References
- 1..Tomek J, Bueno-Orovio A, Passini E, Zhou X, Minchole A, Britton O, et al. Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. Elife. 2019;8:e48890.
- 2..Passini E, Zhou X, Trovato C, Britton OJ, Bueno-Orovio A, Rodriguez B. The virtual assay software for human in silico drug trials to augment drug cardiac testing. J Comput Sci. 2020;45:101163.
- 3..Passini E, Britton OJ, Lu HR, Rohrbacher J, Hermans AN, Gallacher DJ, et al. Human in silico drug trials demonstrate higher accuracy than animal models in predicting clinical pro-arrhythmic cardiotoxicity. Front Physiol.2017;8:668.
- 4..Strauss DG, Gintant G, Li Z, Wu W, Blinova K, Vicente J, et al. Comprehensive in vitro proarrhythmia assay (CiPA) update from a cardiac Safety research consortium/health and environmental sciences institute/FDA meeting. Ther Innov Regul Sci. 2019;53:519–25.
- 5..Zhou X, Qu Y, Passini E, Bueno-Orovio A, Liu Y, Vargas HM, et al. Blinded in silico drug trial reveals the minimum set of ion channels for torsades de pointes risk assessment. Front Pharmacol. 2020;10:643.
- 6..Fang L, Tsakalozou E, Polli JE. Paving the path for increased technological innovation, strategic decision making and regulatory acceptance of modeling and simulation approaches. Pharm Res. 2025;42(5):727–9.
- 7..Serrano R, Feyen DA, Bruyneel AA, Hnatiuk AP, Vu MM, Amatya PL, et al. A deep learning platform to assess drug proarrhythmia risk. Cell Stem Cell.2023;30(1):86-95.e4.
- 8..Sahli-Costabal F, Seo K, Ashley E, Kuhl E. Classifying drugs by their arrhythmogenic risk using machine learning. Biophys J.2020;118(5):1165–76.
- 9..Tang X, Yu Z, Chen J, Cui Y, Shao D, Wang W, et al. CellForge: agentic design of virtual cell models. arXiv: 2508.02276 [Preprint]. 2025
- 10..Bremner SB, Gaffney KS, Sniadecki NJ, Mack DL. A change of heart:human cardiac tissue engineering as a platform for drug development. Curr Cardiol Rep. 2022;24(5):473–86.

