Proarrhythmic risk assessment of drugs by dVm /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-maximal inhibitory concentration and Hill coefficients of 28 drugs released by CiPA groups. Then, we trained the deep CNN model with the differential AP shapes of 12 drugs and tested it with those of 16 drugs. Our model had a better performance for classifying the proarrhythmic risk of drugs than the traditional logistic regression model using qNet. The classification accuracy was 98% for high-risk level drugs, 94% for intermediate-risk level drugs, and 89% for low-risk level drugs.

FIGURE 1

Schematic of proposed algorithms. (a) Flow chart of the whole process; (b) the convolutional neural network model structure. AP, action potential; dV _m /dt, differential action potential; IC₅₀, the half inhibitory concentration

FIGURE 2

Testing algorithm for evaluating the model performance; this algorithm was suggested by the CiPA research group based on the central limit theorem; AUC, area under the receiver operating curves; CiPA, comprehensive in vitro proarrhythmia assay

FIGURE 3

Histogram results of the 10,000‐test using 16 test drugs. Distribution of AUCs in the 10,000 ROC curves for high‐risk drugs (a), intermediate‐risk drugs (b), and low‐risk drugs (c); (d) distribution of final model accuracy; (e) F1 scores distribution of the 10,000 confusion matrices. AUC, area under the ROC curves; ROC, receiver operating curves

FIGURE 4

Confusion matrix for classification of drug's proarrhythmic risk