Evidence map›Paper›PMID 32113846›Full record

ArticleJournal of integrative medicine2020

In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus.

Deng-Hai Zhang, Kun-Lun Wu, Xue Zhang, Sheng-Qiong Deng, Bin Peng

Open access · hybridAbstract read
In one paragraph

Article in Journal of integrative medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 217 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
217citing papers in PubMed, 1 pooled it
79.1field-weighted citation impact, top 1% 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

217 citing papers in PubMed, 1 synthesis or guideline pooled it, 490 citations in OpenAlex.

  1. Pooled it
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  12. Steroidal lactones fromIn silico pharmacology · 2024
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  14. Ayurvedic and Chinese Herbs against Coronaviruses.Current pharmaceutical design · 2024
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  15. Article
  16. Review
  17. Anti-SARS-CoV-2 Activity ofMolecules (Basel, Switzerland) · 2023
    Article
  18. Article
  19. Review
  20. Review

157 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Deng-Hai ZhangShanghai Health Commission Key Lab of Artificial Intelligence (AI)-Based Management of Inflammation and Chronic Diseases, Sino-French Cooperative Central Lab, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China. Electronic address: shanghai_zhang@hotmail.com.
Kun-Lun WuDepartment of Traditional Chinese Medicine, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China.
Xue ZhangShanghai Health Commission Key Lab of Artificial Intelligence (AI)-Based Management of Inflammation and Chronic Diseases, Sino-French Cooperative Central Lab, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China.
Sheng-Qiong DengDepartment of Research Affair Management, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China.
Bin PengShanghai Health Commission Key Lab of Artificial Intelligence (AI)-Based Management of Inflammation and Chronic Diseases, Sino-French Cooperative Central Lab, Shanghai Pudong Gongli Hospital, Secondary Military Medical University, Shanghai 200135, China.
Shanghai Pudong New Area Gongli Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn this study we execute a rational screen to identify Chinese medical herbs that are commonly used in treating viral respiratory infections and also contain compounds that might directly inhibit 2019 novel coronavirus (2019-nCoV), an ongoing novel coronavirus that causes pneumonia.

methodsThere were two main steps in the screening process. In the first step we conducted a literature search for natural compounds that had been biologically confirmed as against sever acute respiratory syndrome coronavirus or Middle East respiratory syndrome coronavirus. Resulting compounds were cross-checked for listing in the Traditional Chinese Medicine Systems Pharmacology Database. Compounds meeting both requirements were subjected to absorption, distribution, metabolism and excretion (ADME) evaluation to verify that oral administration would be effective. Next, a docking analysis was used to test whether the compound had the potential for direct 2019-nCoV protein interaction. In the second step we searched Chinese herbal databases to identify plants containing the selected compounds. Plants containing 2 or more of the compounds identified in our screen were then checked against the catalogue for classic herbal usage. Finally, network pharmacology analysis was used to predict the general in vivo effects of each selected herb.

resultsOf the natural compounds screened, 13 that exist in traditional Chinese medicines were also found to have potential anti-2019-nCoV activity. Further, 125 Chinese herbs were found to contain 2 or more of these 13 compounds. Of these 125 herbs, 26 are classically catalogued as treating viral respiratory infections. Network pharmacology analysis predicted that the general in vivo roles of these 26 herbal plants were related to regulating viral infection, immune/inflammation reactions and hypoxia response.

conclusionChinese herbal treatments classically used for treating viral respiratory infection might contain direct anti-2019-nCoV compounds.

Indexed as

Coronavirus InfectionsCOVID-19Drug DiscoveryDrug Evaluation, PreclinicalDrugs, Chinese HerbalHumansPneumonia, ViralRespiratory Tract InfectionsVirus DiseasesDrugs, Chinese Herbal2019-nCoVChinese herbalDrugsMolecular dockingNatural compoundsNetwork pharmacologyPneumoniaWuhan coronavirus

Identifiers

PMID32113846
PMCPMC7102521
OpenAlexW3006718791

What OpenQuestion 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 OpenQuestion graph.