Prediction of human cardiotoxic QT prolongation using in vitro multiple ion channel data and mathematical models of cardiac myocytes

Thousands of animals are used across the world for the assessment of cardiac toxicity each year. Animals are used at multiple stages of drug development, in every pharmaceutical company. This is primarily for detection of risk of Torsade-de-Pointes (TdP) cardiac arrhythmia. A leading cause of withdrawal of drugs from the market, TdP risk is one of the main causes of attrition during compound development. There are two major reasons that large numbers of animals have traditionally been required: first, there are a large number of potential drug interactions in the heart, which we could not hope to screen without a representation of all of the possible targets in the whole system (with an animal model); and second, the heart's electrophysiology has been considered "too complicated" to predict a drug effect―even given the full list of drug targets and affinities, the whole physiological system must be well represented (again, with an animal model).

Technological advances mean that neither of the points above should remain a stumbling block, and in this project we will reduce animal use by taking advantage of the following techniques: we will work with AstraZeneca and GlaxoSmithKline to assess compounds for multiple cardiac–ion-channel interactions, using high-throughput in vitro screens, to address the first point; mathematical models, quantifying the complex processes involved in generation of cardiac electrical activity, address the second. We will compare our predictions with the human trial results, statistically quantifying the level of predictive power that simulations have for human clinical trials. We will provide all of the generated data, simulation and analysis tools as open-source. There will therefore be no major obstacle to the widespread use of simulation, instead of animal models, for proarrhythmic screening, with additional benefits in terms of more accurate prediction of effects in human physiology.

Johnstone RH et al. (2016). Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models? J Mol Cell Cardiol 96:49-62. doi: 10.1016/j.yjmcc.2015.11.018 

Cooper J et al. (2015). Cellular cardiac electrophysiology modeling with Chaste and CellML. Front Physiol 5:511. doi: 10.3389/fphys.2014.00511 

Walmsley J et al. (2015). Application of stochastic phenomenological modelling to cell-to-cell and beat-to-beat electrophysiological variability in cardiac tissue. Journal of Theoretical Biology 365:325–336. doi: 10.1016/j.jtbi.2014.10.029

Wang K et al. (2015). Cardiac tissue slices: preparation, handling, and successful optical mapping. American Journal of Physiology. Heart and Circulatory Physiology 308(9):H1112-25. doi: 10.1152/ajpheart.00556.2014

Williams G, Mirams GR (2015). A web portal for in-silico action potential predictions. J Pharmacol Toxicol Methods 75:10-6. doi: 10.1016/j.vascn.2015.05.002 

Mirams GR et al. (2014). Prediction of Thorough QT study results using action potential simulations based on ion channel screens. J Pharmacol Toxicol Methods  pii: S1056-8719(14)00235-4. doi: 10.1016/j.vascn.2014.07.002

Osborne JM et al. (2014). Ten simple rules for effective computational research. PLoS computational biology 10(3):e1003506. doi: 10.1371/journal.pcbi.1003506

Beattie KA et al. (2013). Evaluation of an in silico cardiac safety assay: using ion channel screening data to predict QTinterval changes in the rabbit ventricular wedge. J Pharmacol Toxicol Methods 68(1):88-96. doi: 10.1016/j.vascn.2013.04.004

Elkins RC et al. (2013). Variability in high-throughput ion-channel screening data and consequences for cardiac safety assessment. J Pharmacol Toxicol Methods 68(1):112-22. doi: 10.1016/j.vascn.2013.04.007

Mirams GR et al. (2013). Chaste: an open source C++ library for computational physiology and biology. PLoS computational biology 9(3):e1002970. doi: 10.1371/journal.pcbi.1002970

Walmsley J et al. (2013). mRNA expression levels in failing human hearts predict cellular electrophysiological remodeling: a population-based simulation study. PloS one 8(2):e56359. doi: 10.1371/journal.pone.0056359

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Strategic grant

Grant reference number

NC/K001337/1

Award date:

Feb 2013 - Apr 2014

Grant amount

£176,136 (Co-funded by EPSRC)