Skip to main content

Advertisement

SpringerLink
Log in

Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors

  • Full Length Paper
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

A linear Quantitative Structure–Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin-2-yl-thiophene-2-hydroxamic acids. In particular, a five-variable model is produced by using the Multiple Linear Regression (MLR) technique and the Elimination Selection-Stepwise Regression Method (ES-SWR) on a database that consists of 58 recently discovered 5-pyridin-2-yl-thiophene-2-hydroxamic acids and 69 descriptors. The physical meaning of the selected descriptors is discussed in detail. The validity of the proposed MLR model is established using the following techniques: cross validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Based on the produced model, an in silico-screening study explores novel structural patterns and suggests new potent lead compounds.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Santini V, Gozzini A, Ferrari G (2007) Histone deacetylase inhibitors: molecular and biological activity as a premise to clinical application. Curr Drug Metab 8: 383–393. doi:10.2174/138920007780655397

    Article  PubMed  CAS  Google Scholar 

  2. Elaut G, Rogiers V, Vanhaecke T (2007) The pharmaceutical potential of histone deacetylase inhibitors. Curr Pharm Des 13: 2584–2620. doi:10.2174/138161207781663064

    Article  PubMed  CAS  Google Scholar 

  3. An W (2007) Histone acetylation and methylation: combinatorial players for transcriptional regulation. Subcell Biochem 41: 351–369

    PubMed  Google Scholar 

  4. de Ruijter AJM, van Gennip AH, Caron HN, Kemp S, van Kuilenburg ABP (2003) Histone deacetylases (HDACs): characterization of the classical HDAC family. Biochem J 370: 737–749. doi:10.1042/BJ20021321

    Article  PubMed  Google Scholar 

  5. Rasheed WK, Johnstone RW, Prince HM (2007) Histone deacetylase inhibitors in cancer therapy. Expert Opin Investig Drugs 16: 659–678. doi:10.1517/13543784.16.5.659

    Article  PubMed  CAS  Google Scholar 

  6. Balakin KV, Ivanenkov YA, Kiselyov AS, Tkachenko SE (2007) Histone deacetylase inhibitors in cancer therapy: latest developments, trends and medicinal chemistry perspective. Anticancer Agents Med Chem 7: 576–592

    PubMed  CAS  Google Scholar 

  7. Glozak MA, Seto E (2007) Histone deacetylases and cancer. Oncogene 26: 5420–5432. doi:10.1038/sj.onc.1210610

    Article  PubMed  CAS  Google Scholar 

  8. Gallinari P, Di Marco S, Jones P, Pallaoro M, Steinkühler C (2007) HDACs, histone deacetylation and gene transcription: from molecular biology to cancer therapeutics. Cell Res 17:195– 211

    PubMed  CAS  Google Scholar 

  9. Andrade CH, Salum LDB, Castilho MS, Pasqualoto KFM, Ferreira EI, Andricopulo AD (2008) Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents. Mol Divers 12: 47–59

    Article  PubMed  CAS  Google Scholar 

  10. Yap CW, Li H, Ji ZL, Chen YZ (2007) Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties. Mini Rev Med Chem 7: 1097–1107. doi:10.2174/138955707782331696

    Article  PubMed  CAS  Google Scholar 

  11. Castilho MS, Guido RVC, Andricopulo AD (2007) 2D Quantitative structure-activity relationship studies on a series of cholesteryl ester transfer protein inhibitors. Bioorg Med Chem 15: 6242–6252. doi:10.1016/j.bmc.2007.06.021

    Article  PubMed  CAS  Google Scholar 

  12. Melagraki G, Afantitis A, Sarimveis H, Igglessi-Markopoulou O, Alexandridis A (2006) A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri. Mol Divers 10: 213–221. doi:10.1007/s11030-005-9008-y

    Article  PubMed  CAS  Google Scholar 

  13. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) A novel QSAR model for evaluating and predicting the inhibition activity of dipeptidyl aspartyl fluoromethylketones. QSAR Comb Sci 25: 928–935. doi:10.1002/qsar.200530208

    Article  CAS  Google Scholar 

  14. Roy K (2006) Ecotoxicological modeling and risk assessment using chemometric tools. Mol Divers 10: 93–94. doi:10.1007/s11030-006-9025-5

    Article  PubMed  CAS  Google Scholar 

  15. Xia B, Ma W, Zheng B, Zhang X, Fan B (2008) Quantitative structure–activity relationship studies of a series of non-benzodiazepine structural ligands binding to benzodiazepine receptor. Eur J Med Chem 43: 1489–1498. doi:10.1016/j.ejmech.2007.09.004

    Article  PubMed  CAS  Google Scholar 

  16. Melagraki G, Afantitis A, Makridima K, Sarimveis H, Igglessi-Markopoulou O (2006) Prediction of toxicity using a novel RBF neural network training methodology. J Mol Model 12: 297–305. doi:10.1007/s00894-005-0032-8

    Article  PubMed  CAS  Google Scholar 

  17. Tropsha A, Golbraikh A (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13: 3494–3504. doi:10.2174/138161207782794257

    Article  PubMed  CAS  Google Scholar 

  18. Muegge I, Oloff S (2006) Advances in virtual screening. Drug Discov Today Technol 3: 405–411

    Article  Google Scholar 

  19. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) Investigation of substituent effect of 1-(3,3-diphenylpropyl)—piperidinyl phenylacetamides amides on CCR5 binding affinity using QSAR and virtual screening techniques. J Comput Aided Mol Des 20: 83–95. doi:10.1007/s10822-006-9038-2

    Article  PubMed  CAS  Google Scholar 

  20. Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2007) Identification of a series of novel derivatives as potent HCV inhibitors by a ligand-based virtual screening optimized procedure. Bioorg Med Chem 15: 7237–7247. doi:10.1016/j.bmc.2007.08.036

    Article  PubMed  CAS  Google Scholar 

  21. Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2007) Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening. J Comput Aided Mol Des 21: 251–267. doi:10.1007/s10822-007-9112-4

    Article  PubMed  CAS  Google Scholar 

  22. Guido RVC, Oliva G, Andricopulo AD (2008) Virtual screening and its integration with modern drug design technologies. Curr Med Chem 15: 37–46. doi:10.2174/092986708783330683

    Article  PubMed  CAS  Google Scholar 

  23. Chen H-F, Kang J-h, Li Q, Zeng B-s, Yao X-j, Fan B-t, Yuan S-g, Panay A, Doucet JP (2003) 3D-QSAR study on apicidin inhibit histone deacetylase. Chin J Chem 21: 1596–1607

    CAS  Google Scholar 

  24. Xie A, Liao C, Li Z, Ning Z, Hu W, Lu X, Shi L, Zhou J (2004) Quantitative structure-activity relationship study of histone deacetylase inhibitors. Curr Med Chem Anticancer Agents 4: 273–299. doi:10.2174/1568011043352948

    Article  PubMed  CAS  Google Scholar 

  25. Wang D-F, Wiest O, Helquist P, Lan-Hargest H-Y, Wiech NL (2004) QSAR studies of PC-3 cell line inhibition activity of TSA and SAHA-like hydroxamic acids. Bioorg Med Chem Lett 14: 707–711. doi:10.1016/j.bmcl.2003.11.062

    Article  PubMed  CAS  Google Scholar 

  26. Liu B, Lu A-J, Liao C-Z, Liu H-B, Zhou J-J (2005) 3D-QSAR of sulfonamide hydroxamic acid HDAC inhibitors. Acta Physi-Chim Sin 21: 333–337

    Google Scholar 

  27. Guo Y, Xiao J, Guo Z, Chu F, Cheng Y, Wu S (2005) Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses. Bioorg Med Chem 13: 5424–5434. doi:10.1016/j.bmc.2005.05.016

    Article  PubMed  CAS  Google Scholar 

  28. Ragno R, Simeoni S, Valente S, Massa S, Mai A (2006) 3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds. J Chem Inf Model 46: 1420–1430. doi:10.1021/ci050556b

    CAS  Google Scholar 

  29. Ragno R, Simeoni S, Rotili D, Caroli A, Botta G, Brosch G, Massa S, Mai A (2008) Class II-selective histone deacetylase inhibitors. Part 2: alignment-independent GRIND 3-DQSAR, homology and docking studies. Eur J Med Chem 43: 621–632. doi:10.1016/j.ejmech.2007.05.004

    Article  PubMed  CAS  Google Scholar 

  30. Juvale DC, Kulkarni VV, Deokar HS, Wagh NK, Padhye SB, Kulkarni VM (2006) 3D-QSAR of histone deacetylase inhibitors: hydroxamate analogues. Org Biomol Chem 4: 2858–2868. doi:10.1039/b606365a

    Article  PubMed  CAS  Google Scholar 

  31. Wagh NK, Deokar HS, Juvale DC, Kadam SS, Kulkarni VM (2006) 3D-QSAR of histone deacetylase inhibitors as anticancer agents by genetic function approximation. Indian J Biochem Biophys 43: 360–371

    PubMed  CAS  Google Scholar 

  32. Jaiswal D, Karthikeyan C, Shrivastava SK, Trivedi P (2006) QSAR modeling of sulfonamide inhibitors of histone deacetylase. Internet Electron J Mol Des 5: 345–354

    CAS  Google Scholar 

  33. Vadivelan S, Sinha BN, Rambabu G, Boppana K, Jagarlapudi SARP (2008) Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads. J Mol Graph Model 26: 935–946. doi:10.1016/j.jmgm.2007.07.002

    Article  PubMed  CAS  Google Scholar 

  34. Chen Y-D, Jiang Y-J, Zhou J-W, Yu Q-S, You Q-D (2008) Identification of ligand features essential for HDACs inhibitors by pharmacophore modelling. J Mol Graph Model 26: 1160–1168. doi:10.1016/j.jmgm.2007.10.007

    Article  PubMed  CAS  Google Scholar 

  35. Price S, Bordogna W, Bull RJ, Clark DE, Crackett PH, Dyke HJ, Gill M, Harris NV, Gorski J, Lloyd J, Lockey PM, Mullett J, Roach AG, Roussel F, White AB (2007) Identification and optimisation of a series of substituted 5-(1H-pyrazol-3-yl)-thiophene-2-hydroxamic acids as potent histone deacetylase (HDAC) inhibitors. Bioorg Med Chem Lett 17: 370–375. doi:10.1016/j.bmcl.2006.10.048

    Article  PubMed  CAS  Google Scholar 

  36. Price S, Bordogna W, Braganza R, Bull RJ, Dyke HJ, Gardan S, Gill M, Harris NV, Heald RA, van den Heuvel M, Lockey PM, Lloyd J, Molina AG, Roach AG, Roussel F, Sutton JM, White AB (2007) Identification and optimisation of a series of substituted 5-pyridin-2-yl-thiophene-2-hydroxamic acids as potent histone deacetylase (HDAC) inhibitors. Bioorg Med Chem Lett 17: 363–369. doi:10.1016/j.bmcl.2006.10.045

    Article  PubMed  CAS  Google Scholar 

  37. Stewart JJP (2007) Optimization of parameters for semiempirical methods V: modification of NDDO approximations and application to 70 elements. J Mol Model 13: 1173–1213. doi:10.1007/s00894-007-0233-4

    Article  PubMed  CAS  Google Scholar 

  38. CambridgeSoft Corporation ChemOffice. http://www.cambridgesoft.com

  39. MOPAC2007 Stewart JJP, Stewart Computational Chemistry, Version 7.295W web:http://OpenMOPAC.net

  40. Svozil D, Lohninger H TOPIX. http://www.lohninger.com/topix.html

  41. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11: 137–148. doi:10.2307/1266770

    Article  Google Scholar 

  42. Melagraki G, Afantitis A, Makridima K, Sarimveis H, Igglessi-Markopoulou O (2006) Prediction of toxicity using a novel RBF neural network training methodology. J Mol Model 12: 297–305. doi:10.1007/s00894-005-0032-8

    Article  PubMed  CAS  Google Scholar 

  43. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) A novel QSAR model for predicting induction of apoptosis by 4-aryl-4H-chromenes. Bioorg Med Chem 14: 6686–6694. doi:10.1016/j.bmc.2006.05.061

    Article  PubMed  CAS  Google Scholar 

  44. Chakraborti AK, Gopalakrishnan B, Sobhia ME, Malde A (2003) 3D-QSAR studies of indole derivatives as phosphodiesterase IV inhibitors. Eur J Med Chem 38: 975–982. doi:10.1016/j.ejmech.2003.09.001

    Article  PubMed  CAS  Google Scholar 

  45. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) A novel QSAR model for evaluating and predicting the inhibition of dipeptidyl aspartyl fluoromethylketones. QSAR Comb Sci 25: 928–935. doi:10.1002/qsar.200530208

    Article  CAS  Google Scholar 

  46. Ghosh P, Thanadath M, Bagchi MC (2006) On an aspect of calculated molecular descriptors in QSAR studies of quinolone antibacterials. Mol Divers 10: 415–427. doi:10.1007/s11030-006-9018-4

    Article  PubMed  CAS  Google Scholar 

  47. Wu W, Walczak B, Massart DL, Heuerding S, Erni F, Last IR, Prebble KA (1996) Artificial neural networks in classification of NIR spectral data: design of the training set. Chemom Intell Lab Syst 33: 35–46. doi:10.1016/0169-7439(95)00077-1

    Article  CAS  Google Scholar 

  48. Todeschini R, Consonni V, Mannhold R (2000) In: Kubinyi H, Timmerman H (Series Ed.) Handbook of molecular descriptors. Wiley-VCH, Weinheim

  49. Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22: 69–77. doi:10.1002/qsar.200390007

    Article  CAS  Google Scholar 

  50. Golbraikh A, Tropsha A (2002) Beware of q 2(J Mol Graph Model 20): 269–276. doi:10.1016/S1093-3263(01)00123-1

    Google Scholar 

  51. Melagraki G, Afantitis A, Sarimveis H, Igglessi-Markopoulou O, Alexandridis A (2006) A novel RBF neural network training methodology to predict toxicity to Vibrio fischeri. Mol Divers 10: 213–221. doi:10.1007/s11030-005-9008-y

    Article  PubMed  CAS  Google Scholar 

  52. Jalali-Heravi M, Kyani A (2007) Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: activity of carbonic anhydrase II inhibitors. Eur J Med Chem 42: 649–659. doi:10.1016/j.ejmech.2006.12.020

    Article  PubMed  CAS  Google Scholar 

  53. Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006) A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis. Mol Divers 10: 405–414. doi:10.1007/s11030-005-9012-2

    Article  PubMed  CAS  Google Scholar 

  54. Petitjean M (1992) Applications of the radius-diameter diagram to the classification of topological and geometrical shapes of chemical compounds. J Chem Inf Comput Sci 32: 331–337. doi:10.1021/ci00008a012

    CAS  Google Scholar 

  55. Melagraki G, Afantitis A, Sarimveis H, Igglessi-Markopoulou O, Supuran CT (2006) QSAR study on para-substituted aromatic sulfonamides as carbonic anhydrase II inhibitors using topological information indices. Bioorg Med Chem 14: 1108–1114. doi:10.1016/j.bmc.2005.09.038

    Article  PubMed  CAS  Google Scholar 

  56. Balaban AT (1982) Highly discriminating distance-based topological index. Chem Phys Lett 89: 399–404. doi:10.1016/0009-2614(82)80009-2

    Article  CAS  Google Scholar 

  57. Tropsha A, Golbraikh A (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13: 3494–3504. doi:10.2174/138161207782794257

    Article  PubMed  CAS  Google Scholar 

  58. Hewitt M, Cronin MTD, Madden JC, Rowe PH, Johnson C, Obi A, Enoch SJ (2007) Consensus QSAR models: do the benefits outweigh the complexity. J Chem Inf Model 47: 1460–1468. doi:10.1021/ci700016d

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ChemoInformatics, NovaMechanics Ltd, Larnaca, Cyprus

    Georgia Melagraki & Antreas Afantitis

  2. Cyano Research Corporation Ltd, P.O. Box 28670, 2081, Nicosia, Cyprus

    Georgia Melagraki & Antreas Afantitis

  3. School of Chemical Engineering, National Technical University of Athens, Athens, Greece

    Georgia Melagraki, Antreas Afantitis, Haralambos Sarimveis & Olga Igglessi-Markopoulou

  4. Department of Chemistry, University of Cyprus, P.O. Box 20537, 1678, Nicosia, Cyprus

    Panayiotis A. Koutentis

  5. Biomedical Sciences Research Center “Alexander Fleming”, Athens, Greece

    Antreas Afantitis & George Kollias

Authors
  1. Georgia Melagraki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antreas Afantitis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haralambos Sarimveis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Panayiotis A. Koutentis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. George Kollias
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Olga Igglessi-Markopoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Georgia Melagraki.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Melagraki, G., Afantitis, A., Sarimveis, H. et al. Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors. Mol Divers 13, 301–311 (2009). https://doi.org/10.1007/s11030-009-9115-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-009-9115-2

Keywords

Search

Navigation