-02259-6
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Acknowledgements
The authors are grateful to the Scientific Research Deanship at King Khalid University, Abha, Saudi Arabia for their financial support through the Small Research Group Project under grant number (RGP.1/149/45).
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Conceptualization, F.M., F.A., A.A. and S.U.; Formal analysis, S.A.A., A.D., M.S. and F.A.; Funding acquisition, S.P.A.; Methodology, F.M. and S.U.; Validation, S.A.A., F.A., H.A., R.G. and S.A.; Writing – original draft, F.M. and S.U.; Writing – review & editing, S.P.A., S.A.A., J.U. and S.A. All authors reviewed the manuscript.
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Ali, S.P., Mansoor, F., Albaayit, S.F.A. et al. Exploring dihydropyrimidone derivatives as modulators of carbohydrate catabolic enzyme to mitigate diabetes. Sci Rep 14, 31761 (2024). https://doi.org/10.1038/s41598-024-82765-1
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DOI: https://doi.org/10.1038/s41598-024-82765-1
