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Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Research Article

QSAR and DFT Studies of Some Tacrine-Hydroxamate Derivatives as Inhibitors of Cholinesterase (AChEs) in the Treatment of Alzheimer's Disease

Author(s): Imad Hammoudan, Samir Chtita*, Ossama Daoui, Souad Elkhattabi, Mohamed Bakhouch, Mohamed El Yazidi, Farhan Siddique and Driss Riffi-Temsamani

Volume 20, Issue 6, 2023

Published on: 15 August, 2022

Page: [699 - 712] Pages: 14

DOI: 10.2174/1570180819666220512174409

Price: $65

Abstract

Introduction: This work was devoted to an in silico investigation conducted on twenty-eight Tacrine-hydroxamate derivatives as a potential treatment for Alzheimer’s disease using DFT and QSAR modeling techniques.

Methods: The data set was randomly partitioned into a training set (22 compounds) and a test set (6 compounds). Then, fourteen models were built and were used to compute the predicted pIC50 of compounds belonging to the test set.

Results: All built models were individually validated using both internal and external validation methods, including the Y-Randomization test and Golbraikh and Tropsha's model acceptance criteria. Then, one model was selected for its higher R², R²test, and Q²cv values (R² = 0.768, R²adj = 0.713, MSE = 0.304, R²test=0.973, Q²cv = 0.615). From these outcomes, the activity of the studied compounds toward the main protease of Cholinesterase (AChEs) seems to be influenced by 4 descriptors, i.e., the total dipole moment of the molecule (μ), number of rotatable bonds (RB), molecular topology radius (MTR) and molecular topology polar surface area (MTPSA). The effect of these descriptors on the activity was studied, in particular, the increase in the total dipole moment and the topological radius of the molecule and the reduction of the rotatable bond and topology polar surface area increase the activity.

Conclusion: Some newly designed compounds with higher AChEs inhibitory activity have been designed based on the best-proposed QSAR model. In addition, ADMET pharmacokinetic properties were carried out for the proposed compounds, the toxicity results indicate that 7 molecules are nontoxic.

Keywords: Molecular modeling, DFT, QSAR, Alzheimer, cholinesterase inhibitor, ADMET.

Graphical Abstract
[1]
Atri, A. Current and future treatments in Alzheimer’s disease. Semin. Neurol., 2019, 39(2), 227-240.
[http://dx.doi.org/10.1055/s-0039-1678581] [PMID: 30925615]
[2]
Millan, M.J. Linking deregulation of non-coding RNA to the core pathophysiology of Alzheimer’s disease: An integrative review. Prog. Neurobiol., 2017, 156, 1-68.
[http://dx.doi.org/10.1016/j.pneurobio.2017.03.004] [PMID: 28322921]
[3]
Nagaraj, S.; Zoltowska, K.M.; Laskowska-Kaszub, K.; Wojda, U. microRNA diagnostic panel for Alzheimer’s disease and epigenetic trade-off between neurodegeneration and cancer. Ageing Res. Rev., 2019, 49, 125-143.
[http://dx.doi.org/10.1016/j.arr.2018.10.008] [PMID: 30391753]
[4]
McKenna, M.T.; Proctor, G.R.; Young, L.C.; Harvey, A.L. Novel tacrine analogues for potential use against Alzheimer’s disease: Potent and selective acetylcholinesterase inhibitors and 5-HT uptake inhibitors. J. Med. Chem., 1997, 40(22), 3516-3523.
[http://dx.doi.org/10.1021/jm970150t] [PMID: 9357518]
[5]
Silman, I.; Sussman, J.L. Acetylcholinesterase: ‘classical’ and ‘non-classical’ functions and pharmacology. Curr. Opin. Pharmacol., 2005, 5(3), 293-302.
[http://dx.doi.org/10.1016/j.coph.2005.01.014] [PMID: 15907917]
[6]
Selkoe, D.J.; Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med., 2016, 8(6), 595-608.
[http://dx.doi.org/10.15252/emmm.201606210] [PMID: 27025652]
[7]
Ballatore, C.; Lee, V.M-Y.; Trojanowski, J.Q. Tau-mediated neurodegeneration in Alzheimer’s disease and related disorders. Nat. Rev. Neurosci., 2007, 8(9), 663-672.
[http://dx.doi.org/10.1038/nrn2194] [PMID: 17684513]
[8]
Kepp, K.P. Alzheimer’s disease: How metal ions define β-amyloid function. Coord. Chem. Rev., 2017, 351, 127-159.
[http://dx.doi.org/10.1016/j.ccr.2017.05.007]
[9]
Kinney, J.W.; Bemiller, S.M.; Murtishaw, A.S.; Leisgang, A.M.; Salazar, A.M.; Lamb, B.T. Inflammation as a central mechanism in Alzheimer’s disease. Alzheimers Dement. (N. Y.), 2018, 4(1), 575-590.
[http://dx.doi.org/10.1016/j.trci.2018.06.014] [PMID: 30406177]
[10]
Cardoso, S.; Carvalho, C.; Correia, S.C.; Seiça, R.M.; Moreira, P.I. Alzheimer’s disease: From mitochondrial perturbations to mitochondrial medicine. Brain Pathol., 2016, 26(5), 632-647.
[http://dx.doi.org/10.1111/bpa.12402] [PMID: 27327899]
[11]
Wilson, B.; Samanta, M.K.; Santhi, K.; Kumar, K.P.S.; Paramakrishnan, N.; Suresh, B. Targeted delivery of tacrine into the brain with polysorbate 80-coated poly(n-butylcyanoacrylate) nanoparticles. Eur. J. Pharm. Biopharm., 2008, 70(1), 75-84.
[http://dx.doi.org/10.1016/j.ejpb.2008.03.009] [PMID: 18472255]
[12]
Lin, H.; Li, Q.; Gu, K.; Zhu, J.; Jiang, X.; Chen, Y.; Sun, H. Therapeutic agents in Alzheimer’s disease through a multi-targetdirected ligands strategy: Recent progress based on tacrine core. Curr. Top. Med. Chem., 2017, 17(27), 3000-3016.
[http://dx.doi.org/10.2174/1568026617666170717114944] [PMID: 28714419]
[13]
Teponnou, G.A.K.; Joubert, J.; Malan, S.F. Tacrine, trolox and tryptoline as lead compounds for the design and synthesis of multi-target agents for Alzheimer’s disease therapy. Open Med. Chem. J., 2017, 11(1), 24-37.
[http://dx.doi.org/10.2174/1874104501711010024] [PMID: 28567126]
[14]
Jalili-Baleh, L.; Nadri, H.; Moradi, A.; Bukhari, S.N.A.; Shakibaie, M.; Jafari, M.; Golshani, M.; Homayouni Moghadam, F.; Firoozpour, L.; Asadipour, A.; Emami, S.; Khoobi, M.; Foroumadi, A. New racemic annulated pyrazolo[1,2-b]phthalazines as tacrine-like AChE inhibitors with potential use in Alzheimer’s disease. Eur. J. Med. Chem., 2017, 139, 280-289.
[http://dx.doi.org/10.1016/j.ejmech.2017.07.072] [PMID: 28803044]
[15]
Li, G.; Hong, G.; Li, X.; Zhang, Y.; Xu, Z.; Mao, L.; Feng, X.; Liu, T. Synthesis and activity towards Alzheimer’s disease in vitro: Tacrine, phenolic acid and ligustrazine hybrids. Eur. J. Med. Chem., 2018, 148, 238-254.
[http://dx.doi.org/10.1016/j.ejmech.2018.01.028] [PMID: 29466774]
[16]
Viayna, E.; Sola, I.; Bartolini, M.; De Simone, A.; Tapia-Rojas, C.; Serrano, F.G.; Sabaté, R.; Juárez-Jiménez, J.; Pérez, B.; Luque, F.J.; Andrisano, V.; Clos, M.V.; Inestrosa, N.C.; Muñoz-Torrero, D. Synthesis and multitarget biological profiling of a novel family of rhein derivatives as disease-modifying anti-Alzheimer agents. J. Med. Chem., 2014, 57(6), 2549-2567.
[http://dx.doi.org/10.1021/jm401824w] [PMID: 24568372]
[17]
Safarizadeh, H.; Garkani-Nejad, Z. Molecular docking, molecular dynamics simulations and QSAR studies on some of 2-arylethenylquinoline derivatives for inhibition of Alzheimer’s amyloid-beta aggregation: Insight into mechanism of interactions and parameters for design of new inhibitors. J. Mol. Graph. Model., 2019, 87, 129-143.
[http://dx.doi.org/10.1016/j.jmgm.2018.11.019] [PMID: 30537643]
[18]
Tumiatti, V.; Minarini, A.; Bolognesi, M.L.; Milelli, A.; Rosini, M.; Melchiorre, C. Tacrine derivatives and Alzheimer’s disease. Curr. Med. Chem., 2010, 17(17), 1825-1838.
[http://dx.doi.org/10.2174/092986710791111206] [PMID: 20345341]
[19]
Xu, A.; He, F.; Zhang, X.; Li, X.; Ran, Y.; Wei, C.; James Chou, C.; Zhang, R.; Wu, J. Tacrine-hydroxamate derivatives as multitarget-directed ligands for the treatment of Alzheimer’s disease: Design, synthesis, and biological evaluation. Bioorg. Chem., 2020, 98103721
[http://dx.doi.org/10.1016/j.bioorg.2020.103721] [PMID: 32193030]
[20]
Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; Scuseria, G.E.; Robb, M.A.; Cheeseman, J.R.; Scalmani, G.; Barone, V.; Petersson, G.A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A.; Bloino, J.; Janesko, B.G.; Gomperts, R.; Mennucci, B.; Hratchian, H.P.; Ortiz, J.V.; Izmaylov, A.F.; Sonnenberg, J.L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V.G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery, J.A., Jr; Peralta, J.E.; Ogliaro, F.; Bearpark, M.; Heyd, J.J.; Brothers, E.; Kudin, K.N.; Staroverov, V.N.; Keith, T.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J.C.; Iyengar, S.S.; Tomasi, J.; Cossi, M.; Millam, J.M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J.W.; Martin, R.L.; Morokuma, K.; Farkas, O.; Foresman, J.B.; Fox, D.J. Gaussian 09, Revision A.02; Gaussian, Inc.: Wallingford, CT, 2016.
[21]
Hammoudan, I.; Chtita, S.; Riffi-Temsamani, D. QTAIM and IRC studies for the evaluation of activation energy on the C=P, C=N and C=O Diels-Alder reaction. Heliyon, 2020, 6(8)e04655
[http://dx.doi.org/10.1016/j.heliyon.2020.e04655] [PMID: 32904344]
[22]
Chtita, S.; Larif, M.; Ghamali, M.; Bouachrine, M.; Lakhlifi, T. Quantitative structure–activity relationship studies of dibenzo[a,d]cycloalkenimine derivatives for non-competitive antagonists of N-Methyl-d-aspartate based on density functional theory with electronic and topological descriptors. J. Taibah Univ. Sci., 2015, 9(2), 143-154.
[http://dx.doi.org/10.1016/j.jtusci.2014.10.006]
[23]
Chtita, S.; Belhassan, A.; Bakhouch, M.; Taourati, A.I.; Aouidate, A.; Belaidi, S.; Moutaabbid, M.; Belaaouad, S.; Bouachrine, M.; Lakhlifi, T. QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods. Chemom. Intell. Lab. Syst., 2021, 210(15)104266
[http://dx.doi.org/10.1016/j.chemolab.2021.104266] [PMID: 33558778]
[24]
Chtita, S.; Ghamali, M.; Ousaa, A.; Aouidate, A.; Belhassan, A.; Taourati, A.I.; Masand, V.H.; Bouachrine, M.; Lakhlifi, T. QSAR study of anti-human African trypanosomiasis activity for 2-phenylimidazopyridines derivatives using DFT and lipinski’s descriptors. Heliyon, 2019, 5(3)e01304
[http://dx.doi.org/10.1016/j.heliyon.2019.e01304] [PMID: 30899832]
[25]
Becke, A.D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A Gen. Phys., 1988, 38(6), 3098-3100.
[http://dx.doi.org/10.1103/PhysRevA.38.3098] [PMID: 9900728]
[26]
Petersson, G.A. Al‐Laham, M.A. A complete basis set model chemistry. II. Open‐shell systems and the total energies of the first‐row atoms. J. Chem. Phys., 1991, 94(9), 6081-6090.
[http://dx.doi.org/10.1063/1.460447]
[27]
ChemOffice Download ChemDraw and Chem3D. Available from: Http://www.Chem.Ox.Ac.Uk/Software/Chemoffice.Html
[28]
Daoui, O.; Elkhattabi, S.; Chtita, S.; Elkhalabi, R.; Zgou, H.; Benjelloun, A.T. QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase. Heliyon, 2021, 7(7)e07463
[http://dx.doi.org/10.1016/j.heliyon.2021.e07463] [PMID: 34296007]
[29]
Allinger, N.L. Conformational analysis. 130. MM2. A hydrocarbon force field utilizing V1 and V2 torsional terms. J. Am. Chem. Soc., 1977, 99(25), 8127-8134.
[http://dx.doi.org/10.1021/ja00467a001]
[30]
Gramatica, P. Principles of QSAR Models Validation: Internal and External. QSAR Comb. Sci., 2007, 26(5), 694-701.
[http://dx.doi.org/10.1002/qsar.200610151]
[31]
XLSTAT, Software, XLSTAT Company. 2013. Available from: Www.Xlstat.Com (Accessed 6 February 2022).
[32]
Larcher, A.; Bousquet, P-M.; Matrouf, D.; Bonastre, J-F. Analyse. In: Composantes Principales Pour l’extraction Des i-Vecteurs En Vérification Du Locuteur. In Journées d’Étude sur la Parole; JEP: Grenoble, France, 2012.
[33]
Tropsha, A. Best Practices for QSAR Model Development, Validation, and Exploitation. Mol. Inform., 2010, 29(6-7), 476-488.
[http://dx.doi.org/10.1002/minf.201000061] [PMID: 27463326]
[34]
Gourmelon, A.; Ahtiainen, J. Developing test guidelines on invertebrate development and reproduction for the assessment of chemicals, including potential endocrine active substances- the OECD perspective. Ecotoxicology, 2007, 16(1), 161-167.
[http://dx.doi.org/10.1007/s10646-006-0105-1] [PMID: 17219091]
[35]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2001, 46(1-3), 3-26.
[http://dx.doi.org/10.1016/S0169-409X(00)00129-0] [PMID: 11259830]
[36]
Aouidate, A.; Ghaleb, A.; Ghamali, M.; Chtita, S.; Ousaa, A.; Choukrad, M.; Sbai, A.; Bouachrine, M.; Lakhlifi, T. Investigation of Indirubin Derivatives: A combination of 3D-QSAR, molecular docking, and ADMET towards the design of new DRAK2 inhibitors. Struct. Chem., 2018, 29(6), 1609-1622.
[http://dx.doi.org/10.1007/s11224-018-1134-0]
[37]
OECD Guidance Document on the Validation of QSAR Models Organization for Economic Co-Operation & Development; Paris, 2007.
[38]
Roy, K.; Mitra, I.; Ojha, P.K.; Kar, S.; Das, R.N.; Kabir, H. Introduction of Rm2(Rank) metric incorporating rank-order predictions as an additional tool for validation of QSAR/QSPR models. Chemom. Intell. Lab. Syst., 2012, 118, 200-210.
[http://dx.doi.org/10.1016/j.chemolab.2012.06.004]
[39]
Chtita, S.; Aouidate, A.; Belhassan, A.; Ousaa, A.; Taourati, A.I.; Elidrissi, B.; Ghamali, M.; Bouachrine, M.; Lakhlifi, T. QSAR study of N-substituted oseltamivir derivatives as potent avian influenza virus H5N1 inhibitors using quantum chemical descriptors and statistical methods. New J. Chem., 2020, 44(5), 1747-1760.
[http://dx.doi.org/10.1039/C9NJ04909F]
[40]
Bryant, F.B.; Yarnold, P.R. Principal-components analysis and exploratory and confirmatory factor analysis. In: Reading and understanding multivariate statistics; American Psychological Association: Washington, DC, US, 1995; pp. 99-136.
[41]
Roy, K.; Das, R.N.; Ambure, P.; Aher, R.B. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom. Intell. Lab. Syst., 2016, 152, 18-33.
[http://dx.doi.org/10.1016/j.chemolab.2016.01.008]
[42]
Roy, K.; Mitra, I. On the use of the metric rm2 as an effective tool for validation of QSAR models in computational drug design and predictive toxicology. Mini Rev. Med. Chem., 2012, 12(6), 491-504.
[http://dx.doi.org/10.2174/138955712800493861] [PMID: 22587764]
[43]
Shin, I.; Cho, Y.; Jung, H.; Lee, S. Synthesis of singly and doubly spin-labeled maltoses and their EPR spectra. Bull. Korean Chem. Soc., 2001, 22, 355.
[44]
Prasanna, S.; Doerksen, R.J. Topological polar surface area: A useful descriptor in 2D-QSAR. Curr. Med. Chem., 2009, 16(1), 21-41.
[http://dx.doi.org/10.2174/092986709787002817] [PMID: 19149561]
[45]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[46]
Pires, D.E.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem., 2015, 58(9), 4066-4072.
[http://dx.doi.org/10.1021/acs.jmedchem.5b00104] [PMID: 25860834]
[47]
Yang, W.; Gadgil, P.; Krishnamurthy, V.R.; Landis, M.; Mallick, P.; Patel, D.; Patel, P.J.; Reid, D.L.; Sanchez-Felix, M. The evolving druggability and developability space: Chemically modified new modalities and emerging small molecules. AAPS J., 2020, 22(2), 21.
[http://dx.doi.org/10.1208/s12248-019-0402-2] [PMID: 31900602]
[48]
Maple, H.J.; Clayden, N.; Baron, A.; Stacey, C.; Felix, R. Developing degraders: Principles and perspectives on design and chemical space. MedChemComm, 2019, 10(10), 1755-1764.
[http://dx.doi.org/10.1039/C9MD00272C] [PMID: 31867093]
[49]
Fukunishi, Y.; Kurosawa, T.; Mikami, Y.; Nakamura, H. Prediction of synthetic accessibility based on commercially available compound databases. J. Chem. Inf. Model., 2014, 54(12), 3259-3267.
[http://dx.doi.org/10.1021/ci500568d] [PMID: 25420000]
[50]
Ferreira, L.L.G.; Andricopulo, A.D. ADMET modeling approaches in drug discovery. Drug Discov. Today, 2019, 24(5), 1157-1165.
[http://dx.doi.org/10.1016/j.drudis.2019.03.015] [PMID: 30890362]
[51]
Elmeliegy, M.; Vourvahis, M.; Guo, C.; Wang, D.D. Effect of P-glycoprotein (P-gp) inducers on exposure of P-gp substrates: Review of clinical drug-drug interaction studies. Clin. Pharmacokinet., 2020, 59(6), 699-714.
[http://dx.doi.org/10.1007/s40262-020-00867-1] [PMID: 32052379]
[52]
Fromm, M.F. Importance of P-glycoprotein at blood-tissue barriers. Trends Pharmacol. Sci., 2004, 25(8), 423-429.
[http://dx.doi.org/10.1016/j.tips.2004.06.002] [PMID: 15276711]
[53]
Han, Y.; Zhang, J.; Hu, C.Q.; Zhang, X.; Ma, B.; Zhang, P. In silico ADME and toxicity prediction of ceftazidime and its impurities. Front. Pharmacol., 2019, 10, 434.
[http://dx.doi.org/10.3389/fphar.2019.00434] [PMID: 31068821]

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