390
Views
17
CrossRef citations to date
0
Altmetric
Articles

Quantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries

, , , , , & show all
Pages 199-220 | Received 28 Nov 2016, Accepted 08 Feb 2017, Published online: 28 Feb 2017

References

  • C. Choudhary, C. Kumar, F. Gnad, M.L. Nielsen, M. Rehman, T.C. Walther, J.V. Olsen, and M. Mann, Lysine acetylation targets protein complexes and co-regulates major cellular functions, Science 325 (2009), pp. 834–840.
  • K.J. Falkenberg and R.W. Johnstone, Histone deacetylases and their inhibitors in cancer, neurological diseases and immune disorders, Nat. Rev. Drug Discov. 13 (2014), pp. 673–691.
  • X.-J. Yang and E. Seto, The Rpd3/Hda1 family of lysine deacetylases: From bacteria and yeast to mice and men, Nat. Rev. Mol. Cell. Biol. 9 (2008), pp. 206–218.
  • L. Zhang, Y. Han, Q. Jiang, C. Wang, X. Chen, X. Li, F. Xu, Y. Jiang, Q. Wang, and W. Xu, Trend of histone deacetylase inhibitors in cancer therapy: Isoform selectivity or multitargeted strategy, Med. Res. Rev. 35 (2015), pp. 63–84.
  • P. Ma and R.M. Schultz, Histone deacetylase 2 (HDAC2) regulates chromosome segregation and kinetochore function via H4K16 deacetylation during oocyte maturation in mouse, PLoS Genet. 9 (2013), p. e1003377.
  • M.F. Fraga, E. Ballestar, A. Villar-Garea, M. Boix-Chornet, J. Espada, G. Schotta, T. Bonaldi, C. Haydon, S. Ropero, K. Petrie, N.G. Iyer, A. Perez-Rosado, E. Calvo, J.A. Lopez, A. Cano, M.J. Calasanz, D. Colomer, M.A. Piris, N. Ahn, A. Imhof, C. Caldas, T. Jenuwein, and M. Esteller, Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common hallmark of human cancer, Nat. Genet. 37 (2005), pp. 391–400.
  • K.M. Miller, J.V. Tjeertes, J. Coates, G. Legube, S.E. Polo, S. Britton, and S.P. Jackson, Human HDAC1 and HDAC2 function in the DNA-damage response to promote DNA nonhomologous end-joining, Nat. Struct. Mol. Biol. 17 (2010), pp. 1144–1151.
  • M. Mottamal, S. Zheng, L.T. Huang, and G. Wang, Histone deacetylase inhibitors in clinical studies as templates for new anticancer agents, Molecules 20 (2015), pp. 3898–3941.
  • E. Pontiki and D. Hadjipavlou-Litina, Histone deacetylase inhibitors (HDACIs). Structure–activity relationships: History and new QSAR perspectives, Med. Res. Rev. 32 (2012), pp. 1–165.
  • X. Aihua, L. Chenzhong, L. Zhibin, N. Zhiqiang, H. Weiming, L. Xianping, S. Leming, and Z. Jiaju, Quantitative structure-activity relationship study of histone deacetylase inhibitors, Curr. Med. Chem. Anticancer Agents 4 (2004), pp. 273–299.
  • D.-F. Wang, O. Wiest, P. Helquist, H.-Y. Lan-Hargest, and N.L. Wiech, QSAR Studies of PC-3 cell line inhibition activity of TSA and SAHA-like hydroxamic acids, Bioorg. Med. Chem. Lett. 14 (2004), pp. 707–711.
  • 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.
  • N.K. Wagh, H.S. Deokar, D.C. Juvale, S.S. Kadam, and V.M. Kulkarni, 3D-QSAR of histone deacetylase inhibitors as anticancer agents by genetic function approximation, Indian J. Biochem. Biophys. 43 (2006), pp. 360–371.
  • D.C. Juvale, V.V. Kulkarni, H.S. Deokar, N.K. Wagh, S.B. Padhye, and V.M. Kulkarni, 3D-QSAR of histone deacetylase inhibitors: Hydroxamate analogues, Org. Biomol. Chem. 4 (2006), pp. 2858–2868.
  • R. Ragno, S. Simeoni, S. Valente, S. Massa, and A. Mai, 3-D QSAR Studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds, J. Chem. Inf. Model. 46 (2006), pp. 1420–1430.
  • A.R. Katritzky, S.H. Slavov, D.A. Dobchev, and M. Karelson, Comparison between 2D and 3D-QSAR approaches to correlate inhibitor activity for a series of indole amide hydroxamic acids, QSAR Comb. Sci. 26 (2007), pp. 333–345.
  • N. Dessalew, QSAR study on aminophenylbenzamides and acrylamides as histone deacetylase inhibitors: An insight into the structural basis of antiproliferative activity, Med. Chem. Res. 16 (2007), pp. 449–460.
  • R. Ragno, S. Simeoni, D. Rotili, A. Caroli, G. Botta, G. Brosch, S. Massa, and A. Mai, Class II-selective histone deacetylase inhibitors. Part 2: Alignment-independent GRIND 3-D QSAR, homology and docking studies, Eur. J. Med. Chem. 43 (2008), pp. 621–632.
  • A.P. Kozikowski, Y. Chen, A.M. Gaysin, D.N. Savoy, D.D. Billadeau, and K.H. Kim, Chemistry, biology, and QSAR studies of substituted biaryl hydroxamates and mercaptoacetamides as HDAC inhibitors – Nanomolar-potency inhibitors of pancreatic cancer cell growth, ChemMedChem 3 (2008), pp. 487–501.
  • Y.-D. Chen, Y.-J. Jiang, J.-W. Zhou, Q.-S. Yu, and Q.-D. You, Identification of ligand features essential for HDACs inhibitors by pharmacophore modeling, J. Mol. Graph. Model. 26 (2008), pp. 1160–1168.
  • Y. Chen, H. Li, W. Tang, C. Zhu, Y. Jiang, J. Zou, Q. Yu, and Q. You, 3D-QSAR studies of HDACs inhibitors using pharmacophore-based alignment, Eur. J. Med. Chem. 44 (2009), pp. 2868–2876.
  • G. Melagraki, A. Afantitis, H. Sarimveis, P.A. Koutentis, G. Kollias, and O. Igglessi-Markopoulou, Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors, Mol. Divers. 13 (2009), pp. 301–311.
  • H. Tang, X.S. Wang, X.-P. Huang, B.L. Roth, K.V. Butler, A.P. Kozikowski, M. Jung, and A. Tropsha, Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation, J. Chem. Inf. Model. 49 (2009), pp. 461–476.
  • X.H. Liu, H.Y. Song, J.X. Zhang, B.C. Han, X.N. Wei, X.H. Ma, W.K. Cui, and Y.Z. Chen, Identifying novel type ZBGs and nonhydroxamate HDAC inhibitors through a SVM based virtual screening approach, Mol. Inf. 29 (2010), pp. 407–420.
  • A. Beheshti, E. Pourbasheer, M. Nekoei, and A. Banaei, Quantitative structure-activity relationship study of amino acid derivatives as histone deacetylase inhibitors using the genetic algorithm – Multiple linear regression, Anal. Chem. Lett. 2 (2012), pp. 33–43.
  • J.-S. Yang, T.-G. Chun, K.-Y. Nam, H.-M. Kim, and G.-H. Han, Structure-activity relationship of novel lactam based histone deacetylase inhibitors as potential anticancer drugs, Bull. Korean Chem. Soc. 33 (2012), pp. 2063–2066.
  • S.B. Nair, M.K. Teli, H. Pradeep, and G.K. Rajanikant, Computational identification of novel histone deacetylase inhibitors by docking based QSAR, Comput. Biol. Med. 42 (2012), pp. 697–705.
  • Y. Xiang, Z. Hou, and Z. Zhang, Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors, Chem. Biol. Drug Des. 79 (2012), pp. 760–770.
  • L. Zhao, Y. Xiang, J. Song, and Z. Zhang, A novel two-step QSAR modeling work flow to predict selectivity and activity of HDAC inhibitors, Bioorg. Med. Chem. Lett. 23 (2013), pp. 929–933.
  • G.P. Cao, M. Arooj, S. Thangapandian, C. Park, V. Arulalapperumal, Y. Kim, Y.J. Kwon, H.H. Kim, J.K. Suh, and K.W. Lee, A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors, SAR QSAR Environ. Res. 26 (2015), pp. 397–420.
  • Z. Noor, N. Afzal, and S. Rashid, Exploration of novel inhibitors for class I histone deacetylase isoforms by QSAR modeling and molecular dynamics simulation assays, PLOS ONE 10 (2015), p. e0139588.
  • Y. Bi, Z. Liu, X. Liu, X. Zhang, and J. Lu, Molecular docking and 3D-QSAR studies on quinolone-based HDAC inhibitors, Lett. Drug Des. Discov. 13 (2016), pp. 577–586.
  • S. Sinha, S. Goyal, P. Somvanshi, and A. Grover, Mechanistic insights into the binding of class IIa HDAC inhibitors toward spinocerebellar ataxia type-2: A 3D-QSAR and pharmacophore modeling approach, Front. Neurosci. 10 (2017), p. 606.
  • H. Tang, X.S. Wang, X.P. Huang, B.L. Roth, K.V. Butler, A.P. Kozikowski, M. Jung, and A. Tropsha, Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation, J. Chem. Inf. Model. 49 (2009), pp. 461–76.
  • Z. Noor, N. Afzal, and S. Rashid, Exploration of novel inhibitors for class I histone deacetylase isoforms by QSAR modeling and molecular dynamics simulation assays, PLOS ONE. 10 (2015), p. e0139588.
  • A. Lavecchia, Machine-learning approaches in drug discovery: Methods and applications, Drug Discov. Today 20 (2015), pp. 318–331.
  • H. Le-Thi-Thu, G.M. Casanola-Martín, Y. Marrero-Ponce, A. Rescigno, C. Abad, and M.T. Khan, A rational workflow for sequential virtual screening of chemical libraries on searching for new tyrosinase inhibitors, Curr. Top. Med. Chem. 14 (2014), pp. 1473–1485.
  • D.T.K. Oanh, H.V. Hai, S.H. Park, H.-J. Kim, B.-W. Han, H.-S. Kim, J.-T. Hong, S.-B. Han, V.T.M. Hue, and N.-H. Nam, Benzothiazole-containing hydroxamic acids as histone deacetylase inhibitors and antitumor agents, Bioorg. Med. Chem. Lett. 21 (2011), pp. 7509–7512.
  • T. Tung, O.D.T. Kim, and D.P.T. Phuong, H. VT, P. SH, H.B. Woo, K. Y, H. JT, H. Sang-Bae, and N. Nguyen-Hai, New benzothiazole/thiazole-containing hydroxamic acids as potent histone deacetylase inhibitors and antitumor agents, Med. Chem. 9 (2013), pp. 1051–1057.
  • N.-H. Nam, T.L. Huong, and D.T. Mai, Dung, P.T. Phuong Dung, D.T. Kim Oanh, D. Quyen, L.T. Thao, S.H. Park, K.R. Kim, B.W. Han, J. Yun, J.S. Kang, Y. Kim, and S.-B. Han, Novel isatin-based hydroxamic acids as histone deacetylase inhibitors and antitumor agents, Eur. J. Med. Chem. 70 (2013), pp. 477–486.
  • N. Nguyen-Hai, H.T. Lan, D.D.T. Mai, D.P.T. Phuong, O.D.T. Kim, P.S. Ho, K. Kyungrok, H.B. Woo, Y. Jieun, K.J. Soon, K. Youngsoo, and H. Sang-Bae, Synthesis, bioevaluation and docking study of 5-substitutedphenyl-1,3,4-thiadiazole-based hydroxamic acids as histone deacetylase inhibitors and antitumor agents, J. Enzyme Inhib. Med. Chem. 29 (2014), pp. 611–618.
  • Do T.M. Dung, PTP Dung, DTK Oanh, H. Pham-The, H. Le-Thi-Thu, L. Vu-Duc, H. Hahn, B.W. Han, J. Kim, S.B. Han, and N. Nguyen-Hai, Novel 3-substituted-2-oxoindoline-based N-hydroxypropenamides as histone deacetylase inhibitors and antitumor agents, Med. Chem. 11 (2015), pp. 725–735.
  • H.T.T. Lan, D.D.T. Mai, D.P.T. Phuong, H.P. Thanh, V.T. Khac, H. Hyunggu, H.B. Woo, K. Jisung, P. Minji, H. Sang-Bae, and N. Nguyen-Hai, Novel 2-oxoindoline-based hydroxamic acids: Synthesis, cytotoxicity, and inhibition of histone deacetylation, Tetrahedron Lett. 56 (2015), pp. 6425–6429.
  • R.Z. Cer, U. Mudunuri, R. Stephens, and F.J. Lebeda, IC50-to-Ki: A web-based tool for converting IC50 to Ki values for inhibitors of enzyme activity and ligand binding, Nucleic Acids Res. 37 (2009), pp. W441–W445.
  • G.M. Keseru and G.M. Makara, The influence of lead discovery strategies on the properties of drug candidates, Nat. Rev. Drug Discov. 8 (2009), pp. 203–212.
  • Murugan K, Sangeetha S, Ranjitha S, Vimala A, Al-Sohaibani S, and R. G, HDACiDB: A database for histone deacetylase inhibitors, Drug Des. Devel. Ther. 9 (2015), pp. 2257-2264.
  • N. Mills, ChemDraw Ultra 10.0 CambridgeSoft, 100 Cambridge Park Drive, Cambridge, MA 02140. www.cambridgesoft.com. Commercial Price: $1910 for download, $2150 for CD-ROM; Academic Price: $710 for download, $800 for CD-ROM, J. Am. Chem. Soc. 128 (2006), pp. 13649–13650.
  • DRAGON for Windows (Software for Molecular Descriptor Calculator), Version 6.0. Talete srl, Milano Chemometrics and QSAR Research Group, (http://www.talete.mi.it/), 2011.
  • HyperChem (TM) Professional, Version 8.0.5, Hypercube, Inc. Florida, USA, (www.hyper.com), 2008.
  • STATISTICA (data analysis software system), Version 8.0.1, StatSoft, Inc., Tulsa, OK, (www.statsoft.com), 2012.
  • H. Pham-The, I. Gonzalez Diaz, M. Bermejo Sanz, V. Mangas Sanjuan, I. Centelles, T.M. Garriges, and M.A. Cabrera-Perez, In silico prediction of Caco-2 permeability by a classification QSAR approach, Mol. Inf. 30 (2011), pp. 376-385.
  • H.I. Witten, and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann (MK) Publisher, San Francisco, USA, 2005.
  • L. Huma and Y. Yoshihiro, Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, IGI Global, Hershey, PA, USA, 2011.
  • A. Cherkasov, E.N. Muratov, D. Fourches, A. Varnek, I.I. Baskin, M. Cronin, J. Dearden, P. Gramatica, Y.C. Martin, R. Todeschini, and V. Consonni, V.E. Kuz’min, R. Cramer, R. Benigni, C. Yang, J. Rathman, L. Terfloth, J. Gasteiger, A. Richard, and A. Tropsha, QSAR modeling: Where have you been? Where are you going to?, J. Med. Chem. 57 (2014), pp. 4977–5010.
  • OECD, Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] Models, in OECD Series on Testing and Assessment No. 69. ENV/JM/MONO(2007)2, Organisation for Economic Cooperation and Development, Paris, France, 2007, pp. 154.
  • T. Alexander and G. Alexander, Predictive quantitative structure-activity relationships modeling, in Handbook of Chemoinformatics Algorithms, J.L. Faulon and A. Bender, eds., Chapman and Hall/CRC, New York, USA, 2010, pp. 173–210.
  • K.K. Chohan, S.W. Paine, J. Mistry, P. Barton, and A.M. Davis, A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries, J. Med. Chem. 48 (2005), pp. 5154–5161.
  • F. Provost and T. Fawcett, Analysis and visualization of classifier performance: comparison under imprecise class and cost distribution, in Third International Conference on Knowledge Discovery and Data Mining (KDD-97), J. W. Shavlik, ed., Menlo Park, CA: AAAI Press, Newport Beach, California, 1997, pp. 43-48.
  • J.A. Hanley and B.J. McNeil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology 143 (1982), pp. 29–36.
  • R.M. O’Brien, A caution regarding rules of thumb for variance inflation factors, Qual. Quant. 41 (2007), pp. 673–690.
  • S.C. Peterangelo and P.G. Seybold, Synergistic interactions among QSAR descriptors, Int. J. Quantum Chem. 96 (2004), pp. 1–9.
  • Jason D. M. Rennie, L. Shih, J. Teevan, and D.R. Karger, Tackling the poor assumptions of Naive Bayes text classifiers, Proceedings of the Twentieth International Conference on Machine Learning (ICML) 20 (2003), pp. 616-623.
  • C. Wang, L.M. Henkes, L.B. Doughty, M. He, D. Wang, F.-J. Meyer-Almes, and Y.-Q. Cheng, Thailandepsins: Bacterial products with potent histone deacetylase inhibitory activities and broad-spectrum antiproliferative activities, J. Nat. Prod. 74 (2011), pp. 2031–2038.
  • R. Todeschini, and V. Consonni, Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References, Vol. 41, Wiley VCH, Weinheim, Germany, 2010.
  • D. Hadjipavlou-Litina and E. Pontiki, Quantitative structure–activity relationship studies on hydroxamic acids acting as histone deacetylase inhibitors, in Hydroxamic Acids: A Unique Family of Chemicals with Multiple Biological Activities, P.S. Gupta, ed., Springer, Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 205–240.
  • C. Salmi-Smail, A. Fabre, F. Dequiedt, A. Restouin, R. Castellano, S. Garbit, P. Roche, X. Morelli, J.M. Brunel, and Y. Collette, Modified cap group suberoylanilide hydroxamic acid histone deacetylase inhibitor derivatives reveal improved selective antileukemic activity, J. Med. Chem. 53 (2010), pp. 3038–3047.
  • J.C. Bressi, A.J. Jennings, R. Skene, Y. Wu, R. Melkus, R.D. Jong, S. O’Connell, C.E. Grimshaw, M. Navre, and A.R. Gangloff, Exploration of the HDAC2 foot pocket: Synthesis and SAR of substituted N-(2-aminophenyl)benzamides, Bioorg. Med. Chem. Lett. 20 (2010), pp. 3142–3145.
  • R.R. Frey, C.K. Wada, R.B. Garland, M.L. Curtin, M.R. Michaelides, J. Li, L.J. Pease, K.B. Glaser, P.A. Marcotte, J.J. Bouska, S.S. Murphy, and S.K. Davidsen, Trifluoromethyl ketones as inhibitors of histone deacetylase, Bioorg. Med. Chem. Lett. 12 (2002), pp. 3443–3447.
  • G. Derringer and R. Suich, Simultaneous optimization of several response variables, J. Qual. Technol. 12 (1980), pp. 214–219.
  • A.V. Bieliauskas and M.K.H. Pflum, Isoform-selective histone deacetylase inhibitors, Chem. Soc. Rev. 37 (2008), pp. 1402–1413.
  • P.K. Alan and V.B. Kyle, Chemical origins of isoform selectivity in histone deacetylase inhibitors, Curr. Pharm. Des. 14 (2008), pp. 505–528.
  • F. Thaler and C. Mercurio, Towards selective inhibition of histone deacetylase isoforms: What has been achieved, where we are and what will be next?, ChemMedChem 9 (2014), pp. 523–536.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.