Hanum Ulfa Latifa, Qauli Ali Ikhsanul, Fuadah Yunendah Nur, Izza Rahmafatin Nurul, Lim Ki Moo
Department of IT Convergence Engineering, Computational Medicine Lab, Kumoh National Institute of Technology, Gumi, 39177, Republic of Korea.
Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, 60115, Jawa Timur, Indonesia.
Arch Toxicol. 2025 May 22. doi: 10.1007/s00204-025-04079-z.
The comprehensive in vitro proarrhythmia assay (CiPA) has paved the way for integrating in silico trials into drug evaluation processes. In alignment, the International Council for Harmonization (ICH) has initiated efforts to update the ICH S7B and E14 guidelines through a structured Questions and Answers (Q&A) format. A significant challenge in this paradigm is ensuring consistent application and evaluation of diverse proarrhythmia risk prediction models across experimental systems. This study utilized the CiPAORdv1.0 model to predict cardiac toxicity, leveraging in vitro data from 28 drugs for training and validation. A modified O'Hara-Rudy model simulated a virtual population of human ventricular cell models. Seven critical features (qNet, APD50, APD90, Camax, Carest, CaTD50, CaTD90) were extracted as inputs for analysis. CiPAORdv1.0 demonstrated robust performance, achieving predictive accuracies with an area under the curve (AUC) of 1.0 for high risk and 0.95 for low-risk categories. The calibration process was enhanced using normalized Euclidean distances (R1 and R2), effectively distinguishing risk categories. Sensitivity analysis identified key drugs, ensuring a strong calibration drug set to anchor model predictions. The proposed ANN model validated the CiPAORdv1.0 framework as an effective TdP-risk prediction system, ensuring robust and lab-specific validation. This study presents a novel algorithm leveraging artificial neural networks to implement validated cardiac safety models, addressing a critical need for standardized proarrhythmia risk assessment in drug development.
全面体外致心律失常试验（CiPA）为将计算机模拟试验整合到药物评估过程中铺平了道路。与此一致，国际协调理事会（ICH）已开始努力通过结构化问答（Q&A）形式更新ICH S7B和E14指南。这种模式的一个重大挑战是确保在不同实验系统中对各种致心律失常风险预测模型进行一致的应用和评估。本研究利用CiPAORdv1.0模型预测心脏毒性，利用28种药物的体外数据进行训练和验证。一个经过修改的奥哈拉-鲁迪模型模拟了人类心室细胞模型的虚拟群体。提取了七个关键特征（qNet、APD50、APD90、Camax、Carest、CaTD50、CaTD90）作为分析输入。CiPAORdv1.0表现出强大的性能，高风险类别曲线下面积（AUC）的预测准确率为1.0，低风险类别为0.95。使用归一化欧几里得距离（R1和R2）增强了校准过程，有效地区分了风险类别。敏感性分析确定了关键药物，确保了一个强大的校准药物集来锚定模型预测。所提出的人工神经网络（ANN）模型验证了CiPAORdv1.0框架作为一种有效的尖端扭转型室性心动过速（TdP）风险预测系统，确保了强大且针对实验室的验证。本研究提出了一种利用人工神经网络实现经过验证的心脏安全模型的新算法，满足了药物开发中标准化致心律失常风险评估的关键需求。
