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박윤귀Yun-Gwi Park

중앙대학교

J. Adv. Res., Available online 22 July 2025 | https://doi.org/10.1016/j.jare.2025.07.030 A machine learning platform for genotype-specific cardiotoxicity risk prediction using patient-derived iPSC-CMs

Yun-Gwi Park ^a,1, Na Kyeong Park ^b,1, Youngsun Lee ^c,d,1, Muhammad Adnan Pramudito ^e,1, Yeo-Jin Son ^f, Hyeyeon Park ^c,d, Ali Ikhsanul Qauli ^e,g, Seong Woo Choi ^h, Kiwon Banⁱ, Jong-il Choi ^j, Soon-Jung Park ^b, Hun-Jun Park ^k, Ki Moo Lim ^e,g,l, Soo Kyung Koo ^c,d, Jung-Hyun Kim ^c,d,m, Sung-Hwan Moon ^a

^aDepartment of Animal Science and Technology, College of Biotechnology and Natural Resources, Chung-Ang University, Anseong 17546, Republic of Korea
^bR&D Center, Biosolvix Co. Ltd, Seoul 08502, Republic of Korea
^cDepartment of Chronic Diseases Convergence Research, Division of Intractable Diseases Research, Korea National Institute of Health, Osong Health Technology Administration Complex, Cheongju 28159, Republic of Korea
^dKorea National Stem Cell Bank, Cheongju 28159, Republic of Korea
^eComputational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
^fStem Cell Research Institute, T&R Biofab Co. Ltd, Siheung 13487, Republic of Korea
^gDepartment of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Jawa Timur 60115, Indonesia
^hDepartment of Physiology, Dongguk University College of Medicine, Gyeongju 38066, Republic of Korea
ⁱDepartment of Biomedical Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon 999077, Hong Kong Special Administrative Region
^jDivision of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea
^kDepartment of Internal Medicine, Division of Cardiology, Uijeonbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
^lMeta Heart Co., Ltd., Gumi 39177, Republic of Korea
^mCollege of Pharmacy, Ajou University, Suwon 16499, Republic of Korea

¹The authors contributed equally to this work.
Corresponding authors : Soon-Jung Park, Hun-Jun Park, Ki Moo Lim, Soo Kyung Koo, Jung-Hyun Kim, Sung-Hwan Moon

Abstract

Introduction
Drug-induced Torsades de Pointes (TdP) has led to withdrawal of several drugs from the market. Individuals with inherited cardiac channelopathies are at increased risk due to their underlying electrophysiological vulnerability.

Objectives
We aimed to develop a machine learning (ML) platform for disease-specific cardiotoxicity using patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) combined with high-throughput microelectrode array (MEA) recordings.

Methods
We generated genetically confirmed and phenotypically characterized iPSC-CMs from patients with long QT syndrome (LQTS) and Brugada syndrome (BrS). These cells were exposed to 28 compounds with varying TdP risk levels. Electrophysiological responses including field potential duration, corrected field potential duration, beat period and amplitude were measured using MEA. These data were used to train and compare machine learning models, including artificial neural networks (ANN), random forest, and XGBoost. Model performance was optimized by grid search and evaluated by fivefold cross-validation.

Results
The ANN model trained on LQTS iPSC-CMs achieved the highest accuracy (area under the curve [AUC] = 0.94). BrS cell lines showed hypersensitivity to calcium channel blockers, while LQTS lines exhibited heightened responses to potassium channel inhibitors. Previously ambiguous compounds were reclassified based on disease-specific electrophysiological profiles, demonstrating the platform’s utility in genotype-specific cardiotoxicity risk assessment.

Conclusion
This study presents a scalable and individualized approach for cardiotoxicity screening using well-characterized patient-derived iPSC-CMs. The platform enhances drug safety prediction, supports regulatory evaluation, and advances precision medicine in arrhythmia risk assessment.

논문정보

형식Research article
게재일2025년 07월 (BRIC 등록일 2025-07-24)
연구진국내(교신)+국외 연구진
분야 바이오·의료융합 > 다학제 융합/복합

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