{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:47:40Z","timestamp":1772909260386,"version":"3.50.1"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["EIC Project No 190134524"],"award-info":[{"award-number":["EIC Project No 190134524"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["EIC Project No 190134524"],"award-info":[{"award-number":["EIC Project No 190134524"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["EIC Project No 190134524"],"award-info":[{"award-number":["EIC Project No 190134524"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["EIC Project No 190134524"],"award-info":[{"award-number":["EIC Project No 190134524"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["EIC Project No 190134524"],"award-info":[{"award-number":["EIC Project No 190134524"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["EIC Project No 190134524"],"award-info":[{"award-number":["EIC Project No 190134524"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["EIC Project No 190134524"],"award-info":[{"award-number":["EIC Project No 190134524"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-024-01370-8","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T11:15:48Z","timestamp":1735211748000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators"],"prefix":"10.1038","volume":"7","author":[{"given":"Paula","family":"Dominguez-Gomez","sequence":"first","affiliation":[]},{"given":"Alberto","family":"Zingaro","sequence":"additional","affiliation":[]},{"given":"Laura","family":"Baldo-Canut","sequence":"additional","affiliation":[]},{"given":"Caterina","family":"Balzotti","sequence":"additional","affiliation":[]},{"given":"Borje","family":"Darpo","sequence":"additional","affiliation":[]},{"given":"Christopher","family":"Morton","sequence":"additional","affiliation":[]},{"given":"Mariano","family":"V\u00e1zquez","sequence":"additional","affiliation":[]},{"given":"Jazmin","family":"Aguado-Sierra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"1370_CR1","unstructured":"National Health Service. Arrhythmia (2021). https:\/\/www.nhs.uk\/conditions\/arrhythmia\/. Accessed July 31, 2024."},{"key":"1370_CR2","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1097\/00045391-200311000-00013","volume":"10","author":"L Cubeddu","year":"2003","unstructured":"Cubeddu, L. QT Prolongation and Fatal Arrhythmias: A Review of Clinical Implications and Effects of Drugs. American journal of therapeutics 10, 452\u20137 (2003).","journal-title":"American journal of therapeutics"},{"key":"1370_CR3","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.ahj.2013.11.004","volume":"167","author":"PT Sager","year":"2014","unstructured":"Sager, P. T., Gintant, G., Turner, J. R., Pettit, S. & Stockbridge, N. Rechanneling the cardiac proarrhythmia safety paradigm: A meeting report from the cardiac safety research consortium. American Heart Journal 167, 292\u2013300 (2014).","journal-title":"American Heart Journal"},{"key":"1370_CR4","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/nrd.2015.34","volume":"15","author":"G Gintant","year":"2016","unstructured":"Gintant, G., Sager, P. T. & Stockbridge, N. Evolution of strategies to improve preclinical cardiac safety testing. Nature Reviews Drug Discovery 15, 457\u2013471 (2016).","journal-title":"Nature Reviews Drug Discovery"},{"key":"1370_CR5","doi-asserted-by":"crossref","unstructured":"Kaye, G. & Lemery, R.Fast Facts: Cardiac Arrhythmias (S. Karger AG, 2018).","DOI":"10.1159\/isbn.978-1-912776-14-6"},{"key":"1370_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1093\/toxsci\/kfac013","volume":"187","author":"J-P Valentin","year":"2022","unstructured":"Valentin, J.-P. et al. The Challenges of Predicting Drug-Induced QTc Prolongation in Humans. Toxicol. Sci. 187, 3\u201324 (2022).","journal-title":"Toxicol. Sci."},{"key":"1370_CR7","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.vascn.2016.06.002","volume":"81","author":"T Colatsky","year":"2016","unstructured":"Colatsky, T. et al. The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative - Update on progress. Journal of Pharmacological and Toxicological Methods 81, 15\u201320 (2016).","journal-title":"Journal of Pharmacological and Toxicological Methods"},{"key":"1370_CR8","doi-asserted-by":"publisher","first-page":"021502","DOI":"10.1063\/1.5132618","volume":"4","author":"M Hwang","year":"2020","unstructured":"Hwang, M., Lim, C.-H., Leem, C. H. & Shim, E. B. In silico models for evaluating proarrhythmic risk of drugs. APL Bioengineering 4, 021502 (2020).","journal-title":"APL Bioengineering"},{"key":"1370_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1093\/cvr\/cvr044","volume":"91","author":"GR Mirams","year":"2011","unstructured":"Mirams, G. R. et al. Simulation of multiple ion channel block provides improved early prediction of compounds\u2019 clinical torsadogenic risk. Cardiovascular research 91, 53\u201361 (2011).","journal-title":"Cardiovascular research"},{"key":"1370_CR10","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1002\/cpt.1184","volume":"105","author":"Z Li","year":"2019","unstructured":"Li, Z. et al. Assessment of an in silico mechanistic model for proarrhythmia risk prediction under the CiPA initiative. Clinical Pharmacology & Therapeutics 105, 466\u2013475 (2019).","journal-title":"Clinical Pharmacology & Therapeutics"},{"key":"1370_CR11","doi-asserted-by":"publisher","first-page":"668","DOI":"10.3389\/fphys.2017.00668","volume":"8","author":"E Passini","year":"2017","unstructured":"Passini, E. et al. Human in silico drug trials demonstrate higher accuracy than animal models in predicting clinical pro-arrhythmic cardiotoxicity. Front. Physiol. 8, 668 (2017).","journal-title":"Front. Physiol."},{"key":"1370_CR12","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1080\/15376516.2016.1256460","volume":"27","author":"M Abbasi","year":"2017","unstructured":"Abbasi, M., Small, B. G., Patel, N., Jamei, M. & Polak, S. Early assessment of proarrhythmic risk of drugs using the in vitro data and single-cell-based in silico models: proof of concept. Toxicology Mechanisms and Methods 27, 88\u201399 (2017).","journal-title":"Toxicology Mechanisms and Methods"},{"key":"1370_CR13","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.1111\/bph.14786","volume":"176","author":"E Passini","year":"2019","unstructured":"Passini, E. et al. Drug-induced shortening of the electromechanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias. British Journal of Pharmacology 176, 3819\u20133833 (2019).","journal-title":"British Journal of Pharmacology"},{"key":"1370_CR14","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1021\/acs.jcim.7b00440","volume":"58","author":"L Romero","year":"2018","unstructured":"Romero, L. et al. In silico QT and APD prolongation assay for early screening of drug-induced proarrhythmic risk. Journal of Chemical Information and Modeling 58, 867\u2013878 (2018).","journal-title":"Journal of Chemical Information and Modeling"},{"key":"1370_CR15","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1111\/j.1476-5381.2012.02200.x","volume":"168","author":"N Zemzemi","year":"2013","unstructured":"Zemzemi, N. et al. Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials. British Journal of Pharmacology 168, 718\u2013733 (2013).","journal-title":"British Journal of Pharmacology"},{"key":"1370_CR16","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.3389\/fphys.2019.01139","volume":"10","author":"M Hwang","year":"2019","unstructured":"Hwang, M. et al. Three-dimensional heart model-based screening of proarrhythmic potential by in silico simulation of action potential and electrocardiograms. Fron. Physiol. 10, 1139 (2019).","journal-title":"Fron. Physiol."},{"key":"1370_CR17","doi-asserted-by":"publisher","first-page":"e2964","DOI":"10.1002\/cnm.2964","volume":"34","author":"F Sahli Costabal","year":"2018","unstructured":"Sahli Costabal, F., Yao, J. & Kuhl, E. Predicting drug-induced arrhythmias by multiscale modeling. International Journal for Numerical Methods in Biomedical Engineering 34, e2964 (2018).","journal-title":"International Journal for Numerical Methods in Biomedical Engineering"},{"key":"1370_CR18","doi-asserted-by":"publisher","first-page":"3435","DOI":"10.1111\/bph.14357","volume":"175","author":"J-I Okada","year":"2018","unstructured":"Okada, J.-I. et al. Arrhythmic hazard map for a 3D whole-ventricle model under multiple ion channel block. British Journal of Pharmacology 175, 3435\u20133452 (2018).","journal-title":"British Journal of Pharmacology"},{"key":"1370_CR19","doi-asserted-by":"publisher","first-page":"v90","DOI":"10.1093\/europace\/eus281","volume":"14","author":"M Wilhelms","year":"2012","unstructured":"Wilhelms, M., Rombach, C., Scholz, E. P., D\u00f6ssel, O. & Seemann, G. Impact of amiodarone and cisapride on simulated human ventricular electrophysiology and electrocardiograms. EP Europace 14, v90\u2013v96 (2012).","journal-title":"EP Europace"},{"key":"1370_CR20","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s13239-018-0347-0","volume":"9","author":"JP Cranford","year":"2018","unstructured":"Cranford, J. P. et al. Efficient computational modeling of human ventricular activation and its electrocardiographic representation: A sensitivity study. Cardiovascular engineering and technology 9, 447\u2013467 (2018).","journal-title":"Cardiovascular engineering and technology"},{"key":"1370_CR21","doi-asserted-by":"publisher","first-page":"708435","DOI":"10.3389\/fphys.2021.708435","volume":"12","author":"M Peirlinck","year":"2021","unstructured":"Peirlinck, M., Sahli Costabal, F. & Kuhl, E. Sex differences in drug-induced arrhythmogenesis. Front. Physiol. 12, 708435 (2021).","journal-title":"Front. Physiol."},{"key":"1370_CR22","doi-asserted-by":"publisher","first-page":"107860","DOI":"10.1016\/j.cmpb.2023.107860","volume":"242","author":"J Llopis-Lorente","year":"2023","unstructured":"Llopis-Lorente, J. et al. Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity. Computer Methods and Programs in Biomedicine 242, 107860 (2023).","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"1370_CR23","doi-asserted-by":"publisher","first-page":"107498","DOI":"10.1016\/j.vascn.2024.107498","volume":"126","author":"J Aguado-Sierra","year":"2024","unstructured":"Aguado-Sierra, J. et al. Virtual clinical QT exposure-response studies \u2013 a translational computational approach. Journal of Pharmacological and Toxicological Methods 126, 107498 (2024).","journal-title":"Journal of Pharmacological and Toxicological Methods"},{"key":"1370_CR24","doi-asserted-by":"publisher","first-page":"831179","DOI":"10.3389\/fphys.2022.831179","volume":"13","author":"M Peirlinck","year":"2022","unstructured":"Peirlinck, M., Lee, J., Fovargue, D. & Kuhl, E. Sex matters: A comprehensive comparison of female and male hearts. Frontiers in Physiology 13, 831179 (2022).","journal-title":"Frontiers in Physiology"},{"key":"1370_CR25","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1111\/bcp.12201","volume":"77","author":"B Darpo","year":"2014","unstructured":"Darpo, B. et al. Are women more susceptible than men to drug-induced QT prolongation? Concentration-QTc modelling in a phase 1 study with oral rac-sotalol. British Journal of Clinical Pharmacology 77, 522\u2013531 (2014).","journal-title":"British Journal of Clinical Pharmacology"},{"key":"1370_CR26","doi-asserted-by":"crossref","unstructured":"Subasi, A. & Subasi, M. E. Digital twins in healthcare and biomedicine. In Artificial Intelligence, Big Data, Blockchain and 5G for the Digital Transformation of the Healthcare Industry, 365\u2013401 (Elsevier, 2024).","DOI":"10.1016\/B978-0-443-21598-8.00011-7"},{"key":"1370_CR27","doi-asserted-by":"publisher","first-page":"20190334","DOI":"10.1098\/rsta.2019.0334","volume":"378","author":"S Longobardi","year":"2020","unstructured":"Longobardi, S. et al. Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats. Philosophical Transactions of the Royal Society A 378, 20190334 (2020).","journal-title":"Philosophical Transactions of the Royal Society A"},{"key":"1370_CR28","doi-asserted-by":"publisher","first-page":"e0239416","DOI":"10.1371\/journal.pone.0239416","volume":"15","author":"S Fresca","year":"2020","unstructured":"Fresca, S., Manzoni, A., Ded\u00e8, L. & Quarteroni, A. Deep learning-based reduced order models in cardiac electrophysiology. PloS one 15, e0239416 (2020).","journal-title":"PloS one"},{"key":"1370_CR29","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1109\/TBME.2022.3163428","volume":"69","author":"E Karabelas","year":"2022","unstructured":"Karabelas, E. et al. Global sensitivity analysis of four chamber heart hemodynamics using surrogate models. IEEE Transactions on Biomedical Engineering 69, 3216\u20133223 (2022).","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"1370_CR30","doi-asserted-by":"publisher","first-page":"114825","DOI":"10.1016\/j.cma.2022.114825","volume":"393","author":"F Regazzoni","year":"2022","unstructured":"Regazzoni, F., Salvador, M., Ded\u00e8, L. & Quarteroni, A. A machine learning method for real-time numerical simulations of cardiac electromechanics. Computer methods in applied mechanics and engineering 393, 114825 (2022).","journal-title":"Computer methods in applied mechanics and engineering"},{"key":"1370_CR31","doi-asserted-by":"publisher","first-page":"e1011257","DOI":"10.1371\/journal.pcbi.1011257","volume":"19","author":"M Strocchi","year":"2023","unstructured":"Strocchi, M. et al. Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using gaussian processes emulators. PLOS Computational Biology 19, e1011257 (2023).","journal-title":"PLOS Computational Biology"},{"key":"1370_CR32","doi-asserted-by":"publisher","first-page":"107402","DOI":"10.1016\/j.cmpb.2023.107402","volume":"231","author":"M Salvador","year":"2023","unstructured":"Salvador, M., Regazzoni, F., Ded\u00e8, L. & Quarteroni, A. Fast and robust parameter estimation with uncertainty quantification for the cardiac function. Computer Methods and Programs in Biomedicine 231, 107402 (2023).","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"1370_CR33","doi-asserted-by":"publisher","first-page":"e3783","DOI":"10.1002\/cnm.3783","volume":"40","author":"L Cicci","year":"2024","unstructured":"Cicci, L., Fresca, S., Manzoni, A. & Quarteroni, A. Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation. International Journal for Numerical Methods in Biomedical Engineering 40, e3783 (2024).","journal-title":"International Journal for Numerical Methods in Biomedical Engineering"},{"key":"1370_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01084-x","volume":"7","author":"M Salvador","year":"2024","unstructured":"Salvador, M. et al. Whole-heart electromechanical simulations using latent neural ordinary differential equations. NPJ Digital Medicine 7, 90 (2024).","journal-title":"NPJ Digital Medicine"},{"key":"1370_CR35","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1038\/s43588-024-00732-2","volume":"4","author":"M Yin","year":"2024","unstructured":"Yin, M. et al. A scalable framework for learning the geometry-dependent solution operators of partial differential equations. Nat. Comput. Sci. 4, 928\u2013940 (2024).","journal-title":"Nat. Comput. Sci."},{"key":"1370_CR36","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.cma.2019.01.033","volume":"348","author":"F Sahli Costabal","year":"2019","unstructured":"Sahli Costabal, F., Matsuno, K., Yao, J., Perdikaris, P. & Kuhl, E. Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. Computer Methods in Applied Mechanics and Engineering 348, 313\u2013333 (2019).","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"key":"1370_CR37","doi-asserted-by":"publisher","first-page":"RP91911","DOI":"10.7554\/eLife.91911","volume":"12","author":"T Grandits","year":"2024","unstructured":"Grandits, T. et al. Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies. Elife 12, RP91911 (2024).","journal-title":"Elife"},{"key":"1370_CR38","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.vascn.2016.03.009","volume":"81","author":"WJ Crumb","year":"2016","unstructured":"Crumb, W. J., Vicente, J., Johannesen, L. & Strauss, D. G. An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. Journal of Pharmacological and Toxicological Methods 81, 251\u2013262 (2016). Focused Issue on Safety Pharmacology.","journal-title":"Journal of Pharmacological and Toxicological Methods"},{"key":"1370_CR39","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.jocs.2015.12.007","volume":"14","author":"M V\u00e1zquez","year":"2016","unstructured":"V\u00e1zquez, M. et al. Alya: Multiphysics engineering simulation toward exascale. Journal of computational science 14, 15\u201327 (2016).","journal-title":"Journal of computational science"},{"key":"1370_CR40","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1111\/j.1540-8159.1998.tb00150.x","volume":"21","author":"B Houltz","year":"1998","unstructured":"Houltz, B. et al. Electrocardiographic and clinical predictors of Torsades de Pointes induced by almokalant infusion in patients with chronic atrial fibrillation or flutter: A prospective study. Pacing and Clinical Electrophysiology 21, 1044\u20131057 (1998).","journal-title":"Pacing and Clinical Electrophysiology"},{"key":"1370_CR41","doi-asserted-by":"publisher","first-page":"e1002061","DOI":"10.1371\/journal.pcbi.1002061","volume":"7","author":"T O\u2019Hara","year":"2011","unstructured":"O\u2019Hara, T., Vir\u00e1g, L., Varr\u00f3, A. & Rudy, Y. Simulation of the undiseased human cardiac ventricular action potential: model formulation and experimental validation. PLoS computational biology 7, e1002061 (2011).","journal-title":"PLoS computational biology"},{"key":"1370_CR42","doi-asserted-by":"publisher","first-page":"12","DOI":"10.12793\/tcp.2019.27.1.12","volume":"27","author":"J-S Park","year":"2019","unstructured":"Park, J.-S., Jeon, J.-Y., Yang, J.-H. & Kim, M.-G. Introduction to in silico model for proarrhythmic risk assessment under the cipa initiative. Translational and clinical pharmacology 27, 12 (2019).","journal-title":"Translational and clinical pharmacology"},{"key":"1370_CR43","first-page":"1","volume":"45","author":"C Garnett","year":"2018","unstructured":"Garnett, C. et al. Scientific white paper on concentration-QTc modeling. Journal of Pharmacokinetics and Pharmacodynamics 45, 1\u201315 (2018).","journal-title":"Journal of Pharmacokinetics and Pharmacodynamics"},{"key":"1370_CR44","unstructured":"Mirams, G. Action potential durations and QT intervals. https:\/\/mirams.wordpress.com\/2014\/03\/21\/apd_vs_qt\/ (2014). Accessed: 2024-8-1."},{"key":"1370_CR45","doi-asserted-by":"crossref","unstructured":"Lewis-Beck, C. & Lewis-Beck, M.Applied regression: An introduction, vol. 22 (Sage publications, 2015).","DOI":"10.4135\/9781483396774"},{"key":"1370_CR46","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1177\/2168479018795117","volume":"53","author":"DG Strauss","year":"2019","unstructured":"Strauss, D. G. et al. Comprehensive in vitro proarrhythmia assay (CiPA) update from a cardiac safety research consortium\/health and environmental sciences institute\/FDA meeting. Therapeutic Innovation & Regulatory Science 53, 519\u2013525 (2019).","journal-title":"Therapeutic Innovation & Regulatory Science"},{"key":"1370_CR47","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1056\/NEJMra032426","volume":"350","author":"DM Roden","year":"2004","unstructured":"Roden, D. M. Drug-induced prolongation of the QT interval. The New England journal of medicine 350, 1013\u20131022 (2004).","journal-title":"The New England journal of medicine"},{"key":"1370_CR48","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1080\/00401706.2000.10485979","volume":"42","author":"MD McKay","year":"2000","unstructured":"McKay, M. D., Beckman, R. J. & Conover, W. J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42, 55\u201361 (2000).","journal-title":"Technometrics"},{"key":"1370_CR49","unstructured":"Saltelli, A. et al. Global sensitivity analysis. The primer (John Wiley & Sons, Ltd., Chichester, 2008)."},{"key":"1370_CR50","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785\u2013794 (ACM, 2016).","DOI":"10.1145\/2939672.2939785"},{"key":"1370_CR51","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21\u201327 (1967).","journal-title":"IEEE Transactions on Information Theory"},{"key":"1370_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin, M. & Sulaiman, M. N. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process 5, 1 (2015).","journal-title":"International Journal of Data Mining & Knowledge Management Process"},{"key":"1370_CR53","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1002\/cpt.60","volume":"97","author":"B Darpo","year":"2015","unstructured":"Darpo, B. et al. Results from the IQ-CSRC prospective study support replacement of the thorough QT study by QT assessment in the early clinical phase. Clinical Pharmacology and Therapeutics 97, 326\u2013335 (2015).","journal-title":"Clinical Pharmacology and Therapeutics"},{"key":"1370_CR54","doi-asserted-by":"publisher","first-page":"616","DOI":"10.3389\/fphys.2017.00616","volume":"8","author":"S Dutta","year":"2017","unstructured":"Dutta, S. et al. Optimization of an in silico cardiac cell model for proarrhythmia risk assessment. Front. Physiol. 8, 616 (2017).","journal-title":"Front. Physiol."},{"key":"1370_CR55","doi-asserted-by":"publisher","DOI":"10.1038\/srep02100","volume":"3","author":"J Kramer","year":"2013","unstructured":"Kramer, J. et al. Mice models: superior to the herg model in predicting torsade de pointes. Scientific reports 3, 2100 (2013).","journal-title":"Scientific reports"},{"key":"1370_CR56","doi-asserted-by":"publisher","first-page":"e3140","DOI":"10.1002\/cnm.3140","volume":"34","author":"A Santiago","year":"2018","unstructured":"Santiago, A. et al. Fully coupled fluid-electro-mechanical model of the human heart for supercomputers. International journal for numerical methods in biomedical engineering 34, e3140 (2018).","journal-title":"International journal for numerical methods in biomedical engineering"},{"key":"1370_CR57","doi-asserted-by":"crossref","unstructured":"Vicente, J., Zheng, N., Bende, G. & Garnett, C. Chapter 72 - Sex differences in drug-induced QT prolongation. In Malik, M. (ed.) Sex and Cardiac Electrophysiology, 799\u2013806 (Academic Press, 2020).","DOI":"10.1016\/B978-0-12-817728-0.00072-3"},{"key":"1370_CR58","unstructured":"Ghosh, S., Gavaghan, D. & Mirams, G. Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models. arXiv (2018)."},{"key":"1370_CR59","doi-asserted-by":"publisher","first-page":"iv23","DOI":"10.1093\/europace\/eum168","volume":"9","author":"B Darpo","year":"2007","unstructured":"Darpo, B. Detection and reporting of drug-induced proarrhythmias: room for improvement. EP Europace 9, iv23\u2013iv36 (2007).","journal-title":"EP Europace"},{"key":"1370_CR60","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.pbiomolbio.2015.12.002","volume":"120","author":"A Muszkiewicz","year":"2016","unstructured":"Muszkiewicz, A. et al. Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm. Progress in Biophysics and Molecular Biology 120, 115\u2013127 (2016).","journal-title":"Progress in Biophysics and Molecular Biology"},{"key":"1370_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1468-6708-6-1","volume":"6","author":"C Pater","year":"2005","unstructured":"Pater, C. Methodological considerations in the design of trials for safety assessment of new drugs and chemical entities. Current controlled trials in cardiovascular medicine 6, 1 (2005).","journal-title":"Current controlled trials in cardiovascular medicine"},{"key":"1370_CR62","doi-asserted-by":"publisher","first-page":"112762","DOI":"10.1016\/j.cma.2019.112762","volume":"361","author":"F Levrero-Florencio","year":"2020","unstructured":"Levrero-Florencio, F. et al. Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant biomarkers. Computer Methods in Applied Mechanics and Engineering 361, 112762 (2020).","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"key":"1370_CR63","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.pbiomolbio.2020.06.007","volume":"159","author":"F Margara","year":"2021","unstructured":"Margara, F. et al. In-silico human electro-mechanical ventricular modelling and simulation for drug-induced pro-arrhythmia and inotropic risk assessment. Progress in Biophysics and Molecular Biology 159, 58\u201374 (2021).","journal-title":"Progress in Biophysics and Molecular Biology"},{"key":"1370_CR64","doi-asserted-by":"publisher","first-page":"e0263639","DOI":"10.1371\/journal.pone.0263639","volume":"18","author":"P Gonzalez-Martin","year":"2023","unstructured":"Gonzalez-Martin, P. et al. Ventricular anatomical complexity and sex differences impact predictions from electrophysiological computational models. Plos one 18, e0263639 (2023).","journal-title":"Plos one"},{"key":"1370_CR65","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1093\/europace\/euy192","volume":"21","author":"M L\u00f3pez-Yunta","year":"2019","unstructured":"L\u00f3pez-Yunta, M. et al. Infarct transmurality as a criterion for first-line endo-epicardial substrate-guided ventricular tachycardia ablation in ischemic cardiomyopathy. EP Europace 21, 55\u201363 (2019).","journal-title":"EP Europace"},{"key":"1370_CR66","doi-asserted-by":"publisher","first-page":"i143","DOI":"10.1093\/europace\/euaa405","volume":"23","author":"ZJ Wang","year":"2021","unstructured":"Wang, Z. J. et al. Human biventricular electromechanical simulations on the progression of electrocardiographic and mechanical abnormalities in post-myocardial infarction. EP Europace 23, i143\u2013i152 (2021).","journal-title":"EP Europace"},{"key":"1370_CR67","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.rec.2020.05.024","volume":"74","author":"JR Bragard","year":"2021","unstructured":"Bragard, J. R. et al. Cardiac computational modelling. Revista Espa\u00f1ola de Cardiolog\u00eda (English Edition) 74, 65\u201371 (2021).","journal-title":"Revista Espa\u00f1ola de Cardiolog\u00eda (English Edition)"},{"key":"1370_CR68","doi-asserted-by":"crossref","unstructured":"Gil, D. et al. What a difference in biomechanics cardiac fiber makes. In Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges: Third International Workshop, STACOM 2012. Lecture Notes in Computer Science, 253\u2013260 (Springer, 2013).","DOI":"10.1007\/978-3-642-36961-2_29"},{"key":"1370_CR69","doi-asserted-by":"crossref","unstructured":"Aguado-Sierra, J. et al. HPC Framework for Performing in Silico Trials Using a 3D Virtual Human Cardiac Population as Means to Assess Drug-Induced Arrhythmic Risk, vol. 2716 of Methods in Molecular Biology, chap. 14 (Springer US, New York, NY, 2024).","DOI":"10.1007\/978-1-0716-3449-3_14"},{"key":"1370_CR70","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1002\/cnm.1443","volume":"27","author":"M V\u00e1zquez","year":"2011","unstructured":"V\u00e1zquez, M. et al. A massively parallel computational electrophysiology model of the heart. International journal for numerical methods in biomedical engineering 27, 1911\u20131929 (2011).","journal-title":"International journal for numerical methods in biomedical engineering"},{"key":"1370_CR71","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1002\/cnm.1494","volume":"28","author":"P Lafortune","year":"2012","unstructured":"Lafortune, P., Ar\u00eds, R., V\u00e1zquez, M. & Houzeaux, G. Coupled electromechanical model of the heart: parallel finite element formulation. International journal for numerical methods in biomedical engineering 28, 72\u201386 (2012).","journal-title":"International journal for numerical methods in biomedical engineering"},{"key":"1370_CR72","doi-asserted-by":"crossref","unstructured":"V\u00e1zquez, M. et al. Alya Red CCM: HPC-based cardiac computational modelling. In Selected topics of computational and experimental fluid mechanics,189\u2013207 (Springer, 2015).","DOI":"10.1007\/978-3-319-11487-3_11"},{"key":"1370_CR73","doi-asserted-by":"publisher","first-page":"e0160502","DOI":"10.1371\/journal.pone.0166925","volume":"11","author":"L Johannesen","year":"2016","unstructured":"Johannesen, L., Vicente, J., Hosseini, M. & Strauss, D. G. Automated algorithm for j-tpeak and tpeak-tend assessment of drug-induced proarrhythmia risk. PloS one 11, e0160502 (2016).","journal-title":"PloS one"},{"key":"1370_CR74","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1002\/cpt.2240","volume":"110","author":"A Fogli Iseppe","year":"2021","unstructured":"Fogli Iseppe, A. et al. Sex-specific classification of drug-induced Torsade de Pointes susceptibility using cardiac simulations and machine learning. Clinical Pharmacology & Therapeutics 110, 380\u2013391 (2021).","journal-title":"Clinical Pharmacology & Therapeutics"},{"key":"1370_CR75","doi-asserted-by":"publisher","first-page":"107524","DOI":"10.1016\/j.vascn.2024.107524","volume":"128","author":"DJ Leishman","year":"2024","unstructured":"Leishman, D. J. et al. Journal of Pharmacological and Toxicological Methods 128, 107524 (2024).","journal-title":"Journal of Pharmacological and Toxicological Methods"},{"key":"1370_CR76","unstructured":"U. of Minnesota Atlas of Human Cardiac Anatomy. https:\/\/www.vhlab.umn.edu\/atlas\/histories\/histories.shtml (2021). [Accessed 02-08-2024]."},{"key":"1370_CR77","unstructured":"Franzone, P. C., Pavarino, L. F. & Scacchi, S.Mathematical cardiac electrophysiology, vol. 13 (Springer, 2014)."},{"key":"1370_CR78","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.yjmcc.2015.09.003","volume":"96","author":"E Passini","year":"2016","unstructured":"Passini, E. et al. Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti-arrhythmic therapies in human hypertrophic cardiomyopathy. Journal of molecular and cellular cardiology 96, 72\u201381 (2016).","journal-title":"Journal of molecular and cellular cardiology"},{"key":"1370_CR79","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1161\/01.CIR.41.6.899","volume":"41","author":"D Durrer","year":"1970","unstructured":"Durrer, D. et al. Total excitation of the isolated human heart. Circulation 41, 899\u2013912 (1970).","journal-title":"Circulation"},{"key":"1370_CR80","doi-asserted-by":"publisher","first-page":"e3185","DOI":"10.1002\/cnm.3185","volume":"35","author":"R Doste","year":"2019","unstructured":"Doste, R. et al. A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts. International Journal for Numerical Methods in Biomedical Engineering 35, e3185 (2019).","journal-title":"International Journal for Numerical Methods in Biomedical Engineering"},{"key":"1370_CR81","doi-asserted-by":"publisher","first-page":"33341","DOI":"10.3389\/fphys.2012.00360","volume":"3","author":"P-C Yang","year":"2012","unstructured":"Yang, P.-C. & Clancy, C. E. In silico prediction of sex-based differences in human susceptibility to cardiac ventricular tachyarrhythmias. Frontiers in physiology 3, 33341 (2012).","journal-title":"Frontiers in physiology"},{"key":"1370_CR82","unstructured":"Plonsey, R. & Barr, R. C.Bioelectricity: A Quantitative Approach (Springer, New York, NY, 2007)."},{"key":"1370_CR83","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1046\/j.1365-2125.2003.01791.x","volume":"55","author":"M Desai","year":"2003","unstructured":"Desai, M., Li, L., Desta, Z., Malik, M. & Flockhart, D. Variability of heart rate correction methods for the qt interval. British Journal of Clinical Pharmacology 55, 511\u2013517 (2003).","journal-title":"British Journal of Clinical Pharmacology"},{"key":"1370_CR84","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825\u20132830 (2011).","journal-title":"Journal of Machine Learning Research"},{"key":"1370_CR85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-7-91","volume":"7","author":"S Varma","year":"2006","unstructured":"Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7, 1\u20138 (2006).","journal-title":"BMC Bioinformatics"},{"key":"1370_CR86","unstructured":"Zheng, A.Evaluating machine learning models: a beginner\u2019s guide to key concepts and pitfalls (O\u2019Reilly Media, 2015)."},{"key":"1370_CR87","doi-asserted-by":"crossref","unstructured":"Rasmussen, C. E. & Williams, C. K. I.Gaussian processes for machine learning. Adaptive Computation and Machine Learning (MIT Press, Cambridge, MA, 2006).","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"1370_CR88","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/S0010-4655(02)00280-1","volume":"145","author":"A Saltelli","year":"2002","unstructured":"Saltelli, A. Making best use of model evaluations to compute sensitivity indices. Computer physics communications 145, 280\u2013297 (2002).","journal-title":"Computer physics communications"},{"key":"1370_CR89","doi-asserted-by":"publisher","unstructured":"Herman, J. & Usher, W. SALib: An open-source Python library for Sensitivity Analysis. The Journal of Open Source Software2 (2017). https:\/\/doi.org\/10.21105\/joss.00097.","DOI":"10.21105\/joss.00097"},{"key":"1370_CR90","doi-asserted-by":"publisher","first-page":"18155","DOI":"10.18174\/sesmo.18155","volume":"4","author":"T Iwanaga","year":"2022","unstructured":"Iwanaga, T., Usher, W. & Herman, J. Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling 4, 18155 (2022).","journal-title":"Socio-Environmental Systems Modelling"},{"key":"1370_CR91","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1177\/0091270010381498","volume":"51","author":"JA Florian","year":"2011","unstructured":"Florian, J. A., Torn\u00f8e, C. W., Brundage, R., Parekh, A. & Garnett, C. E. Population pharmacokinetic and concentration \u2013 QTc models for moxifloxacin: Pooled analysis of 20 thorough QT studies. The Journal of Clinical Pharmacology 51, 1152\u20131162 (2011).","journal-title":"The Journal of Clinical Pharmacology"},{"key":"1370_CR92","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1002\/cpt.1303","volume":"105","author":"J Vicente","year":"2019","unstructured":"Vicente, J. et al. Assessment of multi-ion channel block in a phase I randomized study design: Results of the CiPA phase I ECG biomarker validation study. Clinical Pharmacology & Therapeutics 105, 943\u2013953 (2019).","journal-title":"Clinical Pharmacology & Therapeutics"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01370-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01370-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01370-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T12:02:49Z","timestamp":1735214569000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01370-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,26]]},"references-count":92,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1370"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01370-8","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,26]]},"assertion":[{"value":"27 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"P.D., A.Z., L.B., C.B., B.D. and J.A. declare no competing interests. M.V. is CTO and co-founder of ELEM Biotech and C.M. is CEO and co-founder of ELEM Biotech.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"380"}}