Advertisement

BET bromodomain inhibitors: fragment-based in silico design using multi-target QSAR models

  • Alejandro Speck-PlancheEmail author
  • Marcus T. Scotti
Original Article
  • 91 Downloads

Abstract

Epigenetics has become a focus of interest in drug discovery. In this sense, bromodomain-containing proteins have emerged as potential epigenetic targets in cancer research and other therapeutic areas. Several computational approaches have been applied to the prediction of bromodomain inhibitors. Nevertheless, such approaches have several drawbacks such as the fact that they predict activity against only one bromodomain-containing protein, using structurally related compounds. Also, there are no reports focused on meaningfully analyzing the physicochemical/structural features that are necessary for the design of a bromodomain inhibitor. This work describes the development of two different multi-target models based on quantitative structure–activity relationships (mt-QSAR) for the prediction and in silico design of multi-target bromodomain inhibitors against the proteins BRD2, BRD3, and BRD4. The first model relied on linear discriminant analysis (LDA) while the second focused on artificial neural networks. Both models exhibited accuracies higher than 85% in the dataset. Several molecular fragments were extracted, and their contributions to the inhibitory activity against the three BET proteins were calculated by the LDA model. Six molecules were designed by assembling the fragments with positive contributions, and they were predicted as multi-target BET bromodomain inhibitors by the two mt-QSAR models. Molecular docking calculations converged with the predictions performed by the mt-QSAR models, suggesting that the designed molecules can exhibit potent activity against the three BET proteins. These molecules complied with the Lipinski’s rule of five.

Keywords

Epigenetics mt-QSAR Molecular fragment BET bromodomain inhibitor Linear discriminant analysis Artificial neural networks Docking 

Notes

Acknowledgments

A. Speck-Planche acknowledges the Spanish Juan de la Cierva program (Grant: FJCI-2015-25572) for the financial support. Marcus T. Scotti acknowledges the Brazilian National Council for Scientific and Technological Development (Grant: CNPq 310919/2016-9).

Supplementary material

11030_2018_9890_MOESM1_ESM.xlsx (5.6 mb)
Supplementary material 1 (XLSX 5783 kb)
11030_2018_9890_MOESM2_ESM.xlsx (5.5 mb)
Supplementary material 2 (XLSX 5683 kb)
11030_2018_9890_MOESM3_ESM.doc (40 kb)
Supplementary material 3 (DOC 40 kb)
11030_2018_9890_MOESM4_ESM.doc (64 kb)
Supplementary material 4 (DOC 64 kb)
11030_2018_9890_MOESM5_ESM.doc (47 kb)
Supplementary material 5 (DOC 47 kb)
11030_2018_9890_MOESM6_ESM.doc (41 kb)
Supplementary material 6 (DOC 41 kb)

References

  1. 1.
    Smith SG, Zhou MM (2016) The bromodomain: a new target in emerging epigenetic medicine. ACS Chem Biol 11:598–608.  https://doi.org/10.1021/acschembio.5b00831 CrossRefPubMedGoogle Scholar
  2. 2.
    Pachaiyappan B, Woster PM (2014) Design of small molecule epigenetic modulators. Bioorg Med Chem Lett 24:21–32.  https://doi.org/10.1016/j.bmcl.2013.11.001 CrossRefPubMedGoogle Scholar
  3. 3.
    Sakaguchi T, Yoshino H, Sugita S, Miyamoto K, Yonemori M, Osako Y, Meguro-Horike M, Horike SI, Nakagawa M, Enokida H (2018) Bromodomain protein BRD4 inhibitor JQ1 regulates potential prognostic molecules in advanced renal cell carcinoma. Oncotarget 9:23003–23017.  https://doi.org/10.18632/oncotarget.25190 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Segatto M, Fittipaldi R, Pin F, Sartori R, Dae Ko K, Zare H, Fenizia C, Zanchettin G, Pierobon ES, Hatakeyama S, Sperti C, Merigliano S, Sandri M, Filippakopoulos P, Costelli P, Sartorelli V, Caretti G (2017) Epigenetic targeting of bromodomain protein BRD4 counteracts cancer cachexia and prolongs survival. Nat Commun 8:1707.  https://doi.org/10.1038/s41467-017-01645-7 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Raina K, Lu J, Qian Y, Altieri M, Gordon D, Rossi AM, Wang J, Chen X, Dong H, Siu K, Winkler JD, Crew AP, Crews CM, Coleman KG (2016) PROTAC-induced BET protein degradation as a therapy for castration-resistant prostate cancer. Proc Natl Acad Sci USA 113:7124–7129.  https://doi.org/10.1073/pnas.1521738113 CrossRefPubMedGoogle Scholar
  6. 6.
    Asangani IA, Dommeti VL, Wang X, Malik R, Cieslik M, Yang R, Escara-Wilke J, Wilder-Romans K, Dhanireddy S, Engelke C, Iyer MK, Jing X, Wu YM, Cao X, Qin ZS, Wang S, Feng FY, Chinnaiyan AM (2014) Therapeutic targeting of BET bromodomain proteins in castration-resistant prostate cancer. Nature 510:278–282.  https://doi.org/10.1038/nature13229 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Filippakopoulos P, Knapp S (2014) Targeting bromodomains: epigenetic readers of lysine acetylation. Nat Rev Drug Discov 13:337–356.  https://doi.org/10.1038/nrd4286 CrossRefPubMedGoogle Scholar
  8. 8.
    Andrieu GP, Denis GV (2018) BET proteins exhibit transcriptional and functional opposition in the epithelial-to-mesenchymal transition. Mol Cancer Res 16:580–586.  https://doi.org/10.1158/1541-7786.MCR-17-0568 CrossRefPubMedGoogle Scholar
  9. 9.
    Roberts TC, Etxaniz U, Dall’Agnese A, Wu SY, Chiang CM, Brennan PE, Wood MJA, Puri PL (2017) BRD3 and BRD4 BET bromodomain proteins differentially regulate skeletal myogenesis. Sci Rep 7:6153.  https://doi.org/10.1038/s41598-017-06483-7 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Deeney JT, Belkina AC, Shirihai OS, Corkey BE, Denis GV (2016) BET bromodomain proteins Brd2, Brd3 and Brd4 selectively regulate metabolic pathways in the pancreatic beta-cell. PLoS ONE 11:e0151329.  https://doi.org/10.1371/journal.pone.0151329 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Prieto-Martínez FD, Gortari EF-D, Méndez-Lucio O, Medina-Franco JL (2016) A chemical space odyssey of inhibitors of histone deacetylases and bromodomains. RSC Adv 6:56225–56239.  https://doi.org/10.1039/c6ra07224k CrossRefGoogle Scholar
  12. 12.
    Schiedel M, Conway SJ (2018) Small molecules as tools to study the chemical epigenetics of lysine acetylation. Curr Opin Chem Biol 45:166–178.  https://doi.org/10.1016/j.cbpa.2018.06.015 CrossRefPubMedGoogle Scholar
  13. 13.
    Garcia-Jacas CR, Martinez-Mayorga K, Marrero-Ponce Y, Medina-Franco JL (2017) Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators. SAR QSAR Environ Res 28:41–58.  https://doi.org/10.1080/1062936X.2017.1278616 CrossRefPubMedGoogle Scholar
  14. 14.
    Zhao H, Gartenmann L, Dong J, Spiliotopoulos D, Caflisch A (2014) Discovery of BRD4 bromodomain inhibitors by fragment-based high-throughput docking. Bioorg Med Chem Lett 24:2493–2496.  https://doi.org/10.1016/j.bmcl.2014.04.017 CrossRefPubMedGoogle Scholar
  15. 15.
    Vidler LR, Filippakopoulos P, Fedorov O, Picaud S, Martin S, Tomsett M, Woodward H, Brown N, Knapp S, Hoelder S (2013) Discovery of novel small-molecule inhibitors of BRD4 using structure-based virtual screening. J Med Chem 56:8073–8088.  https://doi.org/10.1021/jm4011302 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Chung CW, Dean AW, Woolven JM, Bamborough P (2012) Fragment-based discovery of bromodomain inhibitors part 1: inhibitor binding modes and implications for lead discovery. J Med Chem 55:576–586.  https://doi.org/10.1021/jm201320w CrossRefPubMedGoogle Scholar
  17. 17.
    Chung CW, Coste H, White JH, Mirguet O, Wilde J, Gosmini RL, Delves C, Magny SM, Woodward R, Hughes SA, Boursier EV, Flynn H, Bouillot AM, Bamborough P, Brusq JM, Gellibert FJ, Jones EJ, Riou AM, Homes P, Martin SL, Uings IJ, Toum J, Clement CA, Boullay AB, Grimley RL, Blandel FM, Prinjha RK, Lee K, Kirilovsky J, Nicodeme E (2011) Discovery and characterization of small molecule inhibitors of the BET family bromodomains. J Med Chem 54:3827–3838.  https://doi.org/10.1021/jm200108t CrossRefPubMedGoogle Scholar
  18. 18.
    Andrieu G, Belkina AC, Denis GV (2016) Clinical trials for BET inhibitors run ahead of the science. Drug Discov Today Technol 19:45–50.  https://doi.org/10.1016/j.ddtec.2016.06.004 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Speck-Planche A (2018) Recent advances in fragment-based computational drug design: tackling simultaneous targets/biological effects. Future Med Chem 10:2021–2024.  https://doi.org/10.4155/fmc-2018-0213 CrossRefPubMedGoogle Scholar
  20. 20.
    Bediaga H, Arrasate S, Gonzalez-Diaz H (2018) PTML combinatorial model of ChEMBL compounds assays for multiple types of cancer. ACS Comb Sci.  https://doi.org/10.1021/acscombsci.8b00090 CrossRefPubMedGoogle Scholar
  21. 21.
    Speck-Planche A, Cordeiro MNDS (2013) Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry. Curr Top Med Chem 13:1656–1665.  https://doi.org/10.2174/15680266113139990116 CrossRefPubMedGoogle Scholar
  22. 22.
    Speck-Planche A, Cordeiro MNDS (2015) Enabling virtual screening of potent and safer antimicrobial agents against noma: mtk-QSBER model for simultaneous prediction of antibacterial activities and ADMET properties. Mini Rev Med Chem 15:194–202.  https://doi.org/10.2174/138955751503150312120519 CrossRefPubMedGoogle Scholar
  23. 23.
    Jahnke W, Erlanson DA (2006) Fragment-based approaches in drug discovery. Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimCrossRefGoogle Scholar
  24. 24.
    Speck-Planche A, Cordeiro MNDS (2017) Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol Divers 21:511–523.  https://doi.org/10.1007/s11030-017-9731-1 CrossRefPubMedGoogle Scholar
  25. 25.
    Romero-Duran FJ, Alonso N, Yanez M, Caamano O, Garcia-Mera X, Gonzalez-Diaz H (2016) Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology 103:270–278.  https://doi.org/10.1016/j.neuropharm.2015.12.019 CrossRefPubMedGoogle Scholar
  26. 26.
    Tenorio-Borroto E, Penuelas-Rivas CG, Vasquez-Chagoyan JC, Castanedo N, Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2014) Model for high-throughput screening of drug immunotoxicity—study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur J Med Chem 72:206–220.  https://doi.org/10.1016/j.ejmech.2013.08.035 CrossRefPubMedGoogle Scholar
  27. 27.
    Speck-Planche A, Cordeiro MNDS (2014) Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: a chemoinformatic complementary approach for high-throughput screening. ACS Comb Sci 16:78–84.  https://doi.org/10.1021/co400115s CrossRefPubMedGoogle Scholar
  28. 28.
    Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107.  https://doi.org/10.1093/nar/gkr777 CrossRefPubMedGoogle Scholar
  29. 29.
    Prado-Prado F, Garcia-Mera X, Abeijon P, Alonso N, Caamano O, Yanez M, Garate T, Mezo M, Gonzalez-Warleta M, Muino L, Ubeira FM, Gonzalez-Diaz H (2011) Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. Eur J Med Chem 46:1074–1094.  https://doi.org/10.1016/j.ejmech.2011.01.023 CrossRefPubMedGoogle Scholar
  30. 30.
    Marzaro G, Chilin A, Guiotto A, Uriarte E, Brun P, Castagliuolo I, Tonus F, Gonzalez-Diaz H (2011) Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur J Med Chem 46:2185–2192.  https://doi.org/10.1016/j.ejmech.2011.02.072 CrossRefPubMedGoogle Scholar
  31. 31.
    Garcia I, Fall Y, Gomez G, Gonzalez-Diaz H (2011) First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol Divers 15:561–567.  https://doi.org/10.1007/s11030-010-9280-3 CrossRefPubMedGoogle Scholar
  32. 32.
    Prado-Prado FJ, Uriarte E, Borges F, Gonzalez-Diaz H (2009) Multi-target spectral moments for QSAR and complex networks study of antibacterial drugs. Eur J Med Chem 44:4516–4521.  https://doi.org/10.1016/j.ejmech.2009.06.018 CrossRefPubMedGoogle Scholar
  33. 33.
    Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2010) Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 18:2225–2231.  https://doi.org/10.1016/j.bmc.2010.01.068 CrossRefPubMedGoogle Scholar
  34. 34.
    Prado-Prado FJ, Martinez de la Vega O, Uriarte E, Ubeira FM, Chou KC, Gonzalez-Diaz H (2009) Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg Med Chem 17:569–575.  https://doi.org/10.1016/j.bmc.2008.11.075 CrossRefPubMedGoogle Scholar
  35. 35.
    Prado-Prado FJ, Borges F, Perez-Montoto LG, Gonzalez-Diaz H (2009) Multi-target spectral moment: QSAR for antifungal drugs versus different fungi species. Eur J Med Chem 44:4051–4056.  https://doi.org/10.1016/j.ejmech.2009.04.040 CrossRefPubMedGoogle Scholar
  36. 36.
    Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2013) Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer Agents Med Chem 13:791–800.  https://doi.org/10.2174/1871520611313050013 CrossRefPubMedGoogle Scholar
  37. 37.
    Anderson AC (2003) The process of structure-based drug design. Chem Biol 10:787–797.  https://doi.org/10.1016/j.chembiol.2003.09.002 CrossRefPubMedGoogle Scholar
  38. 38.
    ChemAxon (1998–2016) Standardizer (Tool for structure canonicalization and transformation), J Chem. v15.11.16.0, Budapest, HungaryGoogle Scholar
  39. 39.
    Valdes-Martini JR, Marrero-Ponce Y, Garcia-Jacas CR, Martinez-Mayorga K, Barigye SJ, Almeida YSV, Perez-Gimenez F, Morell CA (2017) QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations. J Cheminform 9:35.  https://doi.org/10.1186/s13321-017-0211-5 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Medina Marrero R, Marrero-Ponce Y, Barigye SJ, Echeverria Diaz Y, Acevedo-Barrios R, Casanola-Martin GM, Garcia Bernal M, Torrens F, Perez-Gimenez F (2015) QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents. SAR QSAR Environ Res 26:943–958.  https://doi.org/10.1080/1062936X.2015.1104517 CrossRefPubMedGoogle Scholar
  41. 41.
    Marrero-Ponce Y, Siverio-Mota D, Galvez-Llompart M, Recio MC, Giner RM, Garcia-Domenech R, Torrens F, Aran VJ, Cordero-Maldonado ML, Esguera CV, de Witte PA, Crawford AD (2011) Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: the nitroindazolinone chemotype. Eur J Med Chem 46:5736–5753.  https://doi.org/10.1016/j.ejmech.2011.07.053 CrossRefPubMedGoogle Scholar
  42. 42.
    Casanola-Martin GM, Marrero-Ponce Y, Khan MT, Khan SB, Torrens F, Perez-Jimenez F, Rescigno A, Abad C (2010) Bond-based 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation. Chem Biol Drug Des 76:538–545.  https://doi.org/10.1111/j.1747-0285.2010.01032.x CrossRefPubMedGoogle Scholar
  43. 43.
    Gonzalez-Diaz H, Herrera-Ibata DM, Duardo-Sanchez A, Munteanu CR, Orbegozo-Medina RA, Pazos A (2014) ANN multiscale model of anti-HIV drugs activity versus AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks. J Chem Inf Model 54:744–755.  https://doi.org/10.1021/ci400716y CrossRefPubMedGoogle Scholar
  44. 44.
    Pearson K (1895) Notes on regression and inheritance in the case of two parents. Proc R Soc Lond 58:240–242.  https://doi.org/10.1098/rspl.1895.0041 CrossRefGoogle Scholar
  45. 45.
    Kleandrova VV, Ruso JM, Speck-Planche A, Dias Soeiro Cordeiro MN (2016) Enabling the discovery and virtual screening of potent and safe antimicrobial peptides. simultaneous prediction of antibacterial activity and cytotoxicity. ACS Comb Sci 18:490–498.  https://doi.org/10.1021/acscombsci.6b00063 CrossRefPubMedGoogle Scholar
  46. 46.
    Statsoft-Team (2001) STATISTICA. Data analysis software system. v6.0, TulsaGoogle Scholar
  47. 47.
    Filippakopoulos P, Qi J, Picaud S, Shen Y, Smith WB, Fedorov O, Morse EM, Keates T, Hickman TT, Felletar I, Philpott M, Munro S, McKeown MR, Wang Y, Christie AL, West N, Cameron MJ, Schwartz B, Heightman TD, La Thangue N, French CA, Wiest O, Kung AL, Knapp S, Bradner JE (2010) Selective inhibition of BET bromodomains. Nature 468:1067–1073.  https://doi.org/10.1038/nature09504 CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Jahagirdar R, Zhang H, Azhar S, Tobin J, Attwell S, Yu R, Wu J, McLure KG, Hansen HC, Wagner GS, Young PR, Srivastava RA, Wong NC, Johansson J (2014) A novel BET bromodomain inhibitor, RVX-208, shows reduction of atherosclerosis in hyperlipidemic ApoE deficient mice. Atherosclerosis 236:91–100.  https://doi.org/10.1016/j.atherosclerosis.2014.06.008 CrossRefPubMedGoogle Scholar
  49. 49.
    Picaud S, Wells C, Felletar I, Brotherton D, Martin S, Savitsky P, Diez-Dacal B, Philpott M, Bountra C, Lingard H, Fedorov O, Muller S, Brennan PE, Knapp S, Filippakopoulos P (2013) RVX-208, an inhibitor of BET transcriptional regulators with selectivity for the second bromodomain. Proc Natl Acad Sci USA 110:19754–19759.  https://doi.org/10.1073/pnas.1310658110 CrossRefPubMedGoogle Scholar
  50. 50.
    Filippakopoulos P, Picaud S, Qi J, Keates T, Felletar I, Fedorov O, Muniz J, von Delft F, Arrowsmith CH, Edwards AM, Weigelt J, Bountra C, Bradner JE, Knapp S, Structural_Genomics_Consortium_(SGC) (2011) Crystal structure of the first bromodomain of human BRD3 in complex with the inhibitor JQ1. Protein Data Bank.  https://doi.org/10.2210/pdb3s91/pdb CrossRefGoogle Scholar
  51. 51.
    Filippakopoulos P, Picaud S, Qi J, Keates T, Felletar I, Fedorov O, Muniz J, von Delft F, Arrowsmith CH, Edwards AM, Weigelt J, Bountra C, Bradner JE, Knapp S, Structural_Genomics_Consortium_(SGC) (2011) Crystal Structure of the second bromodomain of human BRD3 in complex with the inhibitor JQ1. Protein Data Bank.  https://doi.org/10.2210/pdb3s92/pdb CrossRefGoogle Scholar
  52. 52.
    Mishra NK, Urick AK, Ember SW, Schonbrunn E, Pomerantz WC (2014) Fluorinated aromatic amino acids are sensitive 19F NMR probes for bromodomain–ligand interactions. ACS Chem Biol 9:2755–2760.  https://doi.org/10.1021/cb5007344 CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321.  https://doi.org/10.1021/jm051197e CrossRefPubMedGoogle Scholar
  54. 54.
    Speck-Planche A, Cordeiro MNDS (2017) Speeding up early drug discovery in antiviral research: a fragment-based in silico approach for the design of virtual anti-hepatitis C leads. ACS Comb Sci 19:501–512.  https://doi.org/10.1021/acscombsci.7b00039 CrossRefPubMedGoogle Scholar
  55. 55.
    Romero FA, Taylor AM, Crawford TD, Tsui V, Cote A, Magnuson S (2016) Disrupting acetyl-lysine recognition: progress in the development of bromodomain inhibitors. J Med Chem 59:1271–1298.  https://doi.org/10.1021/acs.jmedchem.5b01514 CrossRefPubMedGoogle Scholar
  56. 56.
    Xue X, Zhang Y, Liu Z, Song M, Xing Y, Xiang Q, Wang Z, Tu Z, Zhou Y, Ding K, Xu Y (2016) Discovery of benzo[cd]indol-2(1H)-ones as potent and specific BET bromodomain inhibitors: structure-based virtual screening, optimization, and biological evaluation. J Med Chem 59:1565–1579.  https://doi.org/10.1021/acs.jmedchem.5b01511 CrossRefPubMedGoogle Scholar
  57. 57.
    Galdeano C, Ciulli A (2016) Selectivity on-target of bromodomain chemical probes by structure-guided medicinal chemistry and chemical biology. Future Med Chem 8:1655–1680.  https://doi.org/10.4155/fmc-2016-0059 CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Zhao Y, Zhou B, Bai L, Liu L, Yang CY, Meagher JL, Stuckey JA, McEachern D, Przybranowski S, Wang M, Ran X, Aguilar A, Hu Y, Kampf JW, Li X, Zhao T, Li S, Wen B, Sun D, Wang S (2018) Structure-based discovery of CF53 as a potent and orally bioavailable bromodomain and extra-terminal (BET) bromodomain inhibitor. J Med Chem 61:6110–6120.  https://doi.org/10.1021/acs.jmedchem.8b00483 CrossRefPubMedGoogle Scholar
  59. 59.
    Ayoub AM, Hawk LML, Herzig RJ, Jiang J, Wisniewski AJ, Gee CT, Zhao P, Zhu JY, Berndt N, Offei-Addo NK, Scott TG, Qi J, Bradner JE, Ward TR, Schonbrunn E, Georg GI, Pomerantz WCK (2017) BET bromodomain inhibitors with one-step synthesis discovered from virtual screen. J Med Chem 60:4805–4817.  https://doi.org/10.1021/acs.jmedchem.6b01336 CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Jennings LE, Schiedel M, Hewings DS, Picaud S, Laurin CMC, Bruno PA, Bluck JP, Scorah AR, See L, Reynolds JK, Moroglu M, Mistry IN, Hicks A, Guzanov P, Clayton J, Evans CNG, Stazi G, Biggin PC, Mapp AK, Hammond EM, Humphreys PG, Filippakopoulos P, Conway SJ (2018) BET bromodomain ligands: probing the WPF shelf to improve BRD4 bromodomain affinity and metabolic stability. Bioorg Med Chem 26:2937–2957.  https://doi.org/10.1016/j.bmc.2018.05.003 CrossRefPubMedGoogle Scholar
  61. 61.
    Speck-Planche A, Kleandrova VV (2012) QSAR and molecular docking techniques for the discovery of potent monoamine oxidase B inhibitors: computer-aided generation of new rasagiline bioisosteres. Curr Top Med Chem 12:1734–1747.  https://doi.org/10.2174/1568026611209061734 CrossRefPubMedGoogle Scholar
  62. 62.
    Baskin II, Skvortsova MI, Stankevich IV, Zefirov NS (1995) On the basis of invariants of labeled molecular graphs. J Chem Inf Comput Sci 35:527–531.  https://doi.org/10.1021/ci00025a021 CrossRefGoogle Scholar
  63. 63.
    Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791–4810.  https://doi.org/10.3390/molecules17054791 CrossRefPubMedGoogle Scholar
  64. 64.
    Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182.  https://doi.org/10.1021/ci049714+ CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Royal Chemical Society (RCS), ChemSpider (2009). http://www.chemspider.com/. Accessed 14 Aug 2018
  66. 66.
    Liu Z, Wang P, Chen H, Wold EA, Tian B, Brasier AR, Zhou J (2017) Drug discovery targeting bromodomain-containing protein 4. J Med Chem 60:4533–4558.  https://doi.org/10.1021/acs.jmedchem.6b01761 CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    McLure KG, Gesner EM, Tsujikawa L, Kharenko OA, Attwell S, Campeau E, Wasiak S, Stein A, White A, Fontano E, Suto RK, Wong NC, Wagner GS, Hansen HC, Young PR (2013) RVX-208, an inducer of ApoA-I in humans, is a BET bromodomain antagonist. PLoS ONE 8:e83190.  https://doi.org/10.1371/journal.pone.0083190 CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26.  https://doi.org/10.1016/S0169-409X(00)00129-0 CrossRefPubMedGoogle Scholar
  69. 69.
    CambridgeSoft (2003) ChemDraw Ultra. v8.0, Cambridge, MAGoogle Scholar
  70. 70.
    ChemAxon (1998-2016) Marvin Sketch, JChem. v15.11.16.0, Budapest, HungaryGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Research Program on Biomedical Informatics (GRIB)Hospital del Mar Medical Research Institute (IMIM)BarcelonaSpain
  2. 2.Chemistry DepartmentFederal University of ParaíbaJoão PessoaBrazil

Personalised recommendations