New approach to predicting proconvulsant activity with the use of Support Vector Regression.

Antiepileptic drugs are commonly used for many therapeutic indications, including epilepsy, neuropathic pain, bipolar disorder and anxiety. Accumulating data suggests that many of them may lower the seizure threshold in men. In the present paper we deal with the possibility of using Support Vector Regression (SVR) to forecast the proconvulsant activity of compounds exerting anticonvulsant activity in the electroconvulsive threshold test in mice. A new approach to forecast this drug-related toxic effect by means of the support vector machine (SVM) in the regression mode is discussed below. The efficacy of this mathematical method is compared to the results obtained in vivo. Since SVR investigates the anticonvulsant activity of the compounds more thoroughly than it is possible using animal models, this method seems to be a very helpful tool for predicting additional dose ranges at which maximum anticonvulsant activity without toxic effects is observed. Good generalizing properties of SVR allow to assess the therapeutic dose range and toxicity threshold. Noteworthy, this method is very interesting for ethical reasons as this mathematical model enables to limit the use of living animals during the anticonvulsant screening process. Copyright © 2012 Elsevier Ltd. All rights reserved.

Citation

Robert Salat, Kinga Salat. New approach to predicting proconvulsant activity with the use of Support Vector Regression. Computers in biology and medicine. 2012 May;42(5):575-81

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PMID: 22348824

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