Investigational Treatments for COVID‐19 may Increase Ventricular Arrhythmia Risk Through Drug Interactions

  1. Meera Varshneya
  2. Itziar Irurzun-Arana
  3. Chiara Campana
  4. Rafael Dariolli
  5. Amy Gutierrez
  6. Taylor K. Pullinger
  7. Eric A. Sobie

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Abstract

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.

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  1. Version published to 10.1002/psp4.12573
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    SciScore for 10.1101/2020.05.21.20109397: (What is this?)

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    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  3. Version published to 10.1101/2020.05.21.20109397v1 on medRxiv