Electromechanical modelling and simulation of human-induced pluripotent stem cell-derived cardiomyocytes predict

Milda Folkmanaite1, Xin Zhou1, Andreas Koschinski1

  • 1University of Oxford, Oxford, United Kingdom.

PubMed

Insights

New computer models simulate human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) electromechanical behavior, improving drug testing accuracy. These models accurately predict drug effects and reveal novel mechanisms, advancing cardiac research.

Area of Science:

  • Cardiovascular Research
  • Computational Biology
  • Pharmacology

Background:

  • Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are valuable for cardiac disease modeling and drug testing.
  • Existing computational models often lack human-specific mechanical properties crucial for accurate hiPSC-CM simulations.
  • Adult human cardiac tissue is scarce, limiting its use in research and drug development.

Purpose of the Study:

  • To develop and evaluate novel electromechanical models of hiPSC-CMs at different maturation states.
  • To incorporate human-specific mechanical properties into hiPSC-CM computational models.
  • To validate these models against experimental data for reliable in silico drug testing.

Main Methods:

  • Developed two versions of hiPSC-CM electromechanical models based on human cardiomyocyte and hiPSC-CM data.
  • Incorporated mechanical properties specific to hiPSC-CMs.
  • Validated models by comparing simulation outcomes with extensive experimental datasets, including drug responses.

Main Results:

  • The models accurately simulated hiPSC-CM electrophysiology and contraction.
  • Simulations correctly predicted the inotropic effects of 41 out of 48 drugs.
  • Identified previously unrecognized rate-dependent inotropic effects of paliperidone, confirmed experimentally.

Conclusions:

  • The developed in vitro-in silico framework enables accurate simulation of drug-dependent electromechanical effects in hiPSC-CMs.
  • The models enhance drug testing efficiency and accuracy by integrating computational predictions with experimental data.
  • This approach provides mechanistic insights into drug actions and cardiac electrophysiology.