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Letters in Drug Design & Discovery

Editor-in-Chief

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

Research Article

Homology Modelling, Docking-based Virtual Screening, ADME Properties, and Molecular Dynamics Simulation for Identification of Probable Type II Inhibitors of AXL Kinase

Author(s): Heena R. Bhojwani and Urmila J. Joshi*

Volume 19, Issue 3, 2022

Published on: 03 October, 2021

Page: [214 - 241] Pages: 28

DOI: 10.2174/1570180818666211004102043

Price: $65

Abstract

Background: AXL kinase is an important member of the TAM family for kinases which is involved in most cancers. Considering its role in different cancers due to its pro-tumorigenic effects and its involvement in the resistance, it has gained importance recently. Majority of research carried out is on Type I inhibitors and limited studies have been carried out for Type II inhibitors. Taking this into consideration, we have attempted to build Homology models to identify the Type II inhibitors for the AXL kinase.

Methods: Homology Models for DFG-out C-helix-in/out state were developed using SWISS Model, PRIMO, and Prime. These models were validated by different methods and further evaluated for stability by molecular dynamics simulation using Desmond software. Selected models PED1-EB and PEDI1-EB were used for the docking-based virtual screening of four compound libraries using Glide software. The hits identified were subjected to interaction analysis and shortlisted compounds were subjected to Prime MM-GBSA studies for energy calculation. These compounds were also docked in the DFG-in state to check for binding and elimination of any compounds that may not be Type II inhibitors. The Prime energies were calculated for these complexes as well and some compounds were eliminated. ADMET studies were carried out using Qikprop. Some selected compounds were subjected to molecular dynamics simulation using Desmond for evaluating the stability of the complexes.

Results: Out of 78 models inclusive of both DFG-out C-helix-in and DFG-out C-helix-out, 5 models were identified after different types of evaluation as well as validation studies. 1 model representing each type (PED1-EB and PEDI1-EB) was selected for the screening studies. The screening studies resulted in the identification of 29 compounds from the screen on PED1-EB and 10 compounds from the screen on PEDI1-EB. Hydrogen bonding interactions with Pro621, Met623, and Asp690 were observed for these compounds primarily. In some compounds, hydrogen bonding with Leu542, Glu544, Lys567, and Asn677 as well as pi-pi stacking interactions with either Phe622 or Phe691 were also seen. 4 compounds identified from PED1-EB screen were subjected to molecular dynamics simulation and their interactions were found to be consistent during the simulation. 2 compounds identified from PEDI1-EB screen were also subjected to the simulation studies, however, their interactions with Asp690 were not observed for a significant time and in both cases differed from the docked pose.

Conclusion: Multiple models of DFG-out conformations of AXL kinase were built, validated and used for virtual screening. Different compounds were identified in the virtual screening, which may possibly act as Type II inhibitors for AXL kinase. Some more experimental studies can be done to validate these findings in future. This study will play a guiding role in the further development of the newer Type II inhibitors of the AXL kinase for the probable treatment of cancer.

Keywords: AXL kinase, Type II inhibitors, homology modelling, docking-based virtual screen, molecular dynamics, prime MM-GBSA.

Graphical Abstract
[1]
Whiteside, T.L. The tumor microenvironment and its role in promoting tumor growth. Oncogene, 2008, 27(45), 5904-5912.
[http://dx.doi.org/10.1038/onc.2008.271] [PMID: 18836471]
[2]
Myers, K.V.; Amend, S.R.; Pienta, K.J. Targeting Tyro3, Axl and MerTK (TAM receptors): implications for macrophages in the tumor microenvironment. Mol. Cancer, 2019, 18(1), 94.
[http://dx.doi.org/10.1186/s12943-019-1022-2] [PMID: 31088471]
[3]
Paolino, M.; Penninger, J.M. The role of TAM family receptors in immune cell function: Implications for cancer therapy. Cancers (Basel), 2016, 8(10), 97.
[http://dx.doi.org/10.3390/cancers8100097] [PMID: 27775650]
[4]
Gay, C.M.; Balaji, K.; Byers, L.A. Giving AXL the axe: targeting AXL in human malignancy. Br. J. Cancer, 2017, 116(4), 415-423.
[http://dx.doi.org/10.1038/bjc.2016.428] [PMID: 28072762]
[5]
Di Stasi, R.; De Rosa, L.; D’Andrea, L.D. Therapeutic aspects of the Axl/Gas6 molecular system. Drug Discov. Today, 2020, 25(12), 2130-2148.
[http://dx.doi.org/10.1016/j.drudis.2020.09.022] [PMID: 33002607]
[6]
Rankin, E.B.; Giaccia, A.J. The receptor tyrosine kinase AXL in cancer progression. Cancers (Basel), 2016, 8(11), 103-119.
[http://dx.doi.org/10.3390/cancers8110103] [PMID: 27834845]
[7]
Scaltriti, M.; Elkabets, M.; Baselga, J. Molecular Pathways: AXL, a Membrane Receptor Mediator of Resistance to Therapy. Clin. Cancer Res., 2016, 22(6), 1313-1317.
[8]
Zhu, C.; Wei, Y.; Wei, X. AXL receptor tyrosine kinase as a promising anti-cancer approach: functions, molecular mechanisms and clinical applications. Mol. Cancer, 2019, 18(1), 153.
[http://dx.doi.org/10.1186/s12943-019-1090-3] [PMID: 31684958]
[9]
Baladi, T.; Abet, V.; Piguel, S. State-of-the-art of small molecule inhibitors of the TAM family: the point of view of the chemist. Eur. J. Med. Chem., 2015, 105, 220-237.
[http://dx.doi.org/10.1016/j.ejmech.2015.10.003] [PMID: 26498569]
[10]
Roskoski Jr, R. Classification of small molecule protein kinase inhibitors based upon the structures of their drug-enzyme complexes. Pharmacol. Res., 2016, 103, 26-48.
[http://dx.doi.org/10.1016/j.phrs.2015.10.021] [PMID: 26529477]
[11]
Blanc, J.; Geney, R.; Menet, C.; Type, I.I. Type II kinase inhibitors: An opportunity in cancer for rational design. Anticancer. Agents Med. Chem., 2013, 13(5), 731-747.
[http://dx.doi.org/10.2174/1871520611313050008] [PMID: 23094911]
[12]
Davis, M.I.; Hunt, J.P.; Herrgard, S.; Ciceri, P.; Wodicka, L.M.; Pallares, G.; Hocker, M.; Treiber, D.K.; Zarrinkar, P.P. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol., 2011, 29(11), 1046-1051.
[http://dx.doi.org/10.1038/nbt.1990] [PMID: 22037378]
[13]
Gajiwala, K.S.; Grodsky, N.; Bolaños, B.; Feng, J.; Ferre, R.; Timofeevski, S.; Xu, M.; Murray, B.W.; Johnson, T.W.; Stewart, A. 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.
[http://dx.doi.org/10.1074/jbc.M116.771485] [PMID: 28724631]
[14]
Mollard, A.; Warner, S.L.; Call, L.T.; Wade, M.L.; Bearss, J.J.; Verma, A.; Sharma, S.; Vankayalapati, H.; Bearss, D.J. Design, Synthesis and biological evaluation of a series of novel Axl kinase inhibitors. ACS Med. Chem. Lett., 2011, 2(12), 907-912.
[http://dx.doi.org/10.1021/ml200198x] [PMID: 22247788]
[15]
Fatima, G.; Loubna, A.; Wiame, L.; Azeddine, I. In silico inhibition studies of AXL kinase by curcumin and its natural derivatives. J. Appl. Bioinforma. Comput. Biol., 2017, 3, 2.
[http://dx.doi.org/10.4172/2329-9533.1000142]
[16]
Messoussi, A.; Peyronnet, L.; Feneyrolles, C.; Chevé, G.; Bougrin, K.; Yasri, A. Structural elucidation of the DFG-Asp in and DFG-Asp out states of TAM kinases and insight into the selectivity of their inhibitors. Molecules, 2014, 19(10), 16223-16239.
[http://dx.doi.org/10.3390/molecules191016223] [PMID: 25310149]
[17]
Sarukhanyan, E.; Shityakov, S.; Dandekar, T. In silico designed Axl receptor blocking drug candidates against zika virus infection. ACS Omega, 2018, 3(5), 5281-5290.
[http://dx.doi.org/10.1021/acsomega.8b00223] [PMID: 30023915]
[18]
Myers, S.H.; Brunton, V.G. Unciti-broceta, a. AXL inhibitors in cancer: A medicinal chemistry perspective. J. Med. Chem., 2016, 59(8), 3593-3608.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01273] [PMID: 26555154]
[19]
Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol., 1990, 215(3), 403-410.
[http://dx.doi.org/10.1016/S0022-2836(05)80360-2] [PMID: 2231712]
[20]
Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res., 1997, 25(17), 3389-3402.
[http://dx.doi.org/10.1093/nar/25.17.3389] [PMID: 9254694]
[21]
Henikoff, S.; Henikoff, J.G. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA, 1992, 89(22), 10915-10919.
[http://dx.doi.org/10.1073/pnas.89.22.10915] [PMID: 1438297]
[22]
Guex, N.; Peitsch, M.C.; Schwede, T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis, 2009, 30(S1)(Suppl. 1), S162-S173.
[http://dx.doi.org/10.1002/elps.200900140] [PMID: 19517507]
[23]
Hatherley, R.; Brown, D.K.; Glenister, M.; Tastan Bishop, Ö. PRIMO: An interactive homology modeling pipeline. PLoS One, 2016, 11(11)e0166698
[http://dx.doi.org/10.1371/journal.pone.0166698] [PMID: 27855192]
[24]
Release, S. 2017-2: Prime; Schrödinger, LLC: New York, NY, 2017. Available at: https://www.schrodinger.com/citations
[25]
Jacobson, M.P.; Pincus, D.L.; Rapp, C.S.; Day, T.J.F.; Honig, B.; Shaw, D.E.; Friesner, R.A. A hierarchical approach to all-atom protein loop prediction. Proteins, 2004, 55(2), 351-367.
[http://dx.doi.org/10.1002/prot.10613] [PMID: 15048827]
[26]
Jacobson, M.P.; Friesner, R.A.; Xiang, Z.; Honig, B. On the role of the crystal environment in determining protein side-chain conformations. J. Mol. Biol., 2002, 320(3), 597-608.
[http://dx.doi.org/10.1016/S0022-2836(02)00470-9] [PMID: 12096912]
[27]
Vijayan, R.S.K.; He, P.; Modi, V.; Duong-Ly, K.C.; Ma, H.; Peterson, J.R.; Dunbrack, R.L., Jr; Levy, R.M. Conformational analysis of the DFG-out kinase motif and biochemical profiling of structurally validated type II inhibitors. J. Med. Chem., 2015, 58(1), 466-479.
[http://dx.doi.org/10.1021/jm501603h] [PMID: 25478866]
[28]
Modi, V.; Dunbrack, R.L., Jr Defining a new nomenclature for the structures of active and inactive kinases. Proc. Natl. Acad. Sci. USA, 2019, 116(14), 6818-6827.
[http://dx.doi.org/10.1073/pnas.1814279116] [PMID: 30867294]
[29]
Zhang, Y.; Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins, 2004, 57(4), 702-710.
[http://dx.doi.org/10.1002/prot.20264] [PMID: 15476259]
[30]
Xu, J.; Zhang, Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics, 2010, 26(7), 889-895.
[http://dx.doi.org/10.1093/bioinformatics/btq066] [PMID: 20164152]
[31]
Shen, M.Y.; Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci., 2006, 15(11), 2507-2524.
[http://dx.doi.org/10.1110/ps.062416606] [PMID: 17075131]
[32]
Laskowski, R.A.; MacArthur, M.W.; Moss, D.S.; Thornton, J.M. PROCHECK: A Program to Check the Stereochemical Quality of Protein Structures. J. Appl. Cryst., 1993, 26(2), 283-291.
[http://dx.doi.org/10.1107/S0021889892009944]
[33]
Bowie, J. U.; Lüthy, R.; Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional stucture. Science (80), 1991, 253(5016), 164-170.
[http://dx.doi.org/10.1126/science.1853201]
[34]
Lüthy, R.; Bowie, J.U.; Eisenberg, D. Assessment of protein models with three-dimensional profiles. Nature, 1992, 356(6364), 83-85.
[http://dx.doi.org/10.1038/356083a0] [PMID: 1538787]
[35]
Colovos, C.; Yeates, T.O. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci., 1993, 2(9), 1511-1519.
[http://dx.doi.org/10.1002/pro.5560020916] [PMID: 8401235]
[36]
Pontius, J.; Richelle, J.; Wodak, S.J. Deviations from standard atomic volumes as a quality measure for protein crystal structures. J. Mol. Biol., 1996, 264(1), 121-136.
[http://dx.doi.org/10.1006/jmbi.1996.0628] [PMID: 8950272]
[37]
Release, S. 2016-4: Desmond Molecular Dynamics System; D. E. Shaw Research: New York, NY, 2016. Available at: https://www.schrodinger.com/products/desmond
[38]
Bowers, K.J.; Chow, E.; Xu, H.; Dror, R.O.; Eastwood, M.P.; Gregersen, B.A.; Klepeis, J.L.; Kolossvary, I.; Moraes, M.A.; Sacerdoti, F.D.; Salmon, J.K.; Shan, Y.; Shaw, D.E. Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC’06, 2006, p. 3.
[http://dx.doi.org/10.1109/SC.2006.54]
[39]
Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 1983.
[http://dx.doi.org/10.1063/1.445869]
[40]
Ryckaert, J.P.; Ciccotti, G.; Berendsen, H.J.C. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys., 1977.
[http://dx.doi.org/10.1016/0021-9991(77)90098-5]
[41]
Lambrakos, S.G.; Boris, J.P.; Oran, E.S.; Chandrasekhar, I.; Nagumo, M. A modified shake algorithm for maintaining rigid bonds in molecular dynamics simulations of large molecules. J. Comput. Phys., 1989, 79(2), 926-935.
[http://dx.doi.org/10.1016/0021-9991(89)90160-5]
[42]
Hayes, J.M.; Skamnaki, V.T.; Archontis, G.; Lamprakis, C.; Sarrou, J.; Bischler, N.; Skaltsounis, A.L.; Zographos, S.E.; Oikonomakos, N.G. Kinetics, in silico docking, molecular dynamics, and MM-GBSA binding studies on prototype indirubins, KT5720, and staurosporine as phosphorylase kinase ATP-binding site inhibitors: the role of water molecules examined. Proteins, 2011, 79(3), 703-719.
[http://dx.doi.org/10.1002/prot.22890] [PMID: 21287607]
[43]
Tuckerman, M.; Berne, B.J.; Martyna, G.J. Reversible multiple time scale molecular dynamics. J. Chem. Phys., 1992, 97(3), 1990-2001.
[http://dx.doi.org/10.1063/1.463137]
[44]
Martyna, G.J.; Tobias, D.J.; Klein, M.L. Constant pressure molecular dynamics algorithms. J. Chem. Phys., 1994, 101(5), 4177-4189.
[http://dx.doi.org/10.1063/1.467468]
[45]
Release, S. 2017-2: LigPrep; Schrödinger, LLC: New York, NY, 2017. Available at: https://www.schrodinger.com/products/ligprep
[46]
Bhojwani, H.R.; Joshi, U.J. Pharmacophore and docking guided virtual screening study for discovery of type I inhibitors of VEGFR-2 kinase. Curr. Computeraided Drug Des., 2017, 13(3), 186-207.
[http://dx.doi.org/10.2174/1386207319666161214112536] [PMID: 27981900]
[47]
Release, S. 2017-2: Protein Preparation Wizard; Epik, Schrödinger, LLC, New York, NY, 2017; Impact, Schrödinger, LLC, New York, NY, 2017; Prime, Schrödinger, LLC: New York, NY, 2017. Available at: https://www.schrodinger.com/science-articles/protein-preparation-wizard
[48]
Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des., 2013, 27(3), 221-234.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID: 23579614]
[49]
Release, S. 2017-2: Glide; Schrödinger, LLC: New York, NY, 2017. Available at: https://www.schrodinger.com/products/glide
[50]
Halgren, T.A.; Murphy, R.B.; Friesner, R.A.; Beard, H.S.; Frye, L.L.; Pollard, W.T.; Banks, J.L. Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem., 2004, 47(7), 1750-1759.
[http://dx.doi.org/10.1021/jm030644s] [PMID: 15027866]
[51]
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47(7), 1739-1749.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[52]
Friesner, R.A.; Murphy, R.B.; Repasky, M.P.; Frye, L.L.; Greenwood, J.R.; Halgren, T.A.; Sanschagrin, P.C.; Mainz, D.T. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49(21), 6177-6196.
[http://dx.doi.org/10.1021/jm051256o] [PMID: 17034125]
[53]
Release, S. 2017-2: Prime; Schrödinger, LLC: New York, NY, 2017. Available at: https://www.schrodinger.com/products/prime
[54]
Release, S. 2017-2: QikProp; Schrödinger, LLC: New York, NY, 2017. Available at: https://www.schrodinger.com/products/qikprop
[55]
Luo, X.; Zhao, Y.; Tang, P.; Du, X.; Li, F.; Wang, Q.; Li, R.; He, J. Discovery of new small-molecule cyclin-dependent kinase 6 inhibitors through computational approaches. Mol. Divers., 2021, 25(1), 367-382.
[http://dx.doi.org/10.1007/s11030-020-10120-3] [PMID: 32770459]
[56]
Muhammed, M.T.; Aki-Yalcin, E. Homology modeling in drug discovery: Overview, current applications, and future perspectives. Chem. Biol. Drug Des., 2019, 93(1), 12-20.
[http://dx.doi.org/10.1111/cbdd.13388] [PMID: 30187647]
[57]
Ke, Y.Y.; Singh, V.K.; Coumar, M.S.; Hsu, Y.C.; Wang, W.C.; Song, J.S.; Chen, C.H.; Lin, W.H.; Wu, S.H.; Hsu, J.T.A.; Shih, C.; Hsieh, H.P. Homology modeling of DFG-in FMS-like tyrosine kinase 3 (FLT3) and structure-based virtual screening for inhibitor identification. Sci. Rep., 2015, 5(1), 11702.
[http://dx.doi.org/10.1038/srep11702] [PMID: 26118648]
[58]
Modi, V.; Dunbrack, R. A web resource for structural classification of protein kinases and their inhibitors. bioRxiv, 2021.
[http://dx.doi.org/10.1101/2021.02.12.430923]]
[59]
Yang, A.S.; Honig, B. An integrated approach to the analysis and modeling of protein sequences and structures. I. Protein structural alignment and a quantitative measure for protein structural distance. J. Mol. Biol., 2000, 301(3), 665-678.
[http://dx.doi.org/10.1006/jmbi.2000.3973] [PMID: 10966776]
[60]
Webb, B.; Sali, A. Protein structure modeling with MODELLER.In: Methods in Molecular Biology;; , 2017, pp. 39-54.
[http://dx.doi.org/10.1007/978-1-4939-7231-9_4]
[61]
Kruggel, S.; Lemcke, T. Generation and evaluation of a homology model of PfGSK-3. Arch. Pharm. (Weinheim), 2009, 342(6), 327-332.
[http://dx.doi.org/10.1002/ardp.200800158] [PMID: 19475596]
[62]
Jamal, S.; Grover, A.; Grover, S. Machine Learning from Molecu- lar Dynamics Trajectories to Predict Caspase-8 Inhibitors Against Alzheimer’s Disease. Front. Pharmacol., 2019, 10, 780.
[http://dx.doi.org/10.3389/fphar.2019.00780] [PMID: 31354494]
[63]
Podlipnik, C.; Tutino, F.; Bernardi, A.; Seneci, P. DFG-in and DFG-out homology models of TrkB kinase receptor: induced-fit and ensemble docking. J. Mol. Graph. Model., 2010, 29(3), 309-320.
[http://dx.doi.org/10.1016/j.jmgm.2010.09.008] [PMID: 21036641]
[64]
Liu, Y.; Gray, N.S. Rational design of inhibitors that bind to inactive kinase conformations. Nat. Chem. Biol., 2006, 2(7), 358-364.
[http://dx.doi.org/10.1038/nchembio799] [PMID: 16783341]
[65]
Schroeder, G.M.; An, Y.; Cai, Z.W.; Chen, X.T.; Clark, C.; Cornelius, L.A.M.; Dai, J.; Gullo-Brown, J.; Gupta, A.; Henley, B.; Hunt, J.T.; Jeyaseelan, R.; Kamath, A.; Kim, K.; Lippy, J.; Lombardo, L.J.; Manne, V.; Oppenheimer, S.; Sack, J.S.; Schmidt, R.J.; Shen, G.; Stefanski, K.; Tokarski, J.S.; Trainor, G.L.; Wautlet, B.S.; Wei, D.; Williams, D.K.; Zhang, Y.; Zhang, Y.; Fargnoli, J.; Borzilleri, R.M. Discovery of N-(4-(2-amino-3-chloropyridin-4-yloxy)-3-fluorophenyl)-4-ethoxy-1-(4-fluorophenyl)-2-oxo-1,2-dihydropyridine-3-carboxamide (BMS-777607), a selective and orally efficacious inhibitor of the Met kinase superfamily. J. Med. Chem., 2009, 52(5), 1251-1254.
[http://dx.doi.org/10.1021/jm801586s] [PMID: 19260711]
[66]
Qian, F.; Engst, S.; Yamaguchi, K.; Yu, P.; Won, K.A.; Mock, L.; Lou, T.; Tan, J.; Li, C.; Tam, D.; Lougheed, J.; Yakes, F.M.; Bentzien, F.; Xu, W.; Zaks, T.; Wooster, R.; Greshock, J.; Joly, A.H. Inhibition of tumor cell growth, invasion, and metastasis by EXEL-2880 (XL880, GSK1363089), a novel inhibitor of HGF and VEGF receptor tyrosine kinases. Cancer Res., 2009, 69(20), 8009-8016.
[http://dx.doi.org/10.1158/0008-5472.CAN-08-4889] [PMID: 19808973]
[67]
Yan, S.B.; Peek, V.L.; Ajamie, R.; Buchanan, S.G.; Graff, J.R.; Heidler, S.A.; Hui, Y.H.; Huss, K.L.; Konicek, B.W.; Manro, J.R.; Shih, C.; Stewart, J.A.; Stewart, T.R.; Stout, S.L.; Uhlik, M.T.; Um, S.L.; Wang, Y.; Wu, W.; Yan, L.; Yang, W.J.; Zhong, B.; Walgren, R.A. LY2801653 is an orally bioavailable multi-kinase inhibitor with potent activity against MET, MST1R, and other oncoproteins, and displays anti-tumor activities in mouse xenograft models. Invest. New Drugs, 2013, 31(4), 833-844.
[http://dx.doi.org/10.1007/s10637-012-9912-9] [PMID: 23275061]
[68]
Liu, H.; Feng, X.; Ennis, K.N.; Behrmann, C.A.; Sarma, P.; Jiang, T.T.; Kofuji, S.; Niu, L.; Stratton, Y.; Thomas, H.E.; Yoon, S.O.; Sasaki, A.T.; Plas, D.R. Pharmacologic Targeting of S6K1 in PTEN-Deficient Neoplasia. Cell Rep., 2017, 18(9), 2088-2095.
[http://dx.doi.org/10.1016/j.celrep.2017.02.022] [PMID: 28249155]
[69]
Szabadkai, I.; Torka, R.; Garamvölgyi, R.; Baska, F.; Gyulavári, P.; Boros, S.; Illyés, E.; Choidas, A.; Ullrich, A.; Őrfi, L. Discovery of N-[4-(Quinolin-4-yloxy)phenyl]benzenesulfonamides as Novel AXL Kinase Inhibitors. J. Med. Chem., 2018, 61(14), 6277-6292.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00672] [PMID: 29928803]
[70]
Afroze, N.; Pramodh, S.; Hussain, A.; Waleed, M.; Vakharia, K. A Review on Myricetin as a Potential Therapeutic Candidate for Cancer Prevention 3 Biotech., 2020, 10(5), 1-2.
[71]
Goodfellow, V.S.; Loweth, C.J.; Ravula, S.B.; Wiemann, T.; Nguyen, T.; Xu, Y.; Todd, D.E.; Sheppard, D.; Pollack, S.; Polesskaya, O.; Marker, D.F.; Dewhurst, S.; Gelbard, H.A. Discovery, synthesis, and characterization of an orally bioavailable, brain penetrant inhibitor of mixed lineage kinase 3. J. Med. Chem., 2013, 56(20), 8032-8048.
[http://dx.doi.org/10.1021/jm401094t] [PMID: 24044867]
[72]
Haugh, I.M.; Watson, I.T.; Alan Menter, M. Successful treatment of atopic dermatitis with the JAK1 inhibitor oclacitinib. Proc. Bayl. Univ. Med. Cent., 2018, 31(4), 524-525.
[http://dx.doi.org/10.1080/08998280.2018.1480246] [PMID: 30949000]
[73]
Vanajothi, R.; Vedagiri, H.; Al-Ansari, M.M.; Al-Humaid, L.A.; Kumpati, P. Pharmacophore based virtual screening, molecular docking and molecular dynamic simulation studies for finding ROS1 kinase inhibitors as potential drug molecules. J. Biomol. Struct. Dyn., 2020, 13, 1-15.
[http://dx.doi.org/10.1080/07391102.2020.1847195] [PMID: 33200682]
[74]
Feneyrolles, C.; Guiet, L.; Singer, M.; Van Hijfte, N.; Daydé-Cazals, B.; Fauvel, B.; Chevé, G.; Yasri, A. Discovering novel 7-azaindole-based series as potent AXL kinase inhibitors. Bioorg. Med. Chem. Lett., 2017, 27(4), 862-866.
[http://dx.doi.org/10.1016/j.bmcl.2017.01.015] [PMID: 28094183]
[75]
Wang, M.S.; Xu, H.C.; Gong, Y.; Qu, R.Y.; Zhuo, L.S.; Huang, W. Efficient Arylation of 2,7-Naphthyridin-1(2H)-one with Diaryliodonium Salts and Discovery of a New Selective MET/AXL Kinase Inhibitor. ACS Comb. Sci., 2020, 22(9), 457-467.
[http://dx.doi.org/10.1021/acscombsci.0c00074] [PMID: 32589005]
[76]
Choi, M.J.; Roh, E.J.; Hur, W.; Lee, S.H.; Sim, T.; Oh, C.H.; Lee, S.H.; Kim, J.S.; Yoo, K.H. Design, synthesis, and biological evaluation of novel aminopyrimidinylisoindolines as AXL kinase inhibitors. Bioorg. Med. Chem. Lett., 2018, 28(23-24), 3761-3765.
[http://dx.doi.org/10.1016/j.bmcl.2018.10.013] [PMID: 30340900]
[77]
Goff, D.; Zhang, J.; Heckrodt, T.; Yu, J.; Ding, P.; Singh, R.; Holland, S.; Li, W.; Irving, M. Discovery of dual Axl/VEGF-R2 inhibitors as potential anti-angiogenic and anti-metastatic drugs for cancer chemotherapy. Bioorg. Med. Chem. Lett., 2017, 27(16), 3766-3771.
[http://dx.doi.org/10.1016/j.bmcl.2017.06.071] [PMID: 28711351]
[78]
Tan, L.; Zhang, Z.; Gao, D.; Luo, J.; Tu, Z.C.; Li, Z.; Peng, L.; Ren, X.; Ding, K. 4-Oxo-1,4-dihydroquinoline-3-carboxamide Derivatives as New Axl Kinase Inhibitors. J. Med. Chem., 2016, 59(14), 6807-6825.
[http://dx.doi.org/10.1021/acs.jmedchem.6b00608] [PMID: 27379978]
[79]
Tan, L.; Zhang, Z.; Gao, D.; Chan, S.; Luo, J.; Tu, Z.C.; Zhang, Z.M.; Ding, K.; Ren, X.; Lu, X. Quinolone antibiotic derivatives as new selective Axl kinase inhibitors. Eur. J. Med. Chem., 2019, 166, 318-327.
[http://dx.doi.org/10.1016/j.ejmech.2019.01.065] [PMID: 30731400]
[80]
Wang, Y.; Xing, L.; Ji, Y.; Ye, J.; Dai, Y.; Gu, W.; Ai, J.; Song, Z. Discovery of a potent tyrosine kinase AXL inhibitor bearing the 3-((2,3,4,5-tetrahydro-1H-benzo[d]azepin-7-yl)amino)pyrazine core. Bioorg. Med. Chem. Lett., 2019, 29(6), 836-838.
[http://dx.doi.org/10.1016/j.bmcl.2019.01.018] [PMID: 30685094]
[81]
Keung, W.; Boloor, A.; Brown, J.; Kiryanov, A.; Gangloff, A.; Lawson, J.D.; Skene, R.; Hoffman, I.; Atienza, J.; Kahana, J.; De Jong, R.; Farrell, P.; Balakrishna, D.; Halkowycz, P. Structure-based optimization of 1H-imidazole-2-carboxamides as Axl kinase inhibitors utilizing a Mer mutant surrogate. Bioorg. Med. Chem. Lett., 2017, 27(4), 1099-1104.
[http://dx.doi.org/10.1016/j.bmcl.2016.12.024] [PMID: 28082036]
[82]
Pflug, A.; Schimpl, M.; Nissink, J.W.M.; Overman, R.C.; Rawlins, P.B.; Truman, C.; Underwood, E.; Warwicker, J.; Winter-Holt, J.; McCoull, W. A-loop interactions in Mer tyrosine kinase give rise to inhibitors with two-step mechanism and long residence time of binding. Biochem. J., 2020, 477(22), 4443-4452.
[http://dx.doi.org/10.1042/BCJ20200735] [PMID: 33119085]

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