Optimal Biomarkers Design for Drug Safety Evaluation Using Microelectrode Array Measurements

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 (hiPSC), 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. 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 numerical biomarkers. A state-of-the-art machine learning program is used to carry out the classification of drugs using MEA measurements. We show that the numerical biomarkers outperform the classical ones in different classification scenarios. We show that using both synthetic and experimental MEA measurements improves the robustness of the numerical biomarkers and that the classification scores are increased.