Abstract
Immature cardiomyocytes, such as those obtained by stem cell differentiation, have been shown to be useful alternatives to mature cardiomyocytes, which are limited in availability and difficult to obtain, for evaluating the behaviour of drugs for treating arrhythmia. In silico models of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) can be used to simulate the behaviour of the transmembrane potential and cytosolic calcium under drug-treated conditions. Simulating the change in action potentials due to various ionic current blocks enables the approximation of drug behaviour. We used eight machine learning classification models to predict partial block of seven possible ion currents \( (\textit{I}_{\textit{CaL}},\textit{I}_{\textit{Kr}},\textit{I}_{\textit{to}},\textit{I}_{\textit{K1}},\textit{I}_{\textit{Na}},\textit{I}_{\textit{NaL}} and \textit{I}_{\textit{Ks}}) \) in a simulated dataset containing nearly 4600 action potentials represented as a paired measure of transmembrane potential and cytosolic calcium. Each action potential was generated under 1 \( \textit{H}_{\textit{z}} \) pacing. The Convolutional Neural Network outperformed the other models with an average accuracy of predicting partial ionic current block of 93% in noise-free data and 72% accuracy with 3% added random noise. Our results show that \( \textit{I}_{\textit{CaL}} \) and \( \textit{I}_{\textit{Kr}} \) current block were classified with high accuracy with and without noise. The classification of \( \textit{I}_{\textit{to}} \), \( \textit{I}_{\textit{K1}} \) and \( \textit{I}_{\textit{Na}} \) current block showed high accuracy at 0% noise, but showed a significant decrease in accuracy when noise was added. Finally, the accuracy of \( \textit{I}_{\textit{NaL}} \) and \( \textit{I}_{\textit{Ks}} \) classification were relatively lower than the other current blocks at 0% noise and also showed a significant drop in accuracy when noise was added. In conclusion, these machine learning methods may present a pathway for estimating drug response in adult phenotype cardiac systems, but the data must be sufficiently filtered to remove noise before being used with classifier algorithms.
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Singstad, B.J., Dalen, B.S., Sihra, S., Forsch, N., Wall, S. (2022). Identifying Ionic Channel Block in a Virtual Cardiomyocyte Population Using Machine Learning Classifiers. In: McCabe, K.J. (eds) Computational Physiology. Simula SpringerBriefs on Computing(), vol 12. Springer, Cham. https://doi.org/10.1007/978-3-031-05164-7_8
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DOI: https://doi.org/10.1007/978-3-031-05164-7_8
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