In silico evaluation of cell therapy in acute versus chronic infarction: role of automaticity, heterogeneity and Purkinje in human

Holmes, J. W., Borg, T. K. & Covell, J. W. Structure and mechanics of healing myocardial infarcts. Annu. Rev. Biomed. Eng. 7(1), 223–253. https://doi.org/10.1146/annurev.bioeng.7.060804.100453 (2005).Article 
PubMed 

Google Scholar 
Sutton, M. G. S. J. & Sharpe, N. Left ventricular remodeling after myocardial infarction. Circulation 101(25), 2981. https://doi.org/10.1161/01.CIR.101.25.2981 (2000).Article 
PubMed 

Google Scholar 
Foo, K. S. et al. Human ISL1+ ventricular progenitors self-assemble into an in vivo functional heart patch and preserve cardiac function post infarction. Mol. Ther. 26(7), 1644–1659. https://doi.org/10.1016/j.ymthe.2018.02.012 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Querdel, E. et al. Human engineered heart tissue patches remuscularize the injured heart in a dose-dependent manner. Circulation 143(20), 1991–2006. https://doi.org/10.1161/CIRCULATIONAHA.120.047904 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Zimmermann, W.-H. et al. Engineered heart tissue grafts improve systolic and diastolic function in infarcted rat hearts. Nat. Med. 12(4), 1394. https://doi.org/10.1038/nm1394 (2006).Article 

Google Scholar 
Chong, J. J. H. et al. Human embryonic-stem-cell-derived cardiomyocytes regenerate non-human primate hearts. Nature 510(7504), 13233. https://doi.org/10.1038/nature13233 (2014).Article 

Google Scholar 
Romagnuolo, R. et al. Human embryonic stem cell-derived cardiomyocytes regenerate the infarcted pig heart but induce ventricular tachyarrhythmias. Stem Cell Rep. 12(5), 967–981. https://doi.org/10.1016/j.stemcr.2019.04.005 (2019).Article 

Google Scholar 
Arevalo, H., Plank, G., Helm, P., Halperin, H. & Trayanova, N. Tachycardia in post-infarction hearts: Insights from 3D image-based ventricular models. PLoS ONE 8(7), 872. https://doi.org/10.1371/journal.pone.0068872 (2013).Article 

Google Scholar 
Martinez-Navarro, H., Mincholé, A., Bueno-Orovio, A. & Rodriguez, B. High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence. Sci. Rep. 9(1), 2. https://doi.org/10.1038/s41598-019-53221-2 (2019).Article 

Google Scholar 
Wang, Z. J. et al. Human biventricular electromechanical simulations on the progression of electrocardiographic and mechanical abnormalities in post-myocardial infarction. EP Europace 23, 405. https://doi.org/10.1093/europace/euaa405 (2021).Article 

Google Scholar 
Roney, C. H. et al. In silico comparison of left atrial ablation techniques that target the anatomical, structural, and electrical substrates of atrial fibrillation. Front. Physiol. 11, 874. https://doi.org/10.3389/fphys.2020.572874 (2020).Article 

Google Scholar 
O’Hara, R. P. et al. Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy. Elife 11, 73325. https://doi.org/10.7554/eLife.73325 (2022).Article 

Google Scholar 
Dasí, A. et al. In-silico drug trials for precision medicine in atrial fibrillation: From ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria. Front. Physiol. 13, 46. https://doi.org/10.3389/fphys.2022.966046 (2022).Article 

Google Scholar 
Fassina, D. et al. Assessing the arrhythmogenic risk of engineered heart tissue patches through in silico application on infarcted ventricle models. Comput. Biol. Med. 154, 106550. https://doi.org/10.1016/j.compbiomed.2023.106550 (2023).Article 
PubMed 

Google Scholar 
Fassina, D. et al. Modelling the interaction between stem cells derived cardiomyocytes patches and host myocardium to aid non-arrhythmic engineered heart tissue design. PLoS Comput. Biol. 18(4), e1010030. https://doi.org/10.1371/journal.pcbi.1010030 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Gibbs, C. E. et al. Graft-host coupling changes can lead to engraftment arrhythmia: A computational study. J. Physiol. https://doi.org/10.1113/JP284244 (2023).Article 
PubMed 

Google Scholar 
Yu, J. K. et al. A comprehensive, multiscale framework for evaluation of arrhythmias arising from cell therapy in the whole post-myocardial infarcted heart. Sci. Rep. 9(1), 1. https://doi.org/10.1038/s41598-019-45684-0 (2019).Article 

Google Scholar 
Yu, J. K. et al. Assessment of arrhythmia mechanism and burden of the infarcted ventricles following remuscularization with pluripotent stem cell-derived cardiomyocyte patches using patient-derived models. Cardiovasc. Res. https://doi.org/10.1093/cvr/cvab140 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ban, K., Bae, S. & Yoon, Y. Current strategies and challenges for purification of cardiomyocytes derived from human pluripotent stem cells. Theranostics 7(7), 19427. https://doi.org/10.7150/thno.19427 (2017).Article 

Google Scholar 
Jiang, B., Yan, L., Shamul, J. G., Hakun, M. & He, X. Stem cell therapy of myocardial infarction: A promising opportunity in bioengineering. Adv. Ther. (Weinh.) 3(3), 182. https://doi.org/10.1002/adtp.201900182 (2020).Article 

Google Scholar 
Zhou, X. et al. Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations. eLife. https://doi.org/10.7554/eLife.93002.1 (2024).Article 
PubMed 
PubMed Central 

Google Scholar 
Tomek, J. et al. Development, calibration, and validation of a novel human ventricular myocyte model in health, disease, and drug block. Elife 8, 890. https://doi.org/10.7554/eLife.48890 (2019).Article 

Google Scholar 
Trovato, C. et al. Human Purkinje in silico model enables mechanistic investigations into automaticity and pro-arrhythmic abnormalities. J. Mol. Cell Cardiol. 142, 1. https://doi.org/10.1016/j.yjmcc.2020.04.001 (2020).Article 

Google Scholar 
Chew, D. S. et al. Fragmented QRS complexes after acute myocardial infarction are independently associated with unfavorable left ventricular remodeling. J. Electrocardiol. 51(4), 607–612. https://doi.org/10.1016/j.jelectrocard.2018.04.004 (2018).Article 
PubMed 

Google Scholar 
Nable, J. V. & Brady, W. The evolution of electrocardiographic changes in ST-segment elevation myocardial infarction. Am. J. Emerg. Med. 27(6), 734–746. https://doi.org/10.1016/j.ajem.2008.05.025 (2009).Article 
PubMed 

Google Scholar 
Bousseljot, R., Kreiseler, D. & Schnabel, A. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet. Biomed. Tech. 1, 317–318. https://doi.org/10.1515/bmte.1995.40.s1.317 (2009).Article 

Google Scholar 
Zhou, X., Wang, Z., Tomek, J., Wang, L. & Rodriguez, B. Post myocardial infarction ionic remodelling promotes repolarisation dispersions and abnormalities in acute and chronic stages. EP Europace 23, 573. https://doi.org/10.1093/europace/euab116.573 (2021).Article 

Google Scholar 
Doss, M. X. et al. Maximum diastolic potential of human induced pluripotent stem cell-derived cardiomyocytes depends critically on IKr. PLoS ONE 7(7), 40288. https://doi.org/10.1371/journal.pone.0040288 (2012).Article 
ADS 

Google Scholar 
He, J.-Q., Ma, Y., Lee, Y., Thomson, J. A. & Kamp, T. J. Human embryonic stem cells develop into multiple types of cardiac myocytes. Circ. Res. 93(1), 99. https://doi.org/10.1161/01.RES.0000080317.92718.99 (2003).Article 

Google Scholar 
Ma, J. et al. High purity human-induced pluripotent stem cell-derived cardiomyocytes: Electrophysiological properties of action potentials and ionic currents. Am. J. Physiol. Heart Circ. Physiol. 301(5), 1. https://doi.org/10.1152/ajpheart.00694.2011 (2011).Article 

Google Scholar 
Selvakumar, D. et al. Cellular heterogeneity of pluripotent stem cell derived cardiomyocyte grafts is mechanistically linked to treatable arrhythmias. Heart Lung Circ. 31, S37–S38 (2022).Article 

Google Scholar 
O’Hara, T., Virág, L., Varró, A. & Rudy, Y. Simulation of the undiseased human cardiac ventricular action potential: Model formulation and experimental validation. PLoS Comput. Biol. 7(5), 61. https://doi.org/10.1371/journal.pcbi.1002061 (2011).Article 

Google Scholar 
Hinata, Y. et al. Importance of beating rate control for the analysis of drug effects on contractility in human induced pluripotent stem cell-derived cardiomyocytes. J. Pharmacol. Toxicol. Methods 118, 107228. https://doi.org/10.1016/j.vascn.2022.107228 (2022).Article 
PubMed 

Google Scholar 
Poch, C. M. et al. Migratory and anti-fibrotic programmes define the regenerative potential of human cardiac progenitors. Nat. Cell Biol. 24(5), 659–671. https://doi.org/10.1038/s41556-022-00899-8 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Marchiano, S. et al. Gene editing to prevent ventricular arrhythmias associated with cardiomyocyte cell therapy. Cell Stem Cell 30(4), 396–414. https://doi.org/10.1016/j.stem.2023.03.010 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Boyden, P. A., Hirose, M. & Dun, W. Cardiac Purkinje cells. Heart Rhythm 7(1), 127–135. https://doi.org/10.1016/j.hrthm.2009.09.017 (2010).Article 
PubMed 

Google Scholar 
Deo, M., Boyle, P., Plank, G. & Vigmond, E. Arrhythmogenic mechanisms of the Purkinje system during electric shocks: A modeling study. Heart Rhythm 6(12), 1782–1789. https://doi.org/10.1016/j.hrthm.2009.08.023 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Jian, K., Li, C., Hancox, J. C. & Zhang, H. Pro-arrhythmic effects of discontinuous conduction at the Purkinje fiber-ventricle junction arising from heart failure-induced ionic remodelling—Insights from computational modelling. Front. Physiol. 13, 428. https://doi.org/10.3389/fphys.2022.877428 (2022).Article 

Google Scholar 
Riebel, L. L. et al. Modelling and simulation reveals density-dependent re-entry risk in the infarcted ventricles after stem cell-derived cardiomyocyte delivery. In 2022 Computing in Cardiology (CinC). https://doi.org/10.22489/CinC.2022.392 (2022).Mincholé, A., Zacur, E., Ariga, R., Grau, V. & Rodriguez, B. MRI-based computational torso/biventricular multiscale models to investigate the impact of anatomical variability on the ECG QRS complex. Front. Physiol. 10, 1103. https://doi.org/10.3389/fphys.2019.01103 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Doste, R. et al. A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts. Int. J. Numer. Method Biomed. Eng. 35(4), e3185. https://doi.org/10.1002/cnm.3185 (2019).Article 
PubMed 

Google Scholar 
Streeter, D. D. et al. Stress distribution in the canine left ventricle during diastole and systole. Biophys. J. 10(4), 345–363. https://doi.org/10.1016/S0006-3495(70)86306-8 (1970).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Berg, L. A. et al. Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks. Sci. Rep. 13(1), 11788. https://doi.org/10.1038/s41598-023-38653-1 (2023).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Camps, J. et al. Digital twinning of the human ventricular activation sequence to clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials. arXiv e-prints (2023).Taggart, P. et al. Inhomogeneous transmural conduction during early ischaemia in patients with coronary artery disease. J. Mol. Cell Cardiol. 32(4), 105. https://doi.org/10.1006/jmcc.2000.1105 (2000).Article 

Google Scholar 
Joyner, R. W. & Overholt, E. D. Effects of octanol on canine subendocardial Purkinje-to-ventricular transmission. Am. J. Physiol. Heart Circ. Physiol. 249(6), H1228–H1231. https://doi.org/10.1152/ajpheart.1985.249.6.H1228 (1985).Article 

Google Scholar 
Sachetto Oliveira, R. et al. Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating cardiac electrophysiology. Int. J. Numer. Method Biomed. Eng. 34(2), 2913. https://doi.org/10.1002/cnm.2913 (2018).Article 

Google Scholar 
Tomek, J., Bueno-Orovio, A. & Rodriguez, B. ToR-ORd-dynCl: An update of the ToR-ORd model of human ventricular cardiomyocyte with dynamic intracellular chloride. BioRxiv. https://doi.org/10.1101/2020.06.01.127043 (2020).Article 

Google Scholar 
Paci, M. et al. All-optical electrophysiology refines populations of in silico human iPSC-CMs for drug evaluation. Biophys. J. 118(10), 18. https://doi.org/10.1016/j.bpj.2020.03.018 (2020).Article 
ADS 

Google Scholar 
Boukens, B. J. et al. Transmural APD gradient synchronizes repolarization in the human left ventricular wall. Cardiovasc. Res. 108(1), 188–196. https://doi.org/10.1093/cvr/cvv202 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Franz, M. R., Bargheer, K., Rafflenbeul, W., Haverich, A. & Lichtlen, P. R. Monophasic action potential mapping in human subjects with normal electrocardiograms: Direct evidence for the genesis of the T wave. Circulation 75(2), 379–386. https://doi.org/10.1161/01.CIR.75.2.379 (1987).Article 
PubMed 

Google Scholar 
Rog-Zielinska, E. A., Norris, R. A., Kohl, P. & Markwald, R. The living scar—Cardiac fibroblasts and the injured heart. Trends Mol. Med. 22(2), 99–114. https://doi.org/10.1016/j.molmed.2015.12.006 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ringenberg, J. et al. Effects of fibrosis morphology on reentrant ventricular tachycardia inducibility and simulation fidelity in patient-derived models. Clin. Med. Insights Cardiol. 8, 15712. https://doi.org/10.4137/CMC.S15712 (2014).Article 

Google Scholar 
Kohl, P. Heterogeneous cell coupling in the heart. Circ. Res. 93(5), 381–383. https://doi.org/10.1161/01.RES.0000091364.90121.0C (2003).Article 
PubMed 

Google Scholar 
Klesen, A. et al. Cardiac fibroblasts. Herzschrittmacherther. Elektrophysiol. 29(1), 62–69. https://doi.org/10.1007/s00399-018-0553-3 (2018).Article 
PubMed 

Google Scholar 
Yue, L., Xie, J. & Nattel, S. Molecular determinants of cardiac fibroblast electrical function and therapeutic implications for atrial fibrillation. Cardiovasc. Res. 89(4), 744–753. https://doi.org/10.1093/cvr/cvq329 (2011).Article 
PubMed 

Google Scholar 
Cardone-Noott, L. et al. A computational investigation into the effect of infarction on clinical human electrophysiology biomarkers. Comput. Cardiol. 2014, 673–676 (2014).
Google Scholar 
Hill, Y. R. et al. Investigating a novel activation-repolarisation time metric to predict localised vulnerability to reentry using computational modelling. PLoS ONE 11(3), e0149342. https://doi.org/10.1371/journal.pone.0149342 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Brinkman, A. M., Baker, P. B., Newman, W. P., Vigorito, R. & Friedman, M. H. Variability of human coronary artery geometry: An angiographic study of the left anterior descending arteries of 30 autopsy hearts. Ann. Biomed. Eng. 22(1), 34–44. https://doi.org/10.1007/BF02368220 (1994).Article 
PubMed 

Google Scholar 
Ørn, S. et al. Effect of left ventricular scar size, location, and transmurality on left ventricular remodeling with healed myocardial infarction. Am. J. Cardiol. 99(8), 1109–1114. https://doi.org/10.1016/j.amjcard.2006.11.059 (2007).Article 
PubMed 

Google Scholar 
Reindl, M. et al. Impact of infarct location and size on clinical outcome after ST-elevation myocardial infarction treated by primary percutaneous coronary intervention. Int. J. Cardiol. 301, 14–20. https://doi.org/10.1016/j.ijcard.2019.11.123 (2020).Article 
PubMed 

Google Scholar 
Spath, N. B. et al. Assessment of stunned and viable myocardium using manganese-enhanced MRI. Open Heart 8(1), e001646. https://doi.org/10.1136/openhrt-2021-001646 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Tülümen, E. et al. Extent of peri-infarct scar on late gadolinium enhancement cardiac magnetic resonance imaging and outcome in patients with ischemic cardiomyopathy. Heart Rhythm 18(6), 954–961. https://doi.org/10.1016/j.hrthm.2021.01.023 (2021).Article 
PubMed 

Google Scholar 
Aronis, K. N. et al. Accurate conduction velocity maps and their association with scar distribution on magnetic resonance imaging in patients with postinfarction ventricular tachycardias. Circ. Arrhythm. Electrophysiol. 13(4), 792. https://doi.org/10.1161/CIRCEP.119.007792 (2020).Article 

Google Scholar 
Jamil-Copley, S. et al. Application of ripple mapping to visualize slow conduction channels within the infarct-related left ventricular scar. Circ. Arrhythm. Electrophysiol. 8(1), 76–86. https://doi.org/10.1161/CIRCEP.114.001827 (2015).Article 
PubMed 

Google Scholar 
Hansen, K. J., Laflamme, M. A. & Gaudette, G. R. Development of a contractile cardiac fiber from pluripotent stem cell derived cardiomyocytes. Front. Cardiovasc. Med. 5, 52. https://doi.org/10.3389/fcvm.2018.00052 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
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. https://doi.org/10.3389/fphys.2017.00668 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Musuamba, F. T. et al. Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility. CPT Pharmacometr. Syst. Pharmacol. 10(8), 804–825. https://doi.org/10.1002/psp4.12669 (2021).Article 

Google Scholar 
Viceconti, M. et al. In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods 185, 120–127. https://doi.org/10.1016/j.ymeth.2020.01.011 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Trovato, C., Mohr, M., Schmidt, F., Passini, E. & Rodriguez, B. Cross clinical-experimental-computational qualification of in silico drug trials on human cardiac Purkinje cells for proarrhythmia risk prediction. Front. Toxicol. 4, 650. https://doi.org/10.3389/ftox.2022.992650 (2022).Article 

Google Scholar 
Riebel, L. L. et al. In silico identification of the key ionic currents modulating human pluripotent stem cell-derived cardiomyocytes towards an adult phenotype. In 2021 Computing in Cardiology (CinC) 1–4. https://doi.org/10.23919/CinC53138.2021.9662683 (IEEE, 2021).Paci, M. et al. Comparison of the simulated response of three in silico human stem cell-derived cardiomyocytes models and in vitro data under 15 drug actions. Front. Pharmacol. 12, 713. https://doi.org/10.3389/fphar.2021.604713 (2021).Article 

Google Scholar 
Kienzle, M. G., Tan, R. C., Ramza, B. M., Young, M. L. & Joyner, R. W. Alterations in endocardial activation of the canine papillary muscle early and late after myocardial infarction. Circulation 76(4), 860–874. https://doi.org/10.1161/01.CIR.76.4.860 (1987).Article 
PubMed 

Google Scholar 
Bishop, M. J. & Plank, G. Bidomain ECG simulations using an augmented monodomain model for the cardiac source. IEEE Trans. Biomed. Eng. 58(8), 2297–2307. https://doi.org/10.1109/TBME.2011.2148718 (2011).Article 

Google Scholar 
Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101(23), 215. https://doi.org/10.1161/01.CIR.101.23.e215 (2000).Article 

Google Scholar 

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