Evidence map›Paper›PMID 34353886›Full record

ArticleLife science alliance2021

Integrated human/SARS-CoV-2 metabolic models present novel treatment strategies against COVID-19.

Bridget P Bannerman, Jorge Júlvez, Alexandru Oarga, Tom L Blundell, Pablo Moreno, R Andres Floto

Open access · goldAbstract read
In one paragraph

Article in Life science alliance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.5field-weighted citation impact, top 5% 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

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Dynamic metabolic modelling of ATP allocation during viral infection.Journal of the Royal Society, Interface · 2026
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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

6 authors at 3 institutions in 3 countries.

Bridget P BannermanMolecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK bpc28@cam.ac.uk.ORCID 0000-0002-5746-8283
Jorge JúlvezDepartment of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain.ORCID 0000-0002-7093-228X
Alexandru OargaDepartment of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain.ORCID 0000-0002-7271-733X
Tom L BlundellDepartment of Biochemistry, University of Cambridge, Cambridge, UK.
Pablo MorenoEMBL-EBI, European Bioinformatics Institute, Hinxton, UK.
R Andres FlotoMolecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK arf27@cam.ac.uk.ORCID 0000-0002-2188-5659
University of Cambridge · GBUniversidad de Zaragoza · ESEuropean Bioinformatics Institute · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for more than 3 million deaths in 219 countries across the world and with more than 140 million cases. The absence of FDA-approved drugs against SARS-CoV-2 has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and SARS-CoV-2 to provide insight into the virus' pathogenic mechanism and support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first, in its healthy state. We then demonstrate how the entry of SARS-CoV-2 into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. A new computational method that predicts 20 unique reactions as drug targets from our models and provides a platform for future studies on viral entry inhibition, immune regulation, and drug optimisation strategies. The model is available in BioModels (https://www.ebi.ac.uk/biomodels/MODEL2007210001) and the software tool,

Indexed as

COVID-19 Drug TreatmentComputational BiologyCOVID-19Drug DevelopmentDrug Evaluation, PreclinicalHumansModels, BiologicalPandemicsSARS-CoV-2

Identifiers

PMID34353886
PMCPMC8343166
OpenAlexW3187085770

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.