Multidimensional estimation of cardiac arrhythmia potential across space and time

All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. The current safety evaluation for cardiac toxicity involves interrogating effects of the drug on the delayed rectifier potassium current in single cells and the QT interval in healthy volunteers. However, this paradigm for drug safety evaluation is costly, lengthy, conservative, and impede efficient drug development. Here we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a multidimensional risk estimator to quickly and reliably stratify new and existing drugs according to their pro-arrhythmic potential. We capitalize on recent developments in machine learning and integrate information across ten orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 23 common drugs, exclusively on the basis of their concentrations at 50% current block. Our new risk estimator explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the pro-arrhythmic potential of new drugs. Our study paves the way towards establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.