Proarrhythmic risk assessment of drugs by dV_m /dt shapes using the convolutional neural network.

ABSTRACT: Comprehensive in vitro Proarrhythmia Assay (CiPA) projects for assessing proarrhythmic drugs suggested a logistic regression model using qNet as the Torsades de Pointes (TdP) risk assessment biomarker, obtained from in silico simulation. However, using a single in silico feature, such as qNet, cannot reflect whole characteristics related to TdP in the entire action potential (AP) shape. Thus, this study proposed a deep convolutional neural network (CNN) model using differential action potential shapes to classify three proarrhythmic risk levels: high, intermediate, and low, considering both characteristics related to TdP not only in the depolarization phase but also the repolarization phase of AP shape. We performed an in silico simulation and got AP shapes with drug effects using half-max

SUBMITTER: Jeong DU 

PROVIDER: S-EPMC9124356 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature