Evidence map›Paper›PMID 37745410›Full record

ArticlebioRxiv : the preprint server for biology2023

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody.

Tin Phan, Carolin Zitzmann, Kara W Chew, Davey M Smith, Eric S Daar, David A Wohl, Joseph J Eron, Judith S Currier, Michael D Hughes, Manish C Choudhary and 6 more

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 8 institutions in 1 country.

Tin PhanTheoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.
Carolin ZitzmannTheoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.
Kara W ChewDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Davey M SmithDepartment of Medicine, University of California, San Diego, CA, USA.
Eric S DaarLundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.
David A WohlDepartment of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA.
Joseph J EronDepartment of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA.
Judith S CurrierDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Michael D HughesHarvard T.H. Chan School of Public Health, Boston, MA, USA.
Manish C ChoudharyDepartment of Medicine, Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Rinki DeoDepartment of Medicine, Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Jonathan Z LiDepartment of Medicine, Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Ruy M RibeiroTheoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.
Ruian KeTheoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.
Alan S PerelsonTheoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA.
ACTIV-2/A5401 Study Team
Los Alamos National Laboratory · USBrigham and Women's Hospital · USUniversity of California, Los Angeles · USUniversity of North Carolina at Chapel Hill · USHarvard University · USSanta Fe Institute · USUCLA Medical Center · USUniversity of California San Diego · US

Funding

Leadership and Operations Center (LOC), AIDS Clinical Trials Group (ACTG); LOC 1/UM1AI068636 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Joseph J Eron, RAJESH T GANDHI · 2011 to 2026
$1073.1M
Statistical and Data Management Center (SDMC), AIDS Clinical Trials Group (ACTG)UM1AI068634 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Marlene Ann Cooper, Michael David Hughes · 2011 to 2026
$246.6M
The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI BRYAN O BUCHHOLZ, Nathaniel Scott Hafer · 2018 to 2026
$141.0M
Validation, CLIA and Qualification (VQC): Enhancing the RS ratio as a tool for AIDS Clinical Trial Group (ACTG) tuberculosis trialsUM1AI106701 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Grace M Aldrovandi · 2014 to 2026
$116.8M
University of North Carolina Global HIV Prevention and Treatment Clinical Trials Unit 2024 SupplementUM1AI069423 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Joseph J Eron, MINA CHRISTINE HOSSEINIPOUR · 2012 to 2026
$71.8M
NHLBI NIH HHS U54 HL143541NIAID NIH HHS UM1 AI068634NIAID NIH HHS UM1 AI068636NIAID NIH HHS UM1 AI069423NIAID NIH HHS UM1 AI106701
6 · The paper itself

Abstract

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

Indexed as

Bamlanivimabmonoclonal antibody treatmentSARS-CoV-2viral dynamicsviral rebound

Identifiers

PMID37745410
PMCPMC10515893
OpenAlexW4386810475

What OpenQuestion holds

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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.