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Articles

QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors

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Pages 471-489 | Received 15 Feb 2017, Accepted 14 May 2017, Published online: 14 Jun 2017

References

  • K.Y. Wong, A.G. Mercader, L.M. Saavedra, B. Honarparvar, G.P. Romanelli, and P.R. Duchowicz, QSAR analysis on tacrine-related acetylcholinesterase inhibitors, J. Biomed. Sci. 21 (2014): 84. doi: 10.1186/s12929-014-0084-0.
  • A. Zhou, J. Hu, L. Wang, G. Zhong, J. Pan, Z. Wu, and A. Hui, Combined 3D-QSAR, molecular docking, and molecular dynamics study of tacrine derivatives as potential acetylcholinesterase (AChE) inhibitors of Alzheimer’s disease, J. Mol. Model. 21(2015): 277. doi: 10.1007/s00894-015-2797-8.
  • B.J. Vellas and P. Robert, Fédération nationale des centres Mémoire de Ressources et de Recherche, Traité sur la maladie d'Alzheimer, Springer-Verlag France, Paris, 2013.
  • K.K. Roy, A. Dixit, and A.K. Saxena, An investigation of structurally diverse carbamates for acetylcholinesterase (AChE) inhibition using 3D-QSAR analysis, J. Mol. Graph. Model. 27 (2008), pp. 197–208.
  • M.R. Islam, A. Zaman, I. Jahan, R. Chakravorty, and S. Chakraborty, In silico QSAR analysis of quercetin reveals its potential as therapeutic drug for Alzheimer’s disease, J. Young Pharm. 5 (2013), pp. 173–179.
  • M. Estrada, C. Herrera-Arozamena, C. Pérez, D. Viña, A. Romero, J.A. Morales-García, A. Pérez-Castillo, and M.I. Rodríguez-Franco, New cinnamic–N-benzylpiperidine and cinnamic–N, N-dibenzyl (N-methyl) amine hybrids as Alzheimer-directed multitarget drugs with antioxidant, cholinergic, neuroprotective and neurogenic properties, Eur. J. Med. Chem. 121 (2016), pp. 376–386.
  • M.L. Kostochka, J. Zajicek, J.A. Fuselier, M.A. Etienne, L. Sun, and D.H. Coy, Novel tandem aldol intramolecular cyclization of substituted n-benzylpiperidine-4-one: Synthesis of novel-type nitrogen 2, 8-phenanthroline heterocycles, J. Heterocyclic Chem. 52 (2015), pp. 1723–1730.
  • A. Martinez, E. Fernandez, A. Castro, S. Conde, I. Rodriguez-Franco, J.-E. Baños, and A. Badia, N-Benzylpiperidine derivatives of 1, 2, 4-thiadiazolidinone as new acetylcholinesterase inhibitors, Eur. J. Med. Chem. 35 (2000), pp. 913–922.
  • M.I. Rodríguez-Franco, M.I. Fernández-Bachiller, C. Pérez, A. Castro, and A. Martínez, Design and synthesis of N-benzylpiperidine–purine derivatives as new dual inhibitors of acetyl-and butyrylcholinesterase, Bioorgan. Med. Chem. 13 (2005), pp. 6795–6802.
  • D.K. Sukumarapillai, K. Kooi-Yeong, Y. Kia, V. Murugaiyah, and S.K. Iyer, Design, synthesis and cholinesterase inhibitory evaluation study of fluorescent N-benzylpiperidine-4-one derivatives, Med. Chem. Res. 25 (2016), pp. 1705–1715.
  • A. Więckowska, K. Więckowski, M. Bajda, B. Brus, K. Sałat, P. Czerwińska, S. Gobec, B. Filipek, and B. Malawska, Synthesis of new N-benzylpiperidine derivatives as cholinesterase inhibitors with β-amyloid anti-aggregation properties and beneficial effects on memory in vivo, Bioorgan. Med. Chem. 23 (2015), pp. 2445–2457.
  • M. Shidore, J. Machhi, K. Shingala, P. Murumkar, M.K. Sharma, N. Agrawal, A. Tripathi, Z. Parikh, P. Pillai, and M.R. Yadav, Benzylpiperidine-linked diarylthiazoles as potential anti-Alzheimer’s agents: Synthesis and biological evaluation, J. Med. Chem. 59 (2016), pp. 5823–5846.
  • K. Roy, S. Kar, and R.N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic Press, 2015: 1–46.
  • S. Simeon, N. Anuwongcharoen, W. Shoombuatong, A.A. Malik, V. Prachayasittikul, J.E. Wikberg, and C. Nantasenamat, Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking, PeerJ 4 (2016), p. e2322.
  • K. Roy and P.P. Roy, Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques, Eur. J. Med. Chem. 44 (2009), pp. 2913–2922.
  • N. Akula, L. Lecanu, J. Greeson, and V. Papadopoulos, 3D QSAR studies of AChE inhibitors based on molecular docking scores and CoMFA, Bioorgan. Med. Chem. Lett. 16 (2006), pp. 6277–6280.
  • M. Jung, J. Tak, Y. Lee, and Y. Jung, Quantitative structure–activity relationship (QSAR) of tacrine derivatives against acetylcholinesterase (AChE) activity using variable selections, Bioorgan. Med. Chem. Lett. 17 (2007), pp. 1082–1090.
  • M. Saracoglu and F. Kandemirli, The investigation of structure–activity relationships of tacrine analogues: Electronic-topological method, Open Med. J. 2 (2008), pp. 75–80.
  • N. Chen, C. Liu, L. Zhao, and H. Zhang, 3D-QSAR study of multi-target-directed AchE inhibitors based on autodocking, Med. Chem. Res. 21 (2012), pp. 245–256.
  • G. Pasquale, G.P. Romanelli, J.C. Autino, J. García, E.V. Ortiz, and P.R. Duchowicz, Quantitative structure–activity relationships of mosquito larvicidalchalcone derivatives, J. Agric. Food Chem. 60 (2012), pp. 692–697.
  • J. Fang, P. Wu, R. Yang, L. Gao, C. Li, D. Wang, S. Wu, A.-L. Liu, and G.-H. Du, Inhibition of acetylcholinesterase by two genistein derivatives: Kinetic analysis, molecular docking and molecular dynamics simulation, Acta Pharm. Sin. B 4 (2014), pp. 430–437.
  • P. Ambure, S. Kar, and K. Roy, Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer’s agents, Biosystems 116 (2014), pp. 10–20.
  • J. Correa-Basurto, M. Bello, M.C. Rosales-Hernandez, M. Hernández-Rodríguez, I. Nicolás-Vázquez, A. Rojo-Domínguez, J.G. Trujillo-Ferrara, R. Miranda, and C. Flores-Sandoval, QSAR, docking, dynamic simulation and quantum mechanics studies to explore the recognition properties of cholinesterase binding sites, Chem. Biol. Interact. 209 (2014), pp. 1–13.
  • C. Vats, J.K. Dhanjal, S. Goyal, N. Bharadvaja, and A. Grover, Computational design of novel flavonoid analogues as potential AChE inhibitors: Analysis using group-based QSAR, molecular docking and molecular dynamics simulations, Struct. Chem. 26 (2015), pp. 467–476.
  • Y.-R. Jiang, Y.-Y. Yang, Y.-L. Chen, and Z.-J. Liang, CoMFA, CoMSIA and HQSAR studies of acetylcholinesterase inhibitors, Curr. Comput. Aided Drug Des. 9 (2013), pp. 385–395.
  • B.P. Pulikkal, Common SAR derived from linear and non-linear QSAR studies on AChE inhibitors used in the treatment of Alzheimer’s disease, Curr. Neuropharmacol. 14, pp. 1–7.
  • M.G. Cardozo, Y. Imura, H. Sugimoto, Y. Yamanishi, and A.J. Hopfinger, QSAR analyses of the substituted indanone and benzylpiperidine rings of a series of indanone-benzylpiperidine inhibitors of acetylcholinesterase, J. Med. Chem. 35 (1992), pp. 584–589.
  • W. Tong, E.R. Collantes, Y. Chen, and W.J. Welsh, A comparative molecular field analysis study of N-benzylpiperidines as acetylcholinesterase inhibitors, J. Med. Chem. 39 (1996), pp. 380–387.
  • P. Bernard, D.B. Kireev, J.R. Chrétien, P.-L. Fortier, and L. Coppet, Automated docking of 82 N-benzylpiperidine derivatives to mouse acetylcholinesterase and comparative molecular field analysis with ‘natural’ alignment, J. Comput. Aided Mol. Des. 13 (1999), pp. 355–371.
  • A.S. Dimoglo, N.M. Shvets, I.V. Tetko, and D.J. Livingstone, Electronic-topological investigation of the structure – acetylcholinesterase inhibitor activity relationship in the series of n-benzylpiperidine derivatives, Quant. Struct.-Act. Rel. 20 (2001), pp. 31–45.
  • OECD, Guidance document on the validation of (quantitative) structure–activity relationships [(Q)SAR] models, OECD Series on Testing and Assessment No. 69. ENV/JM/MONO (2007) 2 154, OECD, Paris. 2007.
  • J.-M. Contreras, I. Parrot, W. Sippl, Y.M. Rival, and C.G. Wermuth, Design, synthesis, and structure–activity relationships of a series of 3-[2-(1-benzylpiperidin-4-yl) ethylamino] pyridazine derivatives as acetylcholinesterase inhibitors, J. Med. Chem. 44 (2001), pp. 2707–2718.
  • Y. Ishichi, M. Sasaki, M. Setoh, T. Tsukamoto, S. Miwatashi, H. Nagabukuro, S. Okanishi, S. Imai, R. Saikawa, and T. Doi, Novel acetylcholinesterase inhibitor as increasing agent on rhythmic bladder contractions: SAR of 8-{3-[1-(3-fluorobenzyl) piperidin-4-yl] propanoyl}-1, 2, 5, 6-tetrahydro-4H-pyrrolo [3, 2, 1-ij] quinolin-4-one (TAK-802) and related compounds, Bioorgan. Med. Chem. 13 (2005), pp. 1901–1911.
  • M. Shidore, J. Machhi, K. Shingala, P. Murumkar, M.K. Sharma, N. Agrawal, A. Tripathi, Z. Parikh, P. Pillai, and M.R. Yadav, Benzylpiperidine-linked diarylthiazoles as potential anti-Alzheimer’s agents-synthesis and biological evaluation, J. Med. Chem. 59 (2016), pp. 5823–46.
  • S.-S. Xie, J.-S. Lan, X. Wang, Z.-M. Wang, N. Jiang, F. Li, J.-J. Wu, J. Wang, and L.-Y. Kong, Design, synthesis and biological evaluation of novel donepezil–coumarin hybrids as multi-target agents for the treatment of Alzheimer’s disease, Bioorgan. Med. Chem. 24 (2016), pp. 1528–1539.
  • W. Xu, X.-B. Wang, Z.-M. Wang, J.-J. Wu, F. Li, J. Wang, and L.-Y. Kong, Synthesis and evaluation of donepezil–ferulic acid hybrids as multi-target-directed ligands against Alzheimer’s disease, Med. Chem. Comm. 7 (2016), pp. 990–998.
  • G.L. Ellman, K.D. Courtney, V. Andres, and R.M. Featherstone, A new and rapid colorimetric determination of acetylcholinesterase activity, Biochem. Pharmacol. 7 (1961), pp. 88IN191–9095.
  • MOPAC 2012, Version 15.038W. Stewart Computational Chemistry, 2012; software available at http://OpenMOPAC.net.
  • Molecular Operating Environment (MOE), Chemical Computing Group Inc., Montreal, Canada, 2012.
  • R. Todeschini and V. Consonni, Molecular Descriptors for Chemoinformatics, Vol. I and II, (2009), pp. 26–29, 173, 176, 757.
  • I.T. Jolliffe and J. Cadima, Principal component analysis: A review and recent developments, Phil. Trans. R. Soc. A 374 (2016), p. 20150202.
  • A.-L. Boulesteix and K. Strimmer, Partial least squares: A versatile tool for the analysis of high-dimensional genomic data, Brief. Bioinformatics 8 (2007), pp. 32–44.
  • N. Urbach and F. Ahlemann, Structural equation modeling in information systems research using partial least squares, J. Inform. Technol. Theory Appl. 11 (2010), pp. 5–40.
  • H. Stoppiglia, G. Dreyfus, R. Dubois, and Y. Oussar, Ranking a random feature for variable and feature selection, J. Mach. Learn. Res. 3 (2003), pp. 1399–1414.
  • M. Hamadache, S. Hanini, O. Benkortbi, A. Amrane, L. Khaouane, and C.S. Moussa, Artificial neural network-based equation to predict the toxicity of herbicides on rats, Chemometr. Intell. Lab. Syst. 154 (2016), pp. 7–15.
  • D.B. de Oliveira and A.C. Gaudio, BuildQSAR: A new computer program for QSAR analysis, Quant.-Struct.-Act. Rel. 19 (2000), pp. 599–601.
  • G. Snedecor and W. Cochran, Statistical Methods, Oxford and IBH Publishing Co., New Delhi, 1967.
  • D. Rogers and A.J. Hopfinger, Application of genetic function approximation to quantitative structure–activity relationships and quantitative structure–property relationships, J. Chem. Inform. Comput. Sci. 34 (1994), pp. 854–866.
  • M. Hamadache, L. Khaouane, O. Benkortbi, C. Si Moussa, S. Hanini, and A. Amrane, Prediction of acute herbicide toxicity in rats from quantitative structure–activity relationship modeling, Environ. Eng. Sci. 31 (2014), pp. 243–252.
  • Y. Fan, L.M. Shi, K.W. Kohn, Y. Pommier, and J.N. Weinstein, Quantitative structure-antitumor activity relationships of camptothecin analogues: Cluster analysis and genetic algorithm-based studies, J. Med. Chem. 44 (2001), pp. 3254–3263.
  • P. Pratim Roy, S. Paul, I. Mitra, and K. Roy, On two novel parameters for validation of predictive QSAR models, Molecules 14 (2009), pp. 1660–1701.
  • R. Wang, J. Jiang, Y. Pan, H. Cao, and Yi. Cui, Prediction of impact sensitivity of nitro energetic compounds by neural network based on electrotopological-state indices, J. Hazard. Mater. 166 (2009), pp. 155–186.
  • P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring metrics for validation of QSPR models, Chemometr. Intell. Lab. Syst. 107 (2011), pp. 194–205.
  • L. Eriksson, J. Jaworska, A.P. Worth, M.T. Cronin, R.M. McDowell, and P. Gramatica, Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs, Environ. Health Persp. 111 (2003), pp. 1361–1375.
  • K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemometr. Intell. Lab. Syst. 145 (2015), pp. 22–29.
  • A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77.
  • M. Jaiswal, P.V. Khadikar, A. Scozzafava, and C.T. Supuran, Carbonic anhydrase inhibitors: The first QSAR study on inhibition of tumor-associated isoenzyme IX with aromatic and heterocyclic sulfonamides, Bioorgan. Med. Chem. Lett. 14 (2004), pp. 3283–3290.
  • G.D. Garson, Interpreting neural network connection weights, AI Expert 6 (1991), pp. 47–51.
  • A.T. Goh, Back-propagation neural networks for modeling complex systems, Artif. Intell. Eng. 9 (1995), pp. 143–151.
  • K. Roy, I. Mitra, S. Kar, P.K. Ojha, R.N. Das, and H. Kabir, Comparative studies on some metrics for external validation of QSPR models, J. Chem. Inform. Model. 52 (2012), pp. 396–408.

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