Uncovering The Potential Mechanism of Action of Garlic as an

Anti-Diabetic: A Computational Approach

Salam R.*

Department of Pharmacy, Faculty of Medicine, Universitas Brawijaya,Veteran St., 65145, Malang, Indonesia.

*Corresponding Author E-mail: rudy_salam@ub.ac.id

ABSTRACT:

Garlic (Allium sativum L.) is a type of vegetable that is commonly consumed and plays an important role in culinary purposes, as a food additive, and in traditional medicine. The bioactive compounds in garlic, such as allicin, alliin, diallyl sulfide, diallyl disulfide, diallyl trisulfide, and ajoene, have indicated pharmacological effects, particularly on anti-diabetic properties. This study aimed to reveal the potential mechanism of action of bioactive compounds in garlic involved in anti-diabetic properties through a computational approach with reverse docking and molecular dynamics (MD) simulations. Of the 7 proteins known as the target of anti-diabetic drugs, alliin showed the highest docking score compared to other bioactive compounds. In terms of ligand-protein interaction, the interaction of alliin with dipeptidyl peptidase-4 (DPP-4) was similar to the native ligand of DPP-4 compared to alliin and other target proteins. Alliin formed hydrogen bonds with Glu205 and Glu206 which are essential for the inhibitory effect on DPP-4. Furthermore, MD simulations revealed a shift in the position of Alliin in the DPP-4 binding cavity at the end of the simulation. Interestingly, hydrogen bonds between Alliin and Glu205, and Glu206 remained formed. This result provided insight into the potential of bioactive compounds from garlic, alliin, acting as anti-diabetic properties through inhibition of DPP-4.

KEYWORDS: Garlic, alliin, Anti-diabetes, Molecular docking, Molecular dynamic.

INTRODUCTION: 

Garlic (Allium sativum L.) is a type of vegetable that is commonly consumed and widely used for culinary purposes, as a food additive, and in traditional medicine such us antioxidant^1,2. Garlic has been reported to have anti-diabetic properties^1,3,4. Streptozotocin-induced diabetic rats were given garlic extract orally for 14 days at doses of 0.1, 0.25, and 0.5g/kg of body weight demonstrated elevated insulin levels and a decrease in blood glucose levels in diabetic compared to normal rats.

Additionally, the administration of extracts was more effective compared to glibenclamide as a positive control⁵. Patients with type II diabetes who used garlic tablets as a supplement in addition to regular treatment saw better blood sugar control than those who only received regular treatment³. The majority of the therapeutic properties of garlic are attributed to the sulfur-containing organic compounds containing the cysteine structure present in garlic⁶. The bioactive compounds of garlic associated with anti-diabetic effects include allicin, alliin, diallyl sulfide, diallyl disulfide, diallyl trisulfide, and ajoene^7,8.

Based on this evidence, it can be hypothesized that bioactive compounds of garlic have anti-diabetic properties. However, it remains unclear which bioactive compounds have anti-diabetic properties and the mechanism of action. Therefore, this study aimed to determine the potential mechanism of action of the bioactive compounds from garlic associated with anti-diabetic properties through a computational approach. Reverse docking was utilized to understand the potential protein targets of the bioactive compounds. The bioactive compound of garlic with similar interaction with the native ligand of the target protein was selected for molecular dynamics simulation. MD simulations were carried out to determine the stability of the interaction between selected bioactive compounds and protein targets.

MATERIALS AND METHODS:

Molecular docking and Interaction analysis:

Ligand preparation:

All compounds used for docking were downloaded from Pubchem⁹ in SDF format. Subsequently, the compounds were subjected to energy minimization using the MM2 force field feature built-in the Chem3Dv. 22.0 (PerkinElmer, Waltham, Massachusetts, United States). The following, all compounds were prepared for docking using the Python script ‘prepare_ligand4.py’ from AutoDockTools 1.5.6^10-12 was used to prepare all ligands prepared for docking. During this step, Gasteiger charges were assigned, non-polar hydrogens inserted, torsion trees created, and then data exported to PDBQT format.

Protein preparation:

The crystal structures of the proteins used for docking were obtained from the Protein Data Bank¹³. The AutoDockTools standard protocol^10,14 was used to prepare the protein prior to docking. The protein was prepared by extracting water molecules, repairing missing residues, adding hydrogen atoms, and assigning partial charges. 

Molecular docking analysis:

Molecular docking calculations was carried out using AutoDock Vina 1.1.2^10,15. The grid and center set up for each of the target proteins covering the active binding site and all essential residues (Table 1). The docking parameters were set at default values with exhaustiveness increased to 32 from 8 (default setting). The bioactive compounds of garlic with the lowest docking value on each target protein were selected as candidates with anti-diabetic properties. The reference ligand was re-docked in the binding cavity, and the interaction results were compared with selected bioactive compounds of garlic at the same protein target. The protein-ligand interaction was determined using Chimera 1.15¹⁶ by displaying residues within 5 Å from the ligand pose and Discovery Studio Visualizer 2017 for 2D interaction.

Molecular dynamic simulation:

To further evaluate the stability of the complex between selected bioactive compounds and protein targets, molecular dynamics (MD) simulations were performed on the best poses obtained from the docking results. All MD simulations were executed using GROMACS version 2020 with a 50 ns simulation period^17,18. Protein and ligand topology files were prepared in advance, with the protein topology files obtained by applying the CHARMM36 all-atom force field¹⁹ and the ligand topology files generated by adding hydrogen atoms using Avogadro²⁰ and submitting them to the CgenFF web server²¹. All protein and ligand complexes were solved in a 1 nm explicit water box with TIP3P water type with periodic boundary conditions (PBC) dodecahedron box and CL-ion was used to neutralize the system. The energy was then minimized with a maximum of 50000 steps, followed by the standardization of system temperature to 300 K and the application of pressure on the system to the desired density of 1000 kg/m3²².  MD trajectories were analyzed using various scripts built-in to the GROMACS software package. "gmx rms" were used to determine the root mean square deviation (RMSD. Hydrogen bond parameters during the simulation were analyzed using "gmx hbond" and “gmx mindist” were used to determine number of contacts^23,24. All graphs were plotted using Excel version 2108.

Table 1. The grid and center of each target protein.

No.	Protein target	PDB ID	Grid box (Å)	Center box (XYZ)
1.	Dipeptydil peptidase 4 (DPP-4)	1X70	25 x 25 x 25	-4.4 x 62.9 x 37.0
2.	Alpha glucosidase	3A4A	25 x 30 x 30	22.6 x -4.9 x 22.4
3.	Organic cation transporter 1 (OCT1)	8SC4	25 x 30 x 25	112.8 x 103.3 108.4
4.	Sodium–glucose cotransporter 2 (SGLT2)	8HEZ	35 x 30 x 30	66.1 x 65.8 x 76.3
5.	ATP-sensitive potassium (KATP)	6PZA	25 x 30 x 30	202.0 x 281.1 x 220.1
6.	Peroxisome proliferator-activator receptor (PPAR) γ	5Y2O	20 x 25 x 25	-48.2 x -0.4 x 83.3
7.	Glucagon-like peptide-1 (GLP1)	4ZGM	30 x 20 x 25	13.5 x 45.4 x 18.0

RESULT AND DISCUSSION:

Molecular docking and Interaction analysis:

Prior to docking on bioactive compounds from garlic, docking validation was carried out to validate the applied docking method to ensure accuracy²⁵. Docking validation was conducted by re-docking the co-crystallized ligand of one of the target proteins (PDB ID: 1X70) followed by RMSD measurement. The re-docking results showed the re-docked ligand pose overlapped with the co-crystallized ligand (Figure 1) and the RMSD value of 0.473 which was considered successful according to the criteria of RMSD value range < 2.0^25,26.

Table 2. docking score  of garlic bioactive compounds for each target protein

Ligand	Docking Score
	Alpha glucosidase	OCT1	DPP4	GLP1	SGLT2	KATP	PPAR γ
Allicin	-4.5	-4.3	-4.3	-3.3	-4.7	-3.8	-4.8
Alliin	-5.5	-5.1	-5.1	-3.9	-5.9	-4.4	-5.3
Allyl sulfide	-3.9	-3.6	-3.6	-2.9	-4.0	-3.2	-3.9
Diallyl sulfide	-4.0	-3.7	-3.8	-3.0	-4.3	-3.3	-4.1
Diallyl disulfide	-3.8	-3.6	-3.6	-2.9	-4.0	-3.2	-3.8
Diallyl trisulfide	-4.0	-3.8	-3.8	-3.0	-4.5	-3.4	-4.1
E-Ajoene	-4.9	-5.1	-4.6	-3.7	-5.8	-4.3	-5.3
Methylcysteine	-4.6	-4.0	-4.4	-3.4	-5.0	-3.7	-4.2
S-allylcysteine	-5.2	-4.9	-5.2	-3.7	-5.4	-4.2	-4.7
S-allylmercaptocysteine	-5.2	-4.9	-4.8	-3.7	-5.3	-4.1	-4.8
Z-ajoene	-5.0	-4.9	-4.6	-3.7	-5.6	-4.4	-5.3

Figure 1. Superimposition of re-docked sitagliptin (turquoise) and the cocrystallized sitagliptin (brown) in the crystallography structure of DPP4 (PDB ID: 1X70)

Furthermore, eleven garlic bioactive compounds were docked on seven protein targets involved in the mechanism of action of anti-diabetic drugs. The binding affinity increased with the negative value of the docking score²⁷. The docking results obtained that alliin has the lowest score compared to other bioactive compounds on all protein targets as shown in Table 2. These results suggest that alliin tends to interact more easily with all protein targets and could be a potential garlic bioactive compound with anti-diabetic features.

The following step was to analyze the docking pose and molecular interaction of alliin with each target protein and compared with the reference ligand of the target protein. The molecular docking approach allowed for figuring out the interactions between the ligand and essential residues on the protein, enabling the characterization of ligand behaviour in the binding cavity to elucidate fundamental biochemical processes²⁸. Therefore, the docking poses of the ligand overlapped with the reference ligand representing a considerably higher potency for biologically relevant interactions and biochemical effects.

The best docking pose of alliin overlapped with sitagliptin as the reference ligand of DPP4 but was absent in the other six targets, which suggested similarities in the essential interactions occurring in the binding cavity of DPP4 (Figure 2 and Figure 3). The molecular interactions between garlic bioactive compounds and the reference ligand at the binding cavity of each target protein are summarized in Table 3. The DPP4 binding cavity is made up of several residues consisting of Arg125, Glu205, Glu206, Tyr547, Tyr662, Tyr666, Ser630, and Phe357 whereby all inhibitory ligands to DPP4 interact with GLU205 and GLU206 through hydrogen bonds (H-bond) and salt bridges^29,30. Alliin formed H-bonds with GLU205, GLU206, and TYR662 residues similar to sitagliptin which are essential for DPP4 inhibition.

Figure 2. Docking pose of alliin with target proteins: A. ATP-sensitive potassium (KATP); B. Sodium–glucose cotransporter 2 (SGLT2); C. Organic cation transporter 1 (OCT1); D. Glucagon-like peptide-1 (GLP-1); E. Alpha glucosidase; F. Peroxisome proliferator-activator receptor (PPAR) γ. (purple: Alliin; blue: native ligand)

Figure 3. A. Best docking pose of alliin (purple) and sitagliptin (blue) in the binding cavity of dipeptidyl peptidase 4 (DPP-4); B. The overlapping pose of alliin (purple) and sitagliptin (blue); C. 2D representation of molecular interactions between sitagliptin and DPP-4; D. 2D representation of molecular interactions between alliin and DPP-4.

Table 3. Intermolecular interactions of alliin with target proteins.

Ligand		Conventional hydrogen bonds	Carbon hydrogen bond	Pi-Pi Stacked	Pi-Sulfur	Pi-Alkyl	Pi-sigma	Pi-Pi T-Shaped	Other interactions
Dipeptydil peptidase 4 (DPP-4)
Sitagliptin		ARG125 (3.40 Å); SER209 (3.16 Å); ARG358 (3.08 Å); ARG358 (3.70 Å); ASN170 3.38 Å); GLU205 (2.87 Å); TYR662 (2.55 Å); GLU205 (2.79 Å); GLU206 (2.40 Å)	GLU205 (3.76 Å)	TYR662 (4.68 Å); PHE357 (3.98 Å)		PHE357 (4.37 Å)		TYR666 (	Halogen: HIS740 (3.63 Å); Pi-donor: TYR662 (3.30 Å)
Alliin		GLU205 (2.77 Å); GLU206 (2.21 Å); GLU206 (2.77 Å); TYR662 (2.90 Å)
	Alpha glucosidase
Acarbose		GLU277 (2.66 Å); ASP352 (2.74 Å); GLN353 (2.87 Å); GLU411 (2.35 Å); GLN353 (1.93 Å)	ARG442 (3.34 Å)
Alliin		ARG442 (2.94 Å); ARG442 (3.11 Å); GLU277 (2.80 Å); ASP352 (2.28 Å); ASP215 (2.32 Å); GLU277 (2.95 Å); HIS351 (2.54 Å)
Organic cation transporter 1 (OCT1)
Metformin		GLN241 (2.40 Å); THR245 (2.33 Å); GLN241 (2.56 Å)							Electrostatic: GLU386 (3.36 Å)
Alliin		GLN362 (3.04 Å); SER358 (2.00 Å)			TYR361 (4.15 Å)
Sodium–glucose cotransporter 2 (SGLT2)
apagliflozin		TRP291 (3.04 Å); LYS321 (3.34 Å); LYS321 (3.17 Å); SER287 (2.02 Å); HIS80 (2.05 Å);	GLN457 (3.79 Å); ASP454 (3.62 Å); TYR526 (3.52 Å)	HIS80 (3.94 Å); PHE453 (5.88 Å)		PHE453 (4.66 Å); LEU84 (5.25 Å); VAL95 (5.12 Å)	TYR290 (3.77 Å)		Alkyl: VAL157 (4.70 Å)
Alliin		ASN75 (2.92 Å); SER287 (3.02 Å); SER287 (2.88 Å); TRP291 (3.23 Å); LYS321 (3.17 Å); LYS321 (3.32 Å); PHE98 (1.86 Å); GLU99 (2.63 Å)
ATP-sensitive potassium (KATP)
Glibenclamide		ARG388 (3.10 Å); ARG388 (3.32 Å); SER1238 (2.70 Å); ASP1193 (2.18 Å); ASP1193 (2.29 Å)	TYR377 (3.52 Å)	TYR377 (4.06 Å)		TYR377 (4.86 Å); ILE381 (5.37 Å)		TRP430 (5.14 Å)	Alkyl: LEU592 (5.04 Å)
Alliin		THR588 (3.08 Å); ASN1293 (3.09 Å); TYR1294 (3.04 Å); TYR1294 (2.47 Å); ASN1293 (2.18 Å)			TYR1294 (5.60 Å); TRP1297 (5.29 Å)
Peroxisome proliferator-activator receptor (PPAR) γ
Pioglitazone		SER289 (2.80 Å); TYR473 (2.16 Å)	CYS285 (3.57 Å)		MET348 (4.96 Å); MET364 (4.88 Å); PHE363 (4.81 Å); HIS449 (5.02 Å)	CYS285 (3.90 Å); ARG288 (5.44 Å); LEU330 (5.18 Å)	ILE341 (3.50 Å)	TYR327 (5.98 Å)	Alkyl: ILE281 (4.45 Å)
Alliin		LEU228 (3.10 Å); ARG288 (2.95 Å); GLU295 (2.03 Å)
Glucagon-like peptide-1 (GLP-1)
Cochinchinenin C			ARG36 (2.32 Å)	TRP39 (3.68 Å); TRP39 (4.45 Å); TYR69 (5.28 Å); ALA30 (4.50 Å)		LEU123 (5.38 Å); VAL33 (4.30 Å); ALA30 (5.47 Å); LEU32 (4.42 Å)
Alliin		ARG40 (2.36 Å); ARG40 (2.45 Å); ARG43 (2.19 Å); ARG43 (2.93 Å); GLN47 (2.44 Å)

Molecular dynamic analysis:

For further evaluation of the stability of the complex between alliin and DPP4, molecular dynamics (MD) simulations were carried out on the best poses obtained from the docking results. All MD simulations were performed using GROMACS version 2020¹⁷ with a simulation period of 50 ns. The stability of the alliin and DPP4 interaction was assessed using the parameters of RMSD, percentage of H-bond occupancy, distance, and number of contacts of essential residues to alliin over the simulation time.

Following 50 ns of simulation, the RMSD graph (Figure 4A) of alliin in the DPP4 binding cavity remained stable as well as the reference ligand, sitagliptin. The alliin conformation was considered stable as the RMSD fluctuation value was less than 3 Å similar to sitagliptin. According to the percentage of H-bond occupancy (Figure 4B), alliin had the highest percentage of H-bonds with residue GLU206 followed by TYR547 in contrast to sitagliptin in which it formed H-bonds with residue SER630. As noted, the three residues are essential residues composing the binding cavity and involved in the substrate recognition process of DPP4, and the interaction with such residues is associated with the inhibitory effect on DPP4^29,30. Figure 4C showed the distance between alliin and the residues forming the binding cavity of DPP4 in the range of 2.2 - 3 Å, allowing either H-bond or hydrophobic interactions with the residues during the simulation period. This result was in line with the number of contacts between alliin and the residues forming the DPP4 binding cavity (Figure 4D) which ranged from 300 - 500 times similar to sitagliptin.

Figure 4.  Molecular dynamic (MD) simulation results; A. RMSD graph of the heavy atoms of Alliin (blue) and Sitagliptin (orange); B. Hydrogen bond occupancies of polar residues in the TGR5 binding cavity of dipeptidyl peptidase 4 (DPP-4); C. Average distance of active residues in the binding cavity of DPP-4 relative to Alliin; D. Average number of contacts (< 6 Å) of active residues in the binding cavity of DPP-4 with the ligand (blue: alliin; orange: sitagliptin) 

CONCLUSION:

The utilization of plant extracts as therapy is becoming an alternate due to less side-effect risk. However, the challenge is the lack of knowledge of bioactive compounds with therapeutic activity and mechanism of action. A computational approach is expected to provide insight into the bioactive compounds involved in therapeutic effects and its mechanism of action. In this study, alliin, one of the bioactive compounds of garlic, demonstrated potential in DPP4 inhibition, one of the targets of anti-diabetic drugs. The findings of this study are expected to pave the way for further development of alliin as an anti-diabetic agent.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGMENTS:

We thank the Ministry of Education, Youth, and Sport of the Czech Republic for computational resources supplied by the project “e-Infrastruktura CZ” (e-INFRA CZ ID: 90140 and e-INFRA CZ LM2018140). The research was implemented in the MetaCentrum and IT4I supercomputing facilities.

REFERENCES:

1. Cardelle-Cobas, A., et al., A comprehensive survey of garlic functionality. 2009, Nova Science Publishers.

2. Nidhiya, I., et al., Comparative Antioxidant Activity of Three Garlic Varieties: An in vitro Study. Research Journal of Pharmacognosy and Phytochemistry. 2011; 3(4): 162-165.

3. Ashraf, R., R.A. Khan, and I. Ashraf, Garlic (Allium sativum) supplementation with standard antidiabetic agent provides better diabetic control in type 2 diabetes patients. Pak J Pharm Sci. 2011; 24(4): 565-70.

4. Fadheel, Q.J., A comparative study of the effect of metformin and metformin plus garlic on blood glucose level in patients with type 2 diabetes mellitus in Iraq. Research Journal of Pharmacy and Technology. 2019; 12(4): 1806-1810.

5. Eidi, A., M. Eidi, and E. Esmaeili, Antidiabetic effect of garlic (Allium sativum L.) in normal and streptozotocin-induced diabetic rats. Phytomedicine. 2006; 13(9): 624-629.

6. Fenwick, G.R. and A.B. Hanley, The genus Allium--Part 3. Crit Rev Food Sci Nutr. 1985; 23(1): 1-73.

7. Sanie-Jahromi, F., Z. Zia, and M. Afarid, A review on the effect of garlic on diabetes, BDNF, and VEGF as a potential treatment for diabetic retinopathy. Chin Med. 2023; 18(1): 18.

8. Haider, R., Garlic: The Mystical Food in Health Promotion. 2024.

9. Thangapandian, S., et al., Ligand and structure-based pharmacophore modeling to facilitate novel histone deacetylase 8 inhibitor design. European Journal of Medicinal Chemistry. 2010; 45(10): 4409-4417.

10. Trott, O. and A.J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010; 31(2):  455-61.

11. Morris, G.M., et al., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry. 2009; 30(16): 2785-2791.

12. Malik, A., et al., Molecular docking, synthesis, α-amylase inhibition, urease inhibition and antioxidant evaluation of 4-hydroxy-3-methoxy benzoic acid derivatives. Research Journal of Pharmacy and Technology. 2019; 12(12): 5653-5663.

13. Burley, S.K., et al., RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Research. 2021; 49(D1): D437-D451.

14. Pawar, S., et al., Molecular docking using different tools. Asian Journal of Pharmaceutical Research. 2023; 13(4): 292-296.

15. Baul, H.S. and M. Rajiniraja, Favorable binding of quercetin to α-synuclein as potential target in Parkinson disease: an insilico approach. Research Journal of Pharmacy and Technology. 2018: 11(1): 203-206.

16. Pettersen, E.F., et al., UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry. 2004; 25(13): 1605-1612.

17. Van Der Spoel, D., et al., GROMACS: fast, flexible, and free. Journal of Computational Chemistry. 2005; 26(16): 1701-1718.

18. Kesavan, K. and S. Jayanthi, Structure Based Virtual Screening and Molecular Dynamics Studies to Identify Novel APE1 Inhibitor from Seaweeds as Anti-glioma Agent. Research Journal of Pharmacy and Technology. 2017; 10(8): 2474-2478.

19. Huang, J. and A.D. MacKerell Jr, CHARMM36 all‐atom additive protein force field: Validation based on comparison to NMR data. Journal of Computational Chemistry. 2013; 34(25): 2135-2145.

20. Hanwell, M.D., et al., Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics. 2012; 4: 1-17.

21. Vanommeslaeghe, K., E.P. Raman, and A.D. MacKerell Jr, Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges. Journal of Chemical Information and Modeling. 2012; 52(12): 3155-3168.

22. Parrinello, M. and A. Rahman, Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics. 1981; 52(12): 7182-7190.

23. Jayasurya, S.S., et al., Identification of ZAP-70 Inhibitor from Macroalgae through Docking and Molecular Dynamics Studies. Research Journal of Pharmacy and Technology. 2017; 10(12):  4162-4166.

24. Anbarasu, K., D. Senthilkumar, and S. Jayanthi, Deciphering the impact of R324L Mutation in Polycystin-1PKD Domain associated with Autosomal Dominant Polycystic Kidney Disease (ADPKD): A Molecular Dynamics Perspective. Research Journal of Pharmacy and Technology. 2017; 10(9): 3089-3094.

25. Hevener, K.E., et al., Validation of molecular docking programs for virtual screening against dihydropteroate synthase. J Chem Inf Model. 2009; 49(2): 444-60.

26. Sahu, R., S. Jain, and D. Jain, Design of some Apigenin derivatives as selective DPP-IV Inhibitors by Pharmacophore Modelling and its Validation through Molecular Dynamics Simulation. Research Journal of Pharmacy and Technology. 2023; 16(8): 3535-3543.

27. Xue, Q., et al., Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock. Ecotoxicology and Environmental Safety. 2022; 233: 113323.

28. McConkey, B.J., V. Sobolev, and M. Edelman, The performance of current methods in ligand–protein docking. Current Science. 2002: 845-856.

29. Arulmozhiraja, S., et al., Comparative Binding Analysis of Dipeptidyl Peptidase IV (DPP-4) with Antidiabetic Drugs - An Ab Initio Fragment Molecular Orbital Study. PLoS One. 2016; 11(11): e0166275.

30. Kim, D., et al., (2R)-4-Oxo-4-[3-(Trifluoromethyl)-5,6-dihydro[1,2,4] triazolo[4,3-a]pyrazin- 7(8H)-yl]-1-(2,4,5-trifluorophenyl)butan-2-amine:  A Potent, Orally Active Dipeptidyl Peptidase IV Inhibitor for the Treatment of Type 2 Diabetes. Journal of Medicinal Chemistry. 2005; 48(1): 141-151.

Received on 27.08.2024      Revised on 29.01.2025

Accepted on 30.04.2025 Published on 01.12.2025

Available online from December 06, 2025

Research J. Pharmacy and Technology. 2025;18(12):5675-5681.

DOI: 10.52711/0974-360X.2025.00820

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