Evidence map›Paper›PMID 36371228›Full record

ReviewMolecular aspects of medicine2023

Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics.

Yash Gupta, Oleksandr V Savytskyi, Matt Coban, Amoghavarsha Venugopal, Vasili Pleqi, Caleb A Weber, Rohit Chitale, Ravi Durvasula, Christopher Hopkins, Prakasha Kempaiah and 1 more

Open access · hybridAbstract readReview
In one paragraph

Review in Molecular aspects of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
5.4field-weighted citation impact, top 4% 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Structure-based approaches against COVID-19.Journal of the Chinese Medical Association : JCMA · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Cardiovascular Symposium on Perspectives in Long COVID.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Review
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

11 authors at 2 institutions in 1 country.

Yash GuptaDepartment of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA.
Oleksandr V SavytskyiDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; In Vivo Biosystems, Eugene, OR, USA.
Matt CobanDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA.
Amoghavarsha VenugopalDepartment of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA.
Vasili PleqiDepartment of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA.
Caleb A WeberDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Rohit ChitaleDepartment of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA; The Council on Strategic Risks, 1025 Connecticut Ave NW, Washington, DC, USA.
Ravi DurvasulaDepartment of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA.
Christopher HopkinsIn Vivo Biosystems, Eugene, OR, USA.
Prakasha KempaiahDepartment of Medicine, Infectious Diseases, Mayo Clinic, Jacksonville, FL, USA.
Thomas R CaulfieldDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, USA; Department of QHS Computational Biology, Mayo Clinic, Jacksonville, FL, USA; Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA; Department of Clinical Genomics, Mayo Clinic, Rochester, MN, USA; Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA. Electronic address: caulfield.thomas@mayo.edu.
Mayo Clinic in Florida · USJacksonville College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With more than 5 million fatalities and close to 300 million reported cases, COVID-19 is the first documented pandemic due to a coronavirus that continues to be a major health challenge. Despite being rapid, uncontrollable, and highly infectious in its spread, it also created incentives for technology development and redefined public health needs and research agendas to fast-track innovations to be translated. Breakthroughs in computational biology peaked during the pandemic with renewed attention to making all cutting-edge technology deliver agents to combat the disease. The demand to develop effective treatments yielded surprising collaborations from previously segregated fields of science and technology. The long-standing pharmaceutical industry's aversion to repurposing existing drugs due to a lack of exponential financial gain was overrun by the health crisis and pressures created by front-line researchers and providers. Effective vaccine development even at an unprecedented pace took more than a year to develop and commence trials. Now the emergence of variants and waning protections during the booster shots is resulting in breakthrough infections that continue to strain health care systems. As of now, every protein of SARS-CoV-2 has been structurally characterized and related host pathways have been extensively mapped out. The research community has addressed the druggability of a multitude of possible targets. This has been made possible due to existing technology for virtual computer-assisted drug development as well as new tools and technologies such as artificial intelligence to deliver new leads. Here in this article, we are discussing advances in the drug discovery field related to target-based drug discovery and exploring the implications of known target-specific agents on COVID-19 therapeutic management. The current scenario calls for more personalized medicine efforts and stratifying patient populations early on for their need for different combinations of prognosis-specific therapeutics. We intend to highlight target hotspots and their potential agents, with the ultimate goal of using rational design of new therapeutics to not only end this pandemic but also uncover a generalizable platform for use in future pandemics.

Indexed as

COVID-19Antiviral AgentsArtificial IntelligenceDrug DiscoveryHumansSARS-CoV-2Antiviral AgentsArtificial intelligenceCOVID-19Drug targetingMathematical modelingRational improvementSARS-CoV-2Target-based drug discovery

Identifiers

PMID36371228
PMCPMC9613808
OpenAlexW4307457958

What Socratic 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 Socratic graph.