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A population of in silico models to face the variability of human induced pluripotent stem cell−derived cardiomyocytes: The hERG block case study

M Paci‚ E Passini‚ S Severi‚ J Hyttinen and B Rodriguez


Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are characterized by an extreme variability, which cannot be reproduced by a single in silico model. Here we present a population of hiPSC-CM models, calibrated using six different experimental datasets. By sampling the maximum conductances of 11 ionic currents, 10000 parameter sets were obtained. The experimental data-based calibration selected 1355 in silico models to be included in the final population. Such population reproduces the experimental data variability and it is used to assess the different responses to a 90% IKr block. Three different profiles emerged: models still normally beating (562), action potentials with EADs (336) and repolarization failures (457). The models still beating after 800 s since IKr block showed a mean ΔAPD90 of 723±12 ms. We observed significant differences among these three classes in the maximum conductances of ICaL, IKr, IKs, IK1, INaCa and INaK, supporting the idea that hiPSC-CM belonging to the same control population can however show dramatically different responses to an external perturbation, due to the physiological variability. This has to be taken in proper consideration in the perspective of using hiPSC-CMs for safety pharmacology assays.

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Computing in Cardiology
Biological system modeling‚Biomarkers‚Computational modeling‚EAD‚IKr block‚In vitro‚Safety‚Sociology‚Statistics‚action potentials‚bioelectric potentials‚cellular biophysics‚experimental data variability‚experimental data−based calibration‚external perturbation‚final population‚hERG block case study‚hiPSC−CM models‚human induced pluripotent stem cell−derived cardio‚ionic currents‚maximum conductances‚medical computing‚normally beating‚parameter sets‚patient treatment‚physiological variability‚repolarization failures‚safety pharmacology assays‚single in silico model