Evidence map›Paper›PMID 35004621›Full record

ReviewFrontiers in chemistry2021

Inhibition of SARS-CoV-2 by Targeting Conserved Viral RNA Structures and Sequences.

Shalakha Hegde, Zhichao Tang, Junxing Zhao, Jingxin Wang

Erratum issuedAbstract readReview
In one paragraph

Review in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Unwinding circular RNA's role in inflammatory pulmonary diseases.Naunyn-Schmiedeberg's archives of pharmacology · 2024
    Review
  6. Targeting the Main Protease (MACS chemical biology · 2024
    Article
  7. Review
  8. Article
  9. Review
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Shalakha HegdeDepartment of Medicinal Chemistry, University of Kansas, Lawrence, KS, United States.
Zhichao TangDepartment of Medicinal Chemistry, University of Kansas, Lawrence, KS, United States.
Junxing ZhaoDepartment of Medicinal Chemistry, University of Kansas, Lawrence, KS, United States.
Jingxin WangDepartment of Medicinal Chemistry, University of Kansas, Lawrence, KS, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing COVID-19/Severe Acute Respiratory Syndrome CoV-2 (SARS-CoV-2) pandemic has become a significant threat to public health and has hugely impacted societies globally. Targeting conserved SARS-CoV-2 RNA structures and sequences essential for viral genome translation is a novel approach to inhibit viral infection and progression. This new pharmacological modality compasses two classes of RNA-targeting molecules: 1) synthetic small molecules that recognize secondary or tertiary RNA structures and 2) antisense oligonucleotides (ASOs) that recognize the RNA primary sequence. These molecules can also serve as a "bait" fragment in RNA degrading chimeras to eliminate the viral RNA genome. This new type of chimeric RNA degrader is recently named ribonuclease targeting chimera or RIBOTAC. This review paper summarizes the sequence conservation in SARS-CoV-2 and the current development of RNA-targeting molecules to combat this virus. These RNA-binding molecules will also serve as an emerging class of antiviral drug candidates that might pivot to address future viral outbreaks.

Indexed as

antisense oligonucleotideantiviralprogrammed frameshiftRIBOTACRNA-targetingSARS-CoV-2small moleculeuntranslated region

Identifiers

PMID35004621
PMCPMC8733332

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.