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정다운,Aroli Marcellinus,임기무 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.4
심근세포에서 활동전위 간의 교대 현상인 alternans은 일반적으로 심박수가 높을 때 발생하여 심장 부정맥을 유발하는 것으로 알려져 있으나, 임상에서 관찰되는 alternans의 일부는 낮은 심박수 조건에서 발생하여 심장 부정맥의 유발가능성이 증가시킨다. 하지만, 심장의 전기생리학적 조건은 너무 방대하고 다양하여 alternans이 어떠한 조건에서 더 쉽게 발생하는지에 대해서는 밝혀지지 않았다. 따라서, 본 연구에서는 심근세포의 10개의 이온 채널 전도도 변화에 따른 alternans의 발생 여부를 확인하고 각 이온 채널들의 변화 조합을 통해 alternans의 발생에 가장 많은 영향을 미치는 이온 채널을 규명하였다. 이를 위해 Ten Tusscher 인간 심실 세포 모델을 통해 10개의 이온 채널의 전기전도도에 변화가 없을 때와 50% 감소하였을 때, 150% 증가하였을 때에 따른 조합 조건을 구현하였다. 그에 따라 생성된 310개의 이온 채널 전도도 조합 조건에서 alternans 유발 프로토콜을 기반으로 시뮬레이션을 수행하였다. 그후, alternans이 발생하였을 때의 자극 주기(AOCL)와 그 때의 평균 활동 전위기간(AO APD)를 계산하고 다차원 밀도 모델을 통해 alternans의 유발에 가장 많은 영향을 미치는 이온 채널과 전기전도도 조합 조건을 확인하였다. 그 결과, AOCL과 AO APD에 민감하게 변화를 주는 이온 채널은 각각 달랐으나, 공통적으로 inward K+ 채널과 rapid rectifier K+ 채널의 전도도 변화에 가장 민감하게 반응하였다. 본 연구의 결과는 심근 세포에서 alternans 발생의 원인과 메커니즘을 세포의 전기생리학적 가변성을 기반으로 이해하는 데 도움을 줄 수 있을 것으로 예상한다.
약물의 염전성 부정맥 유발 예측 지표로서 심장의 전기생리학적 특징 값들의 검증
유예담,정다운,Aroli Marcellinus,임기무 대한의용생체공학회 2022 의공학회지 Vol.43 No.1
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 data- set, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug data- set, and the entire drug dataset.
합성곱 신경망을 사용한 미분된 활동전위 형상에 따른 약물의 부정맥 위험도 분류
정다운(Da Un Jeong),Aroli Marcellinus,임기무(Ki Moo Lim) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
CiPA projects for assessing proarrhythmic drugs suggested a logistic regression model using qNet as the Torsade de Pointes risk assessment biomarker, obtained from In-silico simulation. However, In-silico simulation requires high-performance computation resources and a lot of times. Thus, this study proposed a deep CNN model using differential action potential (AP) shapes to classify three proarrhythmic risk levels: high, intermediate, and low. We performed an In-silico simulation and got AP shapes with drug effects using IC50 and Hill coefficients of 28 drugs released by CiPA groups. Then, we trained the deep CNN model with the differential AP shapes of 12 drugs and tested it with those of 16 drugs. Our model had a better performance for classifying the proarrhythmic risk of drugs than the traditional logistic regression model using qNet. The classification accuracy was 98% for high-risk level drugs, 94% for intermediate-risk level drugs, and 89% for low-risk level drugs.
Rakha Zharfarizqi Danadibrata,Da Un Jeong,Aroli Marcellinus,Ki Moo Lim 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
As part of the Comprehensive in vitro Proarrhythmia Assay initiative, methodologies for predicting the incidence of Torsade de Pointes (TdP) by drugs were recently developed, leading to the importance of assessment using In-silico simulations. We performed the In-silico simulation using the ventricular cell model suggested by Sara Dutta using IC50 and hill coefficient as the input that we got from the In-vitro experiment of drug-response of ion channels. From the In-silico simulation, we obtained the qNet variability according to pace. To increase drug toxicity evaluation performance, we proposed deep CNN model utilized the qNet variability as an input and classify into high-risk, intermediate-risk, and low-risk. We trained the model with 12 drugs and tested it with the remaining 16 drugs. In the high-risk , the proposed CNN model had an AUC of 0.90, 0.75 in the intermediate-risk, and 0.82 in the low-risk.
심근 세포의 전기생리학적 특징을 이용한 인공 신경망 기반 약물의 심장독성 평가
유예담,정다운,임기무,Yoo, Yedam,Jeong, Da Un,Marcellinus, Aroli,Lim, Ki Moo 대한의용생체공학회 2021 의공학회지 Vol.42 No.6
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.