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Updated: Aug 5, 2025

Preclinical Cardiac Electrophysiology Assessment by Dual Voltage and Calcium Optical Mapping of Human Organotypic Cardiac Slices
Published on: June 16, 2020
Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to
Nanako Kawaguchi1, Toshio Nakanishi1
1Department of Pediatric Cardiology and Adult Congenital Cardiology, Tokyo Women's Medical University, Tokyo 162-8666, Japan.
Animal models are crucial for cardiovascular research but have limitations. Combining animal models, induced pluripotent stem cell (iPSC)-derived models, and machine learning can improve human disease modeling.
Area of Science:
- Cardiovascular research
- Translational medicine
- Disease modeling
Background:
- Traditional animal models (zebrafish, rodents, canines, pigs) offer physiological similarities but face handling, welfare, and species-specific limitations.
- Differences in disease mechanisms and organ characteristics between species and humans necessitate improved preclinical models.
- Existing preclinical studies rely on animal models for drug safety and efficacy evaluation due to a lack of alternatives.
Purpose of the Study:
- To review and explore the utility of various disease models in cardiovascular research.
- To evaluate the potential of induced pluripotent stem cell (iPSC)-derived in vitro models and humanized animals.
- To discuss the challenges and integration of machine learning in disease modeling.
Main Methods:
- Review of current animal disease models in cardiovascular research.
- Exploration of induced pluripotent stem cell (iPSC)-derived in vitro models, including 3D culture systems.
- Discussion of humanized animal models and machine learning applications.
- Comparative analysis of disease mechanisms across species and human relevance.
Main Results:
- Animal models, while valuable, present significant challenges in translation to human physiology.
- iPSC-derived human cardiomyocytes offer a genetically identical in vitro model.
- Humanized animals and advanced in vitro systems show promise for better disease mimicry.
- Machine learning presents new avenues for refining disease models.
Conclusions:
- A multi-modal approach integrating animal models, iPSC technology, humanized animals, and machine learning is essential.
- Combining these diverse modeling strategies can bridge the gap between preclinical data and human disease.
- Future research should focus on synergistic application of these methods to enhance the accuracy and translatability of disease models.
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