Vandetanib, a multi-kinase inhibitor used for the treatment of various cancers, has been reported to induce several adverse cardiac effects. However, the underlying mechanisms of vandetanib-induced cardiotoxicity are unclear. This study aimed to investigate the mechanism of vandetanib-induced cardiotoxicity using intracellular electrophysiological recordings on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), rabbit Purkinje fibers, and HEK293 cells transiently expressing human ether-a-go-go-related gene (hERG; the rapidly activating delayed rectifier K⁺ channel, I_Kr), KCNQ1/KCNE1 (the slowly activating delayed rectifier K⁺ current, I_Ks), KCNJ2 (the inwardly rectifying K⁺ current, I_K1) or SCN5A (the inward Na⁺ current, I_Na). Purkinje fiber assays and ion channel studies showed that vandetanib at concentrations of 1 and 3 μM inhibited the hERG currents and prolonged the action potential duration. Alanine scanning and in silico hERG docking studies demonstrated that Y652 and F656 in the hERG S6 domain play critical roles in vandetanib binding. In hiPSC-CMs, vandetanib markedly reduced the maximum rate of depolarization during the AP upstroke. Ion channel studies revealed that hiPSC-CMs were more sensitive to inhibition of the I_Na by vandetanib than in a heterogeneously expressed HEK293 cell model, consistent with the changes in the AP parameters of hiPSC-CMs. The subclasses of Class I antiarrhythmic drugs inhibited I_Na currents in a dose-dependent manner in hiPSC-CMs and SCN5A-encoded HEK293 cells. The inhibitory potency of vandetanib for I_Na was much higher in hiPSC-CMs (IC₅₀: 2.72 μM) than in HEK293 cells (IC₅₀: 36.63 μM). These data suggest that AP and I_Na assays using hiPSC-CMs are useful electrophysiological models for prediction of drug-induced cardiotoxicity.

Lee HA, et al. Korean J Physiol Pharmacol. 2016 Jan;20(1):119-127. http://dx.doi.org/10.4196/kjpp.2016.20.1.119

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.