Optimising experimental designs for model selection of ion channel drug binding mechanisms

Abstract

The rapid delayed rectifier current carried by the human Ether-a-go-go-Related Gene (hERG) channel is susceptible to drug-induced reduction which can lead to an increased risk of cardiac arrhythmia. Establishing the mechanism by which a specific drug compound binds to hERG can help to reduce uncertainty when quantifying pro-arrhythmic risk. In this study, we introduce a methodology for optimising experimental voltage protocols to produce data that enable different proposed models for the drug-binding mechanism to be distinguished. We demonstrate the performance of this methodology via a synthetic data study. If the underlying model of hERG current is known exactly, then the optimised protocols generated show noticeable improvements in our ability to select the true model when compared to a simple protocol used in previous studies. However, if the model is not known exactly, and we assume a discrepancy between the data-generating hERG model and the hERG model used in fitting the models, then the optimised protocols become less effective in determining the true binding dynamics. While the introduced methodology shows promise, we must be careful to ensure that, if applied in a real data study, we have a well-calibrated model of hERG current gating.