Evidence map›Paper›PMID 32971210›Full record

ArticleFood and chemical toxicology : an international journal published for the British Industrial Biological Research Association2020

Comprehensive assessment of side effects in COVID-19 drug pipeline from a network perspective.

Qihui Wu, Xiude Fan, Honghai Hong, Yong Gu, Zhihong Liu, Shuhuan Fang, Qi Wang, Chuipu Cai, Jiansong Fang

Abstract read
In one paragraph

Article in Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
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

12 citing papers in PubMed.

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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

9 authors.

Qihui WuClinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China; Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China. Electronic address: wuqihui426@qq.com.
Xiude FanLerner Research Institute, Cleveland Clinic, Cleveland, USA. Electronic address: fanxiudexjtu@163.com.
Honghai HongDepartment of Clinical Laboratory, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. Electronic address: gaolaosao@126.com.
Yong GuClinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, China. Electronic address: yonggu@smu.edu.cn.
Zhihong LiuGuangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China. Electronic address: zhihongliu2011@gmail.com.
Shuhuan FangScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China. Electronic address: fshkm@126.com.
Qi WangScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China. Electronic address: wangqi@gzucm.edu.cn.
Chuipu CaiScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China. Electronic address: caichuipu@gzucm.edu.cn.
Jiansong FangScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China; Lerner Research Institute, Cleveland Clinic, Cleveland, USA. Electronic address: fangjs@gzucm.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, coronavirus disease 2019 (COVID-19), has posed an imminent threat to global public health. Although some current therapeutic agents have showed potential prevention or treatment, a growing number of associated adverse events have occurred on patients with COVID-19 in the course of medical treatment. Therefore, a comprehensive assessment of the safety profile of therapeutic agents against COVID-19 is urgently needed. In this study, we proposed a network-based framework to identify the potential side effects of current COVID-19 drugs in clinical trials. We established the associations between 116 COVID-19 drugs and 30 kinds of human tissues based on network proximity and gene-set enrichment analysis (GSEA) approaches. Additionally, we focused on four types of drug-induced toxicities targeting four tissues, including hepatotoxicity, renal toxicity, lung toxicity, and neurotoxicity, and validated our network-based predictions by preclinical and clinical evidence available. Finally, we further performed pharmacovigilance analysis to validate several drug-tissue toxicities via data mining adverse event reporting data, and we identified several new drug-induced side effects without labeling in Food and Drug Administration (FDA) drug instructions. Overall, this study provides forceful approaches to assess potential side effects on COVID-19 drugs, which will be helpful for their safe use in clinical practice and promoting the discovery of antiviral therapeutics against SARS-CoV-2.

Indexed as

PharmacovigilanceAntineoplastic AgentsAntiviral AgentsBetacoronavirusClinical Trials as TopicCoronavirus InfectionsCOVID-19HumansImmunologic FactorsPandemicsPneumonia, ViralSARS-CoV-2Antineoplastic AgentsAntiviral AgentsImmunologic FactorsCMapCOVID-19 drugNetwork proximityOpenVigilPharmacovigilanceSide effect

Identifiers

PMID32971210
PMCPMC7505223

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.