Evidence map›Paper›PMID 32586380›Full record

ArticleJournal of translational medicine2020

Advanced bioinformatics rapidly identifies existing therapeutics for patients with coronavirus disease-2019 (COVID-19).

Jason Kim, Jenny Zhang, Yoonjeong Cha, Sarah Kolitz, Jason Funt, Renan Escalante Chong, Scott Barrett, Rebecca Kusko, Ben Zeskind, Howard Kaufman

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 2 pooled it
2.0field-weighted citation impact, top 8% 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

51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
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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

10 authors at 1 institution in 1 country.

Jason KimImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Jenny ZhangImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Yoonjeong ChaImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Sarah KolitzImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Jason FuntImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Renan Escalante ChongImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Scott BarrettImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Rebecca KuskoImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA. bkusko@immuneering.com.ORCID 0000-0001-6730-5990
Ben ZeskindImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Howard KaufmanImmuneering Corporation, 245 Main Street, Cambridge, MA, 02142, USA.
Immuneering (United States)

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease-2019 (COVID-19).

methodsWe applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform.

resultsTop results for ACE2 binding iincluded several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2.

conclusionsWhile these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes.

Indexed as

Computational BiologyAngiotensin-Converting Enzyme 2AnimalsBetacoronavirusCoronavirus InfectionsCOVID-19Gene Expression RegulationGlutamineHumansMicePandemicsPeptidyl-Dipeptidase APneumonia, ViralSARS-CoV-2Serine EndopeptidasesACE2 protein, humanAce2 protein, mouseAngiotensin-Converting Enzyme 2GlutaminePeptidyl-Dipeptidase ASerine EndopeptidasesTMPRSS2 protein, humanArtificial intelligenceBioinformaticsComputational BiologyCoronavirusDrug therapy

Identifiers

PMID32586380
PMCPMC7315012
OpenAlexW3037167931

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.