Quantitative Structure–Activity Relationship and Molecular Docking Studies of Imidazolopyrimidine Amides as Potent Dipeptidyl Peptidase-4 (DPP4) Inhibitors

Main Article Content

Leila Emami
Razieh Sabet
Amirhossein Sakhteman
Mehdi Khoshnevis Zade

Abstract

Type 2 diabetes (T2DM) is a metabolic disorder disease and DPP-4 inhibitors are a class of oral hypoglycemic that blocks the dipeptidyl peptidase-4 (DPP-4) enzyme.  DPP-4 inhibitors reduce glucagon and blood glucose levels and don’t have side effects such as hypoglycemia or weight gain. In this paper, a series of imidazolopyrimidine amides analogues as DPP4 inhibitors were selected for quantitative structure-activity relationship (QSAR) analysis and docking studies. A collection of chemometric methods such as multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), genetic algorithm for variable selection-MLR (GA-MLR) and partial least squared combined with genetic algorithm for variable selection (GA-PLS), were conducted to make relations between structural features and DPP4 inhibitory of a variety of imidazolopyrimidine amides derivatives. GA-PLS represented superior results with high statistical quality (R2 = 0.94 and Q2 = 0.80) for predicting the activity of the compounds. Docking studies of these compounds reveals and confirms that compounds 15, 18, 25, 26, and 28 are introduced as good candidates for DPP-4 inhibitors were introduced as a good candidate for DPP-4 inhibitory compounds.

Keywords:
Imidazopyrimidine derivatives, DPP-4 inhibitors, QSAR, molecular docking

Article Details

How to Cite
Emami, L., Sabet, R., Sakhteman, A., & Zade, M. (2019). Quantitative Structure–Activity Relationship and Molecular Docking Studies of Imidazolopyrimidine Amides as Potent Dipeptidyl Peptidase-4 (DPP4) Inhibitors. Journal of Pharmaceutical Research International, 27(6), 1-15. https://doi.org/10.9734/jpri/2019/v27i630186
Section
Original Research Article

References

Hansch C, Hoekman D, Gao H. Comparative QSAR: Toward a deeper understanding of chemicobiological interactions. Chem. Rev. 1996;96:1045-1076.

Hansch C, Maloney PP, Fujita T, Muir RM. Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature. 1962;194:178-180.

Fassihi A, Sabet R. QSAR study of p56lck protein tyrosine kinase inhibitory activity of flavonoid derivatives using MLR and GA-PLS, Int. J. Mol. Sci. 2008;9:1876-1892.

Hansch T. Fujita. ρ-σ-π Analysis. A method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 1964;86:1616-1626.

Sabet R, Fassihi A, Moeinifard B. QSAR study of PETT derivatives as potent HIV-reverse transcriptase inhibitors. J. Mol. Graph & Model. 2009;28:146.

Hansch C, Hoekman D, Gao H. Compara-tive QSAR: Toward a deeper under-standing of chemicobiological interactions. Chem. Rev. 1996;96:1045-1075.

Todeschini R, Consonni V. Handbook of molecular descriptors. Wiley-VCH, Weinheim; 2000.

Sabet R, Fassihi A. QSAR study of Isatin analogues as in vitro anti-cancer agents. Eur. J. Med. Chem. 2010;45:1113.

Sabet R, Fassihi A, Hemmateenejad B, Saghaie L, Miri R, Gholami M. Computer-aided drug design of novel antibacterial 3-hydroxypyridine-4-ones: Application of QSAR methods based on the MOLMAP approch. Journal of Computer-Aided Molecular Design. 2012;26:349.

Karbakhsh R, Sabet R. Application of different chemometrics tools in QSAR Study of Azolo-adamantanes against influenza a virus. 2011;6:23.

Visit the Hyperchem official.
Available:http://www.hyper.com

Todeschini R. Milano chemometrics and QSPR group.
Available:http://michem.disat.unimib.it/

Wei Meng, Robert P. Brigance, Hannguang J. Chao, Aberra Fura. Discovery of 6-(Aminome thyl) -5-(2,4-dichlorophe nyl) -7-methy limidazo[1,2-a] pyrimidine-2-carbox amides as Potent, Selective Dipeptidyl Peptidase-4 (DPP4) Inhibitor s. J. Med. Chem. 2010;53:5620–5628.

Morris GM, Huey R, Olson AJ. Using auto dock for ligand-receptor docking. Curr Protoc Bioinformatics. 2008;(Chapter 8): Unit 8.14.

Hikisz P, Szczupak Ł, Koceva-Chyła A, Oehninger L, Ott I, Therrien B, et al. Anticancer and antibacterial activity studies of gold (I)-alkynyl chromones. Molecules. 2015;20:19699-718.

Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen MY, et al. Comparative protein structure modeling using modeller. Curr Protoc Bioinformatics. 2006;(Chapter 5):Unit 5.6.

Sakhteman A. PreAupos SOM.
Available:https://www.biomedicale.univ-paris5.fr/aupossom/

Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemomtr. 2000;14:643-655.

Siedlecki W, Sklansky J. On automatic feature selection. Int. J. Pattern Recog. Artif. Intell. 1988;2:197-220.

Schmidi H. Multivariate prediction for QSAR. Chemom. Intell. Lab. Sys. 1997;37: 125-134

Hansch C, Kurup A, Garg R, Gao H. Chem-bioinformatics and QSAR. A Review of QSAR Lacking Positive Hydrophobic Terms. Chem. Rev. 2001;101:619-672.

Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. Journal of Molecular Graphics. 1996;14(1):33-8.

Fereidoonnezhad M, Faghih Z, Mojaddami A, Sakhteman A, Rezaei Z. A comparative docking studies of dichloroacetate analogues on four isozymes of pyruvate dehydrogenase kinase in humans. Indian J Pharm Educ. 2016;50(2):S32-S8.

Mirjalili BF, Zamani L, Zomorodian K, Khabnadideh S, Haghighijoo Z, Malakotikhah Z, et al. Synthesis, antifungal activity and docking study of 2-amino-4H-benzochromene-3-carbonitrile derivatives. Journal of Molecular Structure. 2016; 1116:102-8.

Li Z, Gu J, Zhuang H, Kang L, Zhao X, Guo Q. Adaptive molecular docking method based on information entropy genetic algorithm. Applied Soft Computing. 2015;26:299-302.

Feng J, Ablajan K, Sali A. 4-Dimethy-laminopyridine-catalyzed multi-component one-pot reactions for the convenient synthesis of spiro[indoline-3,4′-pyrano[2,3-c]pyrazole] derivatives. Tetrahedron. 2014; 70(2):484-9.

Fassihi A, Abedi D, Saghaie L, Sabet R, Fazeli H, Bostaki G, Deilami O, Sadinpour H. Eur. J. Med. Chem; 2008.
DOI: 10.1016/j.ejmech.2008.10.022

Sharaf MA, Illman DL, Kowalski BR. Chemometrics. New York: Wiley. 1986; 332.

Olah M, Bologa C, Oprea TI. An automated PLS search for biologically relevant QSAR descriptors. J. Comput. Aided Mol. Des. 2004;18:437-449.

Mohajeri A, Hemmateenejad B, Mehdipour A, Miri R. Modeling calcium channel antagonistic activity of dihydropyridine derivatives using QTMS indices analyzed by GA-PLS and PC-GA-PLS. J. Mol. Graph. Model. 2008;26:1057-1065.

Brereton R. Chemometrics data analysis for the laboratory and chemical plant. Wiley. 2004;47–54.

Liu Y, Liu T. Recent in non-peptidomimetic dipeptidyl peptidase 4 inhibitors: Medicinal chemistry and preclinical aspects. Current Medicinal Chemistry. 2012;19:3982- 3999.