[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124674-en":3,"doc-seo-124674-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124674,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability","A multi-in silico framework predicts drug-induced Torsade de Pointes (TdP) risk by combining electrophysiological modeling with machine learning. A virtual population of human ventricular cell models, built from a modified O’Hara-Rudy model to represent inter-individual variability, is simulated using IC50 and Hill coefficients from 67 drugs. Fourteen simulation-derived features train KNN, Random Forest, XGBoost, and ANN models with grid-search optimization and five-fold cross-validation. The ANN achieves the strongest performance on unseen drug data, supporting reliable TdP risk assessment.","TYPE Original Research PUBLISHED 04 October 2023  \nDOI 10.3389/fphys.2023.1266084  \nOPEN ACCESS  \nEDITED BY  \nMorten Gram Pedersen, University of Padua, Italy  \nREVIEWED BY  \nFrancisco Sahli Costabal,  \nPontificia Universidad Católica de Chile, Chile  \nFrederique Jos Vanheusden, Nottingham Trent University, United Kingdom  \n*CORRESPONDENCE  \nKi Moo Lim,  \n [kmlim@kumoh.ac.kr](kmlim@kumoh.ac.kr)  \nRECEIVED 24 July 2023  \nACCEPTED 20 September 2023  \nPUBLISHED 04 October 2023  \nCITATION  \nFuadah YN, Qauli AI, Marcellinus A, Pramudito MA and Lim KM (2023), Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability.  \nFront. Physiol. 14:1266084 .  \ndoi: 10.3389/fphys.2023.1266084  \nCOPYRIGHT  \n© 2023 Fuadah, Qauli, Marcellinus, Pramudito and Lim. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability  \nYunendah Nur Fuadah 1,2, Ali Ikhsanul Qauli 1,3, Aroli Marcellinus 1, Muhammad Adnan Pramudito 1 and Ki Moo Lim 1,4,5*  \n1Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea, 2School of Electrical Engineering, Telkom University, Bandung, Indonesia, 3Department of Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Jawa Timur, Indonesia, 4Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Republic of Korea, 5Meta Heart Co., Ltd., Gumi, Republic of Korea  \nIntroduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of druginduced cardiotoxicity.  \nMethod: We generated a virtual population of human ventricular cell models using a modified O ’ Hara-Rudy model, allowing inter-individual variation. IC50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (~~d~~V~~d~~ repol , ~~d~~V~~d~~ max , Vmpeak , Vmresting , APDtri , APD90 , APD50 , Capeak , Cadiastole , Catri , CaD90 , CaD50 , qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN) . Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model ’ s performance with test data from 25 drugs.  \nResult: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908–0.937), sensitivity of 0.926 (0.909–0.942), specificity of 0.921 (0.906–0.935), and AUC score of 0. 964 (0 .954–0. 975) .  \nDiscussion and conclus","cbCaicT5mHtqFDep","https://ap.wps.com/l/cbCaicT5mHtqFDep","pdf",2571542,1,18,"English","en",105,"# Introduction\n## Drug-induced TdP and existing risk guidelines\n# Method\n## Virtual population modeling and input drug parameters\n## Feature extraction and machine learning models\n# Result\n## Predictive performance of the proposed ANN model\n# Discussion and conclusion\n## Generalization on unseen datasets","[{\"question\":\"What modeling approach is used to represent inter-individual variability in drug effects?\",\"answer\":\"A virtual population of human ventricular cell models is generated using a modified O’Hara-Rudy model, enabling inter-individual variation during electrophysiological simulation.\"},{\"question\":\"Which machine learning methods are evaluated in the study?\",\"answer\":\"The study evaluates KNN, Random Forest, XGBoost, and Artificial Neural Networks (ANN), using simulation-derived features as inputs.\"},{\"question\":\"How is model performance assessed and optimized?\",\"answer\":\"Model parameters are selected via grid search, training uses five-fold cross-validation with 42 drugs, and performance is evaluated on test data from 25 unseen drugs.\"}]","Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability | 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