BACKGROUND: The slow and rapid delayed rectifier K+ currents (IKs and IKr, respectively) are responsible for repolarizing the ventricular action potential (AP) and preventing abnormally long APs that may lead to arrhythmias. Although differences in biophysical properties of the two currents have been carefully documented, the respective physiological roles of IKr and IKs are less established. In this study, we sought to understand the individual roles of these currents and quantify how effectively each stabilizes the AP and protects cells against arrhythmias across multiple species. METHODS: We compared 10 mathematical models describing ventricular myocytes from human, rabbit, canine, and guinea pig. We examined variability within heterogeneous cell populations, tested the susceptibility of cells to proarrhythmic behavior, and studied how IKs and IKr responded to changes in the AP. RESULTS: We found that: (1) models with higher baseline IKs exhibited less cell-to-cell variability in action potential duration (APD); (2) models with higher baseline IKs were less susceptible to early afterdepolarizations (EADs) induced by depolarizing perturbations; (3) as APD is lengthened, IKs increases more profoundly than IKr, thereby providing negative feedback that resists excessive AP prolongation; and (4) the increase in IKs that occurs during β-adrenergic stimulation is critical for protecting cardiac myocytes from EADs under these conditions. CONCLUSIONS: Slow delayed rectifier current is uniformly protective across a variety of cell types. These results suggest that IKs enhancement could potentially be an effective antiarrhythmic strategy.

At present, the QT interval on the electrocardiographic (ECG) waveform is the most common metric for assessing an individual’s susceptibility to ventricular arrhythmias, with a long QT, or, at the cellular level, a long action potential duration (APD) considered high risk. However, the limitations of this simple approach have long been recognized. Here, we sought to improve prediction of arrhythmia susceptibility by combining mechanistic mathematical modeling with machine learning (ML). Simulations with a model of the ventricular myocyte were performed to develop a large heterogenous population of cardiomyocytes (n = 10,586), and we tested each variant’s ability to withstand three arrhythmogenic triggers: 1) block of the rapid delayed rectifier potassium current (IKr Block), 2) augmentation of the L-type calcium current (ICaL Increase), and 3) injection of inward current (Current Injection). Eight ML algorithms were trained to predict, based on simulated AP features in preperturbed cells, whether each cell would develop arrhythmic dynamics in response to each trigger. We found that APD can accurately predict how cells respond to the simple Current Injection trigger but cannot effectively predict the response to IKr Block or ICaL Increase. ML predictive performance could be improved by incorporating additional AP features and simulations of additional experimental protocols. Importantly, we discovered that the most relevant features and experimental protocols were trigger specific, which shed light on the mechanisms that promoted arrhythmia formation in response to the triggers. Overall, our quantitative approach provides a means to understand and predict differences between individuals in arrhythmia susceptibility.

Many drugs that have been proposed for treatment of COVID‐19 are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here we explored the potential effects on cardiac electrophysiology of 4 drugs proposed to treat COVID‐19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PK) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both pharmacokinetic and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that females with pre‐existing heart disease are especially susceptible to drug‐induced arrhythmias, compared with diseased males or healthy individuals of either sex. Statistical analysis of population simulations revealed the molecular factors that certain females with heart failure especially susceptible to arrhythmias. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID‐19 therapies.

Several drugs proposed for the treatment of COVID-19 have reported cases of cardiac adverse events such as ventricular arrhythmias. To properly weigh risks against potential benefits in a timely manner, mathematical modeling of drug disposition and drug action can be useful for predicting patient response.Here we explored the potential effects on cardiac electrophysiology of 4 COVID-19 proposed treatments: lopinavir, ritonavir, chloroquine, and azithromycin, including combination therapy involving these drugs. To address this, we combined simulations of pharmacokinetics (PK) with mechanistic mathematical modeling of human ventricular myocytes to predict adverse events caused by these treatments. We utilized a mechanistic model to construct heterogenous populations of 4 patient groups (healthy male, healthy female, diseased male, and diseased female) each with 1000 members, and studied the varied responses of drugs and combinations on each population. To determine appropriate drug concentrations for recommended COVID-19 regimen, we implemented PK models for each drug and incorporated these values into the mechanistic model. We found that: (1) drug combinations can lead to greater cellular action potential (AP) prolongation, analogous to QT prolongation, compared with drugs given in isolation; (2) simulations of chloroquine with azithromycin caused a significantly greater increase in AP duration (DAPDz190 ms) compared to lopinavir with ritonavir (DAPDz6 ms); (3) drug effects on different patient populations revealed that females with preexisting heart disease are more susceptible to drug-induced arrhythmias as 85 members formed arrhythmias, and less than 20 in each of the other three; and (4) logistic regression analysis performed on the population showed that higher levels of the sodium-calcium exchanger may predispose certain females with heart failure to drug-induced arrhythmias. Overall, these results illustrate how PK and mechanistic modeling can be combined to precisely predict cardiac arrhythmia susceptibility of COVID-19 therapies.

Many drugs that have been proposed for treatment of COVID-19 are reported to cause cardiac adverse events, including ventricular arrhythmias. In order to properly weigh risks against potential benefits, particularly when decisions must be made quickly, mathematical modeling of both drug disposition and drug action can be useful for predicting patient response and making informed decisions. Here we explored the potential effects on cardiac electrophysiology of 4 drugs proposed to treat COVID-19: lopinavir, ritonavir, chloroquine, and azithromycin, as well as combination therapy involving these drugs. Our study combined simulations of pharmacokinetics (PK) with quantitative systems pharmacology (QSP) modeling of ventricular myocytes to predict potential cardiac adverse events caused by these treatments. Simulation results predicted that drug combinations can lead to greater cellular action potential prolongation, analogous to QT prolongation, compared with drugs given in isolation. The combination effect can result from both pharmacokinetic and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that females with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared males with disease or healthy individuals of either sex. Overall, the results illustrate how PK and QSP modeling may be combined to more precisely predict cardiac risks of COVID-19 therapies.