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ToxSci Advance Access published online on February 19, 2004
Toxicological Sciences, doi:10.1093/toxsci/kfh082
Toxicological Sciences © Society of Toxicology 2004; all rights reserved
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Received December 17, 2003; accepted January 16, 2004
© 2004 Society of Toxicology

Safety Assessment

Prediction of Torsade-Causing Potential of Drugs by Support Vector Machine Approach

C. W. Yap 1, C. Z. Cai 2, Y. Xue 3, and Y. Z. Chen 1*

1 Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543
2 Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543; Department of Applied Physics, Chongqing University, Chongqing 400044, P. R. China
3 Department of Computational Science, National University of Singapore, Blk SOC1, Level 7, 3 Science Drive 2, Singapore 117543; Department of Chemistry, Sichuan University, Chengdu 610064, P. R. China

* To whom correspondence should be addressed. E-mail: yzchen{at}cz3.nus.edu.sg.


  Abstract

In an effort to facilitate drug discovery, computational methods for facilitating the prediction of various adverse drug reactions (ADRs) have been developed. So far, attention has not been sufficiently paid to the development of methods for the prediction of serious ADRs that occur less frequently. Some of these ADRs, such as torsade de pointes (TdP), are important issues in the approval of drugs for certain diseases. Thus there is a need to develop tools for facilitating the prediction of these ADRs. This work explores the use of a statistical learning method, support vector machine (SVM), for TdP prediction. TdP involves multiple mechanisms and SVM is a method suitable for such a problem. Our SVM classification system used a set of linear solvation energy relationship (LSER) descriptors and was optimized by leave-one-out cross validation procedure. Its prediction accuracy was evaluated by using an independent set of agents and by comparison with results obtained from other commonly used classification methods using the same dataset and optimization procedure. The accuracies for the SVM prediction of TdP-causing agents and non-TdP-causing agents are 97.4% and 84.6% respectively, one is substantially improved against and the other is comparable to the results obtained by other classification methods useful for multiple-mechanism prediction problems. This indicates the potential of SVM in facilitating the prediction of TdP-causing risk of small molecules and perhaps other ADRs that involve multiple mechanisms.

Key Words: support vector machine, torsade de pointes, linear solvation energy relationship, prediction .


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