A greedy classifier optimization strategy to assess ion channel blocking activity and pro-arrhythmia in hiPSC-cardiomyocytes

Novel studies conducting cardiac safety assessment using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising but might be limited by their specificity and predictivity. It is often challenging to correctly classify ion channel blockers or to sufficiently predict the risk for Torsade de Pointes (TdP). In this study, we developed a method combining in vitro and in silico experiments to improve machine learning approaches in delivering fast and reliable prediction of drug-induced ion-channel blockade and proarrhythmic behaviour. The algorithm is based on the construction of a dictionary and a greedy optimization, leading to the definition of optimal classifiers. Finally, we present a numerical tool that can accurately predict compound-induced pro-arrhythmic risk and involvement of sodium, calcium and potassium channels, based on hiPSC-CM field potential data.
Author summary Being able to measure the electrophysiology of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) using multi-electrodes arrays (MEA) is promising in view of introducing novel drug screening methodologies in cardiac safety assessment in a preclinical setting. However, with new opportunities come new challenges. Data generated from hiPSC-CM MEA assays are challenging to interpret and translate to the clinical situation. Moreover, the observed experimental variability of the traces makes it difficult to assess whether we can systematically address classification problems such as channel blockade prediction, or arrhythmia risk. It would be of the utmost importance to understand if, in the Field potentials, there is enough information about these phenomena, and how it could be extracted. The method investigated is a first step towards this, and it is based on the construction of a dictionary of signal features: some of them are known markers used in electrophysiology, some are more agnostical signal characteristics. A goal oriented search is performed, in such a way that the input of the classifiers is found in order to maximise the success rate. Since, in general, the number of available experimental traces is not large enough to cover all the possible scenarios of interest, the experimental training set is complemented by an in silico training set. This method was applied to arrhythmic risk prediction on in silico data and channel blockade prediction on combined in silico and in vitro data. A conceptual scheme of the main points of the present contribution is presented in Fig 1.