INTRODUCTION:

Diabetes mellitus, particularly type 2 diabetes, poses a monumental challenge to global health, demanding relentless innovation in therapeutic strategies.¹ Among the myriads of biological targets, dipeptidyl peptidase-4 (DPP-4) has surged to the forefront as a crucial enzyme in glucose metabolism regulation, making it a prime target for cutting-edge antidiabetic drug development.²

DPP-4 inhibitors, by enhancing the body's innate ability to control glucose levels through the prolongation of incretin hormone activity, represent a revolutionary approach in diabetes management.^3,4This therapeutic class not only amplifies insulin release but also curtails glucagon secretion in a glucose-dependent manner, offering a dual mechanism to combat hyperglycaemia.^5,6,7

Dipeptidyl peptidase-4 (DPP-4), sometimes referred to as CD26, is an enzyme that belongs to the serine protease family. It has a significant impact on the regulation of glucose metabolism and immune system functioning. The crystal structure of DPP-4, identified by the PDB ID: 2OQV, displays a homodimeric arrangement. Each monomer consists of two primary domains: an N-terminal eight-bladed β-propeller domain and a C-terminal α/β hydrolase domain. The β-propeller domain adopts a circular layout reminiscent of a barrel, contributing to the stabilization of the structure and providing support to the catalytic domain. The catalytic triad of Ser630, Asp708, and His740 is positioned at the interface of the two domains that make up the active site of DPP-4. The presence of this triad is crucial for the enzyme's proteolytic action, which entails the removal of dipeptides from the N-terminus of polypeptides. The structure of 2OQV offers comprehensive information about the arrangement of important residues that interact with substrates and inhibitors in the active site. Gaining a comprehensive understanding of the complex intricacies of DPP-4's structure is crucial to develop specific inhibitors that can successfully hinder its action. This is especially important in the context of treating type 2 diabetes and other metabolic disorders.

The high-resolution crystal structure of DPP-4 (PDB ID: 2OQV) provides an unprecedented opportunity to exploit the enzyme's active site for the design of potent inhibitors. The crystal structure 2OQV was chosen for molecular docking studies because it provides a high-resolution, detailed representation of the dipeptidyl peptidase-4 (DPP-4) enzyme, which is crucial for understanding its active site and binding characteristics. This particular structure is well-characterized and has been extensively validated in previous studies, making it a reliable model for docking simulations In this aggressive pursuit of novel therapeutics, derivatives of 2-methyl-N-(1,3-dioxoisoindolin-2-yl) benzamide have emerged as formidable contenders. These derivatives have anti-inflammatory⁸, and Anticancer⁹ activity. Their structural framework suggests intrinsic compatibility with the DPP-4 active site, potentially culminating in high binding affinity and selectivity.

This research embarks on an incisive exploration of the docking interactions between DPP-4 and these benzamide derivatives, employing rigorous molecular docking simulations. Leveraging the detailed crystal structure of DPP-4, this study aims to unveil the key binding interactions, assess binding affinities with precision, and propose strategic modifications to enhance inhibitor efficacy^. By delving into the molecular intricacies of these interactions, we aspire to propel the development of superior DPP-4 inhibitors, thereby advancing the arsenal of therapeutic options for type 2 diabetes.

Our methodology integrates state-of-the-art molecular docking techniques with meticulous computational analysis to dissect the structural determinants of inhibitor binding.^10,11 By systematically evaluating the interaction profiles of these benzamide derivatives, we provide a comprehensive understanding of their binding modes and identify critical molecular features that drive their inhibitory potency. The insights gleaned from this study are poised to significantly augment the current landscape of DPP-4 inhibition and inform future endeavours in the design and synthesis of next-generation antidiabetic agents.

METHODOLOGY:

A library of DPP-4 inhibitors was designed using chemical intuition and literature review and the 3D structure of compounds for molecular docking was prepared from chem-draw ultra 10 software. The crystal structures of proteins DPP-4 with PDB codes 2OQV employed in was obtained from the Protein Data Bank. The input files were prepared using Auto Dock Tools (version 1.5.6).¹² The 3D structure of compounds was minimized using universal force fields, followed by the addition of Gasteiger charges, torsions were defined, and the input files were saved in PDBQT format. The protein was prepared by eliminating heteroatoms, water molecules, and any extra chains. The missing residues were fixed using the Swiss PDB viewer, Kollman charges, and polar charges were added. The prepared proteins were then converted to PDBQT format. The grids were defined with the co-crystallized ligands positioned at the center of the box. The box dimensions measure 30 Å for DPP4 in X, Y, and Z directions. Configuration files were prepared defining the information for proteins, ligands, and grid size. The freely available program Auto Dock Vina was used to carry out molecular docking studies.^13,14 Top ten binding modes were generated and ranked according to the binding free energy (kcal/mol). The discussion included those binding modes that exhibited interactions with crucial residues.

Figure-1 Structure of all the drugs with their code P-1 to P-13 used for Docking Analysis

RESULTS:

The molecular docking simulations investigated the interactions between DPP-4¹⁵ (PDB ID: 2OQV) and a series of 2-methyl-N-(1,3-dioxoisoindolin-2-yl) benzamide derivatives.¹⁶ The docking scores¹⁷ and detailed interaction profiles are summarized in Table 1. The docking scores demonstrated that all derivatives exhibited significant binding affinities towards DPP-4, ranging from -8.0 to -9.0. Among the tested compounds, P-2 displayed the highest binding affinity with a docking score of -9.0, marking it as the most potent DPP-4 inhibitor in this study. Comparatively, Compound P-1, with a docking score of -8.9, formed stable hydrogen bonds with key residues Tyr547 and Tyr662, similar to the high-affinity interactions observed with P-2. P-2's interactions with Tyr662, Glu205, and Ser630 through multiple hydrogen bonds and pi interactions contributed to its superior binding affinity. (Fig-2) Compound P-3, also with a notable docking score of -8.8, exhibited strong interactions with Arg125, Tyr547, and Tyr662, indicating a stable binding mode comparable to P-1 and P-2. Likewise, P-4, with the same docking score of -8.8, formed significant hydrogen bonding and Pi-Sigma interactions with Arg125 and Tyr662, showing a binding profile similar to P-3.

Compound P-5, with a slightly lower docking score of -8.3, demonstrated moderate binding affinity, primarily through hydrogen bonds with Arg125 and Tyr662. P-6, with a docking score of -8.6, exhibited a more extensive interaction network, including multiple hydrogen bonds and a C-H bond, indicating a robust binding profile akin to the higher-scoring compounds. Compound P-8, despite its lower docking score of -8.0, showed a complex binding mechanism with diverse interaction types, including conventional hydrogen bonds, Pi donor H-bonds, H bonds, and Pi-alkyl bonds involving Arg125, Phe357, and Tyr662. Finally, P-9, with a docking score of -8.5, effectively bound to the active site through conventional hydrogen bonds and pi interactions with Arg125, Tyr547, and Tyr662, making it a noteworthy candidate for further development.

DISCUSSION:

The docking studies revealed that the derivatives of 2-methyl-N-(1,3-dioxoisoindolin-2-yl) benzamide possess substantial binding affinities and favourable interaction profiles with DPP-4, particularly involving key residues such as Tyr547, Tyr662, and Arg125. These interactions are essential for the inhibition of DPP-4 activity and highlight the potential of these compounds as promising DPP-4 inhibitors. Comparatively, the high docking score of compound P-2 can be attributed to its ability to form multiple hydrogen bonds and pi interactions with critical residues, especially Tyr662, which plays a significant role in its binding affinity. The interactions of P-1 and P-3 with Tyr547 and Arg125, respectively, underscore the importance of these residues in stabilizing the inhibitors within the enzyme's active site. P-4's effective inhibition is characterized by significant hydrogen bonding and Pi-Sigma interactions, while P-5's moderate binding affinity involves crucial hydrogen bonds with Arg125 and Tyr662.

Compound P-6's extensive interaction network, including multiple hydrogen bonds and a C-H bond, underscores its robust binding profile, making it comparable to the higher-scoring compounds P-1, P-2, P-3, and P-4. Despite its lower docking score, P-8's diverse interaction types indicate a complex binding mechanism within the active site, warranting further investigation. P-9's effective binding, involving conventional hydrogen bonds and pi interactions, highlights its potential for further development. Overall, these findings underscore the potential of 2-methyl-N-(1,3-dioxoisoindolin-2-yl) benzamide derivatives as promising DPP-4 inhibitors. The substantial binding affinities and favourable interaction profiles observed in the docking studies warrant further investigation through in vitro and in vivo studies to confirm their therapeutic efficacy and potential as antidiabetic agents.

Table 1. Binding affinities (kcal/mol) and Key Interactions of 2-methyl-N-(1,3 dioxoisoindolin-2-yl) Benzamide Derivatives with DPP-4.

Compounds	Docking Score (kcal/mol)	H-bond	Type	Hydrophobic bond	Type
Co-crystallized	-10.1	Glu205,Glu 206, Tyr662,Asn710	Conventional H-bond	Arg125, Glu205, Glu206, Phe357, Arg358, Tyr547, Tyr662, Tyr666, Asn710	Pi-Pi Stacked, Amide-Pi Stacked
P-1	-8.9	Tyr547,	Conventional H-bond	Phe357	Pi-Pi Stacked
		Tyr662	Pie donor H-bond	Ser630, Tyr631	Amide-Pi Stacked
				Tyr666	Pi-Pi T-Shaped
				Ser630	Pi-Sigma
P-2	-9	Tyr662	Conventional H-bond	Phe357, Tyr662	Pi-Pi Stacked
		Glu205	Conventional H-bond	Tyr666	Pi-Pi T-Shaped
		Ser630	Pie donor H-bond	Arg-125 (Electrostatic)	Pi-cation
		Tyr662	Pie donor H-bond	Phe357	Pi-Pi Stacked
		Tyr662	Pie donor H-bond	Tyr662	Pi-Pi Stacked
P-3	-8.8	Arg-125	Conventional H-bond	Tyr666	Pi-Pi T-Shaped
		Tyr547,	Conventional H-bond	Ser630	Amide-Pi Stacked
		Tyr662	Pie donor H-bond	Tyr631	Amide-Pi Stacked
				Tyr666	Pi-Pi Stacked
				Phe357	Pi-Pi T-Shaped
P-4	-8.8	Arg-125	Conventional H-bond	His126	Pi-alkyl
P-4	-8.8	Tyr662	Pi-Sigma	Ser630	Amide-Pi Stacked
P-5	-8.3	Arg-125	Conventional H-bond	Tyr631	Amide-Pi Stacked
		Tyr662	Pie donor H-bond	Tyr662	Pi-Pi Stacked
		Asn710	Conventional H-bond	Tyr666	Pi-Pi T-Shaped
				Arg-125 (Electrostatic)	Pi-cation
				Ser630	Amide-Pi Stacked
				Tyr631	Amide-Pi Stacked

Continue- table-1

P-6	-8.6	Arg-125	Conventional H-bond	Tyr666	Pi-Pi T-Shaped
		His126
		Ser209
		Tyr662	Pie donor H-bond
		Asn710	Conventional H-bond	Tyr631	pi-alkyl
		His740	Conventional H-bond	Val656	alkyl
P-8	-8	Arg-125	Conventional H-bond	Tyr666	Pi-Pi T-Shaped
		Phe357	Pie donor H-bond	Ser630	Pi-Pi Stacked
		Tyr662	H bond and pi-alkyl bond	Tyr631	Amide-Pi Stacked
P-9	-8.5	Arg-125	Conventional H-bond	Tyr662	Pi-Pi Stacked
		His126		Tyr666	Pi-Pi T-Shaped
		Glu205
		Ser209		Ser630	Amide-Pi Stacked
		Asn710		Tyr631	Amide-Pi Stacked
P-10	-8.1	Arg-125	Conventional H-bond	Tyr666	Pi-Pi T-Shaped
		Tyr662	Pie donor H-bond	Ser630	Amide-Pi Stacked
		Asn710	Conventional H-bond	Tyr631	Amide-Pi Stacked
P-11	-8.9	Arg-125	Conventional H-bond	Tyr666	Pi-Pi T-Shaped
		Ser209	Conventional H-bond
		Tyr662	Pie donor H-bond
		Asn710	Conventional H-bond	Tyr662	Pi-Pi Stacked
		His740	Conventional H-bond	Tyr666	Pi-Pi T-shaped
P-12	-8.5	Arg-125	Conventional H-bond	Ser630	Amide-Pi Stacked
		Asn710		Tyr631	Amide-Pi Stacked
		His126		Arg-125 (Electrostatic)	Pi-cataion
		Tyr662	Pi-Donor Hydrogen Bond	Tyr662	Pi-Pi Stacked
		Tyr662	Pi-Donor Hydrogen Bond	Phe357	Pi-Pi T-shaped
P-13	-8.1	Arg125	Conventional H-bond	Tyr666	Pi-Pi T-shaped
		Tyr547		Ser630	Amide-Pi Stacked
		Asn710		Tyr631	Amide-Pi Stacked
		Tyr662	Pi-Donor Hydrogen Bond
		Tyr662	Pi-Donor Hydrogen Bond


P-1	P-2

P-3	P-4

P-5	P-6

Figure-2- Important interactions of P-1 to P-6 with Protein PDB ID: 2OQV


P-8	P-9

P-10	P-11

P-12	P-13

Figure-3- Important interactions of P-8 to P-13 with Protein PDB ID: 2OQ (*P-7 shows no interaction)

CONCLUSION:

This study explored the molecular docking¹⁸ interactions between DPP-4¹⁹ (PDB ID: 2OQV) and a series of 2-methyl-N-(1,3-dioxoisoindolin-2-yl) benzamide derivatives²⁰, revealing significant²¹ insights into their potential as DPP-4 inhibitors. The docking simulations demonstrated that these derivatives possess substantial binding affinities towards DPP-4, with docking scores ranging from -8.0 to -9.0. Among the tested compounds, P-2 emerged as the most potent inhibitor with the highest docking score of -9.0, attributed to its ability to form multiple stable hydrogen bonds²² and pi interactions with critical residues, particularly Tyr662.

The comparative analysis highlighted that compounds P-1, P-3, and P-4 also exhibited notable binding affinities, characterized by stable interactions with key residues such as Tyr547, Arg125, and Tyr662. P-6 showed a robust binding profile with an extensive network of interactions, while P-5, P-8, and P-9 demonstrated moderate to significant binding interactions, warranting further investigation.

Overall, the findings from this study underscore the potential of 2-methyl-N-(1,3-dioxoisoindolin-2-yl) benzamide derivatives as promising DPP-4 inhibitors. These compounds demonstrated favourable interaction profiles and substantial binding affinities, making them viable candidates for further in vitro and in vivo studies. The insights gained from this research pave the way for the development of more effective DPP-4 inhibitors, potentially advancing therapeutic options for managing type 2 diabetes^23,24,25. Future work will focus on validating these computational findings^26,27 through experimental studies and optimizing these derivatives to enhance their efficacy and selectivity as antidiabetic agents^28,29,30.

ABBREVIATION:

DPP-4: Dipeptidyl Peptidase-4

PDB: Protein Data Bank

H-bond: Hydrogen Bond

Pi: Pi Interaction

Arg: Arginine

yr: Tyrosine

Glu: Glutamic Acid

Ser: Serine

Asn: Asparagine

His: Histidine

Phe: Phenylalanine

C-H bond: Carbon-Hydrogen Bond

Pi-Sigma: Pi-Sigma Interaction

Pi-alkyl: Pi-Alkyl Interaction

CONFLICTS OF INTEREST:

The authors declare no conflict of interest.

REFERENCES:

1. Chaudhry CS. Emerging Diabetes Pandemic in India: A Case Study for an Integrative Approach. Walden University; 2014.

2. Pradhan R. Incretin-based Drugs and the Incidence of Skin Cancer Among Patients with Type 2 Diabetes (Doctoral dissertation, McGill University (Canada)).

3. Havale SH, Pal M. Medicinal chemistry approaches to the inhibition of dipeptidyl peptidase-4 for the treatment of type 2 diabetes. Bioorganic and Medicinal Chemistry. 2009 Mar 1; 17(5): 1783-802.

4. Tomkin GH. Treatment of type 2 diabetes, lifestyle, GLP1 agonists and DPP4 inhibitors. World Journal of Diabetes. 2014 Oct 10; 5(5): 636.

5. Cho YM, Merchant CE, Kieffer TJ. Targeting the glucagon receptor family for diabetes and obesity therapy. Pharmacology and Therapeutics. 2012 Sep 1;135(3):247-78.

6. Wu P, Liu Z, Jiang X, Fang H. An overview of prospective drugs for type 1 and type 2 diabetes. Current Drug Targets. 2020 Apr 1;21(5):445-57.

7. Bhilare NV, Shedge R, Tambe PM, More A. Unveiling the potential of prodrug and drug-conjugate strategies in treatment of diabetes mellitus and its complications. Medicinal Chemistry Research. 2024 Mar;33(3):337-53.

8. Srivastava A, Mishra A, Rai AK. Nsaids-alendronate based prodrug for bone specific drug targeting. Res J Pharm Technol. 2020; 13(7): 3520-3523.

9. Kumar, S., Ritika A brief review of the biological potential of indole derivatives. Futur J Pharm Sci 6, 121 (2020).

10. Girdhar K, Dehury B, Singh MK, Daniel VP, Choubey A, Dogra S, Kumar S, Mondal P. Novel insights into the dynamics behavior of glucagon-like peptide-1 receptor with its small molecule agonists. Journal of Biomolecular Structure and Dynamics. 2018 Nov 17.

11. Basith S, Cui M, Macalino SJ, Choi S. Expediting the design, discovery and development of anticancer drugs using computational approaches. Current medicinal chemistry. 2017; Dec 1; 24(42): 4753-78.

12. Santoshi S, Mathur P. Recent Trends in Computer-Aided Drug Design. Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design. 2021: 123-51.

13. Zhou S, Tan S, Fang D, Zhang R, Lin W, Wu W, Zheng K. Computational analysis of binding between benzamide-based derivatives and Abl wt and T315I mutant kinases. RSC advances. 2016; 6(88): 85355-66.

14. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010; 31(2): 45

15. Alsamghan AS, Alwabli AS, Abadi M, Alsaleem SA, Anbari DM, Alomari AS, Alzahrani O, Alam Q, Tarique M. From sequence analysis of DPP-4 to molecular docking based searching of its inhibitors. Bioinformation. 2020; Jun 30; 16(6): 444-451. doi: 10.6026/97320630016444

16. Mourad AAE, Khodir AE, Saber S, Mourad MAE. Novel Potent and Selective DPP-4 Inhibitors: Design, Synthesis and Molecular Docking Study of Dihydropyrimidine Phthalimide Hybrids. Pharmaceuticals (Basel). 2021; Feb 11; 14(2): 144. doi: 10.3390/ph14020144.

17. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010; 31(2): 455-461.

18. Firoj Alam, Badruddeen, Anil Kumar Kharya, Akhtar Juber, Mohammad Irfan Khan. Naringin: Sources, Chemistry, Toxicity, Pharmacokinetics, Pharmacological Evidences, Molecular Docking and Cell line Study. Research J. Pharm. and Tech 2020; 13(5):2507-2515. doi: 10.5958/0974-360X.2020.00447.3

19. Ankita, Keerti Bhardwaj, Navneet Khurana, Ashish Sutte, Gopal Khatik. Identification of Dipeptidyl peptidase-4 (DPP-4) inhibitors as Potential Antidiabetic agents using Molecular docking study. Research J. Pharm. and Tech. 2020; 13(11):5257-5262. doi: 10.5958/0974-360X.2020.00919.1.

20. Navjot Kaur, Monika, Kulwinder Singh. 3D-QSAR and Molecular Docking Studies of N-(2-Aminophenyl)-Benzamide Derivatives as Inhibitors of HDAC2. Research J. Pharm. and Tech. 7(7): July 2014 Page 760-770.

21. Srivastava A, Mishra A, Rai AK. Synthesis, characterization and evaluation of ulcerogenic potential for NSAIDs-alendronate based prodrug. Res J Pharm Technol. 2020;13(5):2107-2111. doi:10.5958/0974-360X.2020.00379.0

22. Arfeen M, Bharatam P. Design of Glycogen Synthase Kinase-3 Inhibitors: An Overview on Recent Advancements. Curr Pharm Des. 2013;19(26):4755-4775. doi:10.2174/1381612811319260007

23. U. S. Mahadeva Rao, Thant Zin, Kyi Kyi Win RN, Subbhagganthan A/L Subramaniam, Tan Bin Shan, Kaushilya A/P Mogan, Anis Syafiqah Binti Ismail. Cross-Sectional Study on Knowledge, Attitude and Practice regarding Diabetes mellitus among Medical and Non-Medical Students. Research J. Pharm. and Tech 2018; 11(11): 4837-4841. doi: 10.5958/0974-360X.2018.00879.X

24. Fianza Rezkita, Kadek G. P. Wibawa, Alexander P. Nugraha. Curcumin loaded Chitosan Nanoparticle for Accelerating the Post Extraction Wound Healing in Diabetes Mellitus Patient: A Review. Research J. Pharm. and Tech 2020; 13(2):1039-1042. doi: 10.5958/0974-360X.2020.00191.2

25. Nidaul Hasanah, Zullies Ikawati, Zainol Akbar Zainal. The effectiveness of Smartphone application-based education “Teman Diabetes” on clinical outcomes of Type-2 Diabetes mellitus patients. Research Journal of Pharmacy and Technology. 2021; 14(7):3625-0. doi: 10.52711/0974-360X.2021.00627.

26. Thressia. P.A. (Sr. Tresa Anto) , Rajeev. Kumar. N.. Effect of Sleep Duration on Obesity and the Glycemic level in Patients with Type 2 Diabetes. Asian J. Nur. Edu. and Research 4(4): Oct.- Dec., 2014; Page 502-507.

27. Sindhu L, Jaya Kumar B. Effectiveness of Educational Intervention on Body Mass Index (BMI) of Patients with Type 2 Diabetes Mellitus in South Indian Population. Asian J. Nursing Education and Research. 2018; 8(3):434-436.

28. Sreedevi K. A study to assess the effectiveness of structured Teaching Programme on self-care management of patients with type 2 Diabetes mellitus and Evaluation of prognosis in selected Hospitals. Asian J. Nursing Education and Research. 2020; 10(4):427-431.

29. Gepsi Jain, Nahomi Clement. A Study to assess the knowledge regarding self management behaviour among Type II Diabetes Mellitus patients in prevention of COVID-19 at selected area, Alappuzha District, Kerala, India. Asian Journal of Nursing Education and Research. 2022; 12(4):449-3.

30. .E. Otuokere, F.J. Amaku, C.O. Alisa. In Silico Geometry Optimization, Excited – State Properties of (2E)-N-Hydroxy-3-[3- (Phenylsulfamoyl) Phenyl] prop-2-Enamide (Belinostat) and its Molecular Docking Studies with Ebola Virus Glycoprotein. Asian J. Pharm. Res. 5(3): July- Sept., 2015; Page 131-137.

Received on 03.07.2024 Revised on 26.12.2024

Accepted on 22.03.2025 Published on 05.09.2025

Available online from September 08, 2025

Research J. Pharmacy and Technology. 2025;18(9):4389-4395.

DOI: 10.52711/0974-360X.2025.00629

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.