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In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically

Overview of attention for article published in Drug Design, Development and Therapy, November 2017
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Title
In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically
Published in
Drug Design, Development and Therapy, November 2017
DOI 10.2147/dddt.s149214
Pubmed ID
Authors

Jrhau Lung, Kuan-Liang Chen, Chien-Hui Hung, Chih-Cheng Chen, Ming-Szu Hung, Yu-Ching Lin, Ching-Yuan Wu, Kuan-Der Lee, Neng-Yao Shih, Ying Huang Tsai

Abstract

Unlimited growth of cancer cells requires an extensive nutrient supply. To meet this demand, cancer cells drastically upregulate glucose uptake and metabolism compared to normal cells. This difference has made the blocking of glycolysis a fascinating strategy to treat this malignant disease. α-enolase is not only one of the most upregulated glycolytic enzymes in cancer cells, but also associates with many cellular processes or conditions important to cancer cell survival, such as cell migration, invasion, and hypoxia. Targeting α-enolase could simultaneously disturb cancer cells in multiple ways and, therefore, is a good target for anticancer drug development. In the current study, more than 22 million chemical structures meeting the criteria of Lipinski's rule of five from the ZINC database were docked to α-enolase by virtual screening. Twenty-four chemical structures with docking scores better than that of the enolase substrate, 2-phosphoglycerate, were further screened by the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties prediction. Four of them were classified as non-mutagenic, non-carcinogenic, and capable of oral administration where they showed steady interactions to α-enolase that were comparable, even superior, to the currently available inhibitors in molecular dynamics (MD) simulation. These compounds may be considered promising leads for further development of the α-enolase inhibitors and could help fight cancer metabolically.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 19%
Student > Ph. D. Student 4 19%
Researcher 3 14%
Student > Master 2 10%
Professor 1 5%
Other 3 14%
Unknown 4 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 14%
Chemistry 3 14%
Medicine and Dentistry 3 14%
Pharmacology, Toxicology and Pharmaceutical Science 2 10%
Immunology and Microbiology 2 10%
Other 4 19%
Unknown 4 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 November 2017.
All research outputs
#22,764,772
of 25,382,440 outputs
Outputs from Drug Design, Development and Therapy
#1,753
of 2,268 outputs
Outputs of similar age
#299,290
of 340,752 outputs
Outputs of similar age from Drug Design, Development and Therapy
#32
of 45 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,752 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.