QSAR study of anti-prion activity of 2-aminothiazoles

EXCLI Journal

Volumn 11, Issue , 2012, Pages 453-467

  Mandi, Prasit ^a   Nantasenamat, Chanin ^a   Srungboonmee, Kakanand ^a   Isarankura Na Ayudhya, Chartchalerm ^a   Prachayasittikul, Virapong ^a  

^a MAHIDOL UNIVERSITY   (Thailand)

Author keywords

2 aminothiazole; Anti prion; Artificial neural network; Multiple linear regression; QSAR; Support vector machine

Indexed keywords

2 AMINOTHIAZOLE DERIVATIVE;

ACCURACY; ANTI PRION ACTIVITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CORRELATION COEFFICIENT; DRUG ACTIVITY; DRUG CONCENTRATION; DRUG STRUCTURE; HYDROGEN BOND; LEAVE ONE OUT CROSS VALIDATION; MEDIAN EFFECTIVE CONCENTRATION; MULTIPLE LINEAR REGRESSION ANALYSIS; PHYSICAL CHEMISTRY; PREDICTIVE VALUE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

EID: 84867607816     PISSN: None     EISSN: 16112156     Source Type: Journal    
DOI: None     Document Type: Article

References (59)

1. Butler DA, Scott MR, Bockman JM, Borchelt DR, Taraboulos A, Hsiao KK et al. Scrapie-infected murine neuroblastoma cells produce protease-resistant prion proteins. J Virol 1988;62:1558-64.
2. ChemAxon Ltd. MarvinSketch, version 5.7. Budapest, Hungary, 2011.
3. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273-97.
4. Cristianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge: Cambridge Univ. Press, 2000.
5. Dollinger S, Lober S, Klingenstein R, Korth C, Gmeiner P. A chimeric ligand approach leading to potent antiprion active acridine derivatives: design, synthesis, and biological investigations. J Med Chem 2006;49: 6591-5.
6. Dong N, Lu WC, Chen NY, Zhu YC, Chen KX. Using support vector classification for SAR of fentanyl derivatives. Acta Pharmacol Sin 2005;26:107-12.
7. Du H, Wang J, Watzl J, Zhang X, Hu Z. Classification structure-activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives. Toxicol Lett 2008;177:10-9.
8. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR et al. Gaussian 09, Revision A.1. Wallingford, Connecticut, 2009.
9. Gallardo-Godoy A, Gever J, Fife KL, Silber BM, Prusiner SB, Renslo AR. 2-Aminothiazoles as therapeutic leads for prion diseases. J Med Chem 2011;54:1010-21.
10. Heal W, Thompson MJ, Mutter R, Cope H, Louth JC, Chen B. Library synthesis and screening: 2,4-diphenylthiazoles and 2,4-diphenyloxazoles as potential novel prion disease therapeutics. J Med Chem 2007;50: 1347-53.
11. IBM Corporation. SPSS Statistics, version 18. New York: Somers, 2011.
12. Isarankura-Na-Ayudhya C, Nantasenamat C, Buraparuangsang P, Piacham T, Ye L, Bülow L et al. Computational insights on sulfonamide imprinted polymers. Molecules 2008;13:3077-91.
13. Karelson M, Lobanov VS, Katritzky AR. Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 1996;96:1027-44.
14. Kawasaki Y, Kawagoe K, Chen CJ, Teruya K, Sakasegawa Y, Doh-ura K. Orally administered amyloidophilic compound is effective in prolonging the incubation periods of animals cerebrally infected with prion diseases in a prion strain-dependent manner. J Virol 2007;81:12889-98.
15. Kempster S, Bate C, Williams A. Simvastatin treatment prolongs the survival of scrapie-infected mice. Neuroreport 2007; 18:479-82.
16. Kimata A, Nakagawa H, Ohyama R, Fukuuchi T, Ohta S, Doh-ura K et al. New series of antiprion compounds: pyrazolone derivatives have the potent activity of inhibiting protease-resistant prion protein accumulation. J Med Chem 2007;50:5053-6.
17. Korth C, May BC, Cohen FE, Prusiner SB. Acridine and phenothiazine derivatives as pharmacotherapeutics for prion disease. Proc Natl Acad Sci USA 2001;98:9836-41.
18. Li X, Luan F, Si H, Hu Z, Liu M. Prediction of retention times for a large set of pesticides or toxicants based on support vector machine and the heuristic method. Toxicol Lett 2007;175:136-44.
19. Liu HX, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT. QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines. J Comput Aided Mol Des 2004;18:389-99.
20. May BC, Fafarman AT, Hong SB, Rogers M, Deady LW, Prusiner SB et al. Potent inhibition of scrapie prion replication in cultured cells by bis-acridines. Proc Natl Acad Sci USA 2003;100:3416-21.
21. May BC, Witkop J, Sherrill J, Anderson MO, Madrid PB, Zorn JA et al. Structureactivity relationship study of 9-aminoacridine compounds in scrapie-infected neuroblastoma cells. Bioorg Med Chem Lett 2006;16:4913-6.
22. Miller G. Neurodegeneration. Could they all be prion diseases? Science 2009;326: 1337-9.
23. Nantasenamat C, Naenna T, Isarankura-Na-Ayudhya C, Prachayasittikul V. Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. J Comput Aided Mol Des 2005;19:509-24.
24. Nantasenamat C, Tantimongcolwat T, Naenna T, Isarankura-Na-Ayudhya C, Prachayasittikul V. Prediction of selectivity index of pentachlorophenol-imprinted polymers. EXCLI J 2006;5:150-63.
25. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V. Quantitative structure-imprinting factor relationship of molecularly imprinted polymers. Biosens Bioelectron 2007a;22:3309-17.
26. Nantasenamat C, Isarankura-Na-Ayudhya C, Tansila N, Naenna T, Prachayasittikul V. Prediction of GFP spectral properties using artificial neural network. J Comput Chem 2007b;28:1275-89.
27. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V. Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine. J Mol Graph Model 2008a;27:188-96.
28. Nantasenamat C, Piacham T, Tantimongcolwat T, Naenna T, Isarankura-Na-Ayudhya C, Prachayasittikul V. QSAR model of the quorum-quenching N-acyl-homoserine lactone lactonase activity. J Biol Syst 2008b;16:279-93.
29. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V. A practical overview of quantitative structure-activity relationship. EXCLI J 2009;8:74-88.
30. Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V. Advances in computational methods to predict the biological activity of compounds. Exp Opin Drug Discov 2010;5:633-54.
31. Nantasenamat C, Li H, Isarankura-Na-Ayudhya C, Prachayasittikul V. Exploring the physicochemical properties of templates from molecular imprinting literature using interactive text mining approach. Chemometr Intell Lab Syst 2012;116:128-36.
32. Niu B, Lu WC, Yang SS, Cai YD, Li GZ. Support vector machine for SAR/QSAR of phenethyl-amines. Acta Pharmacol Sin 2007;28:1075-86.
33. Parr RG, Pearson RG. Absolute hardness: companion parameter to absolute electronegativity. J Am Chem Soc 1983;105:7512-16.
34. Parr RG, Donnelly RA, Levy M, Palke WE. Electronegativity: The density functional viewpoint. J Chem Phys 1978;68:3801.
35. Parr RG, Szentpály L v, Liu S. Electrophilicity Index. J Am Chem Soc 1999;121: 1922-4.
36. Piacham T, Isarankura-Na-Ayudhya C, Nantasenamat C, Yainoy S, Ye L, Bülow L et al. Metalloantibiotic Mn(II)-bacitracin complex mimicking manganese superoxide dismutase. Biochem Biophys Res Commun 2006;341:925-30.
37. Piacham T, Nantasenamat C, Suksrichavalit T, Puttipanyalears C, Pissawong T, Maneewas S et al. Synthesis and theoretical study of molecularly imprinted nanospheres for recognition of tocopherols. Molecules 2009;14:2985-3002.
38. Prachayasittikul V, Isarankura-Na-Ayudhya C, Tantimongcolwat T, Nantasenamat C, Galla HJ. EDTA-induced membrane fluidization and destabilization: Biophysical studies on artificial lipid membranes. Acta Biochim Biophys Sin 2007;39:901-13.
39. Prachayasittikul S, Wongsawatkul O, Worachartcheewan A, Nantasenamat C, Ruchirawat S, Prachayasittikul V. Elucidating the structure-activity relationships of the vasorelaxation and antioxidation properties of thionicotinic acid derivatives. Molecules 2010;15:198-214.
40. Prachayasittikul S, Worachartcheewan A, Nantasenamat C, Chinworrungsee M, Sornsongkhram N, Ruchirawat S et al. Synthesis and structure-activity relationship of 2-thiopyrimidine-4-one analogs as antimicrobial and anticancer agents. Eur J Med Chem 2011;46:738-42.
41. Prusiner SB. Prions. Proc Natl Acad Sci USA 1998;95:13363-83.
42. Race RE, Fadness LH, Chesebro B. Characterization of scrapie infection in mouse neuroblastoma cells. J Gen Virol 1987;68:1391-9.
43. Sánchez VD. Advanced support vector machines and kernel methods. Neurocomputing 2003;55:5-20.
44. Suksrichavalit T, Prachayasittikul S, Piacham T, Isarankura-Na-Ayudhya C, Nantasenamat C, Prachayasittikul V. Copper complexes of nicotinic-aromatic carboxylic acids as superoxide dismutase mimetics. Molecules 2008;13:3040-56.
45. Suksrichavalit T, Prachayasittikul S, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul V. Copper complexes of pyridine derivatives with superoxide scavenging and antimicrobial activities. Eur J Med Chem 2009;44:3259-65.
46. Talete srl. DRAGON for Windows (Software for Molecular Descriptor Calculations), version 5.5. Milano, Italy: Talete srl., 2007.
47. Thanikaivelan P, Subramanian V, Raghava Rao J, Unni Nair B. Application of quantum chemical descriptor in quantitative structure activity and structure property relationship. Chem Phys Lett 2000;323:59-70.
48. Thippakorn C, Suksrichavalit T, Nantasenamat C, Tantimongcolwat T, Isarankura-Na-Ayudhya C, Naenna T et al. Modeling the LPS neutralization activity of anti-endotoxins. Molecules 2009;14:1869-88.
49. Thompson MJ, Borsenberger V, Louth JC, Judd KE, Chen B. Design, synthesis, and structure-activity relationship of indole-3-glyoxylamide libraries possessing highly potent activity in a cell line model of prion disease. J Med Chem 2009;52:7503-11.
50. Vapnik V. Statistical learning theory. New York: Wiley, 1998.
51. Weissmann C, Enari M, Klohn PC, Rossi D, Flechsig E. Transmission of prions. Proc Natl Acad Sci USA 2002;99 (Suppl 4): 16378-83.
52. Whitley DC, Ford MG, Livingstone DJ. Unsupervised forward selection: a method for eliminating redundant variables. J Chem Inf Comput Sci 2000;40:1160-8.
53. Witten IH, Frank E, Hall MA. Data mining: Practical machine learning tools and techniques. 3. ed. Amsterdam: Morgan Kaufmann, 2011.
54. 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;44:1664-73.
55. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul S, Prachayasittikul V. Predicting the free radical scavenging activity of curcumin derivatives. Chemometr Intell Lab Syst 2011a;109:207-16.
56. Worachartcheewan A, Prachayasittikul S, Pingaew R, Nantasenamat C, Tantimongcolwat T, Ruchirawat S et al. Antioxidant, cytotoxicity, and QSAR study of 1-adamantylthio derivatives of 3-picoline and phenylpyridines. Med Chem Res 2011b:1-9. DOI 10.1007/s00044-011-9903-y.
57. Yudovin-Farber I, Azzam T, Metzer E, Taraboulos A, Domb AJ. Cationic polysaccharides as antiprion agents. J Med Chem 2005;48:1414-20.
58. Zhao CY, Zhang HX, Zhang XY, Liu MC, Hu ZD, Fan BT. Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 2006;217:105-19.
59. Zupan J, Gasteiger J. Neural networks in chemistry and drug design. Weinheim: Wiley-VCH, 1999.