대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제42권6호
  • /
  • Pages.287-294
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  • 2021
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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심근 세포의 전기생리학적 특징을 이용한 인공 신경망 기반 약물의 심장독성 평가

An Artificial Neural Network-Based Drug Proarrhythmia Assessment Using Electrophysiological Characteristics of Cardiomyocytes

  • 유예담 (금오공과대학교 IT융복합공학과) ;
  • 정다운 (금오공과대학교 IT융복합공학과) ;
  • ;
  • 임기무 (금오공과대학교 메디컬 IT융합공학과)
  • Yoo, Yedam (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Jeong, Da Un (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Marcellinus, Aroli (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Lim, Ki Moo (Dept of Medical IT Convergence Engineering, Kumoh National Institute of Technology)
  • 투고 : 2021.11.10
  • 심사 : 2021.12.29
  • 발행 : 2021.12.31
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초록

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 classifying 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 features, 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.

키워드

과제정보

본 연구는 금오공과대학교 학술연구비에 의하여 연구된 논문 임 (2021).

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