Saturday, 15 May 2021

Modeling the electrical activity of human induced pluripotent stem cell-derived cardiomyocytes

Contributed by

The human induced pluripotent stem cell (hiPSC) technology, discovered in the 2000s, allows us to induce pluripotency and produce stem cells directly from healthy donors and patients. hiPSC-derived cardiomyocytes (hiPSC-CMs) are cardiac cells differentiated from hiPSCs, and they represent a potentially infinite pool of human patient- and disease-specific in vitro models for cardiac studies. Their potential has been acknowledged by academia, regulators, and industry, who considered hiPSC-CMs as a new in vitro model for cardiac safety and proarrhythmia assays within safety pharmacology. An increasing amount of in vitro data has become available over the years, and this allowed us to develop comprehensive in silico models of the electrical activity of hiPSC-CMs.

Since 2013, we have published several in silico models of hiPSC-CM electrophysiology and used them for computational studies focusing on hiPSC-CM biophysics, drugs, and mutations effects.

Selected publications
  • 2013: our first in silico models of ventricular-like and atrial-like hiPSC-CMs (10.1007/s10439-013-0833-3)
  • 2015: the in silico comparison of current blocker effects on ventricular-like and atrial-like hiPSC-CMs, and human adult ventricular cardiomyocytes (10.1111/bph.13282).
  • 2017: our first study where we use control and mutant in silico populations to investigate hiPSC-CM phenotypic variability in long QT syndrome type 3 and the response to antiarrhythmic drugs (10.1016/j.hrthm.2017.07.026).
  • 2018: a new version of our hiPSC-CM model (Paci2018) developed using an automatic optimization algorithm and presenting a more refined Ca2+ handling description (10.3389/FPHYS.2018.00709).
  • 2018: our second paper on LQT syndromes (types 1 and 2) simulated with in silico populations generated with the Paci2018 model as the baseline (10.3390/ijms19113583).
  • 2020: another in silico population-based study presenting the latest Paci2020 model and comparing the results on five in silico drug tests with optically recorded in vitro data (10.1016/j.bpj.2020.03.018).
In silico hiPSC-CM model
Michelangelo Paci

Michelangelo Paci, PhD, Docent, Postdoctoral researcher at Tampere University.

He obtained his Bachelor's (2004) and Master's degrees (2006) in Biomedical Engineering and an additional Bachelor's degree (2007) in Computer engineering from the University of Bologna (Italy).