Evidence map›Paper›PMID 34293006›Full record

ArticlePloS one2021

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19.

Emiko Desvaux, Antoine Hamon, Sandra Hubert, Cheïma Boudjeniba, Bastien Chassagnol, Jack Swindle, Audrey Aussy, Laurence Laigle, Jessica Laplume, Perrine Soret and 4 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

8 citing papers in PubMed, 18 citations in OpenAlex.

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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

14 authors at 2 institutions in 1 country.

Emiko DesvauxServier, Research and Development, Suresnes Cedex, France.
Antoine HamonLincoln, Research and Development, Boulogne-Billancourt Cedex, France.
Sandra HubertServier, Research and Development, Suresnes Cedex, France.
Cheïma BoudjenibaServier, Research and Development, Suresnes Cedex, France.
Bastien ChassagnolServier, Research and Development, Suresnes Cedex, France.
Jack SwindleLincoln, Research and Development, Boulogne-Billancourt Cedex, France.
Audrey AussyServier, Research and Development, Suresnes Cedex, France.ORCID 0000-0002-3432-6223
Laurence LaigleServier, Research and Development, Suresnes Cedex, France.
Jessica LaplumeServier, Research and Development, Suresnes Cedex, France.
Perrine SoretServier, Research and Development, Suresnes Cedex, France.
Pierre Jean-FrançoisServier, Research and Development, Suresnes Cedex, France.
Isabelle Dupin-RogerServier, Research and Development, Suresnes Cedex, France.
Mickaël GuedjServier, Research and Development, Suresnes Cedex, France.ORCID 0000-0002-9742-957X
Philippe MoingeonServier, Research and Development, Suresnes Cedex, France.ORCID 0000-0002-2380-9983
Servier (France) · FRLaboratoire Vision Action Cognition · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1β, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

Indexed as

Drug RepositioningAlarminsAntiviral AgentsCOVID-19HumansPneumoniaAlarminsAntiviral Agents

Identifiers

PMID34293006
PMCPMC8297899
OpenAlexW3186351623

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.