Evidence map›Paper›PMID 35241045›Full record

ArticleBMC complementary medicine and therapies2022

Network pharmacology-based predictions of active components and pharmacological mechanisms of Artemisia annua L. for the treatment of the novel Corona virus disease 2019 (COVID-19).

Yexiao Tang, Xiaobo Li, Yueming Yuan, Hongying Zhang, Yuanyuan Zou, Zhiyong Xu, Qin Xu, Jianping Song, Changsheng Deng, Qi Wang

Open access · goldAbstract read
In one paragraph

Article in BMC complementary medicine and therapies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
5.1field-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

11 citing papers in PubMed, 28 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. SARS-CoV-2 omicron variants succumbbioRxiv : the preprint server for biology · 2022
    Article
  9. Molecules (Basel, Switzerland) · 2022
    Review
  10. Article
  11. 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

10 authors at 2 institutions in 1 country.

Yexiao Tang *Artemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Xiaobo Li *Artemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Yueming YuanArtemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Hongying ZhangArtemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Yuanyuan ZouArtemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Zhiyong XuSci-tech Industrial Park, Guangzhou University of Chinese Medicine, Guangzhou, 510445, China.
Qin XuArtemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Jianping SongArtemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Changsheng DengArtemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Qi WangArtemisinin Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China. wangqi2019@gzucm.edu.cn.
Guangzhou University of Chinese Medicine · CNGuangzhou Chest Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNovel Corona Virus Disease 2019 (COVID-19) is closely associated with cytokines storms. The Chinese medicinal herb Artemisia annua L. (A. annua) has been traditionally used to control many inflammatory diseases, such as malaria and rheumatoid arthritis. We performed network analysis and employed molecular docking and network analysis to elucidate active components or targets and the underlying mechanisms of A. annua for the treatment of COVID-19.

methodsActive components of A. annua were identified through the TCMSP database according to their oral bioavailability (OB) and drug-likeness (DL). Moreover, target genes associated with COVID-19 were mined from GeneCards, OMIM, and TTD. A compound-target (C-T) network was constructed to predict the relationship of active components with the targets. A Compound-disease-target (C-D-T) network has been built to reveal the direct therapeutic target for COVID-19. Molecular docking, molecular dynamics simulation studies (MD), and MM-GBSA binding free energy calculations were used to the closest molecules and targets between A. annua and COVID-19.

resultsIn our network, GO, and KEGG analysis indicated that A. annua acted in response to COVID-19 by regulating inflammatory response, proliferation, differentiation, and apoptosis. The molecular docking results manifested excellent results to verify the binding capacity between the hub components and hub targets in COVID-19. MD and MM-GBSA data showed quercetin to be the more effective candidate against the virus by target MAPK1, and kaempferol to be the other more effective candidate against the virus by target TP53. We identified A. annua's potentially active compounds and targets associated with them that act against COVID-19.

conclusionsThese findings suggest that A. annua may prevent and inhibit the inflammatory processes related to COVID-19.

Indexed as

Artemisia annuaCOVID-19 Drug TreatmentDrugs, Chinese HerbalHumansMolecular Docking SimulationNetwork PharmacologySARS-CoV-2Drugs, Chinese HerbalArtemisia annua L.COVID-19MM-GBSAMolecular dockingMolecule dynamicsNetwork pharmacology

Identifiers

PMID35241045
PMCPMC8893058
OpenAlexW4214824797

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.