Benzo[e]pyrimido[5,4-b][1,4]diazepin-6(11H)-one derivatives as Aurora A kinase inhibitors: LQTA-QSAR analysis and detailed systematic validation of the developed model

Molecular Diversity - Tập 19 - Trang 965-974 - 2015
Ashish M. Kanhed1, Radha Charan Dash2, Nishant Parmar3, Tarun Kumar Das3, Rajani Giridhar1, Mange Ram Yadav1
1Pharmacy Department, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, India
2Visiting Research Associate to Pharmacy Department, The Maharaja Sayajirao University of Baroda, Vadodara, India
3Department of Mathematics, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara, India

Tóm tắt

Aurora kinases are sub-divided into Aurora A, Aurora B, and Aurora C kinases that are considered as prospective targets for a new class of anticancer drugs. In this work, a 4-D-QSAR model using an LQTA-QSAR approach with previously reported 31 derivatives of benzo[e]pyrimido[5,4 -b][1,4]diazepin -6(11H)-one as potent Aurora kinase A inhibitors has been created. Instead of single conformation, the conformational ensemble profile generated for each ligand by using trajectories and topology information retrieved from molecular dynamics simulations from GROMACS package were aligned and used for the calculation of intermolecular interaction energies at each grid point. The descriptors generated on the basis of these Coulomb and Lennard-Jones potentials as independent variables were used to perform a PLS analysis using biological activity as dependent variable. A good predictive model was generated with nine field descriptors and five latent variables. The model showed $${Q}_{\mathrm{LOO}}^{2} = 0.718$$ ; $${R}^{2}= 0.915$$ and $${R}_{\mathrm{pred}}^{2}= 0.839$$ . This model was further validated systematically by using different validation parameters. This 4D-QSAR model gave valuable information to recognize features essential to adapt and develop novel potential Aurora kinase inhibitors.

Tài liệu tham khảo

Andrews PD (2005) Aurora kinases: shining lights on the therapeutic horizon? Oncogene 24:5005–5015. doi:10.1038/sj.onc.1208752

Bischoff JR, Plowman GD (1999) The Aurora/Ipl1p kinase family: regulators of chromosome segregation and cytokinesis. Trends Cell Biol 9:454–459. doi:10.1016/S0962-8924(99)01658-X

Honda K, Mihara H, Kato Y, Yamaguchi A, Tanaka H, Yasuda H, Furukawa K, Urano T (2000) Degradation of human Aurora2 protein kinase by the anaphase-promoting complex-ubiquitin-proteasome pathway. Oncogene 19:2812–2819

Walter AO, Seghezzi W, Korver W, Sheung J, Lees E (2000) The mitotic serine/threonine kinase Aurora2/AIK is regulated by phosphorylation and degradation. Oncogene 19:4906–4916

Sugimoto K, Urano T, Zushi H, Inoue K, Tasaka H, Tachibana M, Dotsu M (2002) Molecular dynamics of Aurora-A kinase in living mitotic cells simultaneously visualized with histone H3 and nuclear membrane protein importinalpha. Cell Struct Funct 27:457–467. doi:10.1247/csf.27.457

Nigg EA (2001) Mitotic kinases as regulators of cell division and its checkpoints. Nat Rev Mol Cell Biol 2:21–32. doi:10.1038/35048096

Goto H, Yasui Y, Kawajiri A, Nigg EA, Terada Y, Tatsuka M, Nagata K, Inagaki M (2003) Aurora-B regulates the cleavage furrow-specific vimentin phosphorylation in the cytokinetic process. J Biol Chem 278:8526–8530. doi:10.1074/jbc.M210892200

Giet R, Petretti C, Prigent C (2005) Aurora kinases, aneuploidy and cancer, a coincidence or a real link? Trends Cell Biol 15:241–250. doi:10.1016/j.tcb.2005.03.004

Sun L, Li D, Dong X, Yu H, Dong J, Zhang C, Lu X, Zhou J (2008) Small-molecule inhibition of Aurora kinases triggers spindle checkpoint-independent apoptosis in cancer cells. Biochem Pharmacol 75:1027–1034. doi:10.1016/j.bcp.2007.11.007

Yan A, Wang L, Xu S, Xu J (2011) Aurora-A kinase inhibitor scaffolds and binding modes. Drug Discov Today 16:260–269. doi:10.1016/j.drudis.2010.12.003

Zhao B, Smallwood A, Yang J, Koretke K, Nurse K, Calamari A, Kirkpatrick RB, Lai Z (2008) Modulation of kinase-inhibitor interactions by auxiliary protein binding: crystallography studies on Aurora A interactions with VX-680 and with TPX2. Protein Sci 17:1791–1797. doi:10.1110/ps.036590.108

Kollareddy M, Zheleva D, Dzubak P, Brahmkshatriya PS, Lepsik M, Hajduch M (2012) Aurora kinase inhibitors: progress towards the clinic. Invest New Drugs 30:2411–2432. doi:10.1007/s10637-012-9798-6

Kanhed AM, Zambre VP, Pawar VA, Sharma MK, Giridhar R, Yadav MR (2014) Structural requirements for imidazo[1,2-a]pyrazine derivatives as Aurora A kinase inhibitors and validation of the model. Med Chem Res 23:5215–5223. doi:10.1007/s00044-014-1094-x

Lan P, Chen W, Chen W (2011) Molecular modeling studies on imidazo[4,5-b]pyridine derivatives as Aurora A kinase inhibitors using 3D-QSAR and docking approaches. Eur J Med Chem 46:77–94. doi:10.1016/j.ejmech.2010.10.017

Lan P, Chen W, Chen W (2011) 3D-QSAR and molecular docking studies of azaindole derivatives as Aurora B kinase inhibitors. J Mol Model 17:1191–1205. doi:10.1007/s00894-010-0820-7

Hopfinger AJ, Wang S, Tokarski JS, Jin B, Albuquerque M, Madhav PJ, Duraiswami C (1997) Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism. J Am Chem Soc 119:10509–10524. doi:10.1021/ja9718937

Barbosa EC, Pasqualoto KFM, Ferreira MCC (2012) The receptor-dependent LQTA-QSAR: application to a set of trypanothione reductase inhibitors. J Comput Aided Mol Des 26:1055–1065. doi:10.1007/s10822-012-9598-2

Martins JPA, Barbosa EG, Pasqualoto KFM, Ferreira MC (2009) LQTA-QSAR: a new 4D-QSAR methodology. J Chem Inf Model 49:1428–1436. doi:10.1021/ci900014f

Kwiatkowski N, Deng X, Wang J, Tan L, Villa F, Santaguida S, Huang HC, Mitchison T, Musacchio A, Gray N (2012) Selective aurora kinase inhibitors identified using a taxol-induced checkpoint sensitivity screen. ACS Chem Biol 7:185–196. doi:10.1021/cb200305u

Gaussian03, Version 6.0, Gaussian Inc., Wallingford, USA

O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: An open chemical toolbox. J Cheminform 3:33–47. doi:10.1186/1758-2946-3-33

Schuttelkopf AW, Van Aalten DMF (2004) PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr D Biol Crystallogr 60:1355–1363. doi:10.1107/S0907444904011679

GROMACS, Version 4.5, http://www.gromacs.org

Ghasemi JB, Safavi-Sohi R, Barbosa EG (2012) 4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling. Mol Divers 16:203–213. doi:10.1007/s11030-011-9340-3

Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodshell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. doi:10.1002/jcc.21256

MATLAB, Version 7.12, MathWorks, Inc

Martins JPA, Ferreira MC (2013) QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR. Quim Nova 36:554–560. doi:10.1590/S0100-40422013000400013

Stahle L, Wold S (1987) Partial least squares analysis with cross validation for the two-class problem: a Monte Carlo study. J Chemom 1:185–196. doi:10.1002/cem.1180010306

Hoskuldsson A (1988) PLS regression methods. J Chemom 2:211–228. doi:10.1002/cem.1180020306

Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopolus J, Igglessi-Markopoulou O (2007) Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening. J Comput Aided Mol Des 21:251–267. doi:10.1007/s10822-007-9112-4

Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701. doi:10.3390/molecules14051660

De Melo EB, Ferreira MC (2012) Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology. J Chem Inf Model 52:1722–1732. doi:10.1021/ci300039a

Golbraikh A, Tropsha A (2002) Beware of q2!. J Mol Graph Model 20:269–276. doi:10.1016/S1093-3263(01)00123-1

Golbraikh A, Tropsha A (2002) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aid Mol Des 16:357–369

Saha S, Raghava G (2006) AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res 34:W202–W209. doi:10.1093/nar/gkl343

Masunari A, Tavares LC (2007) A new class of nifuroxazide analogues: synthesis of 5-nitrothiophene derivatives with antimicrobial activity against multidrug-resistant Staphylococcus aureus. Bioorg Med Chem 15:4229–4236. doi:10.1016/j.bmc.2007.03.068

Vedani A, Dobler M (2002) Multidimensional QSAR: Moving from three- to five-dimensional concepts. Quant Struct-Act Relat 21:382–390. doi:10.1002/1521-3838(200210)21:4<382::AID-QSAR382>3.0.CO;2-L

Raghavan K, Buolamwini JK, Fesen MR, Pommier Y, Kohn KW, Weinstein JN (1995) Three-dimensional quantitative structure-activity relationship (QSAR) of HIV integrase inhibitors: a comparative molecular field analysis (CoMFA) study. J Med Chem 38:890–897. doi:10.1021/jm00006a006

Wold S, Eriksson L (1998) Statistical Validation of QSAR Results, Chemometrics Methods in Molecular Design. In: Van de Waterbeemd H (ed) Chemometric Methods in Molecular Design. Wiley-VCH, Weinheim, pp 309–318

Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF Chimera-a visualization system for exploratory research and analysis. J Comput Chem 25:1605–12. doi:10.1002/jcc.20084

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Molecular Diversity

Tập 19

965-974

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