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SAR and QSAR in Environmental Research Volume 33, 2022 - Issue 7
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Research Article

QSAR analysis and experimental evaluation of new quinazoline-containing hydroxamic acids as histone deacetylase 6 inhibitors

O.V. Tinkova Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Shevchenko Transnistria State University, Tiraspol, MoldovaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-4702-6825View further author information
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V.Y. Grigorevb Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of SciencesDepartment of Computer-aided, Chernogolovka, RussiaORCID Iconhttps://orcid.org/0000-0002-5288-3242View further author information
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L.D. Grigorevac Department of Fundamental Physicochemical Engineering, Moscow State University, Moscow, RussiaORCID Iconhttps://orcid.org/0000-0002-3854-0059View further author information
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V.N. Osipovd Department of Chemical Synthesis, Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Moscow, RussiaORCID Iconhttps://orcid.org/0000-0001-7726-4467View further author information
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A.V. Kolotaeve Laboratory of Natural Compounds, National Research Centre “Kurchatov Institute”, Moscow, Russia;f Laboratory of Natural Compounds, Institute of Chemical Reagents and High Purity Chemical Substances of the National Research Centre “Kurchatov Institute”, Moscow, RussiaORCID Iconhttps://orcid.org/0000-0002-8035-6845View further author information
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D.S. Khachatryane Laboratory of Natural Compounds, National Research Centre “Kurchatov Institute”, Moscow, Russia;f Laboratory of Natural Compounds, Institute of Chemical Reagents and High Purity Chemical Substances of the National Research Centre “Kurchatov Institute”, Moscow, RussiaORCID Iconhttps://orcid.org/0000-0002-5490-5652View further author information
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Pages 513-532 | Received 02 May 2022, Accepted 14 Jun 2022, Published online: 04 Jul 2022

References

  • G.I. Aldana-Masangkay and K.M. Sakamoto, The role of HDAC6 in cancer, J. Biomed. Biotechnol. 2011 (2011), pp. 875824. doi:10.1155/2011/875824.
  • Y. Li, S. Sang, W. Ren, Y. Pei, Y. Bian, Y. Chen, and H. Sun, Inhibition of histone deacetylase 6 (HDAC6) as a therapeutic strategy for Alzheimer’s disease: A review (2010–2020), Eur. J. Med. Chem. 226 (2021), pp. 113874. doi:10.1016/j.ejmech.2021.113874.
  • S. Pulya, A.A. Sk, N. Adhikari, S. Biswas, T. Jha, and B. Ghosh, HDAC6 as privileged target in drug discovery: A perspective, Pharmacol. Res. 163 (2021), pp. 105274. doi:10.1016/j.phrs.2020.105274.
  • M. Mottamal, S. Zheng, T.L. Huang, and G. Wang, Histone deacetylase inhibitors in clinical studies as templates for new anticancer agents, Molecules 20 (2015), pp. 3898–3941. doi:10.3390/molecules20033898.
  • V.N. Osipov, D.S. Khachatryan, and A.N. Balaev, Biologically active quinazoline-based hydroxamic acids, Med. Chem. Res. 29 (2020), pp. 831–845. doi:10.1007/s00044-020-02530-7.
  • D.T. Hieu, D.T. Anh, P.T. Hai, L.T. Huong, E.J. Park, J.E. Choi, J.S. Kang, P.T.P. Dung, S.B. Han, and N.H. Nam, Quinazoline-based hydroxamic acids: Design, synthesis, and evaluation of histone deacetylase inhibitory effects and cytotoxicity, Chem. Biodivers. 15 (2018), pp. e1800027. doi:10.1002/cbdv.201800027.
  • F.W. Peng, T.T. Wu, Z.W. Ren, J.Y. Xue, and L. Shi, Hybrids from 4-anilinoquinazoline and hydroxamic acid as dual inhibitors of vascular endothelial growth factor receptor-2 and histone deacetylase, Bioorg. Med. Chem. Lett. 25 (2015), pp. 5137–5141. doi:10.1016/j.bmcl.2015.10.006.
  • F.W. Peng, J. Xuan, T.T. Wu, J.Y. Xue, Z.W. Ren, D.K. Liu, X.Q. Wang, X.H. Chen, J.W. Zhang, Y.G. Xu, and L. Shi, Design, synthesis and biological evaluation of N–phenylquinazolin–4–amine hybrids as dual inhibitors of VEGFR–2 and HDAC, Eur. J. Med. Chem. 109 (2016), pp. 1–12. doi:10.1016/j.ejmech.2015.12.033.
  • Y. Li, J. Xiao, Q. Zhang, W. Yu, M. Liu, Y. Guo, J. He, and Y. Liu, The association between anti-tumor potency and structure-activity of protein-kinases inhibitors based on quinazoline molecular skeleton, Bioorg. Med. Chem. 27 (2019), pp. 568–577. doi:10.1016/j.bmc.2018.12.032.
  • J.E.G. S.A. Wells Jr, R.F. Gagel, J. Moley, D. Pfister, J.A. Sosa, M. Skinner, A. Krebs, J. Vasselli, and M. Schlumberger, Vandetanib for the treatment of patients with locally advanced or metastatic hereditary medullary thyroid cancer, J. Clin. Oncol. 28 (2010), pp. 767–772. doi:10.1200/jco.2009.23.6604.
  • E.N. Muratov, J. Bajorath, R.P. Sheridan, I.V. Tetko, D. Filimonov, V. Poroikov, T.I. Oprea, I.I. Baskin, A. Varnek, A. Roitberg, O. Isayev, S. Curtalolo, D. Fourches, Y. Cohen, A. Aspuru-Guzik, D.A. Winkler, D. Agrafiotis, A. Cherkasov, and A. Tropsha, QSAR without borders, Chem. Soc. Rev. 49 (2020), pp. 3525–3716. doi:10.1039/d0cs00098a.
  • D. Ruzic, N. Djokovic, and K. Nikolic, Fragment-based drug design of selective HDAC6 inhibitors, Meth. Mol. Biol. 2266 (2021), pp. 155–170. doi:10.1007/978-1-0716-1209-5_9.
  • D. Ruzic, M. Petkovic, D. Agbaba, A. Ganesan, and K. Nikolic, Combined ligand and fragment-based drug design of selective histone deacetylase - 6 inhibitors, Mol. Inf. 38 (2019), pp. e1800083. doi:10.1002/minf.201800083.
  • C. Hu, L. Hong, and W. Du, 3D-QSAR studies of HDAC6 inhibitors using docking-based alignment, Lett. Drug Des. Discov. 14 (2017), pp. 798–810. doi:10.2174/1570180813666161028165151.
  • S. Vijayasarathy and J. Chatterjee, Comparative QSAR analysis of histone deacetylase 6 (HDAC6) inhibitors as anti-cancer agents, Int. J. Curr. Res. 8 (2016), pp. 39618–39623.
  • Y. Guo, J. Xiao, Z. Guo, F. Chu, Y. Cheng, and S. Wu, Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses, Bioorg. Med. Chem. 13 (2005), pp. 5424–5434. doi:10.1016/j.bmc.2005.05.016.
  • A. Xie, C. Liao, Z. Li, Z. Ning, W. Hu, X. Lu, L. Shi, and J. Zhou, Quantitative structure-activity relationship study of histone deacetylase inhibitors, Curr. Med. Chem. Anticancer Agents 4 (2004), pp. 273–299. doi:10.2174/1568011043352948.
  • Z. Yang, T. Wang, F. Wang, T. Niu, Z. Liu, X. Chen, C. Long, M. Tang, D. Cao, X. Wang, W. Xiang, Y. Yi, L. Ma, J. You, and L. Chen, Discovery of selective histone deacetylase 6 inhibitors using the quinazoline as the cap for the treatment of cancer, J. Med. Chem. 59 (2016), pp. 1455–1470. doi:10.1021/acs.jmedchem.5b01342.
  • C. Ding, S. Chen, C. Zhang, G. Hu, W. Zhang, L. Li, Y.Z. Chen, C. Tan, and Y. Jiang, Synthesis and investigation of novel 6-(1,2,3-triazol-4-yl)-4-aminoquinazolin derivatives possessing hydroxamic acid moiety for cancer therapy, Bioorg. Med. Chem. 25 (2017), pp. 27–37. doi:10.1016/j.bmc.2016.10.006.
  • Z. Yuan, S. Chen, Q. Sun, N. Wang, D. Li, S. Miao, C. Gao, Y. Chen, C. Tan, and Y. Jiang, Olaparib hydroxamic acid derivatives as dual PARP and HDAC inhibitors for cancer therapy, Bioorg. Med. Chem. 25 (2017), pp. 4100–4109. doi:10.1016/j.bmc.2017.05.058.
  • C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, and E.Willighagen, Chemistry development kit - Open source modular java libraries for cheminformatics, 2003. Available at https://cdk.github.io/.
  • A. Mauri, alvaDesc KNIME Plugin – Tool for calculates alvaDesc molecular descriptors, 2020. Available at https://www.alvascience.com/knime-alvadesc/.
  • L. Breiman, Random forests, Mach. Learn. 45 (2001), pp. 5–32. doi:10.1023/A:1010933404324.
  • T. Chen and C. Guestrin, XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, B. Krishnapuram and M. Shah, eds., Association for Computing Machinery, New York, 2016, pp. 785–794. doi:10.1145/2939672.2939785.
  • T. Chen and C. Guestrin, Scalable and flexible gradient boosting. Gradient boosting framework, 2016. Available at https://xgboost.ai.
  • A.V. Zakharov, T. Zhao, D.T. Nguyen, T. Peryea, T. Sheils, A. Yasgar, R. Huang, N. Southall, and A. Simeonov, Novel consensus architecture to improve performance of large-scale multitask deep learning QSAR models, J. Chem. Inf. Model. 59 (2019), pp. 4613–4624. doi:10.1021/acs.jcim.9b00526.
  • V.E. Kuz’min, Е.N. Muratov, A.G. Artemenko, E.V. Varlamova, L. Gorb, J. Wang, and J. Leszczynski, Consensus QSAR modeling of phosphor-containing chiral AChE inhibitors, QSAR Comb. Sci. 28 (2009), pp. 664–677. doi:10.1002/qsar.200860117.
  • I. Tetko, OCHEM user’s manual, 2013. Available at http://docs.ochem.eu/display/MAN/OCHEM+Introduction.
  • J. Sadowski, J. Gasteiger, and G. Klebe, Comparison of automatic three-dimensional model builders using 639 x-ray structures, J. Chem. Inf. Comput. Sci. 34 (1994), pp. 1000–1008. doi:10.1021/ci00020a039.
  • A. Rácz, D. Bajusz, and K. Héberger, Intercorrelation limits in molecular descriptor preselection for QSAR/QSPR, Mol. Inf. 38 (2019), pp. e1800154. doi:10.1002/minf.201800154.
  • I.V. Tetko, M.D. Lowe, and A.J. Williams, The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS, J. Cheminf. 8 (2016). article number: 2. doi:10.1186/s13321-016-0113-y.
  • Standardizer, version 5.4. ChemAxon, Budapest, Hungary, 2010. Software available at https://docs.chemaxon.com/display/docs/Standardizer+Command-line+Application.
  • D. Fourches, E. Muratov, and A. Tropsha, Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research, J. Chem. Inf. Model. 50 (2010), pp. 1189–1204. doi:10.1021/ci100176x.
  • D. Fourches, E. Muratov, and A. Tropsha, Trust, but verify II: A practical guide to chemogenomics data curation, J. Chem. Inf. Model. 56 (2016), pp. 1243–1252. doi:10.1021/acs.jcim.6b00129.
  • D. Krstajic, L.J. Buturovic, D.E. Leahy, and S. Thomas, Cross-validation pitfalls when selecting and assessing regression and classification models, J. Cheminf. 6 (2014), pp. 10. doi:10.1186/1758-2946-6-10.
  • I.I. Baskin, G. Marcou, D. Horvath, and A. Varnek, Classification models, in Tutorials in Chemoinformatics, A. Varnek, ed., John Wiley & Sons, Hoboken, 2017, pp. 175–192.
  • O.A. Raevsky, V. Yu, D.B.K. Grigor’ev, and N.S. Zefirov, Complete thermodynamic description of H-bonding in the framework of multiplicative approach, Quant. Struct.-Act. Relat. 11 (1992), pp. 49–63. doi:10.1002/qsar.19920110109.
  • V.Y. Grigorev and L.D. Grigoreva, Calculation and properties of fractal descriptors for C2-C9 alkanes, Moscow Univ. Chem. Bull. 71 (2016), pp. 199–204. doi:10.3103/S0027131416030056.
  • S.N. Khattab, N.S. Haiba, A.M. Asal, A.A. Bekhit, A. Amer, H.M. Abdel-Rahman, and A. El-Faham, Synthesis and evaluation of quinazoline amino acid derivatives as mono amine oxidase (MAO) inhibitors, Bioorg. Med. Chem. 23 (2015), pp. 3574–3585. doi:10.1016/j.bmc.2015.04.021.
  • А. Ganeshpurkar, D. Kumar, and S.K. Singh, Strategies for the synthesis of hydroxamic acids, Curr. Org. Synth. 15 (2018), pp. 154–165. doi:10.2174/1570179414666170614123508.
  • Q. Zhang, Y. Li, B. Zhang, B. Lu, and J. Li, Design, synthesis and biological evaluation of novel histone deacetylase inhibitors incorporating 4-aminoquinazolinyl systems as capping groups, Bioorg. Med. Chem. Lett. 27 (2015), pp. 4885–4888. doi:10.1016/j.bmcl.2017.09.036.
  • Z. Wang, X. Wu, L. Wang, J. Zhang, J. Liu, Z. Song, and Z. Tang, Facile and efficient synthesis and biological evaluation of 4-anilinoquinazoline derivatives as EGFR inhibitors, Bioorg. Med. Chem. Lett. 26 (2016), pp. 2589–2593. doi:10.1016/j.bmcl.2016.04.032.
  • L. Shi, T.T. Wu, Z. Wang, J.Y. Xue, and Y.G. Xu, Discovery of quinazolin-4-amines bearing benzimidazole fragments as dual inhibitors of c-Met and VEGFR-2, Bioorg. Med. Chem. 22 (2014), pp. 4735–4744. doi:10.1016/j.bmc.2014.07.008.
  • M.J. Mphahlele, H.K. Paumo, and Y.S. Choong, Synthesis and in vitro cytotoxicity of the 4-(halogenoanilino)-6-bromoquinazolines and their 6-(4-fluorophenyl) substituted derivatives as potential inhibitors of epidermal growth factor receptor tyrosine kinase, Pharmaceuticals 10 (2017), pp. 87. doi:10.3390/ph10040087.
  • A. Daina, O. Michielin, and V. Zoete, SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules, Sci. Rep. 7 (2017), pp. 42717. doi:10.1038/srep42717.
  • T. Martin, TEST (Toxicity Estimation Software Tool) version 5.1. US Environmental Protection Agency, Cincinnati, OH; 2020. Available at https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test.
  • O.V. Tinkov, V.Y. Grigorev, P.G. Polishchuk, A.V. Yarkov, and O.A. Raevsky, QSAR investigation of acute toxicity of organic compounds during oral administration to mice, Biomed. Khim. 65 (2019), pp. 123–132. doi:10.18097/PBMC20196502123.
  • F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and Éd. Duchesnay, Scikit-learn. Free software machine learning library for the Python programming language, 2011; Software available at https://scikit-learn.org/stable/.
  • C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, and E. Willighagen, Class autocorrelation descriptor charge, 2003. Software available at https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/AutocorrelationDescriptorCharge.html
  • C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, and E. Willighagen, Class XLogP descriptor, 2003. Software available at https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/XLogPDescriptor.html.
  • D.T. Stanton and P.C. Jurs, Development and use of charged partial surface area structural descriptors in computer assissted quantitative structure property relationship studies, Anal. Chem. 62 (1990), pp. 2323–2329. doi:10.1021/ac00220a013.
  • C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, and E. Willighagen, Class CPSA descriptor, 2003. Software available at https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/CPSADescriptor.html.
  • C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, and E. Willighagen, Class weighted path descriptor, 2003. Software available at https://cdk.github.io/cdk/1.5/docs/api/org/openscience/cdk/qsar/descriptors/molecular/WeightedPathDescriptor.html.
  • H.H Lowell and B.K. Lemont, Electrotopological state indices for atom types: A novel combination of electronic, topological, and valence state information, J. Chem. Inf. Comput. Sci. 35 (1995), pp. 1039–1045. doi:10.1021/ci00028a014.
  • P.A. Moran, Notes on continuous stochastic phenomena, Biometrika 37 (1950), pp. 17–23. doi:10.1093/biomet/37.1-2.17.
  • O. Devinyak, D. Havrylyuk, and R. Lesyk, 3D-MoRSE descriptors explained, J. Mol. Graphics 54 (2014), pp. 194–203. doi:10.1016/j.jmgm.2014.10.006.
  • M. Reutlinger, C.P. Koch, D. Reker, N. Todoroff, P. Schneider, T. Rodrigues, and G. Schneider, Chemically advanced template search (CATS) for scaffold-hopping and prospective target prediction for ‘orphan’ molecules, Mol. Inf. 32 (2013), pp. 133–138. doi:10.1002/minf.201200141.
  • G. Sliwoski, J. Mendenhall, and J. Meiler, Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign, J. Comput. Aided Mol. Des. 30 (2016), pp. 209–217. doi:10.1007/s10822-015-9893-9.
  • Zolinza (vorinostat) Capsules, Full prescribing information, 2020. Merck & Co. Available at https://www.merck.com/product/usa/pi_circulars/z/zolinza/zolinza_pi.pdf
  • A. Váradi, G. Szakács, E. Bakos, and B. Sarkadi, P glycoprotein and the mechanism of multidrug resistance, Novartis Found Symp. 243 (2002), pp. 54–185. doi:10.1002/2F0470846356.ch5.
  • P.F. Hollenberg, Characteristics and common properties of inhibitors, inducers, and activators of CYP enzymes, Drug Metab. Rev. 34 (2002), pp. 17–35. doi:10.1081/DMR-120001387.
  • The Environmental Protection Agency, Office of pesticide. Programs label review manual. (2018). Available at https://www.epa.gov/sites/default/files/2018-04/documents/lrm-complete-mar-2018.pdf

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