Characterizing viral clearance kinetics in acute influenza

Pharmacometric assessment of antiviral efficacy in acute influenza informs treatment decisions and pandemic preparedness. We characterized natural viral clearance in acute influenza to guide phase II trial design using simulations based upon observed data. Standardized duplicate oropharyngeal swabs were collected daily over 14 days from 80 untreated low-risk Thai adults, with viral densities measured using quantitative polymerase chain reaction. We evaluated three models to describe viral clearance: exponential, bi-exponential and growth-and-decay. The growth-and-decay model provided the best fit, but the exponential decay model was the most parsimonious. The median viral clearance half-life was 10.3 h (interquartile range (IQR): 6.8-15.4h), varying by influenza type: 9.6 h (IQR: 6.2-13.0 h) for influenza A and 14.0 h (IQR: 10.3-19.3 h) for influenza B. Simulated trials using parameters from the exponential decay model showed that 148 patients per arm provide over 90% power to detect treatments accelerating viral clearance by 40%. Variation in clearance rates strongly impacted the power; doubling this variation would require 232 patients per arm for an antiviral with a 60% effect size. A sampling strategy with four swabs per day reduces the required sample size to 81 per arm while maintaining over 80% power. We recommend this approach to assess and compare current anti-influenza drugs. This article is part of the Theo Murphy meeting issue 'Evaluating anti-infective drugs'.

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Publication Details

Journal
Philosophical Transactions of the Royal Society B Biological Sciences
Published
2026-04-30
DOI
https://doi.org/10.1098/rstb.2024.0351
Citations
1
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
20.27

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article

Characterizing viral clearance kinetics in acute influenza

Podjanee Jittamala, Phrutsamon Wongnak, Nicholas John White, William Schilling et al.
1 citations
Philosophical Transactions of the Royal Society B Biological Sciences
SARS-CoV-2 detection and testing
20.27
article

Characterizing viral clearance kinetics in acute influenza

Podjanee Jittamala, Phrutsamon Wongnak, Nicholas John White, William Schilling, Tim Seers, Mallika Imwong, James Watson
article en
1 citations

Abstract

Pharmacometric assessment of antiviral efficacy in acute influenza informs treatment decisions and pandemic preparedness. We characterized natural viral clearance in acute influenza to guide phase II trial design using simulations based upon observed data. Standardized duplicate oropharyngeal swabs were collected daily over 14 days from 80 untreated low-risk Thai adults, with viral densities measured using quantitative polymerase chain reaction. We evaluated three models to describe viral clearance: exponential, bi-exponential and growth-and-decay. The growth-and-decay model provided the best fit, but the exponential decay model was the most parsimonious. The median viral clearance half-life was 10.3 h (interquartile range (IQR): 6.8-15.4h), varying by influenza type: 9.6 h (IQR: 6.2-13.0 h) for influenza A and 14.0 h (IQR: 10.3-19.3 h) for influenza B. Simulated trials using parameters from the exponential decay model showed that 148 patients per arm provide over 90% power to detect treatments accelerating viral clearance by 40%. Variation in clearance rates strongly impacted the power; doubling this variation would require 232 patients per arm for an antiviral with a 60% effect size. A sampling strategy with four swabs per day reduces the required sample size to 81 per arm while maintaining over 80% power. We recommend this approach to assess and compare current anti-influenza drugs. This article is part of the Theo Murphy meeting issue 'Evaluating anti-infective drugs'.

Philosophical Transactions of the Royal Society B Biological SciencesVol. 381(1949)
Mahidol University (TH), St George’s University Hospitals NHS Foundation Trust (GB), University of Oxford (GB), Infectious Diseases Data Observatory (GB)
Wellcome Trust
Good health and well-being
Openalex Percentile: Top 1%
SARS-CoV-2 detection and testing
20.27
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