Prediction of torsade-causing potential of drugs by support vector machine approach

Toxicological Sciences

Volumn 79, Issue 1, 2004, Pages 170-177

  Yap, C W ^a   Cai, C Z ^a,b   Xue, Y ^a,c   Chen, Y Z ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b CHONGQING UNIVERSITY   (China)

^c SICHUAN UNIVERSITY   (China)

Author keywords

Linear solvation energy relationship; Prediction; Support vector machine; Torsade de pointes

Indexed keywords

ACARBOSE; AMIKACIN; DESMOPRESSIN; GENTAMICIN; KANAMYCIN; MEDROXYPROGESTERONE; MEDRYSONE; METIROSINE; NEOMYCIN; OCTREOTIDE; PAROMOMYCIN; PENICILLAMINE; PRENYLAMINE; PYRIDOXINE; RIMEXOLONE; STREPTOMYCIN; TOBRAMYCIN; ZANAMIVIR;

ACCURACY; ANALYTICAL EQUIPMENT; ARTICLE; DISEASE SEVERITY; DRUG APPROVAL; DRUG STRUCTURE; DRUG SURVEILLANCE PROGRAM; FREQUENCY ANALYSIS; HUMAN; MATHEMATICAL COMPUTING; PREDICTION; PROCESS OPTIMIZATION; STATISTICAL ANALYSIS; STRUCTURE ANALYSIS; TORSADE DES POINTES; VALIDATION PROCESS;

ALGORITHMS; AMINOGLYCOSIDES; ANTI-BACTERIAL AGENTS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DEAMINO ARGININE VASOPRESSIN; DRUG EVALUATION, PRECLINICAL; MODELS, THEORETICAL; OCTREOTIDE; TORSADES DE POINTES;

EID: 2442700335     PISSN: 10966080     EISSN: None     Source Type: Journal    
DOI: 10.1093/toxsci/kfh082     Document Type: Article

References (72)

1. Abraham, M. H. (1993). Scales of solute hydrogen-bonding: Their construction and appplication to physicochemical and biochemical processes. Chem. Soc. Rev. 22, 73-83.
2. AHFS Drug Information (2001). American Society of Health-System Pharmacists, Bethesda, MD.
3. ArizonaCERT. Drugs that prolong the QT interval and/or induce torsades de pointes ventricular arrhythmia, Vol. 2003. University of Arizona CERT.
4. Benigni, R., Guiliani, A., Franke, R., and Gruska, A. (2000). Quantitative structure-activity relationships of mutagenic and carcinogenic aromatic amines. Chem. Rev. 100, 3697-3714.
5. Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C., Ares, J. M., and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. U.S.A. 97, 262-267.
6. Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001). Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 5-14.
7. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 127-167.
8. Cai, C. Z., Han, L. Y., Ji, Z. L., Chen, X., and Chen, Y. Z. (2003a). SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 31, 3692-3697.
9. Cai, C. Z., Wang, W. L., and Zong, C. Y. (2003b). Support vector machine classification of physical and biological datasets. Inter. J. Mod. Phys. C. 14, 575-585.
10. Cavalli, A., and Poluzzi, E. (2002). Toward a pharmacophore for drugs inducing the long QT syndrome: Insights from a CoMFA study of HERG K+ channel blockers. J. Med. Chem. 45, 3844-3853.
11. Chapelle, O., Vapnik, V., Bousquet, O., and Mukherjee, S. (2002). Choosing multiple parameters for support vector machines. Mach. Learn. 46, 131-159.
12. Chen, Y. Z., and Ung, C. Y. (2001). Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. J. Mol. Graph. Mod. 20, 199-218.
13. Cramer, C. J., and Truhlar, D. G. (1992). An SCF solvation model for the hydrophobic effect and absolute free energies of aqueous solvation. Science 256, 213-217.
14. Cronin, M. T. D., and Basketter, D. A. (1994). Multivariate QSAR analysis of a skin sensitization database. SAR QSAR Environ. Res. 2, 159-179.
15. Cruciani, G., Crivori, P., Carrupt, P. A., and Testa, B. (2000). Molecular fields in quantitative structure-permeation relationships: The VolSurf approach. J. Mol. Struc.-Theochem. 503, 17-30.
16. Czerminski, R., Yasri, A., and Hartsough, D. (2001). Use of support vector machine in pattern classification: Application to QSAR studies. Quant. Struct.-Act. Relat. 20, 227-240.
17. Dai, J., Jin, L., Yao, S., and Wang, L. (2001). Prediction of partition coefficient and toxicity for benzaldehyde compounds by their capacity factors and various molecular descriptors. Chemosphere 42, 899-907.
18. Darpo, B. (2001). Spectrum of drugs prolonging QT interval and the incidence of torsade de pointes. Eur. Heart J. 2001, K70-K80.
19. De Ponti, F., Poluzzi, E., Cavalli, A., Recanatini, M., and Montanaro, N. (2002). Safety of non-antiarthythmic drugs that prolong the QT interval or induce torsade de pointes: An overview. Drug Saf. 25, 263-286.
20. De Ponti, F., Poluzzi, E., and Montanaro, N. (2001). Organising evidence on QT prolongation and occurrence of torsades de pointes with non-antiarthythmic drugs: A call for consensus. Eur. J. Clin. Pharmacol. 57, 185-209.
21. Devillers, J. (2000). A neural network SAR model for allergic contact dermatitis. Toxicol. Methods 10, 181-193.
22. Doniger, S., Hofmann, T., and Yeh, J. (2002). Predicting CNS permeability of drug molecules: Comparison of neural network and support vector machine algorithms. J. Comp. Biol. 9, 849-864.
23. Dorland's Illustrated Medical Dictionary (2000). W. B. Saunders, London.
24. Dukes, M. N. G. (1996). Meyler's Side Effects of Drugs. Excerpta Medica, Amsterdam.
25. Evgeniou, T., and Pontil, M. (2001). Support vector machines: Theory and applications. In Machine Learning and Its Applications. Advanced Lectures (G. Paliouras, V. Karkaletsis, and C. D. Spyropoulos, Eds.), pp. 249-257. Springer, New York.
26. Fix, E., and Hodges, J. L. (1951). Discriminatory analysis: Non-parametric discrimination: Consistency properties, pp. 261-279. USAF School of Aviation Medicine, Randolph Field, TX.
27. Furey, T. S., Cristiani, N., Duffy, N., Bednarski, D. W., Schummer, M., and Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906-914.
28. Gratton, J. A., Abraham, M. H., Bradbury, M. W., and Chadha, H. S. (1997). Molecular factors influencing drug transfer across the blood-brain barrier. J. Pharm. Pharmacol. 49, 1211-1216.
29. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389-422.
30. He, Y. B., Wang, L. S., Liu, Z. T., and Zhang, Z. (1995). Acute toxicity of alkyl (1-phenylsulfonyl)cycloalkane-carboxylates to Daphnia magna and quantitative structure-activity relationships. Chemosphere 31, 2739-2746.
31. Johnson, D. E., and Wolfgang, G. H. (2000). Predicting human safety: Screening and computational approaches. Drug Discov. Today 5, 445-454.
32. Kamlet, M. J., Abbound, J.-L. M., and Taft, R. W. (1981). An examination of linear solvation energy relationships. In Progress in Physical Organic Chemistry (R. W. Taft, Ed.), Vol. 13, pp. 485-630. Wiley, New York.
33. Kamlet, M. J., Doherty, P. J., Taft, R. W., Abraham, M. H., Veith, G. D., and Abraham, D. J. (1987). Solubility properties in polymers and biological media. 8. An analysis of the factors that influence toxicities of organic nonelectrolytes to the golden orfe fish (Leuciscus idus melanotus). Environ. Sci. Technol. 21, 149-155.
34. Kennedy, T. (1997). Managing the drug discovery/development interface. Drug Discov. Today 2, 436-444.
35. Kier, L. B. (1985). A shape index from molecular graphs. Quant. Struct.-Act. Relat. 4, 109-116.
36. Kier, L. B. (1990). Indexes of molecular shape from chemical graphs. In Computational Chemical Graph Theory (D. H. Rouvray, Ed.), pp. 151-174. Nova Science Publishers, New York.
37. Kier, L. B., and Hall, L. H. (1986). Molecular Connectivity in Structure-Activity Analysis. Research Studies Press, Wiley, Letchworth, Hertfordshire, England.
38. Kier, L. B., and Hall, L. H. (1999). Molecular Structure Description: The Electrotopological State. Academic Press, San Diego.
39. Klopman, G. (1992). MULTI-CASE: 1. A hierarchical computer automated structure evaluation program. Quant. Struct.-Act. Relat. 11, 176-184.
40. Kramer, S. D., and Wunderli-Allenspach, H. (2001). Physicochemical properties in pharmacokinetic lead optimization. Farmaco 56, 145-148.
41. Kulkarni, A. S., and Hopfinger, A. J. (1999). Membrane-interaction QSAR analysis: Application to the estimation of eye irritation by organic compounds. Pharm. Res. 16, 1244-1252.
42. Lacy, C. F., Armstrong, L. L., Goldman, M. P., and Lance, L. L. (2002). Drug information handbook. Lexi-Comp, Hudson, OH.
43. Layton, D., Key, C., and Shakir, S. A. (2003). Prolongation of the QT interval and cardiac arrhythmias associated with cisapride: Limitations of the pharmacoepidemiological studies conducted and proposals for the future. Pharmacoepidemiol. Drug Saf. 12, 31-40.
44. Liu, X. H., Wang, B., Huang, Z., Han, S. K., and Wang, L. S. (2003). Acute toxicity and quantitative structure-activity relationships of alpha-branched phenylsulfonyl acetates to Daphnia magna. Chemosphere 50, 403-408.
45. Lowrey, A. H., Famini, G. R., Loumbev, V., Wilson, L. Y., and Tosk, J. M. (1997). Modeling drug-melanin interaction with theoretical linear solvation energy relationships. Pigm. Cell Res. 10, 251-256.
46. Malik, M., and Camm, A. J. (2001). Evaluation of drug-induced QT interval prolongation: Implications for drug approval and labelling. Drug Saf. 24, 323-351.
47. MICROMEDEX (Edition expires 12/2003). MICROMEDEX. MICROMEDEX, Greenwood Village, CO.
48. Moss, A. J. (1999). The QT interval and torsade de pointes. Drug Saf. 21, 5-10.
49. Muzikant, A. L., and Penland, R. C. (2002). Models for profiling the potential QT prolongation risk of drugs. Curr. Opin. Drug Discov. Devel. 5, 127-135.
50. Parzen, E. (1962). On estimation of a probability density function and mode. Ann. Math. Stat. 33, 1065-1076.
51. Platts, J. A., Abraham, M. H., Hersey, A., and Butina, D. (2000). Estimation of molecular linear free energy relationship descriptors. 4. Correlation and prediction of cell permeation. Pharm. Res. 17, 1013-1018.
52. Platts, J. A., Abraham, M. H., Zhao, Y. H., Hersey, A., Ijaz, L., and Butina, D. (2001). Correlation and prediction of a large blood-brain distribution data set - an LFER study. Eur. J. Med. Chem. 36, 719-730.
53. Platts, J. A., Butina, D., Abraham, M. H., and Hersey, A. (1999). Estimation of molecular free energy relation descriptors using a group contribution approach. J. Chem. Inf. Comput. Sci. 39, 835-845.
54. Prival, M. J. (2001). Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals. Environ. Mol. Mutagen. 37, 55-69.
55. Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
56. Roche, O., Trube, G., Zuegge, J., Pflimlin, P., Alanine, A., and Schneider, G. (2002). A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. Chembiochem. 3, 455-459.
57. Rockey, W. M., and Elcock, A. H. (2002). Progress toward virtual screening for drug side effects. Proteins 48, 664-671.
58. Sanderson, D. M., and Earnshaw, C. G. (1991). Computer prediction of possible toxic action from chemical structure: The DEREK system. Hum. Exp. Toxicol. 10, 261-273.
59. Scridhar, R., Pablo, T., Rifkin, R., and et al. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad Sci. U.S.A. 98, 15149-15154.
60. Sirius (2000). Absolv. Sirius Analytical Instruments.
61. Sixt, S., Altschuh, J., and Brueggemann, R. (1995). Quantitative structure-toxicity relationships for 80 chlorinated compounds using quantum chemical descriptors. Chemosphere 30, 2397-2414.
62. Smithing, M. P., and Darvas, F. (1992). HazardExpert: An expert system for predicting chemical toxicity. In Food Safety Assessment (J. W. Finlay, S. F. Robinson, and D. J. Armstrong, Eds.), pp. 191-200. American Chemical Society, Washington, DC.
63. Somers, E., Kasparek, M. C., and Pound, J. (1990). Drug regulation - the Canadian approach. Regul. Toxicol. Pharmacol. 12, 214-223.
64. Specht, D. F. (1990). Probabilistic neural networks. Neural Netw. 3, 109-118.
65. Trotter, M. W. B., Buxton, B. F., and Holden, S. B. (2001). Support vector machines in combinatorial chemistry. Measurement and Control 34, 235-239.
66. van de Waterbeemd, H., and Gifford, E. (2003). ADMET in silico modelling: Towards prediction paradise? Nat. Rev. Drug Discov. 2, 192-204.
67. Vandenberg, J. I., Walker, B. D., and Campbell, T. J. (2001). HERG K+ channels: Friend and foe. Trends Pharmacol. Sci. 22, 240-246.
68. Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer, New York.
69. Wilson, L. Y., and Famini, G. R. (1991). Using theoretical descriptors in quantitative structure-activity relationships: Some toxicological indices. J. Med. Chem. 34, 1668-1674.
70. Wold, S., Esbensen, K., and Geladi, P. (1987). Principal component analysis. Chemom. Intell. Lab. Sys. 2, 37-52.
71. Yu, R. L., Hu, G. R., and Zhao, Y. H. (2002). Comparative study of four QSAR models of aromatic compounds to aquatic organisms. J Environ Sci (China) 14, 552-557.
72. Zhao, Y. H., Le, J., Abraham, M. H., Hersey, A., Eddershaw, P. J., Luscombe, C. N., Boutina, D., Beck, G., Sherborne, B.; Cooper, I., and Platts, J. A. (2001). Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J. Pharm. Sci. 90, 749-784.