A Viroimmunologic Model to Characterize the Antiviral Effect of Molnupiravir in Outpatients Infected With SARS-CoV-2: Implication for Treatment Duration
Bibliographic record
Abstract
BACKGROUND: The antiviral efficacy of molnupiravir against SARS-CoV-2 is controversial. Here, we develop a model integrating viral and immune dynamics to characterize the mechanism of action of molnupiravir in vivo and its impact on viral dynamics during and after treatment. METHODS: We analyzed data from the PANORAMIC trial, where 577 outpatients were randomized shortly after symptom onset to receive usual care or molnupiravir for 5 days, with viral and immunologic data collected within 2 weeks. We developed a mathematical model that characterized virus-host interaction, accounting for the impact of molnupiravir on viral replication and mutagenesis. The model was used to explore the impact of longer treatment duration. RESULTS: Molnupiravir reduced RNA replication with an efficacy that reached 93% at the end of a 5-day treatment. This effect was mediated through 2 pathways: 1 that increased transition mutation frequency and 1 that directly inhibited viral production. Accordingly, 5-day treatment shortened the median time to clearance of RNA and infectious virus by approximately 2 days. Ten-day treatment could reduce the time to RNA clearance by 5 days and the occurrence of viral rebounds. Longer treatment durations might be needed for postexposure prophylaxis. CONCLUSIONS: Our model suggests that molnupiravir acts primarily on viral replication, and not specifically on viral infectivity. Longer administration of molnupiravir may reduce the rebound rate, shortening the time to viral clearance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".