Evidence map›Paper›PMID 28984210›Full record

ArticleBMC systems biology2017

A systematic analysis of FDA-approved anticancer drugs.

Jingchun Sun, Qiang Wei, Yubo Zhou, Jingqi Wang, Qi Liu, Hua Xu

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 141 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
141citing papers in PubMed, 3 pooled it
24.4field-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

141 citing papers in PubMed, 3 syntheses or guidelines pooled it, 318 citations in OpenAlex.

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

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

6 authors at 3 institutions in 2 countries.

Jingchun SunSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Qiang WeiSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Yubo ZhouNational Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, People's Republic of China.
Jingqi WangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
Qi LiuDepartment of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37203, USA.
Hua XuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. Hua.Xu@uth.tmc.edu.
The University of Texas Health Science Center at Houston · USNational Center for Drug Screening · CNVanderbilt University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.

resultsIn this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.

conclusionsIn this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

Indexed as

United States Food and Drug AdministrationAntineoplastic AgentsDrug ApprovalHumansMolecular Targeted TherapyNeoplasmsUnited StatesAntineoplastic AgentsAnticancer drugsCancer-drug-target networkDrug-cancer networkDrug repurposing

Identifiers

PMID28984210
PMCPMC5629554
OpenAlexW2763145619

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.