Conformation-independent QSAR on c-Src tyrosine kinase inhibitors

Chemometrics and Intelligent Laboratory Systems

Volumn 134, Issue , 2014, Pages 47-52

  Comelli, Nieves C ^a   Ortiz, Erlinda V ^a   Kolacz, Magdalena ^b   Toropova, Alla P ^c   Toropov, Andrey A ^c   Duchowicz, Pablo R ^b   Castro, Eduardo A ^b  

^a UNIVERSIDAD NACIONAL DE CATAMARCA   (Argentina)

^b INSTITUTO DE INVESTIGACIONES FISICOQUÍMICAS TEÓRICAS Y APLICADAS INIFTA   (Argentina)

^c MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

Author keywords

C Src tyrosine kinase; Multivariable linear regression analysis; Pyrrolo pyrimidine derivatives; QSAR theory; Structural descriptors

Indexed keywords

C SRC TYROSINE KINASE INHIBITOR; PROTEIN TYROSINE KINASE; PROTEIN TYROSINE KINASE INHIBITOR; PYRROLOPYRIMIDINE DERIVATIVE; UNCLASSIFIED DRUG;

ARTICLE; CONFORMATIONAL TRANSITION; ENZYME INHIBITION; LINEAR REGRESSION ANALYSIS; MOLECULAR DOCKING; PRIORITY JOURNAL; PROTEIN TARGETING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RANDOMIZATION; THEORETICAL STUDY; THREE DIMENSIONAL IMAGING; VALIDATION STUDY; X RAY CRYSTALLOGRAPHY;

EID: 84897033563     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2014.03.003     Document Type: Article

References (67)

1. Drewry D.H., Bamborough P., Schneider K., Smith G.K. The kinome and its impact on medicinal chemistry. Royal Society of Chemistry 2011, W1J 0BA:1-53. Burlington House, Piccadilly, London. R.A. Ward, F. Goldberg (Eds.).
2. Hunter T. Tyrosine phosphorylation: thirty years and counting. Curr. Opin. Cell Biol. 2009, 21:140-146.
3. Boggon T.J., Eck M.J. Structure and regulation of Src family kinases. Oncogene 2004, 23:7918-7927.
4. Parsons S.J., Parsons J.T. Src family kinases, key regulators of signal transduction. Oncogene 2004, 23:7906-7909.
5. Sen B., Johnson F.M. Regulation of SRC family kinases in human cancers. J. Signal Transd. 2011, 2011:865819-865832.
6. Tsygankov A.Y., Shore S.K. Src: regulation, role in human carcinogenesis and pharmacological inhibitors. Curr. Pharm. Des. 2004, 10:1745-1756.
7. Summy J.M., Gallick G.E. Src family kinases in tumor progression and metastasis. Cancer Metastasis Rev. 2003, 22:337-358.
8. Irby R.B., Yeatman T.J. Increased Src activity disrupts cadherin/catenin-mediated homotypic adhesion in human colon cancer and transformed rodent cells. Cancer Res. 2002, 62:2669-2674.
9. Rahimi N., Hung W., Tremblay E., Saulnier R., Elliott B. c-Src kinase activity is required for hepatocyte growth factor-induced motility and anchorage-independent growth of mammary carcinoma cells. J. Biol. Chem. 1998, 273:33714-33721.
10. Jones R.J., Avizienyte E., Wyke A.W., Owens D.W., Brunton V.B., Frame M.C. Elevated c-Src is linked to altered cell-matrix adhesion rather than proliferation in KM12C human colorectal cancer cells. Br. J. Cancer 2002, 87:1128-1135.
11. Brunton V.G., Avizienyte E., Fincham V.J., Serrels B., Metcalf C.A., Sawyer T.K. Frame identification of Src-specific phosphorylation site on focal adhesion kinase: dissection of the role of Src SH2 and catalytic functions and their consequences for tumor cell behavior. Cancer Res. 2005, 65:1335-1342.
12. Duxbury M.S., Ito H., Zinner M.J., Ashley S.W., Whang E.E. Inhibition of SRC tyrosine kinase impairs inherent and acquired gemcitabine resistance in human pancreatic adenocarcinoma cells. Clin. Cancer Res. 2004, 10:2307-2318.
13. Levitzk A., Gazit A. Tyrosine kinase inhibition: an approach to drug development. Science 1995, 267:1782-1788.
14. Al-Obeidi F.A., Lam K.S. Development of inhibitors for protein tyrosine kinases. Oncogene 2000, 19:5690-5701.
15. Dow R.L., Bechle B.M., Chou T.T., Goddard C., Larson E.R. Selective inhibition of the tyrosine kinase pp60src by analogs of 5,10-dihydropyrimido[4,5-b]quinolin-4(1H)-one. Bioorg. Med. Chem. Lett. 1995, 5:1007-1010.
16. Carraro F., Naldini A., Pucci A., Locatelli G.A., Maga G., Schenone S., Bruno O., Ranise A., Bondavalli F., Brullo C., Fossa P., Menozzi G., Mosti L., Modugno M., Tintori C., Manetti F., Botta M. Pyrazolo[3,4-d]pyrimidines as potent antiproliferative and proapoptotic agents toward A431 and 8701-BC cells in culture via inhibition of c-Src phosphorylation. J. Med. Chem. 2006, 49:1549-1561.
17. Altmann E., Widler L., Missbach M. N7-substituted-5-aryl-pyrrolo[2,3-d]pyrimidines represent a versatile class of potent inhibitors of the tyrosine kinase c-Src. Mini-Rev. Med. Chem. 2002, 2:201-208.
18. Missbach M., Altmann E., Widler L., Susa M., Buchdunger E., Mett H., Meyer T., Green J. Substituted 5,7-diphenyl-pyrrolo[2,3d]pyrimidines: potent inhibitors of the tyrosine kinase c-Src. Bioorg. Med. Chem. Lett. 2000, 10:945-949.
19. Widler L., Green J., Missbach M., Susa M., Altmann E. 7-Alkyl- and 7-cycloalkyl-5-aryl-pyrrolo[2,3-d]pyrimidines-potent inhibitors of the tyrosine kinase c-Src. Bioorg. Med. Chem. Lett. 2001, 11:849-852.
20. Altmann E., Missbach M., Green J., Susa M., Wagenknecht H.A., Widler L. 7-Pyrrolidinyl- and 7-piperidinyl-5-aryl-pyrrolo[2,3-d]pyrimidines-potent inhibitors of the tyrosine kinase c-Src. Bioorg. Med. Chem. Lett. 2001, 11:853-856.
21. Myers M.R., Setzer N.N., Spada A.P., Zulli A.L., Hsu C.J., Zilberstein A., Johnson S.E., Hook L.E., Jacoski M.V. The preparation and sar of 4-(anilino), 4-(phenoxy), and 4-(thiophenoxy)-quinazolines: inhibitors of p56lck and EGF-R tyrosine kinase activity. Bioorg. Med. Chem. Lett. 1997, 7:417-420.
22. Schenone S., Zanoli S., Brullo C., Crespan E., Maga G. Current advances in the development of anticancer drugs targeting tyrosine kinases of the Src family. Curr. Drug Ther. 2008, 3:158-176.
23. Boyce B.F., Xing L., Yao Z., Yamashita T., Shakespeare W.C., Wang Y., Metcalf C.A., Sundaramoorthi R., Dalgarno D.C., Iuliucci J.D., Sawyer T.K. SRC inhibitors in metastatic bone disease. Clin. Cancer Res. 2006, 12:6291s-6295s.
24. Bohari M.H., Srivastava H.K., Sastry G.N. Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models. Org. Med. Chem. Lett. 2011, 1:3.
25. Bairy S.K., Suneel Kumar B.V., Bhalla J.U., Pramod A.B., Ravikumar M. Three-dimensional quantitative structure-activity relationship studies on c-Src inhibitors based on different docking methods. Chem. Biol. Drug Des. 2009, 73:416-427.
26. Tintori C., Magnani M., Schenone S., Botta M. Docking, 3D-QSAR studies and in silico ADME prediction on c-Src tyrosine kinase inhibitors. Eur. J. Med. Chem. 2009, 44:990-1000.
27. Cao R., Mi N., Zhang H. 3D-QSAR study of c-Src kinase inhibitors based on docking. J. Mol. Model. 2010, 16:361-375.
28. Cao H., Zhang H., Zheng X., Gao D. 3D QSAR studies on a series of potent and high selective inhibitors for three kinases of RTK family. J. Mol. Graph. Model. 2007, 26:236-245.
29. Fernandez M., Ahmad S., Sarai A. Proteochemometric recognition of stable kinase inhibition complexes using topological autocorrelation and support vector machines. J. Chem. Inf. Model. 2010, 50:1179-1188.
30. Cronin M.T.D. Quantitative structure-activity relationships (QSARs). Applications and methodology. Recent Advances in QSAR Studies: Methods and Applications 2010, Springer Science & Business Media, New York. T. Puzyn, J. Leszczynski, M.T.D. Cronin (Eds.).
31. Oprea T.I. 3D QSAR modeling in drug design. Computational Medicinal Chemistry for Drug Discovery 2004, 571-616. Marcel Dekker, Inc., New York, USA. P. Bultinck, H.D. Winter, W. Langenaeker, J.P. Tollenaere (Eds.).
32. Verma J., Khedkar V.M., Coutinho E.C. 3D-QSAR in drug design-a review. Curr. Top. Med. Chem. 2010, 10:95-115.
33. Puzyn T., Leszczynski J., Cronin M.T.D. Recent advances in QSAR studies: methods and applications. Challenges and Advances in Computational Chemistry and Physics 2010, Springer Science&Business Media B.V., Netherlands.
34. Subramanian J., Sharma S., B-Rao C. A novel computational analysis of ligand-induced conformational changes in the ATP binding sites of cyclin dependent kinases. J. Med. Chem. 2006, 49:5434-5441.
35. Talevi A., Bellera C.L., Ianni M.D., Duchowicz P.R., Bruno-Blanch L.E., Castro E.A. An integrated drug development approach applying topological descriptors. Curr. Comput. Aided Drug Des. 2012, 8:172-181.
36. Duchowicz P.R., Comelli N.C., Ortiz E.V., Castro E.A. QSAR study for carcinogenicity in a large set of organic compounds. Curr. Drug Saf. 2012, 7:282-288.
37. HyperChem version 7.5 2002, Hypercube, Inc., (http://www.hyper.com).
38. O'boyle N.M., Banck M., James C.A., Morley C., Vandermeersch T., Hutchison G.R. Open Babel: an open chemical toolbox. J. Cheminf. 2011, 3:1-14.
39. E-Dragon version 1.0,VCCLAB 2005, Virtual Computational Chemistry Laboratory, (http://michem.disat.unimib.it/chm).
40. Todeschini R., Consonni V. Molecular descriptors for chemoinformatics 2009, Weinheim.
41. Recon version 5.5 2002, Rensselaer Polytechnic Institute, New York, USA, (http://reccr.chem.rpi.edu/Software/RECON/recondoc/).
42. Lavine B.K., Davidson C.E., Breneman C., Katt W. Electronic Van der Waals surface property descriptors and genetic algorithms for developing structure-activity correlations in olfactory databases. J. Chem. Inf. Comput. Sci. 2003, 43:1890-1905.
43. Worachartcheewan A., Nantasenamat C., Naenna T., Isarankura-Na-Ayudhya C., Prachayasittikul V. Modeling the activity of furin inhibitors using artificial neural network. Eur. J. Med. Chem. 2009, 44:1664-1673.
44. Bader R.F.W. Atoms in Molecules-A Quantum Theory 1990, Clarendon Press, U.K.
45. Breneman C.M., Weber L.W. Transferable atom equivalents. Assembling accurate electrostatic potential fields for large molecules from ab initio and PROAIMS results on model systems. The Application of Charge Density Research to Chemistry and Drug Design 1991, Plenum, New York. G.A. Jeffrey, J.F. Piniella (Eds.).
46. Duchowicz P.R., Castro E.A., Fernández F.M. Alternative algorithm for the search of an optimal set of descriptors in QSAR-QSPR studies. MATCH Commun. Math. Comput. Chem. 2006, 55:179-192.
47. Duchowicz P.R., Castro E.A., Fernández F.M., González M.P. A new search algorithm of QSPR/QSAR theories: normal boiling points of some organic molecules. Chem. Phys. Lett. 2005, 412:376-380.
48. Duchowicz P.R., Talevi A., Bruno-Blanch L.E., Castro E.A. New QSPR study for the prediction of aqueous solubility of drug-like compounds. Bioorg. Med. Chem. Lett. 2008, 16:7944-7955.
49. Goodarzi M., Duchowicz P.R., Wu C.H., Fernández F.M., Castro E.A. New hybrid genetic based Support Vector Regression as QSAR approach for analyzing flavonoids-GABA(A) complexes. J. Chem. Inf. Model. 2009, 49:1475-1485.
50. Pomilio A.B., Giraudo M.A., Duchowicz P.R., Castro E.A. QSPR analyses for aminograms in food: citrus juices and concentrates. Food Chem. 2010, 123:917-927.
51. Talevi A., Goodarzi M., Ortiz E.V., Duchowicz P.R., Bellera C.L., Pesce G., Castro E.A., Bruno-Blanch L.E. Prediction of drug intestinal absorption by new linear and non-linear QSPR. Eur. J. Med. Chem. 2011, 46:218-228.
52. Pasquale G., Romanelli G.P., Autino J.C., García J., Ortiz E.V., Duchowicz P.R. Quantitative structure-activity relationships on chalcone derivatives: mosquito larvicidal studies. J. Agric. Food Chem. 2012, 60:692-697.
53. Mercader A.G., Duchowicz P.R., Fernandez F.M., Castro E.A. Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories. J. Chem. Inf. Model. 2010, 50:1542-1548.
54. Mercader A.G., Duchowicz P.R., Fernandez F.M., Castro E.A. Advances in the replacement and enhanced replacement method in QSAR and QSPR theories. J. Chem. Inf. Model. 2011, 51:1575-1581.
55. Matlab version 7.12.0.635 2011, The MathWorks Inc., Masachussetts, USA, (http://www.mathworks.com).
56. Toropov A.A., Benfenati E. Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: using rare SMILES attributes to define the applicability domain. Bioorg. Med. Chem. 2008, 26:4801-4809.
57. Toropov A.A., Benfenati E. SMILES in QSPR/QSAR modeling: results and perspectives. Curr. Drug Discov. Technol. 2007, 4:77-116.
58. Toropov A.A., Benfenati E. Additive SMILES-based optimal descriptors in QSAR modelling bee toxicity: using rare SMILES attributes to define the applicability domain. Bioorg. Med. Chem. 2008, 26:4801-4809.
59. Toropov A.A., Toropova A.P., Benfenati E. Simplified molecular input line entry system-based optimal descriptors: quantitative structure-activity relationship modeling mutagenicity of nitrated polycyclic aromatic hydrocarbons. Chem. Biol. Drug Des. 2009, 73:515-525.
60. Toropov A.A., Toropova A.P., Benfenati E., Leszczynska D., Leszczynski J. InChI-based optimal descriptors: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors by correlation balance. Eur. J. Med. Chem. 2010, 45:1387-1394.
61. Roy K. On some aspects of validation of predictive quantitative structure-activity relationship models. Expert. Opin. Drug Discov. 2007, 2:1567-1577.
62. Wold S., Eriksson L. Statistical validation of QSAR results. Chemometrics Methods in Molecular Design 1995, 309-318. Weinheim VCH. H. van de Waterbeemd (Ed.).
63. Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 2007, 26:694-701.
64. Eriksson L., Jaworska J., Worth A.P., Cronin M.T., McDowell R.M., Gramatica P. Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect. 2003, 111:1361-1375.
65. Draper N.R., Smith H. Applied Regression Analysis 1981, John Wiley and Sons, New York.
66. Rice J.A. Mathematical Statistics and Data Analysis 1995, Duxbury Press, Belmont, California.
67. Duchowicz P.R., Fernández F.M., Castro E.A. Orthogonalization methods in QSPR-QSAR studies. QSPR-QSAR Studies on Desired Properties for Drug Design 2010, 189-203.