Quantitative Structure-Activity Relationship Study of DPP-4 Enzyme Inhibitors as Drugs in Therapy of Type 2 Diabetes Mellitus

Rogić, Sanja; Nukic, Miljana; Gagić, Žarko

doi:10.1007/978-3-030-73909-6_56

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…14 Sitagliptin is first DPP-4 inhibitor introduced in DMT2 therapy in 2006 and since then various DPP-4 inhibitors have been marketed, but research in this area is continuing in an effort to discover more efficient antidiabetic drugs. [15][16][17][18] Computer aided drug design (CADD) comprises various in silico techniques that have been used with great success in modern drug discovery process (especially in its early stages) to increase efficiency and save time and money. [19][20][21] The aim of this study was to employ an in silico 3D-quantitative structure-activity relationship study (3D-QSAR) to identify key structural elements that are responsible for DPP-4 inhibitory activity and design new molecules that can lead to discovery of new drugs in therapy of DMT2.…”

Section: Data Setmentioning

confidence: 99%

3D-QSAR-based pharmacophore determination and design of novel DPP-4 inhibitors

Rogić¹,

Gagić²

2022

Scripta Medica

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Background/Aim: Therapy of diabetes mellitus type 2 includes drugs that act as inhibitors of dipeptidyl peptidase 4 (DPP-4) enzyme. Several DPP-4 inhibitors are marketed today and although they have favourable safety profile and tolerability, they show moderate activity in controlling glycaemia. The 3D quantitative structure-activity relationship (3D-QSAR) methodology was employed in order to find pharmacophore responsible for good DPP-4 inhibitory activity and designed new compounds with enhanced activity. Methods: For 3D-QSAR model development, 48 compounds structurally related to sitagliptin were collected from ChEMBL database. Structures of all compounds were optimised in order to find the best 3D conformations prior to QSAR modelling. To establish correlation between structure and biological activity Partial Least Squares (PLS) regression method integrated in Pentacle software was used. Results: Parameters of internal and external validation (R2 = 0.80, Q2 = 0.64 and R2 pred = 0.610) confirmed reliability of developed QSAR model. Analysis of obtained structural descriptors enabled identification of key structural characteristics that influenced DPP-4 inhibitory activity. Based on that information, new compounds were designed, of which 35 compounds had a better predicted activity, compared to sitagliptin. Conclusion: This QSAR model can be used for DPP-4 inhibitory activity prediction of structurally related compounds and resulting pharmacophore contains information useful for optimisation and design of new DPP-4 inhibitors. Finally, authors propose designed compounds for further synthesis, in vitro and in vivo testing, as new potential DPP-4 inhibitors.

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Section: Data Setmentioning

confidence: 99%

3D-QSAR-based pharmacophore determination and design of novel DPP-4 inhibitors

Rogić¹,

Gagić²

2022

Scripta Medica

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“…Therefore, there is a need to find new DPP-4 inhibitors with improved pharmacological profiles. Over the past few years, in silico techniques (Quantitative Structure-Activity Relationship (QSAR) [32][33][34][35], ligand-based and structure-based virtual screening [36,37], pharmacophore modeling [7,38,39], molecular docking [40][41][42], semiempirical/DFT calculations [43][44][45][46], molecular dynamics simulation [47,48], etc.) have been widely used in the early phase of drug discovery due to their efficiency and low costs.…”

Section: Introductionmentioning

confidence: 99%

Natural Compounds as DPP-4 Inhibitors: 3D-Similarity Search, ADME Toxicity, and Molecular Docking Approaches

Istrate

Crisan

2022

Symmetry

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Type 2 diabetes mellitus is one of the most common diseases of the 21st century, caused by a sedentary lifestyle, poor diet, high blood pressure, family history, and obesity. To date, there are no known complete cures for type 2 diabetes. To identify bioactive natural products (NPs) to manage type 2 diabetes, the NPs from the ZINC15 database (ZINC-NPs DB) were screened using a 3D shape similarity search, molecular docking approaches, and ADMETox approaches. Frequently, in silico studies result in asymmetric structures as “hit” molecules. Therefore, the asymmetrical FDA-approved diabetes drugs linagliptin (8-[(3R)-3-aminopiperidin-1-yl]-7-but-2-ynyl-3-methyl-1-[(4-methylquinazolin-2-yl)methyl]purine-2,6-dione), sitagliptin ((3R)-3-amino-1-[3-(trifluoromethyl)-6,8-dihydro-5H-[1,2,4]triazolo[4,3-a]pyrazin-7-yl]-4-(2,4,5-trifluorophenyl)butan-1-one), and alogliptin (2-[[6-[(3R)-3-aminopiperidin-1-yl]-3-methyl-2,4-dioxopyrimidin-1-yl]methyl]benzonitrile) were used as queries to virtually screen the ZINC-NPs DB and detect novel potential dipeptidyl peptidase-4 (DPP-4) inhibitors. The most promising NPs, characterized by the best sets of similarity and ADMETox features, were used during the molecular docking stage. The results highlight that 11 asymmetrical NPs out of 224,205 NPs are potential DPP-4 candidates from natural sources and deserve consideration for further in vitro/in vivo tests.

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Quantitative Structure-Activity Relationship Study of DPP-4 Enzyme Inhibitors as Drugs in Therapy of Type 2 Diabetes Mellitus

Cited by 2 publications

References 15 publications

3D-QSAR-based pharmacophore determination and design of novel DPP-4 inhibitors

3D-QSAR-based pharmacophore determination and design of novel DPP-4 inhibitors

Natural Compounds as DPP-4 Inhibitors: 3D-Similarity Search, ADME Toxicity, and Molecular Docking Approaches

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