약물의 염전성 부정맥 유발 예측 지표로서 심장의 전기생리학적 특징 값들의 검증

• Published in: Journal of biomedical engineering research Vol. 43; no. 1; pp. 19 - 26
• Main Authors: 유예담, 정다운, 임기무, Yoo, Yedam, Jeong, Da Un, Marcellinus, Aroli, Lim, Ki Moo
• Format: Journal Article
• Language: Korean
• Published: 대한의용생체공학회 01.02.2022
• Subjects: 의공학; In silico; Torsades de pointes; Electrophysiological features; CiPA
• ISSN: 1229-0807; 2288-9396

The Comprehensive in vitro Proarrhythmic Assay(CiPA) project was launched for solving the hERG assay problem of being classified as high-risk groups even though they are low-risk drugs due to their high sensitivity. CiPA presented a protocol to predict drug toxicity using physiological data calculated based on the in-silico model. in this study, features calculated through the in-silico model are analyzed for correlation of changing action potential in the near future, and features are verified through predictive performance according to drug datasets. Using the O'Hara Rudy model modified by Dutta et al., Pearson correlation analysis was performed between 13 features(dVm/dtmax, APpeak, APresting, APD90, APD50, APDtri, Capeak, Caresting, CaD90, CaD50, CaDtri, qNet, qInward) calculated at 100 pacing, and between dVm/dtmax_repol calculated at 1,000 pacing, and linear regression analysis was performed on each of the 12 training drugs, 16 verification drugs, and 28 drugs. Indicators showing high coefficient of determination(R2) in the training drug dataset were qNet 0.93, AP resting 0.83, APDtri 0.78, Ca resting 0.76, dVm/dtmax 0.63, and APD90 0.61. The indicators showing high determinants in the validated drug dataset were APDtri 0.94, APD90 0.92, APD50 0.85, CaD50 0.84, qNet 0.76, and CaD90 0.64. Indicators with high coefficients of determination for all 28 drugs are qNet 0.78, APD90 0.74, and qInward 0.59. The indicators vary in predictive performance depending on the drug dataset, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug dataset, and the entire drug dataset.