Abstract
Drug-induced eosinophilia is a potentially life-threatening adverse effect; clinical manifestations, eosinophilia–myalgia syndrome, mainly include severe skin eruption, fever, hematologic abnormalities, and organ system dysfunction. Using experimental methods to evaluate drug-induced eosinophilia is very complicated, time-consuming, and costly in the early stage of drug development. Thus, in this investigation, we established computational prediction models of drug-induced eosinophilia using SVM and naïve Bayesian approaches. For the SVM modeling, the overall prediction accuracy for the training set by means of fivefold cross-validation is 91.6 and for the external test set is 82.9 %. For the naïve Bayesian modeling, the overall prediction accuracy for the training set is 92.5 and for the external test set is 85.4 %. Moreover, some molecular descriptors and substructures considered as important for drug-induced eosinophilia were identified. Thus, we hope the prediction models of drug-induced eosinophilia built in this work should be applied to filter early-stage molecules for potential eosinophilia adverse effect, and the selected molecular descriptors and substructures of toxic compounds should be taken into consideration in the design of new candidate drugs to help medicinal chemists rationally select the chemicals with the best prospects to be effective and safe.
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Acknowledgments
This work was supported by the Project for Enhancing the Research Capability of Young Teachers in Northwest Normal University (NWNU-LKQN-12-7).
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The authors declare that there are no conflicts of interest.
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The structures of the training set (TrainingSet_107.sd) and test set (TestSet_41.sd) molecules, which is available to authorized users. (RAR 63 kb)
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Zhang, H., Yu, P., Xiang, ML. et al. Prediction of drug-induced eosinophilia adverse effect by using SVM and naïve Bayesian approaches. Med Biol Eng Comput 54, 361–369 (2016). https://doi.org/10.1007/s11517-015-1321-8
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DOI: https://doi.org/10.1007/s11517-015-1321-8