Using induced pluripotent stem cells for drug discovery in arrhythmias

Expert Opin Drug Discov. 2024 Jul;19(7):827-840. doi: 10.1080/17460441.2024.2360420. Epub 2024 Jun 2.

Abstract

Introduction: Arrhythmias are disturbances in the normal rhythm of the heart and account for significant cardiovascular morbidity and mortality worldwide. Historically, preclinical research has been anchored in animal models, though physiological differences between these models and humans have limited their clinical translation. The discovery of human induced pluripotent stem cells (iPSC) and subsequent differentiation into cardiomyocyte has led to the development of new in vitro models of arrhythmias with the hope of a new pathway for both exploration of pathogenic variants and novel therapeutic discovery.

Areas covered: The authors describe the latest two-dimensional in vitro models of arrhythmias, several examples of the use of these models in drug development, and the role of gene editing when modeling diseases. They conclude by discussing the use of three-dimensional models in the study of arrythmias and the integration of computational technologies and machine learning with experimental technologies.

Expert opinion: Human iPSC-derived cardiomyocytes models have significant potential to augment disease modeling, drug discovery, and toxicity studies in preclinical development. While there is initial success with modeling arrhythmias, the field is still in its nascency and requires advances in maturation, cellular diversity, and readouts to emulate arrhythmias more accurately.

Keywords: Arrythmia; cardiomyocytes; disease modeling; drug development; human induced pluripotent stem cells.

Publication types

  • Review

MeSH terms

  • Animals
  • Anti-Arrhythmia Agents / pharmacology
  • Arrhythmias, Cardiac* / drug therapy
  • Arrhythmias, Cardiac* / physiopathology
  • Cell Differentiation*
  • Drug Development* / methods
  • Drug Discovery* / methods
  • Gene Editing / methods
  • Humans
  • Induced Pluripotent Stem Cells*
  • Machine Learning
  • Models, Biological
  • Myocytes, Cardiac* / drug effects

Substances

  • Anti-Arrhythmia Agents