Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure–activity relationship modelling

References

• H. Boulebd, L. Ismaili, H. Martin, A. Bonet, M. Chioua, J. Marco Contelles, and A. Belfaitah, New (benz) imidazolopyridino tacrines as nonhepatotoxic, cholinesterase inhibitors for Alzheimer disease, Future Med. Chem. 9 (2017), pp. 723–729.
   Google Scholar
• I. McDowell, Alzheimer’s disease: Insights from epidemiology, Aging Clin. Exp. Res. 13 (2001), pp. 143–162.
   Web of Science ®Google Scholar
• T. Arendt, M.K. Brückner, M. Lange, and V. Bigl, Changes in acetylcholinesterase and butyrylcholinesterase in Alzheimer's disease resemble embryonic development: A study of molecular forms, Neurochem. Int. 21 (1992), pp. 381–396.
   PubMed Web of Science ®Google Scholar
• E.K. Perry, R.H. Perry, G. Blessed, and B.E. Tomlinson, Changes in brain cholinesterases in senile dementia of Alzheimer type, Neuropathol. Appl. Neurobiol. 4 (1978), pp. 273–277.
   PubMed Web of Science ®Google Scholar
• A. Enz, R. Amstutz, H. Boddeke, G. Gmelin, and J. Malanowski, Brain selective inhibition of acetylcholinesterase: A novel approach to therapy for Alzheimer's disease, Prog. Brain Res. 98 (1993), pp. 431–438.
   PubMed Web of Science ®Google Scholar
• N.H. Greig, D.K. Lahiri, and K. Sambamurti, Butyrylcholinesterase: An important new target in Alzheimer's disease therapy, Int. Psychogeriatr. 14 (2002), pp. 77–91.
   PubMed Web of Science ®Google Scholar
• P.W. Elsinghorst, C.M. Tanarro, and M. Gütschow, Novel heterobivalent tacrine derivatives as cholinesterase inhibitors with notable selectivity toward butyrylcholinesterase, J. Med. Chem. 49 (2006), pp. 7540–7544.
   PubMed Web of Science ®Google Scholar
• S. Hamulakova, L. Janovec, M. Hrabinova, K. Spilovska, J. Korabecny, P. Kristian, K. Kuca, and J. Imrich, Synthesis and biological evaluation of novel tacrine derivatives and tacrine–coumarin hybrids as cholinesterase inhibitors, J. Med. Chem. 57 (2014), pp. 7073–7084.
   PubMed Web of Science ®Google Scholar
• J.L. Marco, C. de los Rı́os, M.C. Carreiras, J.E. Baños, A. Badı́a, and N.M. Vivas, Synthesis and acetylcholinesterase/butyrylcholinesterase inhibition activity of new tacrine-like analogues, Bioorg. Med. Chem. 9 (2001), pp. 727–732.
   PubMed Web of Science ®Google Scholar
• S. Thiratmatrakul, C. Yenjai, P. Waiwut, O. Vajragupta, P. Reubroycharoen, M. Tohda, and C. Boonyarat, Synthesis, biological evaluation and molecular modeling study of novel tacrine-carbazole hybrids as potential multifunctional agents for the treatment of Alzheimer's disease, Eur. J. Med. Chem. 75 (2014), pp. 21–30.
   PubMed Web of Science ®Google Scholar
• K. Roy, S. Kar, and R.N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic Press, San Diego, CA, 2015.
   Google Scholar
• S. Simeon, N. Anuwongcharoen, W. Shoombuatong, A.A. Malik, V. Prachayasittikul, J.E.S. Wikberg, and C. Nantasenamat, Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking, PeerJ 4 (2016), e2322.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• N. Akula, L. Lecanu, J. Greeson, and V. Papadopoulos, 3D QSAR studies of AChE inhibitors based on molecular docking scores and CoMFA, Bioorg. Med. Chem. Lett. 16 (2006), pp. 6277–6280.
   PubMed Web of Science ®Google Scholar
• M. Fernández, M.C. Carreiras, J.L. Marco, and J. Caballero, Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized genetic neural networks and ensemble averaging, J. Enzyme Inhib. Med. Chem. 21 (2006), pp. 647–661.
   PubMed Web of Science ®Google Scholar
• M. Saracoglu and F. Kandemirli, The investigation of structure–activity relationships of tacrine analogues: Electronic-topological method, Open Med. Chem. J. 2 (2008), pp. 75–80.
   PubMedGoogle Scholar
• M. Jung, J. Tak, Y. Lee, and Y. Jung, Quantitative structure–activity relationship (QSAR) of tacrine derivatives against acetylcholinesterase (AChE) activity using variable selections, Bioorg. Med. Chem. Lett. 17 (2007), pp. 1082–1090.
   PubMed Web of Science ®Google Scholar
• D. Chekmarev, V. Kholodovych, S. Kortagere, W.J. Welsh, and S. Ekins, Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors, Pharm. Res. 26 (2009), pp. 2216–2224.
   PubMed Web of Science ®Google Scholar
• 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), p. 84.
   PubMed Web of Science ®Google Scholar
• M.S. Castilho, R.V.C. Guido, and A.D. Andricopulo, Classical and hologram QSAR studies on a series of tacrine derivatives as butyrylcholinesterase inhibitors, Lett. Drug Des. Discov. 4 (2007), pp. 106–113.
   Web of Science ®Google Scholar
• J. Fang, R. Yang, L. Gao, D. Zhou, S. Yang, A.L. Liu, and G.H. Du, Predictions of BuChE inhibitors using support vector machine and naive bayesian classification techniques in drug discovery, J. Chem. Inf. Model. 53 (2013), pp. 3009–3020.
   PubMed Web of Science ®Google Scholar
• OECD, Guidance Document on the Validation of (Quantitative) Structure–Activity Relationship [(Q) SAR] Models, Series on Testing and Assessment N° 69, OECD, Paris, 2007.
   Google Scholar
• T. Liu, Y. Lin, X. Wen, R.N. Jorissen, and M.K. Gilson, BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities, Nucleic Acids Res. 35 (2007), D198–D201.
   PubMed Web of Science ®Google Scholar
• G.L. Ellman, K.D. Courtney, V.Jr. Andres, and R.M. Feather-stone, A new and rapid colorimetric determination of acetylcholinesterase activity, Biochem. Pharmacol. 7 (1961), pp. 88–95.
   PubMed Web of Science ®Google Scholar
• A.A. Toropov and A.P. Toropova, The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models?, Mutat. Res. 819 (2017), pp. 31–37.
   PubMed Web of Science ®Google Scholar
• MOE, Molecular Operating Environment, Chemical Computing Group, Montreal, Canada, 2007.
   Google Scholar
• S. Bitam, M. Hamadache, and S. Hanini, QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors, SAR QSAR Environ. Res. 28 (2017), pp. 471–489.
   PubMed Web of Science ®Google Scholar
• D.B. de Oliveira and A.C. Gaudio, BuildQSAR: A new computer program for QSAR analysis, Mol. Inform. 19 (2000), pp. 599–601.
   Google Scholar
• G. Snedecor and W. Cochran, Statistical Methods, 6th ed., Oxford and IBH Publishing Co, New Delhi, 1967.
   Google Scholar
• C.W. Yap, H. Li, Z.L. Ji, and Y.Z. Chen, Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties, Mini Rev. Med. Chem. 7 (2007), 1097–1107.
   PubMed Web of Science ®Google Scholar
• C.J.C. Burges, A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Discov. 2 (1998), pp. 121–167.
   Web of Science ®Google Scholar
• V.N. Vapnik and S. Kotz, Estimation of Dependences Based on Empirical Data, Springer Series in Statistics, Springer-Verlag, New York, 1982.
   Google Scholar
• J.C.G. Boot, Quadratic Programming: Algorithms, Anomalies, Applications, North-Holland, Amsterdam, 1964.
   Google Scholar
• B.E. Boser, I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifiers, in Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM, Pittsburgh, PA, 1992, pp. 144–152.
   Google Scholar
• V. Vapnik, The Nature of Statistical Learning Theory, Springer Science & Business Media, 2013.
   Google Scholar
• L.V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, Prentice-Hall, Upper Saddle River, NJ, 1994.
   Google Scholar
• R. Wang, J. Jiang, Y. Pan, H. Cao, and Y. 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.
   PubMed Web of Science ®Google Scholar
• P.K. Ojha, I. Mitra, R.N. Das, and K. Roy, Further exploring rm2 metrics for validation of QSPR models, Chemom. Intell. Lab. 107 (2011), pp. 194–205.
   Web of Science ®Google Scholar
• 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 Perspect. 111 (2003), pp. 1361–1375.
   PubMed Web of Science ®Google Scholar
• 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. Inf. Model. 52 (2012), pp. 396–408.
   PubMed Web of Science ®Google Scholar
• N. Chirico, and P. Gramatica, Real external predictivity of QSAR models: How to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient, J. Chem. Inf. Model. 51 (2011), pp. 2320–2335.
   PubMed Web of Science ®Google Scholar
• L.I. Lin, A concordance correlation coefficient to evaluate reproducibility, Biometrics 45 (1989), pp. 255–268.
   PubMed Web of Science ®Google Scholar
• 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, Mol. Inform. 22 (2003), pp. 69–77.
   Google Scholar
• V.K. Agrawal and P.V. Khadikar, QSAR prediction of toxicity of nitrobenzenes, Bioorg. Med. Chem. 9 (2001), pp. 3035–3040.
   PubMed Web of Science ®Google Scholar
• I. Mitra, A. Saha, and K. Roy, Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants, Mol. Simul. 36 (2010), pp. 1067–1079.
   Web of Science ®Google Scholar
• 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, Chemom. Intell. Lab. 154 (2016), pp. 7–15.
   Web of Science ®Google Scholar
• F. Sahigara, K. Mansouri, D. Ballabio, A. Mauri, V. Consonni, and R. Todeschini, Comparison of different approaches to define the applicability domain of QSAR models, Molecules 17 (2012), pp. 4791–4810.
   PubMed Web of Science ®Google Scholar
• F. Sahigara, D. Ballabio, R. Todeschini, and V. Consonni, Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions, J. Cheminform. 5 (2013), p. 27.
   PubMed Web of Science ®Google Scholar
• K. Roy, S. Kar, and P. Ambure, On a simple approach for determining applicability domain of QSAR models, Chemom. Intell. Lab. 145 (2015), pp. 22–29.
   Web of Science ®Google Scholar