Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR

References

• R.O. Hynes, Integrins: Bidirectional, allosteric signaling machines, Cell 110 (2002), pp. 673–687.
   Google Scholar
• M. Larsen, V.V. Artym, J.A. Green, and K.M. Yamada, The matrix reorganized: Extracellular matrix remodeling and integrin signaling, Curr. Opin. Cell Biol. 18 (2006), pp. 463–471.
   PubMed Web of Science ®Google Scholar
• W. Guo and F.G. Giancotti, Integrin signalling during tumour progression, Nat. Rev. Mol. Cell Biol. 5 (2004), pp. 816–826.
   PubMed Web of Science ®Google Scholar
• P.J. Reddig and R.L. Juliano, Clinging to life: Cell to matrix adhesion and cell survival, Cancer Metastasis Rev. 24 (2005), pp. 425–439.
   Google Scholar
• S.T. Lim, D. Mikolon, D.G. Stupack, and D.D. Schlaepfer, FERM control of FAK function: Implications for cancer therapy, Cell Cycle 7 (2008), pp. 2306–2314.
   Google Scholar
• M.A. Cabrita, L.M. Jones, J.L. Quizi, L.A. Sabourin, B.C. McKay, and C.L. Addison, Focal adhesion kinase inhibitors are potent anti-angiogenic agents, Mol. Onco. 5 (2011), pp. 517–526.
   Web of Science ®Google Scholar
• P. Dao, D. Lietha, M. Etheve-Quelquejeu, C. Garbay, and H. Chen, Synthesis of novel 1,2,4-triazine scaffold as FAK inhibitors with antitumor activity, Bio. Med. Chem. Lett. 27 (2017), pp. 1727–1730.
   PubMed Web of Science ®Google Scholar
• N.E. Uko, O.F. Güner, L.M.A. Barnett, D.F. Matesic, and J.P. Bowen, Discovery and biological activity of computer-assisted drug designed Akt pathway inhibitors, Bio. Med. Chem. Lett. 28 (2018), pp. 3247–3250.
   PubMed Web of Science ®Google Scholar
• J.F. Ellenbarger, I.V. Krieger, H.-L. Huang, S. Gómez-Coca, T.R. Ioerger, J.C. Sacchettini, S.E. Wheeler, and K.R. Dunbar, Anion-π interactions in computer-aided drug design: Modeling the inhibition of malate synthase by phenyl-diketo acids, J. Chem. Inf. Mod. (2018), pp. 2085–2091.
   PubMed Web of Science ®Google Scholar
• A. Daina, M.-C. Blatter, V. Baillie Gerritsen, P.M. Palagi, D. Marek, I. Xenarios, T. Schwede, O. Michielin, and V. Zoete, Drug design workshop: A web-based educational tool to introduce computer-aided drug design to the general public, J. Chem. Edu. 94 (2017), pp. 335–344.
   Web of Science ®Google Scholar
• A. Abdolmaleki, J.B. Ghasemi, and F. Ghasemi, Computer aided drug design for multi-target drug design: SAR/QSAR, molecular docking and pharmacophore methods, Curr. Drug Target. 18 (2017), pp. 556–575.
   Web of Science ®Google Scholar
• H. Gao, J.A. Katzenellenbogen, R. Garg, and C. Hansch, Comparative QSAR analysis of estrogen receptor ligands, Chem. Rev. 99 (1999), pp. 723–744.
   PubMed Web of Science ®Google Scholar
• D. Hadjipavlou-Litina, R. Garg, and C. Hansch, Comparative quantitative structure–activity relationship studies (QSAR) on non-benzodiazepine compounds binding to benzodiazepine receptor (BzR), Chem. Rev. 104 (2004), pp. 3751–3794.
   PubMed Web of Science ®Google Scholar
• C. Hansch, D. Hoekman, and H. Gao, Comparative QSAR: Toward a deeper understanding of chemicobiological interactions, Chem. Rev. 96 (1996), pp. 1045–1076.
   PubMed Web of Science ®Google Scholar
• S. Thareja, Steroidal 5-alpha-reductase inhibitors: A comparative 3D-QSAR study review, Chem. Rev. 115 (2015), pp. 2883–2894.
   PubMed Web of Science ®Google Scholar
• S. Agatonovic-Kustrin, D.W. Morton, and D. Celebic, QSAR: An in silico approach for predicting the partitioning of pesticides into breast milk, Comb. Chem. High Thro. Scr. 16 (2013), pp. 223–232.
   PubMed Web of Science ®Google Scholar
• A. Aouidate, A. Ghaleb, M. Ghamali, A. Ousaa, M. Choukrad, A. Sbai, M. Bouachrine, and T. Lakhlifi, 3D QSAR studies, molecular docking and ADMET evaluation, using thiazolidine derivatives as template to obtain new inhibitors of PIM1 kinase, Comput. Biol. Chem. 74 (2018), pp. 201–211.
   Google Scholar
• V. Stoickov, D. Stojanovic, I. Tasic, S. Saric, D. Radenkovic, P. Babovic, D. Sokolovic, and A.M. Veselinovic, QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT(1) receptor antagonists based on the Monte Carlo method, Struct. Chem. 29 (2018), pp. 441–449.
   Web of Science ®Google Scholar
• A. Kumar and S. Chauhan, QSAR differential model for prediction of SIRT1 modulation using Monte Carlo method, Drug Res. 67 (2017), pp. 156–162.
   Google Scholar
• A. Kumar and S. Chauhan, Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors, SAR QSAR Environ. Res. 28 (2017), pp. 179–197.
   Google Scholar
• A. Kumar and S. Chauhan, Use of the Monte Carlo method for OECD principles-guided QSAR modeling of SIRT1 inhibitors, Arch Pharm. 350 (2017), doi: 10.1002/ardp.201600268 [ Epub ahead of print].
   Google Scholar
• A.P. Toropova, A.A. Toropov, A.M. Veselinovic, J.B. Veselinovic, D. Leszczynska, and J. Leszczynski, Monte Carlo-based quantitative structure–activity relationship models for toxicity of organic chemicals to Daphnia magna, Environ. Toxicol. Chem. 35 (2016), pp. 2691–2697.
   PubMed Web of Science ®Google Scholar
• P. Kumar and A. Kumar, Monte Carlo method based QSAR studies of mer kinase inhibitors in compliance with OECD principles, Drug Res. (Stuttg). 68 (2018), pp. 189–195.
   Google Scholar
• P. Kumar, R. Bhatia, R. Khanna, A. Dalal, D. Kumar, P. Surain, and R.C. Kamboj, Synthesis of some benzothiazoles by developing a new protocol using urea nitrate as a catalyst and their antimicrobial activities, J. Sul. Chem. 38 (2017), pp. 585–596.
   Web of Science ®Google Scholar
• P. Kumar, M. Duhan, K. Kadyan, J. Sindhu, S. Kumar, and H. Sharma, Synthesis of novel inhibitors of α-amylase based on thiazolidine-4-one skeleton containing pyrazole moiety and their configurational studies, Med. Chem. Comm. 8 (2017), pp. 1468–1476.
   Web of Science ®Google Scholar
• R. Kumar, R. Khanna, P. Kumar, V. Kumar, and R.C. Kamboj, Synthesis of some 4‐quinolinyl pyridines and their antimicrobial and docking studies, J. Het. Chem. 54 (2017), pp. 2740–2747.
   Web of Science ®Google Scholar
• P. Kumar, M. Duhan, K. Kadyan, J.K. Bhardwaj, P. Saraf, and M. Mittal, Multicomponent synthesis of some molecular hybrid containing thiazole pyrazole as apoptosis inducer, Drug Res. (Stuttg). 68 (2018), pp. 72–79.
   Google Scholar
• P. Cheng, J. Li, J. Wang, X. Zhang, and H. Zhai, Investigations of FAK inhibitors: A combination of 3D-QSAR, docking, and molecular dynamics simulations studies, J. Biol. Struct. Dyn. 36 (2018), pp. 1529–1549.
   PubMed Web of Science ®Google Scholar
• P. Dao, R. Jarray, J. Le Coq, D. Lietha, A. Loukaci, Y. Lepelletier, R. Hadj-Slimane, C. Garbay, F. Raynaud, and H. Chen, Synthesis of novel diarylamino-1,3,5-triazine derivatives as FAK inhibitors with anti-angiogenic activity, Bio. Med. Chem. Lett. 23 (2013), pp. 4552–4556.
   PubMed Web of Science ®Google Scholar
• P. Dao, N. Smith, C. Tomkiewicz-Raulet, E. Yen-Pon, M. Camacho-Artacho, D. Lietha, J.-P. Herbeuval, X. Coumoul, C. Garbay, and H. Chen, Design, synthesis, and evaluation of novel imidazo[1,2-a][1,3,5]triazines and their derivatives as focal adhesion kinase inhibitors with antitumor activity, J. Med. Chem. 58 (2015), pp. 237–251.
   Web of Science ®Google Scholar
• N.M. O’Boyle, M. Banck, C.A. James, C. Morley, T. Vandermeersch, and G.R. Hutchison, Open Babel: An open chemical toolbox, J. Cheminform. 3:33(2011), pp. 1–14. doi:10.1186/1758-2946-3-33
   Google Scholar
• A.A. Toropov, A.P. Toropova, E. Benfenati, G. Gini, and R. Fanelli, The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method, Struct. Chem. 24 (2013), pp. 1369–1381.
   Web of Science ®Google Scholar
• P. Kumar, A. Kumar, J. Sindhu, and S. Lal, QSAR models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on Monte Carlo method, Drug Res. (Stuttg). (2018) doi: 10.1055/a-0652-5290 [ Epub ahead of print] .
   Google Scholar
• A.P. Toropova and A.A. Toropov, The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability?, Sci. Total Environ. 586 (2017), pp. 466–472.
   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.
   Google Scholar
• A.P. Toropova and A.A. Toropov, CORAL: Monte Carlo method to predict endpoints for medical chemistry, Mini Rev. Med. Chem. 18 (2018), pp. 382–391.
   Web of Science ®Google Scholar
• A.A. Toropov, R. Carbó-Dorca, and A.P. Toropova, Index of ideality of correlation: New possibilities to validate QSAR: A case study, Struct. Chem. 29 (2018), pp. 33–38.
   Web of Science ®Google Scholar
• K. Roy, On some aspects of validation of predictive quantitative structure–activity relationship models, Expert Opin. Drug Discov. 2 (2007), pp. 1567–1577.
   Google Scholar
• P.P. Roy, J.T. Leonard, and K. Roy, Exploring the impact of size of training sets for the development of predictive QSAR models, Chemom. Intell. Lab. Syst. 90 (2008), pp. 31–42.
   Web of Science ®Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J Mol Graph Model. 20 (2002), pp. 269–276.
   Google Scholar
• P.K. Ojha and K. Roy, Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection, Chemom. Intell. Lab. Syst. 109 (2011), pp. 146–161.
   Web of Science ®Google Scholar
• OECD, Guidance document on the validation of (quantitative) structure–activity relationship [(Q)SAR] models, OECD Series on Testing and Assessment, No. 69, (2014) OECD Publishing, Paris, https://doi.org/10.1787/9789264085442-en.
   Google Scholar
• A.M. Veselinovic, A. Toropov, A. Toropova, D.S. Dordevic, and J.B. Veselinovic, Design and development of novel antibiotics based on FtsZ inhibition – In silico studies, New J. Chem.42 (2018), pp. 10976–10982.
   Google Scholar
• O. Trott and A.J. Olson, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, J. Comput. Chem. 31 (2010), pp. 455–461.
   Google Scholar
• A. Pedretti, L. Villa, and G. Vistoli, VEGA: A versatile program to convert, handle and visualize molecularstructure on WINDOWS-based PCs, J. Mol. Graph. 21 (2002), pp. 47–49.
   Google Scholar
• P. Dao, R. Jarray, N. Smith, Y. Lepelletier, J. Le Coq, D. Lietha, R. Hadj-Slimane, J. Herbeuval, C. Garbay, F. Raynaud, and H. Chen, Inhibition of both focal adhesion kinase and fibroblast growth factor receptor 2 pathways induces anti-tumor and anti-angiogenic activities, Cancer Lett. 348 (2014), pp. 88–99.
   Google Scholar