Evidence map›Paper›PMID 32228680›Full record

ReviewJournal of hematology & oncology2020

The design and development of covalent protein-protein interaction inhibitors for cancer treatment.

Sha-Sha Cheng, Guan-Jun Yang, Wanhe Wang, Chung-Hang Leung, Dik-Lung Ma

Erratum issuedAbstract readReview
In one paragraph

Review in Journal of hematology & oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
–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

48 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  5. Ubiquitin-conjugating Enzymes in Cancer.International journal of biological sciences · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Sha-Sha ChengInstitute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao, SAR, China.
Guan-Jun YangInstitute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao, SAR, China.
Wanhe WangDepartment of Chemistry, Hong Kong Baptist University, Kowloon, 999077, Hong Kong, China.
Chung-Hang LeungInstitute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao, SAR, China. duncanleung@um.edu.mo.ORCID http://orcid.org/0000-0003-2988-3786
Dik-Lung MaDepartment of Chemistry, Hong Kong Baptist University, Kowloon, 999077, Hong Kong, China. edmondma@hkbu.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

Indexed as

Drug DesignDrug DevelopmentAnimalsAntineoplastic AgentsHumansModels, MolecularNeoplasmsProtein Interaction MapsAntineoplastic AgentsCancer therapyCovalent inhibitorsProtein-protein interaction

Identifiers

PMID32228680
PMCPMC7106679

What OpenQuestion holds

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Registered trials

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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.