Composite biomarkers improve classification of drug-induced channel block
Résumé
The Micro-Electrode Array device enables high-throughput electrophysiology measurements that are
less labour-intensive than patch-clamp based techniques. Combined with human-induced pluripotent
stem cells cardiomyocytes (hiPSC-CM), it represents a new and promising paradigm for automated and
accurate in vitro drug safety evaluation. In this article, the following question is addressed: which features
of the MEA signals should be measured to better classify the effects of drugs? A framework for the
classification of drugs using MEA measurements is proposed. Such a classification is based on the drugs
predicted ion channels blocks. It relies on an in silico electrophysiology model of the MEA, a feature
selection algorithm and automatic classification tools. An in silico model of the MEA is developed and
is used to generate synthetic measurements. An algorithm that extracts MEA measurements features
designed to perform well in a classification context is described. These features are called composite
biomarkers. A state-of-the-art machine learning program is used to carry out the classification of drugs
using experimental MEA measurements. The experiments are carried out using five different drugs:
Mexiletine, Flecainide, Diltiazem, Moxifloxacin and Dofetilide. We show that the composite biomarkers
outperform the classical ones in different classification scenarios. We show that using both synthetic and
experimental MEA measurements improves the robustness of the composite biomarkers and that the
classification scores are increased.
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