Global, local and novel consensus quantitative structure-activity relationship studies of 4-(Phenylaminomethylene) isoquinoline-1, 3 (2H, 4H)-diones as potent inhibitors of the cyclin-dependent kinase 4

Analytica Chimica Acta

Volumn 644, Issue 1-2, 2009, Pages 17-24

  Lei, Beilei ^a   Xi, Lili ^a   Li, Jiazhong ^a   Liu, Huanxiang ^a   Yao, Xiaojun ^a  

^a LANZHOU UNIVERSITY   (China)

Author keywords

Consensus models; Cyclin dependent kinase 4 (CDK4); Local lazy regression (LLR); Quantitative structure activity relationship (QSAR)

Indexed keywords

AVERAGE CONSENSUS MODELS; CONSENSUS MODELS; CONSENSUS STRATEGIES; CYCLIN-DEPENDENT KINASE 4 (CDK4); GLOBAL MODELING; ISOQUINOLINE; LOCAL LAZY REGRESSION (LLR); MULTIPLE LINEAR REGRESSIONS; POTENT INHIBITORS; QSAR MODELS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP STUDIES; SELECTIVE INHIBITORS; SUB-MODELS; WEIGHTED CONSENSUS MODELS;

MOLECULAR GRAPHICS; REGRESSION ANALYSIS; SULFUR COMPOUNDS; SUPPORT VECTOR MACHINES;

LUNAR SURFACE ANALYSIS;

4 (PHENYLAMINOMETHYLENE)ISOQUINOLINE 1,3 (2H,4H) DIONE; CYCLIN DEPENDENT KINASE 4; CYCLIN DEPENDENT KINASE INHIBITOR; UNCLASSIFIED DRUG;

ACCURACY; ARTICLE; CONSENSUS; HAT WEIGHTED CONSENSUS MODEL; INTERMETHOD COMPARISON; LINEAR REGRESSION ANALYSIS; LOCAL LAZY REGRESSION; PERFORMANCE; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SIMPLEWEIGHTED CONSENSUS MODEL; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

CYCLIN-DEPENDENT KINASE 4; ISOQUINOLINES; LINEAR MODELS; MODELS, CHEMICAL; MODELS, STATISTICAL; PROTEIN KINASE INHIBITORS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 65549140680     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2009.04.019     Document Type: Article

References (59)

1. Sherr C.J., Kato J., Quelle D.E., Matsuoka M., and Roussel M.F. D-type cyclins and their cyclin-dependent kinases: G1 phase integrators of the mitogenic response. Cold Spring Harbor Symp. Quant. Biol. 59 (1994) 11-19
2. Sherr C.J. Cancer cell cycles. Science 274 (1996) 1672-1677
3. Weinberg R.A. The retinoblastoma protein and cell cycle control. Cell 81 (1995) 323-330
4. Yu Q., Sicinska E., Geng Y., Ahnström M., Zagozdzon A., Kong Y., Gardner H., Kiyokawa H., Harris L., and Stål O. Requirement for CDK4 kinase function in breast cancer. Cancer Cell 9 (2006) 23-32
5. Soni R., Muller L., Furet P., Schoepfer J., Stephan C., Zumstein-Mecker S., Fretz H., and Chaudhuri B. Inhibition of cyclin-dependent kinase 4 (CDK4) by fascaplysin, a marine natural product. Biochem. Biophys. Res. Commun. 275 (2000) 877-884
6. Aubry C., Wilson A.J., Jenkins P.R., Mahale S., Chaudhuri B., Maréchal J.-D., and Sutcliffe M.J. Design, synthesis and biological activity of new CDK4-specific inhibitors, based on fascaplysin. Org. Biomol. Chem. 4 (2006) 787-801
7. Aubry C., Jenkins P.R., Mahale S., Chaudhuri B., Maréchal J.-D., and Sutcliffe M.J. New fascaplysin-based CDK4-specific inhibitors: design, synthesis and biological activity. Chem. Commun. 15 (2004) 1696-1697
8. Aubry C., Patel A., Mahale S., Chaudhuri B., Maréchal J.-D., Sutcliffe M.J., and Jenkins P.R. The design and synthesis of novel 3-[2-indol-1-yl-ethyl]-1H-indole derivatives as selective inhibitors of CDK4. Tetrahedron Lett. 46 (2005) 1423-1425
9. Breault G.A., Ellston R.P.A., Green S., James S.R., Jewsbury P.J., Midgley C.J., Pauptit R.A., Minshull C.A., Tucker J.A., and Pease J.E. Cyclin-dependent kinase 4 inhibitors as a treatment for cancer. Part 2: identification and optimisation of substituted 2,4-bis anilino pyrimidines. Bioorg. Med. Chem. Lett. 13 (2003) 2961-2966
10. Beattie J.F., Breault G.A., Ellston R.P.A., Green S., Jewsbury P.J., Midgley C.J., Naven R.T., Minshull C.A., Pauptit R.A., Tucker J.A., and Pease J.E. Cyclin-dependent kinase 4 inhibitors as a treatment for cancer. Part 1: identification and optimisation of substituted 4,6-Bis anilino pyrimidines. Bioorg. Med. Chem. Lett. 13 (2003) 2955-2960
11. Carini D.J., Kaltenbach R.F., Liu J., Benfield P.A., Boylan J., Boisclair M., Brizuela L., Burton C.R., Cox S., Grafstrom R., Harrison B.A., Harrison K., Akamike E., Markwalder J.A., Nakano Y., Seitz S.P., Sharp D.M., Trainor G.L., and Sielecki T.M. Identification of selective inhibitors of cyclin dependent kinase 4. Bioorg. Med. Chem. Lett. 11 (2001) 2209-2211
12. Mahale S., Aubry C., Wilson A.J., Jenkins P.R., Maréchal J.-D., Sutcliffe M.J., and Chaudhuri B. CA224, a non-planar analogue of fascaplysin, inhibits Cdk4 but not Cdk2 and arrests cells at G0/G1 inhibiting pRB phosphorylation. Bioorg. Med. Chem. Lett. 16 (2006) 4272-4278
13. García M.D., Wilson A.J., Emmerson D.P.G., Jenkins P.R., Mahale S., and Chaudhuri B. Synthesis, crystal structure and biological activity of b-carboline based selective CDK4-cyclin D1 inhibitors. Org. Biomol. Chem. 4 (2006) 4478-4484
14. Mahale S., Aubry C., Wilson J., Jenkins P.R., Chaudhuri B., Marechal J.-D., and Sutcliffe M.J. Inhibition of cancer cell growth by CDK4 inhibitors synthesised based on the structure of fascaplysin. Bioorg. Chem. 34 (2006) 287-297
15. Soni R., O'Reilly T., Furet P., Muller L., Stephan C., Zumstein-Mecker S., Fretz H., Fabbro D., and Chaudhuri B. Selective in vivo and in vitro effects of a small molecule inhibitor of cyclin-dependent kinase 4. J. Natl. Cancer Inst. 93 (2001) 436-446
16. Singha S.K., Dessalewb N., and Bharatamb P.V. 3D-QSAR CoMFA study on indenopyrazole derivatives as cyclin dependent kinase 4 (CDK4) and cyclin dependent kinase 2 (CDK2) inhibitors. Eur. J. Med. Chem. 41 (2006) 1310-1319
17. Dessalew N., and Bharatam P.V. 3D-QSAR and molecular docking study on bisarylmaleimide series as glycogen synthase kinase 3, cyclin dependent kinase 2 and cyclin dependent kinase 4 inhibitors: an insight into the criteria for selectivity. Eur. J. Med. Chem. 42 (2007) 1014-1027
18. Caballero J., Fernández M., and González-Nilo F.D. Structural requirements of pyrido[2,3-d]pyrimidin-7-one as CDK4/D inhibitors: 2D autocorrelation, CoMFA and CoMSIA analyses. Bioorg. Med. Chem. Lett. 16 (2008) 6103-6115
19. Mascarenhas N.M., Ghoshal N., and Combined Ligand. Structure based approaches for narrowing on the essential physicochemical characteristics for CDK4 inhibition. J. Chem. Inf. Model. 48 (2008) 1325-1336
20. Gurney K. An Introduction to Neural Networks (1997), UCL Press. 1 85728 503 4
21. Vapnik V. Statistical Learning Theory (1998), Wiley-Interscience, New York
22. Friedman J.H., and Tukey J.W. A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. C-23 (1974) 881-890
23. He L., and Jurs P.C. Assessing the Reliability of a QSAR Model's Predictions. J. Mol. Graphics Model. 23 (2005) 503-523
24. Guha R., Dutta D., Jurs P.C., and Chen T. Local lazy regression: making use of the neighborhood to improve QSAR predictions. J. Chem. Inf. Model. 46 (2006) 1836-1847
25. Du H., Zhang X., Wang J., Yao X., and Hu Z. Novel approaches to predict the retention of histidine-containing peptides in immobilized metal-affinity chromatography. Proteomics 8 (2008) 2185-2195
26. Yuan H., Wang Y., and Cheng Y. Lcal and Global Quantitative Structure-Activity Relationship Modeling and Prediction for the Baseline Toxicity. J. Chem. Inf. Model. 47 (2007) 159-169
27. Birattari M., Bontempi G., and Bersini H. Lazy learning meets the recursive least squares algorithm. Proceedings of the 1998 conference on Advances in neutral information processing systems II. Adv. Neural Inf. Process. Syst. 11 (1999) 375-381
28. Gramatica P., Giania E., and Papa E. Statistical external validation and consensus modeling: a QSPR case study for Koc prediction. J. Mol. Graphics Model. 25 (2007) 755-766
29. Li J., Lei B., Liu H., Li S., Yao X., Liu M., and Gramatica P. QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling. J. Comput. Chem. 29 (2008) 2636-2647
30. Zhu H., Tropsha A., Fourches D., Varnek A., Papa E., Gramatica P., berg T.O., Dao P., Cherkasov A., and Tetko I.V. Combinatorial QSAR modeling of chemical toxicants tested against tetrahymena pyriformis. J. Chem. Inf. Model. 48 (2008) 766-784
31. Tsou H.-R., Otteng M., Tran T., Floyd Jr. M.B., Reich M., Birnberg G., Kutterer K., Ayral-Kaloustian S., Ravi M., Nilakantan R., Grillo M., McGinnis J.P., and Rabindran S.K. 4-(Phenylaminomethylene)isoquinoline-1,3(2H,4H)-diones as Potent and selective inhibitors of the cyclin-dependent kinase 4 (CDK4). J. Med. Chem. 51 (2008) 3507-3525
32. HyperChem(TM), Hypercube, Inc., 1115 NW 4th Street, Gainesville, Florida 32601, USA.
33. Todeschini R., Consonni V., Mauri A., and Pavan M. DRAGON software for the calculation of molecular descriptors. Version 5.4 for Windows (2005), Talete Srl, Milan, Italy
34. Stewart J.P.P. MOPAC 6.0, Quantum Chemistry Program Exchange, QCPE No. 455 (1989), Indiana University, Bloomington, IN
35. A.R. Katritzky, V.S. Lobanov, M. Karelson, Comprehensive Descriptors for Structural and Statistical Analysis, Version 2.0 (Workstation) and 2.13 (PC), CODESSA Reference Manual, Semichem/The University of Florida, 1995-1997.
36. Ferré J., and Rius F.X. Selection of the best calibration sample subset for multivariate regression. Anal. Chem. 68 (1996) 1565-1571
37. Kennard R.W., and Stone L.A. Computer aided design of experiments. Technometrics 11 (1969) 137-148
38. Næs T. The design of calibration in NIR reflectance analysis by clustering. J. Chemom. 1 (1987) 121-134
39. Puchwein G. Selection of calibration samples for near-infrared spectrometry by factor analysis of spectra. Anal. Chem. 60 (1988) 569-573
40. Snee R.D. Validation of regression models: methods and examples. Technometrics 19 (1977) 415-428
41. Maesschalck R.D., Estienne F., Verdú-Andrés J., Candolfi A., Centner V., Despagne F., Jouan-Rimbaud D., Walczak B., Massart D.L., Jong S.d., Noord O.E.d., Puel C., and Vandeginste B.M.G. The development of calibration models for spectroscopic data using proncipal component regression. Internet J. Chem. 2 (1999) 19
42. Ren Y., Liu H., Yao X., and Liu M. Prediction of ozone tropospheric degradation rate constants by projection pursuit regression. Anal. Chim. Acta 589 (2007) 150-158
43. Hemmateenejad B., Safarpour M.A., Miri R., and Taghavi F. Application of Ab Initio theory to QSAR Study of 1,4-dihydropyridine-based calcium channel blockers using GA-MLR and PC-GA-ANN procedures. J. Comput. Chem. 25 (2004) 1495-1503
44. Sutter J.M., Dixon S.L., and Jurs P.C. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J. Chem. Inf. Comput. Sci. 35 (1995) 77-84
45. Liu H.X., and Gramatica P. QSAR study of selective ligands for the thyroid hormone receptor β. Bioorg. Med. Chem. 15 (2007) 5251-5261
46. 3 radicaltropospheric degradability of organic pollutants by theoretical molecular descriptors. Atmos. Environ. 37 (2003) 3115-3124
47. Han J., and Kamber M. Datamining: Concepts and techniques (2001), Morgan Kaufmann Publishers, San Francisco, CA
48. Choi J.H., Ahn H., and Han I. Utility-based double auction mechanism using genetic algorithms. Expert Syst. Appl. 34 (2008) 150-158
49. Todeschini R., Consonni V., and Pavan M. Software for Multilinear Regression Analysis and Variable Subset Selection by Genetic Algorithm. MOBY DIGS, Version 1. 2 for Windows (2002), Talete Srl, Milan, Italy
50. Fan R.E., Chen P.H., and Lin C.J. Working set selection using the second order information for training SVM. J. Mach. Learn. Res. 6 (2005) 1889-1918
51. A.J. Smola, B. Scholkopf, A tutorial on support vector regression, NeuroCOLT2 Technical Report Series, NC2- TR-1998-030.
52. Martin Y.C., Kofron J.L., and Traphagen L.M. Do structurally similar molecules have similar biological activity?. J. Med. Chem 45 (2002) 4350-4358
53. Aha D.W. Lazy learning: special issue editorial. Artif. Intell. Rev. 11 (1997) 7-10
54. Atkeson C.G., Moore A.W., and Schaal S. Locally weighted learning. Artif. Intell. Rev. 11 (1997) 11-73
55. Bontempi G., Birattari M., and Bersini H. Lazy learning for modeling and control design. Int. J. Control 72 (1999) 643-658
56. M. Birattari, G. Bontempi, The lazy learning toolbox, for use with matlab, version 1.1, 1999, available at http://iridia.ulb.ac.be/∼lazy/.
57. Atkinson A.C. Plots, Transformations and Regression (1985), Clarendon Press, Oxford (UK)
58. Todeschini R., and Consonni V. Handbook of Molecular Descriptors: Methods and Principles in Medicinal Chemistry (2000), WILEY-VCH Verlag GmbH, D-69469 Weinheim
59. Voet V.d. Comparing the predictive accuracy of models using a simple randomization test. Chemom. Intell. Lab. Syst. 25 (1994) 313-323