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A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models (2018)

  • Authors:
  • USP affiliated authors: OLIVEIRA, PATRÍCIA RUFINO - EACH ; HONORIO, KÁTHIA MARIA - EACH ; SILVA, ALBÉRICO BORGES FERREIRA DA - IQSC
  • USP Schools: EACH; EACH; IQSC
  • DOI: 10.1007/s11224-017-1072-2
  • Subjects: MICROTÚBULOS; NEOPLASIAS; BIOLOGIA MOLECULAR
  • Language: Inglês
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    Informações sobre o DOI: 10.1007/s11224-017-1072-2 (Fonte: oaDOI API)
    • Este periódico é de assinatura
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    Versões disponíveis em Acesso Aberto do: 10.1007/s11224-017-1072-2 (Fonte: Unpaywall API)

    Título do periódico: Structural Chemistry

    ISSN: 1040-0400,1572-9001



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    Informações sobre o Citescore
  • Título: Structural Chemistry

    ISSN: 1040-0400

    Citescore - 2017: 1.63

    SJR - 2017: 0.504

    SNIP - 2017: 0.525


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    • ABNT

      LIPINSKI, Célio Fernando; SILVA, Albérico Borges Ferreira da; OLIVEIRA, Aline Alves; HONORIO, Kathia Maria; OLIVEIRA, Patrícia Rufino. A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models. Structural Chemistry: computational and experimental studies of chemical and biological systems, New York, v. 29, p. 01-09, 2018. Disponível em: < http://dx.doi.org/10.1007/s11224-017-1072-2 > DOI: 10.1007/s11224-017-1072-2.
    • APA

      Lipinski, C. F., Silva, A. B. F. da, Oliveira, A. A., Honorio, K. M., & Oliveira, P. R. (2018). A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models. Structural Chemistry: computational and experimental studies of chemical and biological systems, 29, 01-09. doi:10.1007/s11224-017-1072-2
    • NLM

      Lipinski CF, Silva ABF da, Oliveira AA, Honorio KM, Oliveira PR. A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models [Internet]. Structural Chemistry: computational and experimental studies of chemical and biological systems. 2018 ; 29 01-09.Available from: http://dx.doi.org/10.1007/s11224-017-1072-2
    • Vancouver

      Lipinski CF, Silva ABF da, Oliveira AA, Honorio KM, Oliveira PR. A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models [Internet]. Structural Chemistry: computational and experimental studies of chemical and biological systems. 2018 ; 29 01-09.Available from: http://dx.doi.org/10.1007/s11224-017-1072-2

    Referências citadas na obra
    Wilson L, Jordan MA (2004) Microtubules as a target for anticancer drugs. Nature 4:252–265. https://doi.org/10.1038/nrc1317
    Sharma R et al (2016) A review on mechanisms of anti tumor activity of chalcones. Anti Cancer Agents Med Chem 16:200–211. https://doi.org/10.2174/1871520615666150518093144
    Kello M et al (2016) Chalcone derivatives cause accumulation of colon cancer cells in the G2/M phase and induce apoptosis. Life Sci 150:32–38. https://doi.org/10.1016/j.lfs.2016.02.073
    Bai R, Covell DG, Pei XF, Ewell JB, Nguyen NY, Brossi A, Hamel E (2000) Mapping the binding site of colchicinoids on beta-tubulin. 2-chloroaetyl-2-demethylthiocolchicine covalently reacts predominantly with cysteine 239 and secondarily with cysteine 354. J Biol Chem 51:40443–40452
    Gupta S, Bhattacharyya B (2003) Antimicrotubular drugs binding to vinca domain of tubulin. Mol Cell Biochem 1-2:41–47. https://doi.org/10.1074/jbc.M005299200
    Rahman MA (2011) Chalcone: a valuable insight into the recent advances and potential pharmacological activities. Chem Sci J CSJ-21. doi: https://doi.org/10.4172/2150-3494.1000021
    Ou-Yang S et al (2012) Computacional drug discovery. Acta Pharmacol Sin 33:1131–1140. https://doi.org/10.1038/aps.2012.109
    Pettit GR, Singh SB, Hamel E, Lin CM, Alberts DS, Kendall DG (1989) Isolation and structure of the strong cell growth and tubulin inhibitor combretastatin A-4. Experientia 45. doi: https://doi.org/10.1007/BF01954881
    Pettit GR, Singh SB, Boyd MR, Hamel E, Pettit RK, Schmidt JM, Hogan F (1995) Antineoplastic agents. 291. Isolation and synthesis of combretastatins A-4, A-5 and A-6. J Med Chem 38:1666–1672. https://doi.org/10.2174/1871520615666150518093144
    Lin CM, Singh SB, Chu PS, Dempcy RO, Schmidt JM, Pettit GR, Hamel E (1988) Interactions of tubulin with potent natural and synthetic analogs of the antimitotic agent combretastatin: a structure-activity study. Mol Pharmacol 34:200–208
    Pettit GR, Singh SB, Schmidt JM (1988) Isolation, structure, synthesis and antimitotic properties of Combretastatins B-3 and B-4 form Combretum Caffrum. J Nat Prod 51:517–527. https://doi.org/10.1021/np50057a011
    Pettit GR, Cragg GM, Herald DL, Schmidt JM, Lohavanuaya P (1982) Isolation and structure of combretastatin. Can J Chem 60. https://doi.org/10.1139/v82-202
    Pettit GR, Singh SB (1987) Isolation, structure, and synthesis of combretastatin A-2, A-3 and B-2. Can J Chem 65:2390. https://doi.org/10.1139/v87-399
    Ducki S, Rennison D, Woo M, Kendall A, Chabert JFD, McGown AT, Lawrence N (2009) Combretastatin-like chalcones as inhibitors of microtubule polymerization. Part 1: synthesis and biological evaluation of antivascular activity. Bioorg Med Chem 17:7698–7710. https://doi.org/10.1016/j.bmc.2009.09.039
    González-Díaz H et al (2007) ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur J Med Chem 42:580–585. https://doi.org/10.1016/j.ejmech.2006.11.016
    Pasomub E et al (2010) The application of artificial neural networks for phenotypic drug resistance prediction: evaluation and comparison with other interpretation systems. Jpn J Infect Dis 63:87–94
    Oliveira AA et al (2017) New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists. J Mol Model 23:302. https://doi.org/10.1007/s00894-017-3444-3
    Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26(5):694–701. https://doi.org/10.1002/qsar.200610151
    Frisch MJ, Trucks GW, Schlegel HB et al (2009) Gaussian, Inc., Wallingford CT
    Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B 37. doi: https://doi.org/10.1103/PhysRevB.37.785
    Poople JA, Seeger R, Binkley JS, Krishnan R (1980) Self-consistent molecular orbital methods. XX. A basis set for correlated wave functions. J Chem Phys 72:650. https://doi.org/10.1063/1.1677527
    Tetko IV et al (2005) Virtual computational chemistry laboratory—design and description. J Comput Aid Mol Des 19:453–463. https://doi.org/10.1007/s10822-005-8694-y
    Oliveira DB, Gaudio AC (2001) BuildQSAR: a new computer program for QSAR analysis. Quantitative Structure-Activity Relationships 6:599–601. https://doi.org/10.1002/1521-3838
    Infometrix INC. (2002) Pirouette 3.11. Woodinville
    Martins JPA, Ferreira MMC (2013) QSAR modeling: um novo pacote computacional open source para gerar e validar modelos QSAR. Química Nova 36:554. https://doi.org/10.1590/S0100-40422013000400013
    Mathworks (2011). Matlab:7.12
    Deeb O, Hemmateenejad B (2007) ANN-QSAR model of drug-binding to human serum albumin. Chem Biol Drug Des 70:19–29. https://doi.org/10.1111/j.1747-0285.2007.00528.x
    Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Molecular Informatics 29:476–488. https://doi.org/10.1002/minf.201000061
    Jagiello K, Grzonkowska M, Swirog M et al (2016) Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives. J Nanopart Res 18:256. https://doi.org/10.1007/s11051-016-3564-1
    Jagiello K, Sosnowska A et al (2014) Direct QSPR: the most efficient way of predicting organic carbon/water partition coefficient (log KOC) for polyhalogenated POPs. Struct Chem 25:997–1004. https://doi.org/10.1007/s11224-014-0419-1
    Kiralj R, Ferreira MMC (2009) Basic validation procedures for regression models in QSAR and QSPR studies: theory and application. J Braz Chem Soc 20:770–787. https://doi.org/10.1590/S0103-50532009000400021
    Gerova MS et al (2016) Combretastatin A-4 analogues with benzoxazolone scaffold: synthesis, structure and biological activity. Eur J Med Chem 120:121–133. https://doi.org/10.1016/j.ejmech.2016.05.012
    Guan Q et al (2014) Synthesis and biological evaluation of novel 3,4-diaryl-1,2,5-selenadiazol analogues of combretastatin A-4. Eur J Med Chem 87:1–9. https://doi.org/10.1016/j.ejmech.2014.09.046
    Hemmer MC, Steinhauer V, Gasteiger J (1999) Deriving the 3D structure of organic molecules from their infrared spectra. J Vibrational Spectroscopy 19:151–164. https://doi.org/10.1016/S0924-2031(99)00014-4
    Abreu RMV, Ferreira ICFR, Queiroz MJRP (2009) QSAR model for predicting radical scavenging activity of di(hetero)arylamines derivatives of benzo[b]thiophenes. Eur J Med Chem 44:1952–1958. https://doi.org/10.1016/j.ejmech.2008.11.011
    Randic M (1995) Molecular shape profiles. J Chem Inf Comput Sci 35:373–382. https://doi.org/10.1021/ci00025a005
    Randic M, Basak SC (1999) Optimal molecular descriptors based on weighted path numbers. J Chem Inf Comput Sci 39:261–266. https://doi.org/10.1021/ci9800763
    Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682–692. https://doi.org/10.1021/ci015504a
    Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies. J Chem Inf Comput Sci 42:693–705. https://doi.org/10.1021/ci0155053
    Skorobogatov VA, Dobrynin AA (1988) Metric analysis of graphs. Match Commun Math Comp Chem 23:105–151
    Hill NE (1953) Dielectric relaxation time of polar molecules in solution. Nature 4358:836–837. https://doi.org/10.1038/171836b0
    Mahal K et al (2016) Combretastatin A-4 derived 5-(1-methyl-4-phenyl-imidazol-5-yl) indoles with superior cytotoxic and anti-vascular effects on chemoresistant cancer cells and tumors. Eur J Med Chem 118:9–20. https://doi.org/10.1016/j.ejmech.2016.04.045
    Jung E et al (2016) Synthesis and biological activity of pyrole analogues of combretastatin A-4. Bioorg Med Chem Lett 26:3001–3005. https://doi.org/10.1016/j.bmcl.2016.05.026
    Kamal A et al (2016) Synthesis and biological evaluation of arylcinnamide linked combretastatin-A4 hybrids as tubulin polymerization inhibitors and apoptosis inducing agents. Bioorg Med Chem Lett 26:2957–2964. https://doi.org/10.1016/j.bmcl.2016.03.049
    Madadi N et al (2016) Dioxol and dihydrodioxin analogs of 2- and 3-phenylacetonitriles as potent anti-cancer agents with nanomolar activity against a variety of human cancer cells. Bioorg Med Chem Lett 26:2164–2169. https://doi.org/10.1016/j.bmcl.2016.03.068
    Shobeiri N et al (2016) Synthesis and biological evaluation of quinoline analogues of flavones as potential anticancer agents and tubulin polymerization inhibitors. Eur J Med Chem 114:14–23. https://doi.org/10.1016/j.ejmech.2016.02.069