Novel heterocyclic DPP-4 inhibitors for the treatment of type 2 diabetes

Sutton, Jon M.; Clark, David E.; Dunsdon, Stephen J.; Fenton, Garry; Fillmore, Amanda; Harris, Neil V.; Higgs, Chris; Hurley, Chris A.; Krintel, Sussie L.; MacKenzie, Robert E.; Duttaroy, Alokesh; Gangl, Eric; Maniara, Wiesia; Sedrani, Richard; Namoto, Kenji; Ostermann, Nils; Gerhartz, Bernd; Sirockin, Finton; Trappe, Jörg; Hassiepen, Ulrich; Baeschlin, Daniel K.

doi:10.1016/j.bmcl.2011.11.054

Cited by 61 publications

(52 citation statements)

References 16 publications

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“…The lipophilic S 1 pocket is considered a crucial molecular anchor point for DPP‐IV inhibitors, and the residues that constitute this pocket are conserved among the peptidases DPP‐IV, DPP8, and DPP9 . Figure and Supporting Information Figure describe two different ways for increasing the bioactivity of DPP‐IV inhibitors through hydrophobic interactions with the S 1 pocket: (a) replacing a but‐2‐yn‐1‐yl substituent by a prenyl group (with associated improvements in bioactivity in a 1.7 to 125‐fold range; see Figure A and Supporting Information Figure A–G); and (b) replacing a monobutyl substituent by either an m‐tolyl or a phenyl group (with respectively 113‐ and 2.6‐fold associated improvements in bioactivity; see Figure B and Supporting Information Figure H) . In all the comparisons in Figure and Supporting Information Figure we observe a tendency in which those compounds presenting a better occupancy of the S 1 pocket achieved higher bioactivities.…”

Section: How To Favor Potent and Selective Dpp‐iv Inhibitors Accordinmentioning

confidence: 85%

“…The blue dashed line shows the π–π interaction between the 22f‐trans ligand and Tyr666 (this π–π interaction was calculated with the help of Maestro using default options). In panel A a but‐2‐yn‐1‐yl substituent in the S 1 subsite is replaced by a prenyl substituent, while in panel B a monobutyl group is replaced by an m‐tolyl group . The ligand orientations are the result of their superposition with co‐crystallized ligands from the same or very similar chemical series (i.e., 4A5S for panel A and 3KWF for panel B ; residue locations in each panel are also taken from this PDB file).…”

Section: How To Favor Potent and Selective Dpp‐iv Inhibitors Accordinmentioning

confidence: 99%

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Activity and selectivity cliffs for DPP‐IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

Ojeda-Montes

Gimeno

Tomás-Hernández

et al. 2018

Medicinal Research Reviews

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The inhibition of dipeptidyl peptidase-IV (DPP-IV) has emerged over the last decade as one of the most effective treatments for type 2 diabetes mellitus, and consequently (a) 11 DPP-IV inhibitors have been on the market since 2006 (three in 2015), and (b) 74 noncovalent complexes involving human DPP-IV and drug-like inhibitors are available at the Protein Data Bank (PDB). The present review aims to (a) explain the most important activity cliffs for DPP-IV noncovalent inhibition according to the binding site structure of DPP-IV, (b) explain the most important selectivity cliffs for DPP-IV noncovalent inhibition in comparison with other related enzymes (i.e., DPP8 and DPP9), and (c) use the information deriving from this activity/selectivity cliff analysis to suggest how virtual screening protocols might be improved to favor the early identification of potent and selective DPP-IV inhibitors in molecular databases (because they have not succeeded in identifying selective DPP-IV inhibitors with IC ≤ 100 nM). All these goals are achieved with the help of available homology models for DPP8 and DPP9 and an analysis of the structure-activity studies used to develop the noncovalent inhibitors that form part of some of the complexes with human DPP-IV available at the PDB.

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Section: How To Favor Potent and Selective Dpp‐iv Inhibitors Accordinmentioning

confidence: 85%

Section: How To Favor Potent and Selective Dpp‐iv Inhibitors Accordinmentioning

confidence: 99%

Activity and selectivity cliffs for DPP‐IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

Ojeda-Montes

Gimeno

Tomás-Hernández

et al. 2018

Medicinal Research Reviews

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“…The docking score (GBVI/ WSA dG) for all food peptides (IPP, VPP, IY and VY) was less than À 10, supporting that peptides bind to the active site in the ACE (Table 5). [54] Figure 8 shows that the food peptide interacts with the binding site of the known inhibitor in DPP4. The results support that the peptides inhibited the function of the ACE and hydrolytic cleavage of angiotensin I, and were consistent with the GCNN prediction.…”

Section: Structural Analysis Of Peptide-target Interactions Using Docmentioning

confidence: 99%

Prediction of the Health Effects of Food Peptides and Elucidation of the Mode‐of‐action Using Multi‐task Graph Convolutional Neural Network

Fukunaga

Sawada

Shibata

et al. 2019

Molecular Informatics

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Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In this study, we propose a novel deep learning-based method to predict the health effects of food peptides and elucidate the mode-of-action. In the algorithm, we estimate potential target proteins of food peptides using a multi-task graph convolutional neural network, and predict its health effects using information about therapeutic targets for diseases. We constructed predictive models based on 21,103 peptide-protein interactions involving 10,950 peptides and 2,533 proteins, and applied the models to food peptides (e. g., lactotripeptide, isoleucyltyrosine and sardine peptide) defined in food for specified health use. The models suggested potential effects such as blood-pressure lowering effects, blood glucose level lowering effects, and anti-cancer effects for several food peptides. The interactions of food peptides with target proteins were confirmed by docking simulations.Keywords: foods · peptides · health effects · peptide-protein interaction · graph convolutional neural network · deep learning[a] I.

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“…Inhibitors stimulate insulin secretion in a glucose-dependent manner, DPP-4 inhibitors present lower risks on hypoglycemia and may not lead to weight gain, also protect pancreatic beta cells. The up-regulation of gastric inhibitory polypeptide and pituitary adenylate cyclase activating polypeptide by DPP-4 inhibitor also result in blood glucose reduction [6,7]. Clinical data indicate that treatment with DPP-4 inhibitors, in combination with other hypoglycemic drugs result in significantly improved glycemic control compared with DPP-4 inhibitor or metformin alone in patients with type 2 diabetes mellitus (T2DM) [8,9].…”

Section: Introductionmentioning

confidence: 96%

Synergism in Pharmacokinetics of Retagliptin and Metformin Observed during Clinical Trials of their Combination Therapy

Yong¹,

Hu²,

Feng³

et al. 2015

Trop. J. Pharm Res

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Novel heterocyclic DPP-4 inhibitors for the treatment of type 2 diabetes

Cited by 61 publications

References 16 publications

Activity and selectivity cliffs for DPP‐IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

Activity and selectivity cliffs for DPP‐IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

Prediction of the Health Effects of Food Peptides and Elucidation of the Mode‐of‐action Using Multi‐task Graph Convolutional Neural Network

Synergism in Pharmacokinetics of Retagliptin and Metformin Observed during Clinical Trials of their Combination Therapy

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