Evidence map›Paper›PMID 39033482›Full record

ArticleInterdisciplinary sciences, computational life sciences2024

Viral Rebound After Antiviral Treatment: A Mathematical Modeling Study of the Role of Antiviral Mechanism of Action.

Aubrey Chiarelli, Hana Dobrovolny

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Article in Interdisciplinary sciences, computational life sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Aubrey ChiarelliDepartment of Physics and Astronomy, Texas Christian University, Fort Worth, 76129, USA.
Hana DobrovolnyDepartment of Physics and Astronomy, Texas Christian University, Fort Worth, 76129, USA. h.dobrovolny@tcu.edu.ORCID http://orcid.org/0000-0003-3592-6770

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of antiviral treatments for SARS-CoV-2 was an important turning point for the pandemic. Availability of safe and effective antivirals has allowed people to return back to normal life. While SARS-CoV-2 antivirals are highly effective at preventing severe disease, there have been concerning reports of viral rebound in some patients after cessation of antiviral treatment. In this study, we use a mathematical model of viral infection to study the potential of different antivirals to prevent viral rebound. We find that antivirals that block production are most likely to result in viral rebound if the treatment time course is not sufficiently long. Since these antivirals do not prevent infection of cells, cells continue to be infected during treatment. When treatment is stopped, the infected cells will begin producing virus at the usual rate. Antivirals that prevent infection of cells are less likely to result in viral rebound since cells are not being infected during treatment. This study highlights the role of antiviral mechanism of action in increasing or reducing the probability of viral rebound.

Indexed as

Antiviral AgentsCOVID-19 Drug TreatmentSARS-CoV-2COVID-19HumansModels, BiologicalModels, TheoreticalVirus ReplicationAntiviral AgentsAntiviralMathematical modelMechanism of actionViral rebound

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.