Mechanistic prediction of drug-induced proarrhythmic risk via simulation of afterdepolarisations in cardiac myocytes

McMillan, E

Thesis

Mechanistic prediction of drug-induced proarrhythmic risk via simulation of afterdepolarisations in cardiac myocytes

Abstract:: During the drug development process, many promising compounds are discarded due to risk of drug-induced Torsades de Pointes. Testing for block of the hERG potassium current is a sensitive way to rule out torsadogenic risk, but it lacks specificity, meaning that safe, useful medicines may be unduly prevented from coming to the market. The safety of some compounds which block the hERG channel but are not proarrhythmic may be due to their effects on other cardiac ion currents. Computational methods which integrate early-stage ion current screening data with electrophysiological modelling of cardiomyocytes offer a fast, low-cost method for prediction, and could reduce the requirements for animal experimentation. In this thesis, we present a novel method for predicting proarrhythmic risk by simulating early afterdepolarisations in human ventricular cardiac cell models. By applying ion current drug block to the cell model and provoking early afterdepolarisations using a range of interventions, we developed several metrics for the prediction of risk.

Interventions included simulation of the Long QT syndromes and injury current. We assessed the accuracy of prediction using machine learning techniques and leave-one-out cross-validation. We also compared our metrics to previous metrics that have been suggested in the literature, and investigated the effect of combining metrics together and adding pro- and anti-arrhythmic modifiers on classification accuracy.

The EAD metrics outperformed every other metric we calculated using this dataset, with the exception of APD90. The EAD metrics were also able to classify correctly some drugs which APD90 gets wrong, showing that they can provide extra information over the APD90 metric in some cases.

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APA Style

McMillan, E. (2022). Mechanistic prediction of drug-induced proarrhythmic risk via simulation of afterdepolarisations in cardiac myocytes [PhD thesis]. University of Oxford.

MLA Style

McMillan, E. Mechanistic Prediction of Drug-Induced Proarrhythmic Risk via Simulation of Afterdepolarisations in Cardiac Myocytes. University of Oxford, 2022.

Chicago Style

McMillan, E. 2022. “Mechanistic Prediction of Drug-Induced Proarrhythmic Risk via Simulation of Afterdepolarisations in Cardiac Myocytes.” PhD thesis, University of Oxford.
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