Exploring potency and selectivity receptor antagonist profiles using a multilabel classification approach: The human adenosine receptors as a key study

Journal of Chemical Information and Modeling

Volumn 49, Issue 12, 2009, Pages 2820-2836

  Miclielan, Lisa ^a   Stephanie, Federico ^b   Terfloth, Lothar ^e   Hristozov, Dimitar ^e   Cacciari, Barbara ^c   Klotz, Karl Norbert ^d   Spalluto, Giampiero ^b   Gasteiger, Johann ^e   Moro, Stefano ^a  

^a UNIVERSITY OF PADOVA   (Italy)

^b UNIVERSITY OF TRIESTE   (Italy)

^c UNIVERSITY OF FERRARA   (Italy)

^d UNIVERSITY OF WÜRZBURG   (Germany)

^e Molecular Networks GmbH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AROMATIC COMPOUNDS; FORECASTING; LIGANDS; SUPPORT VECTOR MACHINES;

ADENOSINE RECEPTOR; MEDICINAL CHEMISTRY; MOLECULAR DESCRIPTORS; MOLECULAR ELECTROSTATIC POTENTIALS; MULTI-LABEL CLASSIFICATIONS; NOVEL APPLICATIONS; RECEPTOR ANTAGONISTS; SUPPORT VECTOR MACHINE (SVMS);

CLASSIFICATION (OF INFORMATION);

ADENOSINE RECEPTOR; PROTEIN SUBUNIT; PYRIMIDINE; PYRIMIDINE DERIVATIVE; XANTHINE;

ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; CHEMISTRY; DRUG ANTAGONISM; DRUG DEVELOPMENT; ENZYME SPECIFICITY; HUMAN; METHODOLOGY; REPRODUCIBILITY; STATIC ELECTRICITY; TIME;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DRUG DISCOVERY; HUMANS; PROTEIN SUBUNITS; PYRIMIDINES; RECEPTORS, PURINERGIC P1; REPRODUCIBILITY OF RESULTS; STATIC ELECTRICITY; SUBSTRATE SPECIFICITY; TIME FACTORS; XANTHINE;

EID: 73349125784     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci900311j     Document Type: Article

References (64)

1. Jacobson, K. A.; Gao, Z. G. Adenosine receptor as therapeutic targets. Nat. Rm Drug Discovery 2006, 5, 247-264.
2. Moro, S.; Gao, Z. G.; Jacobson, K. A.; Spalluto, G. Progress in pursuit of therapeutic adenosine receptor antagonists. Med. Res. Rev. 2006, 26, 131-159. (Pubitemid 43334639)
3. Kadam. R. U.; Chavan, A. G.; Monga, V.; Kaur, N.; Jain, R.; Roy, N. Selectivity-based QSAR approach for screening and evaluation of TRH analogs for TRH-Rl and TRH-R2 receptors subtypes. J. Mol. Graphics Modell.- 2008, 27, 309-320.
4. 1 adenosine receptors. Bioorg. Med. Chem. 2009, 17, 1817-1830.
5. Moro, S.; Bacilieri, M.; Deflorian, F.; Spalluto, G. G protein-coupled receptors as challenging druggable targets: insights from in silico studies. New J. Chem. 2006, 30, 301-308. (Pubitemid 43383317)
6. Moro, S.; Deflorian, F.; Bacilieri, M.; Spalluto, G. Ligand-based homology modeling as attractive tool to inspect GPCR structural plasticity. Curr. Pharm. Des. 2006, 12, 2175-2185. (Pubitemid 43891410)
7. 3 receptor pyrazolo-triazolo- pyrimidine antagonists binding sites. Bioorg. Med. Chem. 2009, 17, 5259-5274.
8. Fredholm, B. B.; Arslan, G.; Halldner, L.; Kull, B.; Schulte, G.; Wasserman, W. Structure and function of adenosine receptors and their genes. Naunvn-Schmiedebere's Arch. Pharmacol. 2000, 362, 364374.
9. Maemoto, T.; Tada, M.; Mihara, T.; Ueyama, N.; Matsuoka, H.; Harada, K.; Yamaji, T.; Shirakawa, K.; Kuroda, S.; Akahane, A.; Iwashita, A.; Matsuoka, N.; Mutoh, S. Pharmacological characterization of FRl94921, a new potent, selective, and orally active antagonist for central adenosine A1 receptors. J. Pharmacol. Sci. 2004, 96, 4252. (Pubitemid 39318777)
10. 2 receptors decreases the affinity of dopamine D2 receptors in rat striatal membranes. Proc. Natl. Acad. Sci. U.S.A. 1991, 88, 7238-7241.
11. 2B receptor as a novel therapeutic target in asthma. Br. J. Pharmacol. 2005, 145, 1009-1015.
12. 2A receptor antagonists in Parkinson's disease. Pharmacol. Ther. 2005, 105, 267-310.
13. Johnston, T. H.; Brotchie, J. M. Drugs in development for Parkinson's disease: an update. Curr. Opin. Invest. Drugs 2006, 7, 25-32. (Pubitemid 43115777)
14. Ribeiro, J. A.; Sebastiao, A. M.; de Mendonca, A. Adenosine receptors in the nervous system: pathophysiological implications. Prog. Neurobiol. 2002, 68, 377-392. (Pubitemid 36165128)
15. 3 receptor ligands. Curr. Top. Med. Chem. 2003, 3, 445-462.
16. 3 adenosine receptors. Annual Reports in Medicinal Chemistry; Elsevier: San Diego, CA, 2003.
17. 3 receptor ligands: new potential therapy for the treatment of glaucoma. Bioorg. Med. Chem. Lett. 2004, 14, 3775-3779.
18. 3 adenosine receptor antagonists. Eur. J. Med. Che.m 2003, 38, 367-382.
19. 2B adenosine receptor antagonists. Bioorg. Med. Chem. 2008, 16, 2419-2430.
20. 3 adenosine receptor antagonists. J. Med. Chem. 2002, 45, 770-780.
21. 1 receptor antagonists: xanthine modified at position 3 and related pyrimido[l,2,3-cd]puri.nediones. Chem. Med. Chem. 2006, 1, 891-902.
22. 2B adenosine receptor antagonists. Bioorg, Med. Chem. Lett. 2006, 16, 302-306.
23. 2B adenosine receptor antagonists: N-1 monosubstituted 8-(pyrazoI-4-yl)xanthines. Bioorg. Med. Chem. 2008, 18, 1397-1401.
24. 2B adenosine receptor antagonists. J. Med. Chem. 2004, 47, 1434-1477.
25. 3 adenosine receptor antagonists. Bioorg. Med. Chem. 2008, 16, 10281-10294.
26. Moro, S.; Bacilieri, M.; Ferrari, C.; Spalluto, G. Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs. Curr. Drug. Discovery Technol. 2005, 2, 1321. (Pubitemid 40488618)
27. Moro, S.; Bacilieri, M.,; Cacciari, B.; Spalluto, G. Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as new strategy for the prediction of the activity of human A3 adenosine receptor antagonists. J. Med. Chem. 2005, 48, 5698-5704. (Pubitemid 41298344)
28. Moro, S.; Bacilieri, M.; Cacciari, B.; Bolcato, C.; Cusan, C.; Pastorin, G.; Klotz, K. N.; Spalluto, G. The application of a 3D-QSAR (autoMEP/PLS) approach as an efficient pharmacodynamic-driven filtering method for small-sized virtual library: application to a lead optimization of a human A3 adenosine receptor antagonist. Bioorg. Med. Chem. 2006, 14, 4923-4932. (Pubitemid 43767133)
29. 3 adenosine receptor antagonists as a key study, Lett. Drug Des. Discovery 2007, 4, 122127.
30. Michielan, L.; Bacilieri, M.; Schiesaro, A.; Bolcato, C.; Pastorin, G.; Spalluto, G.; Cacciari, B.; Klotz, K. N.; Kaseda, C.; Moro, S. Linear and non-linear 3D-QSAR approaches in tandem with ligandbased homology modeling as computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor. J. Comput. Inf. Model. 2008, 48, 350-363.
31. Burges, C. J. C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 1998, 2, 121-167. (Pubitemid 128695475)
32. Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines; Cambridge University Press: New York, 20O0.
33. Czerminski, R.; Yasri, A.; Hartsough, D. Use of support vector machine in pattern classification: application to QSAR studies. Ouant. Struct.Act. Relat. 2001, 20, 227-240. (Pubitemid 33051752)
34. Warmuth, M. K.; Liao, J.; Ratsch, G.; Mathieson, M.; Putta, S.; Lemmen, C. Active learning with support vector machine in the drug discovery process. J. Chem. Inf. Comput. Sci. 2003, 43, 667673.
35. Jorissen, R. N.; Gilson, M. K. Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model. 2005, 45, 549561.
36. Bruce, C. L.; Melville, J. L.; Pickett, S. D.; Hirst, J. D. Contemporary QSAR classifiers compared. J. Chem. Inf. Model. 2007, 47, 219227.
37. Boutell, M. R.; Luo, J.; Shen, X.; Brown, C. M. C. Learning multilabel scene classification. Pattern Recognit. 2004, 37, 1757-1771.
38. Hristozov, D.; Gasteiger, J.; Da Costa, F. B. Multilabeled classification approach to find a plant source for terpenoids. J. Chem. Inf. Model. 2008, 48, 56-67.
39. 2A adenosine receptor antagonists: a study on the importance of modifications at the side chain on the activity and solubility. J. Med. Chem. 2002, 45, 115-126.
40. Baraldi, P. G.; Cacciari, B.; Romagnoli, R.; Spalluto, G.; Varani, K.; Gessi, S.; Merighi, S.; Borea, P. A. Pyrazolo[4,3-e]l,2,4-triazolo[l, 5-c]pyrimidine derivatives: a new pharmacological tool for the characterization of the human A3 adenosine receptor. Drug.Dev. Res. 2001, 52, 406-415. (Pubitemid 32401966)
41. 8 pyrazole nitrogen. J. Med. Chem. 2000, 43, 4768-4780.
42. 2B adenosine receptor antagonists. Drug Dev. Res. 2001, 53, 225-235.
43. 3 adenosine receptors antagonists. J. Med. Chem. 2003, 46, 1229-1241.
44. OpenMosix; http://www.openMosix.org, 2004.
45. ADRIANA.Code, version 2.2; Molecular Networks GmbH: Erlangen, Germany, 2008.
46. R Development Core Team. R: a language and environment for statistical computing, version 2.8.1, 2008. URL: http://www.r-project.org (accessed February, 2009).
47. Dimitriadou, E.; Hornik, K.; Leisch, F.; Meyer, D.; Weingessel, A. el071: Misc functions of the Department of Statistics (el071); TU Wien, 2005.
48. Gasteiger, J.; Li, X.; Rudolph, C.; Sadowski, J.; Zupan, J. Representation of molecular electrostatic potentials by topological feature maps. J. Am. Chem. Soc. 1994, 116, 4608-4620. (Pubitemid 24981033)
49. Gasteiger, J.; Marsili, M. Iterative partial equalization of orbital electronegativity - a rapid access to atomic charges. Tetrahedron 1980, 36, 3219-3228.
50. Gasteiger, J.; Sailer, H. Calculation of the charge distribution in conjugated systems by a quantification, of the resonance concept. Angew. Chem., Int. Ed. Engl.-1985,.24, 687-689.
51. Moreau, G.; Broto, P. Autocorrelation of molecular structures, application to SAR studies. Nouv. J. Chim. 1980, 4, 757-764.
52. Moreau, G.; Broto, P. The autocorrelation of a topological structure: a new molecular descriptor. Nouv. J. Chim. 1980, 4, 359-360.
53. Wagener, M.; Sadowski, J.; Gasteiger, J. Autocorrelation of molecular surface properties for modeling corticosteroid binding globulin and cytosolic All receptor activity by neural networks. J. Am. Chem. Soc. 1995, 117, 7769-7775.
54. Bauknecht, H.; Zell, A.; Bayer, H.; Levi, P.; Wagener, M.; Sadowski, J.; Gasteiger, J. Locating biologically active compounds in mediumsized heterogeneous datasets by topological autocorrelation vectors: dopamine and benzodiazepine agonists. J. Chem. Inf. Comput. Sci. 1996, 36, 1205-1213. (Pubitemid 126581788)
55. Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, 1995.
56. Vapnik, V. Statistical Learning Theory; Wiley: New York, 1998.
57. Smola, A. J.; Scholkopft, B. Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond; MIT Press: Cambridge, MA, 2002.
58. 2B adenosine receptor subtypes: a molecular modeling investigation. J. Med. Chem. 2003, 46, 4287-4296.
59. Karaneswsky, C.; Thomson, D.; Michellys, A.; Ruppar, P.; Chen, J. H. Preparation of benzoic acid phenylacetic derivatives as HNT-4° modulators. PCT Int. Appl. 2005, WO2005009104200-2005009140716.
60. Klotz, K. N.; Hessling, J.; Hegler, J.; Owman, C.; KuIl, B.; Fredholm, B. B.; Lohse, M. J. Comparative pharmacology of human adenosine receptor subtypes-Characterization of stably transfected receptors in CHO cells. Naunyn-Schmiedeberg's Arch. Pharmacol. 1998, 357, 1-9.
61. De Lean, A.; Hancock, A. A.; Lefkowitz, R. J. Validation and statistical analysis of a computer modeling method for quantitative analysis of radioligand binding data for mixtures of pharmacological receptor subtypes. Mol. Pharmacol. 1982, 21, 5-16.
62. 1 adenosine receptors. J. Biol. Chem.1985, 260, 14659-14664.
63. Cheng, Y. C.; Prusoff, H. R. Relationships between the inhibition constant (Ki) and the concentration of inhibitor which causes 50% inhibition (IC50) of an enzymatic reaction. Biochem. Pharmacol. 1973, 22, 3099-3108.
64. Ivanciuc, O. Application of support vector machines in chemistry. Rev. Comput Chem. 2007, 23, 291-400.