Login Register Cart 0
Generic placeholder image

Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Three Dimensional Quantitative Structure Activity Relationship and Pharmacophore Modeling of Tacrine Derivatives as Acetylcholinesterase Inhibitors in Alzheimer's Treatment

Author(s): Fatemeh Ansari, Jahan B. Ghasemi* and Ali Niazi

Volume 16, Issue 2, 2020

Page: [155 - 168] Pages: 14

DOI: 10.2174/1573406415666190513100646

Price: $65

Abstract

Background: Three dimensional quantitative structure activity relationship and pharmacophore modeling are studied for tacrine derivatives as acetylcholinesterase inhibitors.

Methods: The three dimensional quantitative structure–activity relationship and pharmacophore methods were used to model the 68 derivatives of tacrine as human acetylcholinesterase inhibitors. The effect of the docked conformer of each molecule in the enzyme cavity was investigated on the predictive ability and statistical quality of the produced models.

Results: The whole data set was divided into two training and test sets using hierarchical clustering method. 3D-QSAR model, based on the comparative molecular field analysis has good statistical parameters as indicated by q2 =0.613, r2 =0.876, and r2pred =0.75. In the case of comparative molecular similarity index analysis, q2, r2 and r2pred values were 0.807, 0.96, and 0.865 respectively. The statistical parameters of the models proved that the inhibition data are well fitted and they have satisfactory predictive abilities.

Conclusion: The results from this study illustrate the reliability of using techniques in exploring the likely bonded conformations of the ligands in the active site of the protein target and improve the understanding over the structural and chemical features of AChE.

Keywords: Alzheimer disease, tacrine, inhibitor, 3D-QSAR, pharmacophore modeling, molecular docking.

« Previous Next »
Graphical Abstract

[1]
Kovacs, G. Current Concepts of Neurodegenerative Diseases. 2014.
[2]
Wang, Y.; Guan, X-L.; Wu, P-F.; Wang, C-M.; Cao, H.; Li, L.; Guo, X-J.; Wang, F.; Xie, N.; Jiang, F-C.; Chen, J.G. Multifunctional mercapto-tacrine derivatives for treatment of age-related neurodegenerative diseases. J. Med. Chem., 2012, 55(7), 3588-3592.
[http://dx.doi.org/10.1021/jm300124p] [PMID: 22420827]
[3]
Tabet, N. Acetylcholinesterase inhibitors for Alzheimer’s disease: anti-inflammatories in acetylcholine clothing! Age Ageing, 2006, 35(4), 336-338.
[http://dx.doi.org/10.1093/ageing/afl027] [PMID: 16788077]
[4]
Francis, P.T.; Palmer, A.M.; Snape, M.; Wilcock, G.K. The cholinergic hypothesis of Alzheimer’s disease: a review of progress. J. Neurol. Neurosurg. Psychiatry, 1999, 66(2), 137-147.
[http://dx.doi.org/10.1136/jnnp.66.2.137] [PMID: 10071091]
[5]
O’Brien, R.J.; Wong, P.C. Amyloid precursor protein processing and Alzheimer’s disease. Annu. Rev. Neurosci., 2011, 34, 185-204.
[http://dx.doi.org/10.1146/annurev-neuro-061010-113613] [PMID: 21456963]
[6]
Coyle, J.T.; Price, D.L.; DeLong, M.R. Alzheimer’s disease: a disorder of cortical cholinergic innervation. Science, 1983, 219(4589), 1184-1190.
[http://dx.doi.org/10.1126/science.6338589] [PMID: 6338589]
[7]
Sterling, J.; Herzig, Y.; Goren, T.; Finkelstein, N.; Lerner, D.; Goldenberg, W.; Miskolczi, I.; Molnar, S.; Rantal, F.; Tamas, T.; Toth, G.; Zagyva, A.; Zekany, A.; Finberg, J.; Lavian, G.; Gross, A.; Friedman, R.; Razin, M.; Huang, W.; Krais, B.; Chorev, M.; Youdim, M.B.; Weinstock, M. Novel dual inhibitors of AChE and MAO derived from hydroxy aminoindan and phenethylamine as potential treatment for Alzheimer’s disease. J. Med. Chem., 2002, 45(24), 5260-5279.
[http://dx.doi.org/10.1021/jm020120c] [PMID: 12431053]
[8]
Dvir, H.; Silman, I.; Harel, M.; Rosenberry, T.L.; Sussman, J.L. Acetylcholinesterase: from 3D structure to function. Chem. Biol. Interact., 2010, 187(1-3), 10-22.
[http://dx.doi.org/10.1016/j.cbi.2010.01.042] [PMID: 20138030]
[9]
Kryger, G.; Silman, I.; Sussman, J.L. Structure of acetylcholinesterase complexed with E2020 (Aricept): implications for the design of new anti-Alzheimer drugs. Structure, 1999, 7(3), 297-307.
[http://dx.doi.org/10.1016/S0969-2126(99)80040-9] [PMID: 10368299]
[10]
Cheung, J.; Rudolph, M.J.; Burshteyn, F.; Cassidy, M.S.; Gary, E.N.; Love, J.; Franklin, M.C.; Height, J.J. Structures of human acetylcholinesterase in complex with pharmacologically important ligands. J. Med. Chem., 2012, 55(22), 10282-10286.
[http://dx.doi.org/10.1021/jm300871x] [PMID: 23035744]
[11]
Fernández-Bachiller, M.I.; Pérez, C.; Monjas, L.; Rademann, J.; Rodríguez-Franco, M.I. New tacrine-4-oxo-4H-chromene hybrids as multifunctional agents for the treatment of Alzheimer’s disease, with cholinergic, antioxidant, and β-amyloid-reducing properties. J. Med. Chem., 2012, 55(3), 1303-1317.
[http://dx.doi.org/10.1021/jm201460y] [PMID: 22243648]
[12]
Vitorović-Todorović, M.D.; Cvijetić, I.N.; Juranić, I.O.; Drakulić, B.J. The 3D-QSAR study of 110 diverse, dual binding, acetylcholinesterase inhibitors based on alignment independent descriptors (GRIND-2). The effects of conformation on predictive power and interpretability of the models. J. Mol. Graph. Model., 2012, 38, 194-210.
[http://dx.doi.org/10.1016/j.jmgm.2012.08.001] [PMID: 23073222]
[13]
Eagger, S.A.; Levy, R.; Sahakian, B.J. Tacrine in Alzheimer’s disease. Lancet, 1991, 337(8748), 989-992.
[http://dx.doi.org/10.1016/0140-6736(91)92656-M] [PMID: 1673209]
[14]
Shao, D.; Zou, C.; Luo, C.; Tang, X.; Li, Y. Synthesis and evaluation of tacrine-E2020 hybrids as acetylcholinesterase inhibitors for the treatment of Alzheimer’s disease. Bioorg. Med. Chem. Lett., 2004, 14(18), 4639-4642.
[http://dx.doi.org/10.1016/j.bmcl.2004.07.005] [PMID: 15324879]
[15]
Camps, P.; Morral, J.; Muñoz-Torrero, D.; Badia, A.; Baños, J.E.; Vivas, N.M.; Barril, X.; Orozco, M.; Luque, F.J.; Luque, F.J.; New Tacrine−Huperzine, A. New tacrine-huperzine A hybrids (huprines): highly potent tight-binding acetylcholinesterase inhibitors of interest for the treatment of Alzheimer’s disease. J. Med. Chem., 2000, 43(24), 4657-4666.
[http://dx.doi.org/10.1021/jm000980y] [PMID: 11101357]
[16]
Girek, M.; Szymański, P. Tacrine hybrids as multi-target-directed ligands in Alzheimer’s disease: influence of chemical structures on biological activities. Chem. Pap., 2019, 73(2), 269-289.
[http://dx.doi.org/10.1007/s11696-018-0590-8]
[17]
DiMasi, J.A.; Hansen, R.W.; Grabowski, H.G. The price of innovation: new estimates of drug development costs. J. Health Econ., 2003, 22(2), 151-185.
[http://dx.doi.org/10.1016/S0167-6296(02)00126-1] [PMID: 12606142]
[18]
Adams, C.P.; Brantner, V.V. Estimating the cost of new drug development: is it really 802 million dollars? Health Aff. (Millwood), 2006, 25(2), 420-428.
[http://dx.doi.org/10.1377/hlthaff.25.2.420] [PMID: 16522582]
[19]
Bajorath, J. Computer-aided drug discovery. F1000Research,., 2015, 4, pii: F1000.
[20]
Moro, S.; Bacilieri, M.; Deflorian, F. Combining ligand-based and structure-based drug design in the virtual screening arena. Expert Opin. Drug Discov., 2007, 2(1), 37-49.
[http://dx.doi.org/10.1517/17460441.2.1.37] [PMID: 23496036]
[21]
Acharya, C.; Coop, A.; Polli, J.E.; Mackerell, A.D., Jr Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr. Comput. Aided Drug Des., 2011, 7(1), 10-22.
[http://dx.doi.org/10.2174/157340911793743547] [PMID: 20807187]
[22]
Merz, K.M., Jr; Ringe, D.; Reynolds, C.H.; Reynolds, C.H. Drug design: structure-and ligand-based approaches; In: Cambridge University Press, 2010.
[http://dx.doi.org/10.1017/CBO9780511730412]
[23]
Donkin, P. Quantitative Structure-Activity Relationships. In: In: Handbook of Ecotoxicology; , 1997; p. pp. 785-811.
[24]
Ambure, P.; Roy, K. Advances in quantitative structure-activity relationship models of anti-Alzheimer’s agents. Expert Opin. Drug Discov., 2014, 9(6), 697-723.
[http://dx.doi.org/10.1517/17460441.2014.909404] [PMID: 24754675]
[25]
Patel, H.M.; Noolvi, M.N.; Sharma, P.; Jaiswal, V.; Bansal, S.; Lohan, S.; Kumar, S.S.; Abbot, V.; Dhiman, S.; Bhardwaj, V. Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery. Med. Chem. Res., 2014, 23(12), 4991-5007.
[http://dx.doi.org/10.1007/s00044-014-1072-3]
[26]
Akamatsu, M. Current state and perspectives of 3D-QSAR. Curr. Top. Med. Chem., 2002, 2(12), 1381-1394.
[http://dx.doi.org/10.2174/1568026023392887] [PMID: 12470286]
[27]
Martin, Y.C. 3D QSAR: current state, scope, and limitations. Perspect. Drug. Discov, 1998, 12, 3-23.
[28]
Böhm, M. St rzebecher, J.; Klebe, G. Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J. Med. Chem., 1999, 42(3), 458-477.
[http://dx.doi.org/10.1021/jm981062r] [PMID: 9986717]
[29]
Nikolova, N.; Jaworska, J. Approaches to measure chemical similarity–a review. QSAR Comb. Sci., 2003, 22(9-10), 1006-1026.
[http://dx.doi.org/10.1002/qsar.200330831]
[30]
Li, Y-P.; Weng, X.; Ning, F-X.; Ou, J-B.; Hou, J-Q.; Luo, H-B.; Li, D.; Huang, Z-S.; Huang, S-L.; Gu, L-Q. 3D-QSAR studies of azaoxoisoaporphine, oxoaporphine, and oxoisoaporphine derivatives as anti-AChE and anti-AD agents by the CoMFA method. J. Mol. Graph. Model., 2013, 41, 61-67.
[http://dx.doi.org/10.1016/j.jmgm.2013.02.003] [PMID: 23500628]
[31]
Zaki, H.; Belhassan, A.; Aouidate, A.; Lakhlifi, T.; Benlyas, M.; Bouachrine, M. Antibacterial study of 3-(2-amino-6-phenyl-pyrimidin-4-yl)-N-cyclopropyl-1-methyl-1H-indole-2-carboxamide derivatives: CoMFA, CoMSIA analyses, molecular docking and ADMET properties prediction. J. Mol. Struct., 2019, 1177, 275-285.
[http://dx.doi.org/10.1016/j.molstruc.2018.09.073]
[32]
Kubinyi, H.; Martin, Y.C.; Folkers, G. 3D QSAR in drug design: volume 1: theory methods and applications. In: In: Springer Science & Business Media, 1; , 1993.
[33]
Suh, M-E.; Park, S-Y.; Lee, H-J. Comparison of QSAR methods (CoMFA, CoMSIA, HQSAR) of anticancer 1-N-substituted imidazoquinoline-4, 9-dione derivatives. B. Kor. Chem. Soc., 2002, 23(3), 417-422.
[http://dx.doi.org/10.5012/bkcs.2002.23.3.417]
[34]
Viswanadhan, V.N.; Ghose, A.K.; Revankar, G.R.; Robins, R.K. Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J. chem. Inf. Comp. Sci., 1989, 29(3), 163-172.
[http://dx.doi.org/10.1021/ci00063a006]
[35]
Daoud, I.; Melkemi, N.; Salah, T.; Ghalem, S. Combined QSAR, molecular docking and molecular dynamics study on new Acetylcholinesterase and Butyrylcholinesterase inhibitors. Comput. Biol. Chem., 2018, 74, 304-326.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.03.021] [PMID: 29747032]
[36]
Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins, 2002, 47(4), 409-443.
[http://dx.doi.org/10.1002/prot.10115] [PMID: 12001221]
[37]
Morris, G.M.; Green, L.G.; Radić, Z.; Taylor, P.; Sharpless, K.B.; Olson, A.J.; Grynszpan, F. Automated docking with protein flexibility in the design of femtomolar “click chemistry” inhibitors of acetylcholinesterase. J. Chem. Inf. Model., 2013, 53(4), 898-906.
[http://dx.doi.org/10.1021/ci300545a] [PMID: 23451944]
[38]
Schneidman-Duhovny, D.; Nussinov, R.; Wolfson, H.J. Predicting molecular interactions in silico: II. Protein-protein and protein-drug docking. Curr. Med. Chem., 2004, 11(1), 91-107.
[http://dx.doi.org/10.2174/0929867043456223] [PMID: 14754428]
[39]
Zamora, I.; Oprea, T.; Cruciani, G.; Pastor, M.; Ungell, A-L. Surface descriptors for protein-ligand affinity prediction. J. Med. Chem., 2003, 46(1), 25-33.
[http://dx.doi.org/10.1021/jm011051p] [PMID: 12502357]
[40]
Meng, X-Y.; Zhang, H-X.; Mezei, M.; Cui, M. Molecular docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des., 2011, 7(2), 146-157.
[http://dx.doi.org/10.2174/157340911795677602] [PMID: 21534921]
[41]
Huang, S-Y.; Zou, X. Annual Reports in Computational Chemistry; Wheeler, R.A., Ed.; Elsevier, 2010, Vol. 6, pp. 280-296.
[42]
Pirhadi, S.; Shiri, F.; Ghasemi, J.B. Methods and applications of structure based pharmacophores in drug discovery. Curr. Top. Med. Chem., 2013, 13(9), 1036-1047.
[http://dx.doi.org/10.2174/1568026611313090006] [PMID: 23651482]
[43]
Xu, Z.; Cheng, F.; Da, C.; Liu, G.; Tang, Y. Pharmacophore modeling of human adenosine receptor A(2A) antagonists. J. Mol. Model., 2010, 16(12), 1867-1876.
[http://dx.doi.org/10.1007/s00894-010-0690-z] [PMID: 20224910]
[44]
Sirci, F.; Goracci, L.; Rodríguez, D.; van Muijlwijk-Koezen, J.; Gutiérrez-de-Terán, H.; Mannhold, R. Ligand-, structure- and pharmacophore-based molecular fingerprints: a case study on adenosine A(1), A (2A), A (2B), and A (3) receptor antagonists. J. Comput. Aided Mol. Des., 2012, 26(11), 1247-1266.
[http://dx.doi.org/10.1007/s10822-012-9612-8] [PMID: 23065321]
[45]
Halder, A.K.; Moura, A.S.; Cordeiro, M.N.D. QSAR modelling: A therapeutic patent review 2010-present. Expert Opin. Ther. Pat., 2018.
[46]
Lambrinidis, G.; Tsantili-Kakoulidou, A. Challenges with multi-objective QSAR in drug discovery. Expert Opin. Drug Discov., 2018, 13(9), 851-859.
[http://dx.doi.org/10.1080/17460441.2018.1496079] [PMID: 29996683]
[47]
Gemperline, P. Practical guide to chemometrics., http://www. crcnetbase.com/isbn/9781420018301
[http://dx.doi.org/10.1201/9781420018301]
[48]
Wang, R.; Gao, Y.; Liu, L.; Lai, L. All-Orientation Search and All-Placement Search in Comparative Molecular Field Analysis. Mol. Model. Annual., 1998, 4(8), 276-283.
[http://dx.doi.org/10.1007/s008940050085]
[49]
Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110(18), 5959-5967.
[http://dx.doi.org/10.1021/ja00226a005] [PMID: 22148765]
[50]
Brooks, B.R.; Brooks, C.L., III; Mackerell, A.D., Jr; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A.R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M. Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R.W.; Post, C.B.; Pu, J.Z.; Schaefer, M.; Tidor, B.; Venable, R.M.; Woodcock, H.L.; Wu, X.; Yang, W.; York, D.M.; Karplus, M. CHARMM: the biomolecular simulation program. J. Comput. Chem., 2009, 30(10), 1545-1614.
[http://dx.doi.org/10.1002/jcc.21287] [PMID: 19444816]
[51]
Momany, F.A.; Rone, R. Validation of the general purpose QUANTA® 3.2/CHARMm® force field. J. Comput. Chem., 1992, 13(7), 888-900.
[http://dx.doi.org/10.1002/jcc.540130714]
[52]
Lu, P.; Wei, X.; Zhang, R. CoMFA and CoMSIA 3D-QSAR studies on quionolone caroxylic acid derivatives inhibitors of HIV-1 integrase. Eur. J. Med. Chem., 2010, 45(8), 3413-3419.
[http://dx.doi.org/10.1016/j.ejmech.2010.04.030] [PMID: 20488589]
[53]
Roy, K.; Chakraborty, P.; Mitra, I.; Ojha, P.K.; Kar, S.; Das, R.N. Some case studies on application of “r(m)2” metrics for judging quality of quantitative structure-activity relationship predictions: emphasis on scaling of response data. J. Comput. Chem., 2013, 34(12), 1071-1082.
[http://dx.doi.org/10.1002/jcc.23231] [PMID: 23299630]

Rights & Permissions Print Cite
Article Metrics
22
3
© 2024 Bentham Science Publishers | Privacy Policy