A Deep Learning Platform to Assess Drug Proarrhythmia Risk

Overview

Journal Cell Stem Cell

Publisher Cell Press

Specialty Cell Biology

Date 2022 Dec 23

PMID 36563695

Authors

Ricardo Serrano

Dries A M Feyen

Arne A N Bruyneel

Anna P Hnatiuk

Michelle M Vu

Prashila L Amatya

Isaac Perea-Gil

Maricela Prado

Timon Seeger

Joseph C Wu

Ioannis Karakikes

Mark Mercola

Affiliations

Soon will be listed here.

Abstract

Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro model for predicting drug-induced cardiac arrhythmia. However, the extent to which human-defined features of in vitro arrhythmia predict actual clinical risk has been much debated. Here, we trained a convolutional neural network classifier (CNN) to learn features of in vitro action potential recordings of hiPSC-CMs that are associated with lethal Torsade de Pointes arrhythmia. The CNN classifier accurately predicted the risk of drug-induced arrhythmia in people. The risk profile of the test drugs was similar across hiPSC-CMs derived from different healthy donors. In contrast, pathogenic mutations that cause arrhythmogenic cardiomyopathies in patients significantly increased the proarrhythmic propensity to certain intermediate and high-risk drugs in the hiPSC-CMs. Thus, deep learning can identify in vitro arrhythmic features that correlate with clinical arrhythmia and discern the influence of patient genetics on the risk of drug-induced arrhythmia.

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