Evidence map›Paper›PMID 32864815›Full record

ReviewMedicinal research reviews2021

COVID-19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays.

Xueqing Wang, Yuanfang Guan

Open access · bronzeAbstract readReview
In one paragraph

Review in Medicinal research reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 7% 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

46 citing papers in PubMed, 1 synthesis or guideline pooled it, 98 citations in OpenAlex.

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  9. Monoclonal neutralizing antibodies against SARS-COV-2 S protein.American journal of translational research · 2024
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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

2 authors at 2 institutions in 1 country.

Xueqing WangDepartment of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Yuanfang GuanDepartment of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0001-8275-2852
Michigan Medicine · USUniversity of Michigan · US

Funding

Machine Learning for Drug Response PredictionR35GM133346 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yuanfang Guan · 2019 to 2026
$3.2M
NIGMS NIH HHS R35 GM133346NIH/NIGMS R35-GM133346
6 · The paper itself

Abstract

The situation of coronavirus disease 2019 (COVID-19) pandemic is rapidly evolving, and medical researchers around the globe are dedicated to finding cures for the disease. Drug repurposing, as an efficient way for drug development, has received a lot of attention. However, the huge amount of studies makes it challenging to keep up to date with the literature on COVID-19 therapeutic development. This review addresses this challenge by grouping the COVID-19 drug repurposing research into three large groups, including clinical trials, computational research, and in vitro protein-binding experiments. Particularly, to facilitate future drug discovery and the creation of effective drug combinations, drugs are organized by their mechanisms of action and reviewed by their efficacy measured by clinical trials. Providing this subtyping information, we hope this review would serve the scientists, clinicians, and the pharmaceutical industry who are looking at the new therapeutics for COVID-19 treatment.

Indexed as

Clinical Trials as TopicCOVID-19 Drug TreatmentDrug RepositioningProtein Interaction MapsComputational BiologyDrug Evaluation, PreclinicalHumansclinical trialcomputational researchCOVID-19drug repurposingin vitro protein interaction assay

Identifiers

PMID32864815
PMCPMC8049524
OpenAlexW3082713933

What OpenQuestion holds

Textmetadata
LicenceTDM
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.