A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound

Benjamin L. Ranard, Carson C. Chow, Murad Megjhani, Shadnaz Asgari, Soojin Park, Yoram Vodovotz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of “Paxlovid rebound,” a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

Original languageEnglish
Article numbere28854
JournalJournal of Medical Virology
Volume95
Issue number6
DOIs
StatePublished - Jun 2023
Externally publishedYes

Keywords

  • SARS coronavirus: virus classification
  • antiviral agents
  • computer modeling: biostatistics & bioinformatics
  • immune responses

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