Exploring QSARs of vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors by MLR, PLS and PC-ANN

Current Pharmaceutical Design

Volumn 19, Issue 12, 2013, Pages 2237-2244

  Deeb, Omar ^a   Jawabreh, Sana ^a   Goodarzi, Mohammad ^b  

^a AL QUDS UNIVERSITY   (Palestine)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Multiple linear regression (MLR) and Partial least square (PLS); Principal component artificial neural network (PC ANN); Quantitative structure activity relationship; Vascular endothelial growth factor receptor 2 (VEGFR 2)

Indexed keywords

PROTEIN TYROSINE KINASE INHIBITOR; VASCULOTROPIN RECEPTOR 2;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPETITIVE INHIBITION; CONTROLLED STUDY; CORRELATION COEFFICIENT; PARTIAL LEAST SQUARES REGRESSION; PREDICTIVE VALUE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, CHEMICAL; DRUG DESIGN; HUMANS; LEAST-SQUARES ANALYSIS; LINEAR MODELS; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; PROTEIN KINASE INHIBITORS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SOFTWARE; VASCULAR ENDOTHELIAL GROWTH FACTOR RECEPTOR-2;

EID: 84877797234     PISSN: 13816128     EISSN: 18734286     Source Type: Journal    
DOI: 10.2174/1381612811319120010     Document Type: Article

References (45)

1. Folkman J. Angiogenesis in cancer, vascular, rheumatoid and other diseases. Nat Med 1995, 1, 27-31.
2. Carmeliet P, Jain R K. Angiogenesis in cancer and other diseases. Nature 2000; 407: 249-57.
3. Traxler P. Tyrosine kinases as targets in cancer therapy-successes and failures. Exp. Opin. Ther. Targets 2003; 7: 215.
4. Shawver LK, Lipson KE, Fong TAT, McMahon G, Plowman GD, Strawn LM. Receptor tyrosine kinases as targets for inhibition of angiogenesis. Drug DiscoVery Today 1997; 2: 50-63.
5. Ferrara N, Gerber H P, Lecouter J Nat Med 2003; 9: 669
6. Sepp-Lorenzino L, Thomas KA. Antiangiogenic agents targeting vascular endothelial growth factor and its receptors in clinical development. Exp. Opin. InVestig. Drugs 2002; 11: 1447-65.
7. Bilodeau MT, Fraley ME, Hartman GD. Kinase insert domain-containing receptor kinase inhibitors as anti-angiogenic agents. Exp Opin InVest Drugs 2002; 11: 737-745.
8. Underiner TL, Ruggeri B, Gingrich DE. Development of vascular endothelial growth factor receptor (VEGFR) kinase inhibitors as antiangiogenic agents in cancer therapy. Curr Med Chem 2004; 11: 731-45.
9. Ferrara N, Hillan KJ, Gerber HP, Novotny W. Discovery and development of bevacizumab, an anti-VEGF antibody for treating cancer. Nat Rev Drug Discov 2004; 3: 391-400.
10. Yang JC, Haworth L, Sherry RM, et al. New Engl J Med 2003; 349: 427-34.
11. Hurwitz H, Fehrenbacher L, Novotny W, et al. New Engl J Med 2004; 350: 2335-42.
12. Deeb O, Goodarzi M. Exploring QSARs for Inhibitory Activity of Nonpeptide HIV-1 Protease Inhibitors by GA-PLS and GA-SVM. Chemical Biology and Drug Design 2010; 75(5): 506-14.
13. Deeb O, Drabh M. Exploring QSARs of Some Analgesic compounds by PC-ANN. Chemical Biology and Drug Design 2010; 76(3): 255-62.
14. Deeb O, Goodarzi M, Khadikar PV. Quantum chemical QSAR models to distinguish between inhibitory activities of sulfonamides against human Carbonic Anhydrase I, II and bovine IV isozymes. Chemical Biology and Drug Design 2012; 79(4): 514-22.
15. Deeb O, Goodarzi M, Alfalah S, Prediction of melting point for drug-like compounds via QSPR methods, Molecular Physics 2011; 109(4): 507-16.
16. Deeb O, Goodarzi M, Predicting the solubility of pesticides compounds in water via QSPR methods Molecular Physics 2010; 108 (2): 181-92.
17. Deeb O. Correlation ranking and stepwise regression procedures in principal components artificial neural networks modeling with application to predict toxic activity and human serum albumin binding affinity. Chemometrics and Intelegent Laboratory Systems 2010; 104: 181-94.
18. Todeschini R, Consonni V, Mannhold R, Kubinyi H, Timmerman H. Eds, Handbook of Molecular Descriptors in Methods and Principles in Medicinal Chemistry. Wiley-VCH: Weinheim 2000.
19. Gemperline PJ, Long JRV, Gregoriou G. Nonlinear multivariate calibration using principal components regression and artificial neural networks. Anal Chem 1991; 63:2313-23.
20. Malinowski E R. Determination of the number of factors and the experimental error in a data matrix. Anal Chem 1977; 49: 612-17.
21. Xie YL, Kalivas JH. Evaluation of principal component selection methods to form a global prediction model by principal component regression. Anal Chim Acta 1997; 348: 19-27.
22. Sutter JM, Kalivas JH, Lang PM. Which principal components to utilize for principal component regression. J Chemometr. 1992; 6: 217-25.
23. Kuo G H, Prouty C, Wang A, et al. Synthesis and Structure-Activity Relationships of Pyrazine-Pyridine Biheteroaryls as Novel, Potent, and Selective Vascular Endothelial Growth Factor Receptor-2 Inhibitors. J Med Chem 2005; 48: 4892-909.
24. Sun L, Liang C, Shirazian S, et al. Discovery of 5-[5-Fluoro-2-oxo-1,2-dihydroindol-(3Z) ylidenemethyl]-2,4-dimethyl-1H-pyrrole-3-carboxylic Acid (2-Diethylaminoethyl)amide, a Novel Tyrosine Kinase Inhibitor Targeting Vascular Endothelial and Platelet-Derived Growth Factor Receptor Tyrosine Kinase. J Med Chem 2003; 46: 1116-9.
25. Sun L, Tran N, Liang C, et al. Design, Synthesis, and Evaluations of Substituted 3-[(3-or 4-Carboxyethylpyrrol-2-yl)methylidenyl] indolin-2-ones as Inhibitors of VEGF, FGF, and PDGF Receptor Tyrosine Kinases. J Med Chem 1999; 42: 5120-30.
26. Bhide RS, Cai ZW, Zhang YZ, et al. Discovery and Preclinical Studies of (R)-1-(4-(4-Fluoro-2-methyl-1H-indol-5-yloxy)-5-methylpyrrolo[2,1-f][1,2,4]triazin-6-yloxy)propan-2-ol (BMS-540215), an in vivo Active Potent VEGFR-2 Inhibitor. J. Med. Chem. 2006; 49: 2143-6.
27. Bilodeau MT, Balitza AE, Koester TJ, et al. Potent N-(1,3 Thiazol-2-yl)pyridin-2-amine Vascular Endothelial Growth Factor Receptor Tyrosine Kinase Inhibitors with Excellent Pharmacokinetics and Low Affinity for the hERG Ion Channel. J Med Chem 2004; 47: 6363-72.
28. Borzilleri RM, Cai ZW, Ellis C, et al. Synthesis and SAR of 4-(3 hydroxyphenylamino)pyrrolo-[2,1-f][1,2,4]triazine based VEGFR-2 kinase inhibitors. Bioorganic & Medicinal Chemistry Letters 2005; 15: pp 1429-33.
29. McBride CM, Renhowe PA, Heise C, et al. Design and structureactivity relationship of 3-benzimidazol-2-yl-1H-indazoles as inhibitors of receptor tyrosine kinases. Bioorganic Med Chem Letters 2006; 16: 3595-9.
30. Underiner TL, Ruggeri B, Aimone L, et al. TIE-2/VEGF-R2 SAR and in vitro activity of C3-acyl dihydroindazolo[5,4-a]pyrrolo[3,4-c]carbazole analogs. Bioorganic Med Chem Letters 2008; 18: 2368-72.
31. Gracias V, Ji Z, Zanze IA, et al. Scaffold oriented synthesis. Part 2: Design, synthesis and biological evaluation of pyrimido-diazepines as receptor tyrosine kinase inhibitors. Bioorganic Med Chem Letters 2008; 18: 2691-5.
32. Ruel R, Thibeault C, LHeureux A, et al. Discovery and preclinical studies of 5-isopropyl-6-(5-methyl-1,3,4-oxadiazol-2-yl)-N-(2-methyl-1H-pyrrolo[2,3-b]pyridin-5-yl)pyrrolo[2,1-f][1,2,4]triazin-4-amine (BMS 645737), an in vivo active potent VEGFR-2 inhibitor. Bioorganic Med Chem Letters 2008; 18: 2985-9.
33. Deeb O, Hemmateenejad B. ANN-QSAR Model of Drug-binding to Human Serum Albumin. Chem Biol Drug Des 2007; 70: 19-29.
34. Hemmateenejad B, Safarpour MA, Miri R, Nesari N. Toward an optimal procedure for PC-ANN model building: prediction of the carcinogenic activity of a large set of drugs. J Chem Inf Model 2005; 45: 190-9.
35. Deeb O, Hemmateenejad B, Jaber A, Garduno-Juarez R, Miri R. Effects of electronic and physicochemical parameters on the carcinogenic activity of some sulfa drugs using QSAR analysis based on genetic-MLR and genetic-PLS. Chemosphere. 2007; 67L: 2122-30.
36. Goodarzi M, Freitas MP. Predicting Boiling Points of Aliphatic Alcohols through Multivariate Image Analysis Applied to Quantitative Structure-Property Relationships. J Phys Chem A 2008; 112: 11263-5.
37. Golbraikh A, Tropsha A. Beware of q2!. J Mol Graphics Modelling 2002; 20 (4): 269-76.
38. Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors. Nature1986; 323: 33-6.
39. Dua J, Lei B, Qin J, Liu H, Yao X. Molecular modeling studies of vascular endothelial growth factor receptor tyrosine kinase inhibitors using QSAR and docking, Journal of Molecular Graphics and Modelling 2009; 27: 642-54.
40. Sun M, Chen J, Cai J, Cao M, Yin S, Ji M. Simultaneously Optimized Support Vector Regression Combined With Genetic Algorithm for QSAR Analysis of KDR / VEGFR-2 Inhibitors, Chem Biol Drug Des 2010; 75: 494-505.
41. Ul-Haq Z, Mahmood U, Reza S, Uddin R, Aleem M. Ligand-Based 3D-QSAR Studies of Diaryl Acylsulfonamide Analogues as Human Umbilical Vein Endothelial Cells Inhibitors Stimulated by VEGF, Chem Biol Drug Des 2011; 77: 288-94.
42. Tian Y, Xu J, Li Z, Zhu Z, Zhang J, Wu S. Combined 3D-QSAR and Docking Modelling Study on Indolocarbazole Series Compounds as Tie-2 Inhibitors, Int. J. Mol. Sci. 2011; 12: 5080-97.
43. Jiazhong Li, Jin Qin, Huanxiang Liu, Xiaojun Yao, Mancang Liu and Zhide Hu. In Silico Prediction of Inhibition Activity of Pyrazine-Pyridine Biheteroaryls as VEGFR-2 Inhibitors Based on Least Squares Support Vector Machines. QSAR Comb Sci. 2008; 27(2): 157-64
44. Mitra I, Saha A, Roy K. Exploring quantitative structure-activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Mol Sim 2010; 36(13): 1067-79
45. Zhang C, Tan C, Ding H, Xin T, Jiang Y. Selective VEGFR inhibitors for anticancer therapeutics in clinical use and clinical trials. Curr Pharm Des 2012; 18(20): 2921-35.