Evidence map›Paper›PMID 39577689›Full record

ArticleMathematical biosciences2025

A theory for viral rebound after antiviral treatment: A study case for SARS-CoV-2.

Mara Perez, Marcelo Actis, Ignacio Sanchez, Esteban A Hernandez-Vargas, Alejandro H González

Abstract read
In one paragraph

Article in Mathematical biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Mara PerezInstituto de Desarrollo Tecnológico para la Industria Química (INTEC), Consejo Nacional de Investigaciones científicas y técnicas (CONICET) and Universidad Nacional del Litoral (UNL), Santa Fe, Argentina. Electronic address: mperez@fiq.unl.edu.ar.
Marcelo ActisFacultad de Ingeniería Química (FIQ), Universidad Nacional del Litoral (UNL) and Consejo Nacional de Investigaciones científicas y técnicas (CONICET), Santa Fe, Argentina.
Ignacio SanchezInstituto de Desarrollo Tecnológico para la Industria Química (INTEC), Consejo Nacional de Investigaciones científicas y técnicas (CONICET) and Universidad Nacional del Litoral (UNL), Santa Fe, Argentina.
Esteban A Hernandez-VargasDepartment of Mathematics and Statistical Science, University of Idaho, Moscow, ID, USA.
Alejandro H GonzálezInstituto de Desarrollo Tecnológico para la Industria Química (INTEC), Consejo Nacional de Investigaciones científicas y técnicas (CONICET) and Universidad Nacional del Litoral (UNL), Santa Fe, Argentina.

Funding

Novel Hybrid Computational Models to Disentangle Complex Immune ResponsesR01GM152736 · NIGMS · UNIVERSITY OF IDAHO · PI HERNANDEZ VARGAS, ESTEBAN ABELARDO · 2023 to 2025
$600k
NIGMS NIH HHS R01 GM152736
6 · The paper itself

Abstract

A fraction of individuals infected with SARS-CoV-2 experienced rebounds when treated with effective antivirals such as Nirmatrelvir/Ritonavir (Paxlovid). Although this phenomenon has been studied from biological and statistical perspectives, the underlying dynamical mechanism is not yet fully understood. In this work, we characterize the dynamic behavior of a target-cell model to explain post-treatment rebounds from the perspective of set-theoretic stability analysis. Without relying on the effects of the adaptive immune system or the resistance through viral mutations, we develop mathematical conditions for antiviral treatments to avoid viral rebound. Simulation results illustrate the critical role of dosage (i.e., the doses and timing of administration) in taking advantage of highly effective drugs and tailoring therapies.

Indexed as

Antiviral AgentsCOVID-19 Drug TreatmentRitonavirSARS-CoV-2Computer SimulationCOVID-19Drug CombinationsHumansLopinavirModels, BiologicalAntiviral AgentsDrug CombinationsLopinavirlopinavir-ritonavir drug combinationRitonavirAntiviral treatmentIn-host infectionRebound thresholdViral rebound

Identifiers

PMID39577689
PMCPMC12150815

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Registered trials

None linked

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.