Evidence map›Paper›PMID 37131698›Full record

ArticlebioRxiv : the preprint server for biology2023

An

Kristina E Atanasoff, Luca Brambilla, Daniel C Adelsberg, Shreyas Kowdle, Christian S Stevens, Chuan-Tien Hung, Yanwen Fu, Reyna Lim, Linh Tran, Robert Allen and 4 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, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

  • Updated by
    An2024
5 · Who and what money

Authors and funding

14 authors at 2 institutions in 1 country.

Kristina E Atanasoff
Luca Brambilla
Daniel C Adelsberg
Shreyas Kowdle
Christian S StevensORCID 0000-0003-2472-6984
Chuan-Tien Hung
Yanwen Fu
Reyna Lim
Linh Tran
Robert Allen
J Andrew Duty
Domenico Tortorella
Icahn School of Medicine at Mount Sinai · USSorrento Therapeutics (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has led to over 760 million cases and >6.8 million deaths worldwide. We developed a panel of human neutralizing monoclonal antibodies (mAbs) targeting the SARS-CoV-2 Spike protein using Harbour H2L2 transgenic mice immunized with Spike receptor binding domain (RBD) (1). Representative antibodies from genetically-distinct families were evaluated for inhibition of replication-competent VSV expressing SARS-CoV-2 Spike (rcVSV-S) in place of VSV-G. One mAb (denoted FG-10A3) inhibited infection of all rcVSV-S variants; its therapeutically-modified version, STI-9167, inhibited infection of all tested SARS-CoV-2 variants, including Omicron BA.1 and BA.2, and limited virus proliferation Importance: The COVID-19 pandemic remains a significant public health concern for the global population; development and characterization of therapeutics, especially ones that are broadly effective, will continue to be essential as SARS-CoV-2 variants emerge. Neutralizing monoclonal antibodies remain an effective therapeutic strategy to prevent virus infection and spread with the caveat that they interact with the circulating variants. The epitope and binding specificity of a broadly neutralizing anti-SARS-CoV-2 Spike RBD antibody clone against many SARS-CoV-2 VOC was characterized by generating antibody-resistant virions coupled with cryo-EM structural analysis. This workflow can serve to predict the efficacy of antibody therapeutics against emerging variants and inform the design of therapeutics and vaccines.

Identifiers

PMID37131698
PMCPMC10153249
OpenAlexW4366725160

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.