[2]
Penalver J, Timon I, Collantes C, Canizo-Gomez F. Update on the treatment of type 2 diabetes mellitus. World J Diabetes 2016; 7: 354-95.
[3]
Cahn A, Cernea S, Raz I. An update on DPP-4 inhibitors in the management of type 2 diabetes. Expert Opin Emerg Drugs 2016; 121: 1-12.
[4]
Jose T, Inzucchi S. Cardiovascular effects of the DPP-4 inhibitors. Diab Vasc Dis Res 2012; 9: 109-16.
[5]
Stonehouse A, Darsow T, Maggs D. Incretin-based therapies. J Diabetes 2012; 4: 55-67.
[6]
Daniel J, Drucker D, Nauck M. The incretin system: Glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes. Lancet 2006; 368: 1696-705.
[7]
Cox M, Chu H, Kuethe J, et al. The discovery of novel 5, 6, 5- and 5, 5, 6-tricyclic pyrrolidines as potent and selective DPP-4 inhibitors. Bioorg Med Chem Lett 2016; 26: 2622-6.
[8]
Kim D, Wang L, Beconi M, 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. J Med Chem 2005; 48: 141-51.
[9]
Villhauer E, Brinkman J, Naderi G, et al. 1-[[(3-Hydroxy-1-adamantyl)amino]acetyl]-2-cyano-(S)-pyrrolidine: A potent, selective, and orally bioavailable dipeptidyl peptidase IV inhibitor with antihyperglycemic properties. J Med Chem 2003; 46: 2774-89.
[10]
Augeri D, Robl J, David A, et al. Discovery and Preclinical Profile of Saxagliptin (BMS-477118): A highly potent, long-acting, orally active dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. J Med Chem 2005; 48: 5025-37.
[11]
Feng J, Zhang Z, Wallace M, et al. Discovery of alogliptin: a potent, selective, bioavailable, and efficacious inhibitor of dipeptidyl peptidase IV. J Med Chem 2007; 50: 2297-300.
[12]
Eckhardt M, Langkopf E, Mark M, et al. 8-(3-(R)-Aminopiperidin-1-yl)-7-but-2-ynyl-3-methyl-1-(4-methyl-quinazolin-2-ylmethyl)-3,7-dihydropurine-2,6-dione (BI 1356), a highly potent, selective, long-acting, and orally bioavailable DPP-4 inhibitor for the treatment of type 2 diabetes. J Med Chem 2007; 50: 6450-3.
[13]
Yoshida T, Akahoshi F, Sakashita H, et al. Discovery and preclinical profile of teneligliptin (3-[(2s,4s)-4-[4-(3-methyl-1-phenyl-1h-pyrazol-5-yl)piperazin-1-yl]pyrrolidin-2-ylcarbonyl]thiazolidine): A highly potent, selective, long-lasting and orally active dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. Bioorg Med Chem 2012; 20: 5705-19.
[14]
Kato N, Oka M, Murase T, et al. Discovery and pharmacological characterization of N-[2-(2-[(2S)-2-cyanopyrrolidin-1-yl]-2-oxoethylamino)-2-methylpropyl]-2-methylpyrazolo[1,5-a]pyrimidine-6-carboxamide hydrochloride (anagliptin hydrochloride salt) as a potent and selective DPP-IV inhibitor. Bioorg Med Chem 2011; 19: 7221-7.
[15]
Engel M, Hoffmann T, Wagner L, et al. The crystal structure of dipeptidyl peptidase IV (CD26) reveals its functional regulation and enzymatic mechanism. Proc Natl Acad Sci 2003; 100: 5063-8.
[16]
Gupta S, Patil V. Specificity of binding with matrix metalloproteinases. In: Gupta S, Ed. Matrix metalloproteinases Inhibitors. New York: Springer International Publishing AG 2012; Vol. 103: pp. 35-56.
[17]
Nabeno M, Akahoshi F, Kishida H, et al. A comparative study of the binding modes of recently launched dipeptidyl peptidase IV inhibitors in the active site. Biochem Biophys Res Commun 2013; 434: 191-6.
[18]
Jeanneret L. Dipeptidyl peptidase IV and its inhibitors: Therapeutics for type 2 diabetes and what else? J Med Chem 2014; 57: 2197-212.
[19]
Arulmozhiraja S, Matsuo N, Ishitsubo E, Okazaki S, Shimano H, Tokiwa H. Comparative binding analysis of dipeptidyl peptidase IV (DPP-IV) with antidiabetic drugs – an ab initio fragment molecular orbital study. PLoS One 2016; 11: 1-15.
[20]
Nojima H, Kanou K, Terashi G, et al. Comprehensive analysis of the co-structures of dipeptidyl peptidase IV and its inhibitor. BMC Struct Biol 2016; 16: 11-24.
[21]
Kushwaha R, Haq W, Katti S. Sixteen-years of clinically relevant dipeptidyl peptidase-iv (DPP-IV) inhibitors for treatment of type-2 diabetes: a perspective. Curr Med Chem 2014; 21: 1-33.
[22]
Tseng C. Sitagliptin and pancreatic cancer risk in patients with type 2 diabetes. Eur J Clin Invest 2016; 46: 70-9.
[23]
Swann S, Brown P, Muchmore S, et al. A unified, probabilistic framework for structure- and ligand-based virtual screening. J Med Chem 2011; 54: 1223-32.
[24]
Lionta E, Spyrou G, Vassilatis D, Cournia Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem 2014; 14: 1923-38.
[25]
Noha S, Fischer K, Koeberle A, Garscha U, Werz O, Schuster D. Discovery of novel, non-acidic mPGES-1 inhibitors by virtual screening with a multistep protocol. Bioorg Med Chem 2015; 23: 4839-45.
[26]
Discovery Studio Modelling Environment, Version 4.1, Accelrys Software: San Diego, CA, 2005-2014.
[27]
PipelinePilot, Version 9.2, SciTegic, San Diego, CA, 2001-2014.
[28]
FRED, V 2.2.5, OpenEye Scientific, Santa Fe, 1997-2018.
[30]
Edmondson S, Mastracchio A, Mathvink R, et al. (2S, 3S)-3-Amino-4-(3,3-difluoropyrrolidin-1-yl)-N,N-dimethyl-4-oxo-2-(4-[1,2,4]triazolo[1,5-a]-pyridin-6-ylphenyl)butanamide: a selective r-amino amide dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes. J Med Chem 2006; 49: 3614-27.
[31]
Biftu T, Roy R, Chen P, et al. Omarigliptin (MK-3102): A novel long-acting DPP-4 inhibitor for once-weekly treatment of type 2 diabetes. J Med Chem 2014; 57: 3205-12.
[32]
Martis E, Chandarana R, Shaikh M, et al. Quantifying ligand–receptor interactions for gorge-spanning acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease. J Biomol Struct Dyn 2015; 33: 1107-25.
[33]
Guner O. History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem 2002; 2: 1321-32.
[34]
Guner O, Bowen J. Pharmacophore modeling for ADME. Curr Top Med Chem 2013; 13: 1-17.
[35]
Braga R, Andrade C. Assessing the performance of 3d pharmacophore models in virtual screening: How good are they? Curr Top Med Chem 2013; 13: 1127-38.
[36]
Yao T, Xie J, Liu X, et al. Integration of pharmacophore mapping and molecular docking in sequential virtual screening: towards the discovery of novel JAK2 inhibitors. RSC Advances 2017; 7: 10353-60.
[37]
Patel B, Ghate M. Computational studies on structurally diverse dipeptidyl peptidase IV inhibitors: An approach for new antidiabetic drug development. Med Chem Res 2013; 22: 4505-21.
[40]
Onnis V, Kinsella G, Carta G, et al. Virtual screening for the identification of novel nonsteroidal glucocorticoid modulators. J Med Chem 2010; 53: 3065-74.
[41]
Almasri I, Taha M, Mohammad M. New leads for DPP IV inhibition: Structure-based pharmacophore mapping and virtual screening study. Arch Pharm Res 2013; 36: 1326-37.
[42]
McCann M. FRED pose prediction and virtual screening accuracy. J Chem Inf Model 2011; 51: 578-96.
[43]
Elokely K, Doerksen R. Docking challenge: Protein sampling and molecular docking performance. J Chem Inf Model 2013; 53: 1934-45.
[44]
Bickerton G, Paolini G, Besnard J, Muresan S, Andrew L, Hopkins A. Quantifying the chemical beauty of drugs. Nat Chem 2012; 4: 90-8.