Why the study?
Many proposed COVID-19 drugs are reported to cause cardiac adverse events, including ventricular arrhythmias, necessitating mathematical modeling to weigh risks against benefits.
Do COVID-19 investigational treatments (lopinavir, ritonavir, chloroquine, azithromycin) and their combinations increase the risk of cellular action potential prolongation and arrhythmias in simulated patient models?
Do COVID-19 investigational treatments (lopinavir, ritonavir, chloroquine, azithromycin) and their combinations increase the risk of cellular action potential prolongation and arrhythmias in simulated patient models?
Pharmacokinetic and quantitative systems pharmacology modeling predicts that combinations of COVID-19 therapies (lopinavir, ritonavir, chloroquine, azithromycin) increase the risk of QT prolongation and arrhythmias, particularly in women with pre-existing heart disease.
Simulations predict greater arrhythmia risk with COVID-19 drug combinations via PK/PD interactions; leaves open clinical relevance pending patient validation.
Many drugs that have been proposed for treatment of coronavirus disease 2019 (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 four 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 (PKs) 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 PK and pharmacodynamic drug interactions. Importantly, simulations of different patient groups predicted that women with pre-existing heart disease are especially susceptible to drug-induced arrhythmias, compared with diseased men or healthy individuals of either sex. Statistical analysis of population simulations revealed the molecular factors that make certain women 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.
No takes yet. Share an insight, caveat, or question.
Varshneya et al. (2020) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: