2023
DOI: 10.3389/fphys.2023.1266084
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Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Yunendah Nur Fuadah,
Ali Ikhsanul Qauli,
Aroli Marcellinus
et al.

Abstract: Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing mu… Show more

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