A retrospective cohort study of Paxlovid efficacy depending on treatment time in hospitalized COVID-19 patients

  1. Zhanwei Du
  2. Lin Wang
  3. Yuan Bai
  4. Yunhu Liu
  5. Eric HY Lau
  6. Alison P Galvani
  7. Robert M Krug
  8. Benjamin John Cowling  Is a corresponding author
  9. Lauren A Meyers  Is a corresponding author
  1. University of Hong Kong, Hong Kong
  2. University of Cambridge, United Kingdom
  3. Yale University, United States
  4. The University of Texas at Austin, United States
  5. University of Hong Kong, China

Abstract

Paxlovid, a SARS-CoV-2 antiviral, not only prevents severe illness but also curtails viral shedding, lowering transmission risks from treated patients. By fitting a mathematical model of within-host Omicron viral dynamics to electronic health records data from 208 hospitalized patients in Hong Kong, we estimate that Paxlovid can inhibit over 90% of viral replication. However, its effectiveness critically depends on the timing of treatment. If treatment is initiated three days after symptoms first appear, we estimate a 17% chance of a post-treatment viral rebound and a 12% (95% CI: 0%-16%) reduction in overall infectiousness for non-rebound cases. Earlier treatment significantly elevates the risk of rebound without further reducing infectiousness, whereas starting beyond five days reduces its efficacy in curbing peak viral shedding. Among the 104 patients who received Paxlovid, 62% began treatment within an optimal three-to-five-day day window after symptoms appeared. Our findings indicate that broader global access to Paxlovid, coupled with appropriately timed treatment, can mitigate the severity and transmission of SARS-Cov-2.

Data availability

All data used in this study can be accessed through Github: https://github.com/ZhanweiDU/PaxHK/.

Article and author information

Author details

  1. Zhanwei Du

    Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2020-767X
  2. Lin Wang

    Department of Genetics, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5371-2138
  3. Yuan Bai

    Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  4. Yunhu Liu

    Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  5. Eric HY Lau

    Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
    Competing interests
    No competing interests declared.
  6. Alison P Galvani

    Center for Infectious Disease Modeling and Analysis, Yale University, New Haven, United States
    Competing interests
    No competing interests declared.
  7. Robert M Krug

    Department of Molecular Biosciences, The University of Texas at Austin, Austin, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3754-5034
  8. Benjamin John Cowling

    Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
    For correspondence
    bcowling@hku.hk
    Competing interests
    Benjamin John Cowling, reports honoraria from AstraZeneca, Fosun Pharma, GlaxoSmithKline, Moderna, Pfizer,Sanofi Pasteur, and Roche. The authors report no other potential conflicts of interest.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6297-7154
  9. Lauren A Meyers

    Department of Integrative Biology, The University of Texas at Austin, Austin, United States
    For correspondence
    laurenmeyers@austin.utexas.edu
    Competing interests
    No competing interests declared.

Funding

Innovation and Technology Commission (AIR@InnoHK)

  • Zhanwei Du

Health and Medical Research Fund (07210147)

  • Zhanwei Du

National Natural Science Foundation of China (82304204)

  • Yuan Bai

National Institutes of Health (AI151176)

  • Lauren A Meyers

Centers for Disease Control and Prevention (U01IP001136)

  • Lauren A Meyers

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: Individual patient-informed consent was not required in this study using anonymized data.

Copyright

© 2024, Du et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Zhanwei Du
  2. Lin Wang
  3. Yuan Bai
  4. Yunhu Liu
  5. Eric HY Lau
  6. Alison P Galvani
  7. Robert M Krug
  8. Benjamin John Cowling
  9. Lauren A Meyers
(2024)
A retrospective cohort study of Paxlovid efficacy depending on treatment time in hospitalized COVID-19 patients
eLife 13:e89801.
https://doi.org/10.7554/eLife.89801

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https://doi.org/10.7554/eLife.89801