Sahli Costabal, F. | Search bioRxiv

bioRxiv2021

How drugs modulate the performance of the human heart

Many drugs interact with ion channels in the cells of the heart and trigger heart rhythm disorders with potentially fatal consequences. Computational modeling can provide mechanistic insight into the onset and propagation of drug-induced arrhythmias, but the effect of drugs on the mechanical behavior of the heart remains poorly understood. Here we establish a multiphysics framework that integrates the biochemical, electrical, and mechanical effects of drugs from single cardiac cells to the overall response of the whole heart. For the example of the drug dofetilide, we show that drug concentrations of 3.0x and 4.8x increase the heart rate to 122 and 114 beats per minute, increase the myofiber stretches up to 10%, and decrease tissue relaxation by 6%. Strikingly, the drug-induced interventricular and atrial-ventricular dyssynchrony results in a 2.5% decreased and 7% increased cardiac output, respectively. Our results demonstrate the potential for multiphysics, multiscale modeling towards understanding the mechanical implications of drug-induced arrhythmias. Knowing how differing drug concentrations affect the performance of the heart has important clinical implications in drug safety evaluation and personalized medicine.

biophysics↗

bioRxiv2019

Classifying drugs by their arrhythmogenic risk using machine learning

Abstract.An undesirable side effect of drugs are cardiac arrhythmias, in particular a condition called torsades de pointes. Current paradigms for drug safety evaluation are costly, lengthy, and conservative, and impede efficient drug development. Here we combine multiscale experiment and simulation, high-performance computing, and machine learning to create an easy-to-use risk assessment diagram to quickly and reliable stratify the pro-arrhythmic potential of new and existing drugs. 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 of 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 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 assessment diagram 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 shapes the way towards establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

biophysics↗