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KMID : 1137820210420060287
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의공학회지
2021 Volume.42 No. 6 p.287 ~ p.294
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An Artificial Neural Network-Based Drug Proarrhythmia Assessment Using Electrophysiological Characteristics of Cardiomyocytes
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Yoo Ye-Dam
Jeong Da-Un
Marcellinus Aroli
Lim Ki-Moo
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Abstract
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Cardiotoxicity assessment of all drugs has been performed according to the ICH guidelines since 2005. Non-clinical evaluation S7B has focused on the hERG assay, which has a low specificity problem. The comprehensive in vitro proarrhythmia assay (CiPA) project was initiated to correct this problem, which presented a model for clas- sifying the Torsade de pointes (TdP)-induced risk of drugs as biomarkers calculated through an in silico ventricular model. In this study, we propose a TdP-induced risk group classifier of artificial neural network (ANN)-based. The model was trained with 12 drugs and tested with 16 drugs. The ANN model was performed according to nine fea- tures, seven features, five features as an individual ANN model input, and the model with the highest performance was selected and compared with the classification performance of the qNet input logistic regression model. When the five features model was used, the results were AUC 0.93 in the high-risk group, AUC 0.73 in the intermediate- risk group, and 0.92 in the low-risk group. The model's performance using qNet was lower than the ANN model in the high-risk group by 17.6% and in the low-risk group by 29.5%. This study was able to express performance in the three risk groups, and it is a model that solved the problem of low specificity, which is the problem of hERG assay.
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KEYWORD
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Drug safety assessment, Drug cardiovascular toxicity assessment, Artificial neural network, Machine learning, CiPA
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