Tailoring mathematical models to stem-cell derived cardiomyocyte lines can improve predictions of drug-induced changes to their electrophysiology

Lei, Chon-Lok and Wang, Ken and Clerx, Michael and Johnstone, Ross H. and Hortigon-Vinagre, Maria P. and Zamora, Victor and Allan, Andrew and Smith, Godfrey L. and Gavaghan, David J. and Mirams, Gary R. and Polonchuk, Liudmila (2017) Tailoring mathematical models to stem-cell derived cardiomyocyte lines can improve predictions of drug-induced changes to their electrophysiology. Frontiers in Physiology, 8 . 986/1-986/13. ISSN 1664-042X

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution.
Download (2MB) | Preview

Abstract

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results.

Item Type: Article
Keywords: cardiomyocytes, stem cell derived, electrophysiology, mathematical model, pharmacology, variability, computational model
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: 10.3389/fphys.2017.00986
Depositing User: Mirams, Gary
Date Deposited: 12 Dec 2017 12:34
Last Modified: 13 Dec 2017 05:41
URI: http://eprints.nottingham.ac.uk/id/eprint/48668

Actions (Archive Staff Only)

Edit View Edit View