Evidence map›Paper›PMID 36618412›Full record

ArticleFrontiers in immunology2022

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Yun-Ti Chen, Yu-Hsiu Chang, Nikhil Pathak, Shey-Cherng Tzou, Yong-Chun Luo, Yen-Chao Hsu, Tian-Neng Li, Jung-Yu Lee, Yi-Cyun Chen, Yu-Wei Huang and 22 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
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

32 authors at 4 institutions in 1 country.

Yun-Ti ChenInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yu-Hsiu ChangInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Nikhil PathakInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Shey-Cherng TzouDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yong-Chun LuoInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yen-Chao HsuInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Tian-Neng LiInstitute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan.
Jung-Yu LeeInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yi-Cyun ChenInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yu-Wei HuangInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Hsin-Ju YangInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Nung-Yu HsuInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Hui-Ping TsaiInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Tein-Yao ChangInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Shu-Chen HsuInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Ping-Cheng LiuInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Yuan-Fan ChinInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Wen-Chin LinInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Chuen-Mi YangInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Hsueh-Ling WuInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Chia-Ying LeeInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Hui-Ling HsuInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Yi-Chun LiuInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Jhih-Wei ChuInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Lily Hui-Ching WangInstitute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan.
Jann-Yuan WangDepartment of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Chih-Heng HuangInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Chi-Hung LinDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Po-Shiuan HsiehGraduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan.
Yan-Hwa Wu LeeDepartment of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yi-Jen HungInstitute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan.
Jinn-Moon YangInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
National Defense Medical Center · TWNational Yang Ming Chiao Tung University · TWNational Tsing Hua University · TWNational Taiwan University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of Results: Our Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Indexed as

COVID-19SARS-CoV-2AnimalsAntiviral AgentsComputational BiologyCricetinaeInflammationMethotrexateAntiviral AgentsMethotrexatedrug repurposinginflammationmethotrexatemulti-target drugsSARS-CoV-2

Identifiers

PMID36618412
PMCPMC9811668
OpenAlexW4313468524

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.