Development linear and non-linear QSAR models for predicting AXL kinase inhibitory activity of N-[4-(quinolin-4-yloxy)phenyl]benzenesulfonamides

References

• Thangue L, Bandara Lr NB. Targets for cancer chemotherapy: transcription factors and other nuclear proteins. Totowa: Humana Press; 2002.
   Google Scholar
• Siegel RL, Miller KD, Jema A. Cancer statistics, 2015, Ca. Cancer J Clin. 2015;65(1):5–29.
   PubMed Web of Science ®Google Scholar
• Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016, Ca. Cancer J Clin. 2016;66(1):7–30.
   PubMed Web of Science ®Google Scholar
• Chabner BA, Roberts TG. Timeline: chemotherapy and the war on cancer. Nat Rev Cancer. 2005;5(1):65–72.
   PubMed Web of Science ®Google Scholar
• Jemal A, Bray F, Center MM, et al. Global cancer statistics, Ca. Cancer J Clin. 2011;61(2):69–90.
   PubMed Web of Science ®Google Scholar
• Groner B. Targeted interference with signal transduction events. Berlin: Springer, 2007.
   Google Scholar
• Hubbard SR. Structural analysis of receptor tyrosine kinases. Prog Biophys Mol Biol. 1999;71(3–4):343–358.
   PubMed Web of Science ®Google Scholar
• Schlessinger J. Cell signaling by receptor tyrosine kinases. Cell. 2000;103(2):211–225.
   PubMed Web of Science ®Google Scholar
• Bennasroune A, Gardin A, Aunis D, et al. Tyrosine kinase receptors as attractive targets of cancer therapy. Crit Rev Oncol Hematol. 2004;50(1):23–38.
   PubMed Web of Science ®Google Scholar
• Linger RMA, Keating AK, Earp HS, et al. TAM receptor tyrosine kinases: biologic functions, signaling, and potential therapeutic targeting in human cancer. Adv Cancer Res. 2008;100:35–83.
   PubMed Web of Science ®Google Scholar
• Zwick E, Bange J, Ullrich A. Receptor tyrosine kinase signalling as a target for cancer intervention strategies. Endocr Relat Cancer. 2001;8:161–173.
   PubMed Web of Science ®Google Scholar
• Janning M, Ben-Batalla I, Loges S. Axl inhibition: a potential road to a novel acute myeloid leukemia therapy? Expert Rev Hematol. 2015;8(2):135–138.
   PubMed Web of Science ®Google Scholar
• Wheeler DL, Yarden Y. Receptor tyrosine kinases: family and subfamilies. Totowa: Humana Press; 2015.
   Google Scholar
• Axelrod H, Pienta KJ. Axl as a mediator of cellular growth and survival. Oncotarget. 2014;5(19):8818–8852.
   PubMedGoogle Scholar
• Wu X, Liu X, Koul S, et al. Axl kinase as a novel target for cancer therapy. Oncotarget. 2014;5(20):9546–9563.
   PubMed Web of Science ®Google Scholar
• Okimoto RA, Bivona TG. AXL receptor tyrosine kinase as a therapeutic target in NSCLC. Lung Cancer Targ Therapy. 2015;6:27–34.
   Web of Science ®Google Scholar
• Haybaeck J. Mechanisms of molecular carcinogenesis. vol 1. Berlin: Springer; 2017.
   Google Scholar
• Feneyrolles C, Spenlinhauer A, Guiet L, et al. Axl kinase as a key target for oncology: focus on small molecule inhibitors. Mol Cancer Ther. 2014;13:1–8.
   Web of Science ®Google Scholar
• Fatima G, Loubna A, Wiame L, et al. In silico inhibition studies of axl kinase by curcumin and its natural derivatives. J Appl Bioinforma Comput Biol. 2017;6:3.
   Google Scholar
• Divine LM, Nguyen MR, Meller E, et al. AXL modulates extracellular matrix protein expression and is essential for invasion and metastasis in endometrial cancer. Oncotarget. 2016;7(47):77291–77305.
   PubMedGoogle Scholar
• Rankin EB, Giaccia AJ. The receptor tyrosine kinase AXL in cancer progression. Cancer. 2016;8(11):103.
   Google Scholar
• Akslen LA, Watnick RS. Biomarkers of the tumor microenvironment. Berlin: Springer; 2017.
   Google Scholar
• Shen Y, Chen X, He J, et al. Axl inhibitors as novel cancer therapeutic agents. Life Sci. 2018;198:99–111.
   PubMed Web of Science ®Google Scholar
• Sepehri B, Rezaei M, Ghavami R. The in silico identification of potent anti-cancer agents by targeting the ATP binding site of the N-domain of HSP90. SAR QSAR Environ Res. 2018;29(7):551–565.
   PubMed Web of Science ®Google Scholar
• Sepehri B, Ghavami R. The identification of new ATAD2 bromodomain inhibitors: the application of combined ligand and structure-based virtual screening. SAR QSAR Environ Res. 2017;28(12):957–971.
   PubMed Web of Science ®Google Scholar
• Modi SJ, Kulkarni VM. 3D-QSAR analysis of pyrimidine derivatives as AXL kinase inhibitors as anticancer agents. J Appl Pharm Sci. 2018;8:015–027.
   Google Scholar
• Ghrifi F, Allam L, Wiame L, et al. Curcumin-synthetic analogs library screening by docking and quantitative structure–activity relationship studies for AXL tyrosine kinase inhibition in cancers. J Comput Biol. 2019. doi:10.1089/cmb.2019.0052.
   PubMed Web of Science ®Google Scholar
• Szabadkai I, Torka R, Garamvölgyi R, et al. Discovery of N-[4-(Quinolin-4-yloxy)phenyl]benzenesulfonamides as novel AXL kinase inhibitors. J Med Chem. 2018;61(14):6277–6292.
   PubMed Web of Science ®Google Scholar
• HyperChem 7.1. Gainesville, USA: Hypercube, Inc. Available from: http://www.hyper.com.
   Google Scholar
• O’Boyle NM, Banck M, James CA, et al. Open Babel: An open chemical toolbox. J Cheminformatics 2011;3:33. Available from: http://openbabel.or.
   PubMed Web of Science ®Google Scholar
• Tosco P, Balle T. Open3DALIGN 2.27. 2011. Available from: http://open3dalign.sourceforge.net.
   Google Scholar
• Tosco P, Balle T, Shiri F. Open3DALIGN: an open-source software aimed at unsupervised ligand alignment. J Comput Aided Mol Des. 2011;25(8):777–783.
   PubMed Web of Science ®Google Scholar
• Tosco P, Balle T. Open3DQSAR 2.282. 2011. Available from: http://open3dqsar.sourceforge.net.
   Google Scholar
• Tosco P, Balle T. Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model. 2011;17(1):201–208.
   PubMed Web of Science ®Google Scholar
• Schrödinger, PyMOL (1.7.0.0). New York (NY), USA; 2008. Available from: http://www.pymol.org.
   Google Scholar
• Milano chemometrics and QSAR research group, 2007. Available from http://www.talete.mi.it/dragon.htm
   Google Scholar
• Discovery Studio 16.1.0; Accelrys, BIOVIA, San Diego (CA), USA; 2016. Available from: www.accelrys.com.
   Google Scholar
• The UniProt Consortium. The universal protein resource (UniProt). Nucleic Acids Res. 2008;36:D190–D195.
   PubMed Web of Science ®Google Scholar
• The UniProt Consortium, 2008. Available from https://www.uniprot.org/
   Google Scholar
• Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis. 2009;30(S1):S162–S173.
   PubMed Web of Science ®Google Scholar
• Bienert S, Waterhouse A, de Beer TAP, et al. 2017. Available from: https://swissmodel.expasy.org/
   Google Scholar
• Bienert S, Waterhouse A, de Beer TAP, et al. The SWISS-MODEL repository-new features and functionality. Nucleic Acids Res. 2017;45(D1):D313–D319.
   PubMed Web of Science ®Google Scholar
• Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 2011;39(suppl):W270–W277.
   PubMedGoogle Scholar
• Grosdidier A, Zoete V, Michielin O. 2011. Available from http://www.swissdock.ch/
   Google Scholar
• Suykens JAK, Van Gestel T, Brabanter JD, Moor BD, Vandewalle J. 2002. Available from http://www.esat.kuleuven.be/sista/lssvmlab/
   Google Scholar
• The MathWorks, Inc., Natick, Massachusetts (MA), United States. 2010. Available from https://www.mathworks.com/
   Google Scholar
• The SPSS Inc., Chicago (IL), USA, 2007. Available from http://www.spss.com
   Google Scholar
• Richmond NJ, Willett P, Clark RD. Alignment of three-dimensional molecules using an image recognition algorithm. J Mol Grap Model. 2004;23(2):199–209.
   PubMed Web of Science ®Google Scholar
• Cruciani G. Molecular interaction fields: applications in drug discovery and ADME prediction. Weinheim: WILEY-VCH Verlag GmbH & Co., KGaA; 2006.
   Google Scholar
• Kastenholz MA, Pastor M, Cruciani G, et al. GRID/CPCA: a new computational tool to design selective ligands. J Med Chem. 2000;43(16):3033–3044.
   PubMed Web of Science ®Google Scholar
• Baroni M, Clementi S, Cruciani G, et al. Predictive ability of regression models. Part II: selection of the best predictive PLS model. J Chemometrics. 1992;6(6):347–356.
   Web of Science ®Google Scholar
• Pastor M, Cruciani G, Clementi S. Smart region definition: a new way to improve the predictive ability and interpretability of three-dimensional quantitative structure-activity relationships. J Med Chem. 1997;40(10):1455–1464.
   PubMed Web of Science ®Google Scholar
• Baroni M, Costantino G, Cruciani G, et al. Generating optimal linear PLS estimations (GOLPE): an advanced chemometric tool for handling 3D-QSAR problems. Quant Struct-Act Relat. 1993;12:9–20.
   Web of Science ®Google Scholar
• Sepehri B, Omidikia N, Kompany-Zareh M, et al. Predictive and descriptive CoMFA models: the effect of variable selection. CCHTS. 2018;21(2):117–124.
   Google Scholar
• Gajiwala KS, Grodsky N, Bolaños B, et al. The Axl kinase domain in complex with a macrocyclic inhibitor offers first structural insights into an active TAM receptor kinase. J Biol Chem. 2017;292(38):15705–15716.
   PubMed Web of Science ®Google Scholar
• Leardi R. Genetic algorithms in chemistry. J Chromatogr A. 2007;1158(1–2):226–233.
   PubMed Web of Science ®Google Scholar
• Mehmood T, Liland KH, Snipen L, et al. A review of variable selection methods in partial least squares regression. Chemometr Intell Lab Syst. 2012;118:62–69.
   Web of Science ®Google Scholar
• Leardi R, González AL. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemometr Intell Lab Syst. 1998;41(2):195–207.
   Web of Science ®Google Scholar
• Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J Chemometrics. 2000;14(5–6):643–655.
   Web of Science ®Google Scholar
• Ghavami R, Sepehri B. Investigation of retention behavior of polychlorinated biphenyl congeners on 18 different HRGC columns using molecular surface average local ionization energy descriptors. J Chromatogr A. 2012;1233:116–125.
   PubMed Web of Science ®Google Scholar
• Ghavami R, Sepehri B. QSPR/QSAR solely based on molecular surface electrostatic potentials for benzenoid hydrocarbons. J Iran Chem SOC. 2016;13(3):519–529.
   Web of Science ®Google Scholar
• Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst. 2001;58(2):109–130.
   Web of Science ®Google Scholar
• Goodarzi M, Freitas MP, Wu CH, et al. Duchowicz, pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression. Chemometr Intell Lab Syst. 2010;101(2):102–109.
   Web of Science ®Google Scholar
• Suykens JAK, Gestel TV, Brabanter JD, et al. Least squares support vector machines. Singapore: World Scientific Publishing Co. Pte. Ltd; 2002.
   Google Scholar
• Sepehri B, Rasouli Z, Hassanzadeh Z, et al. Molecular docking and QSAR analysis of naphthyridone derivatives as ATAD2 bromodomain inhibitors: application of CoMFA, LS-SVM, and RBF neural network. Med Chem Res. 2016;25(12):2895–2905.
   Web of Science ®Google Scholar
• Hagan MT, Demuth HB, Beale MH, et al. Neural network design. 2nd ed. Oklahoma (OK), USA; 2014. https://hagan.okstate.edu/nnd.html.
   Google Scholar
• Beale MH, Hagan MT, Demuth HB. Neural network toolbox™ getting started guide. Natick: The MathWorks, Inc; 2016.
   Google Scholar
• Golbraikh A, Tropsha A. Beware of q2! J Mol Graph Model. 2002;20(4):269–276.
   PubMed Web of Science ®Google Scholar
• Melagraki G, Afantitis A. Enalos KNIME nodes: exploring corrosion inhibition of steel in acidic medium. Chemom Intel Lab. 2013;123:9–14.
   Web of Science ®Google Scholar
• Melagraki G, Afantitis A. Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles. RSC Adv. 2014;4(92):50713–50725.
   Web of Science ®Google Scholar
• Ojha PK, Mitra I, Das RN, et al. Further exploring rm2 metrics for validation of QSPR models. Chemom Intell Lab. 2011;107(1):194–205.,
   Web of Science ®Google Scholar
• Tropsha A. Best practices for QSAR model development, validation, and exploitation. Mol Inform. 2010;29(6–7):476–488.
   PubMed Web of Science ®Google Scholar
• Sepehri B, Ghavami R. Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4-amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides. SAR QSAR Environ Res. 2019;30(1):21–38.
   PubMed Web of Science ®Google Scholar
• Sepehri B, Ghavami R. Design new P-glycoprotein modulators based on molecular docking and CoMFA study of α, β-unsaturated carbonyl-based compounds and oxime analogs as anticancer agents. J Mol Struct. 2017;1130:922–928.
   Web of Science ®Google Scholar