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A computational study of the inhibition mechanisms of P-glycoprotein mediated paclitaxel efflux by kinase inhibitors

Overview of attention for article published in BMC Systems Biology, November 2017
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Title
A computational study of the inhibition mechanisms of P-glycoprotein mediated paclitaxel efflux by kinase inhibitors
Published in
BMC Systems Biology, November 2017
DOI 10.1186/s12918-017-0498-x
Pubmed ID
Authors

Joe Bender, Jianwen Fang, Richard Simon

Abstract

Drug resistance mediated by P-glycoprotein (P-gp) renders many cancer therapies ineffective. One P-gp substrate is the widely used chemotherapy drug paclitaxel. Co-administration of paclitaxel and another drug that inhibits P-gp may enhance the therapeutic effectiveness of paclitaxel by preventing its efflux from tumor cells. Here we present a computational approach that combines docking studies with mass action kinetic modeling to investigate how kinase inhibitors may inhibit P-gp mediated paclitaxel efflux. The results show that the inhibition can be attributed to competition between paclitaxel and a tyrosine kinase inhibitor (TKI) for the substrate binding domain (SBD) as well as competition between the kinase inhibitor and ATP for the nuclear (ATP) binding domain (NBD). The relative scales of these two competitions are TKI dependent and determined by the relative affinities of paclitaxel and TKIs to the SBD and NBD of P-gp, and their membrane partition coefficients. Additional simulations suggested that there is no single strategy to further improve the ability of TKIs to inhibit paclitaxel efflux and the most efficient way likely depends on the properties of the TKIs. The developed model fits existing experimental results well and thus detailed analyses of isolated parameters provide insight into the mechanisms of rather important drug efflux. It can be used to guide how to design better TKIs or develop feasible drug combination strategies for targeting P-gp induced drug resistance.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 41%
Student > Bachelor 3 18%
Researcher 3 18%
Student > Master 1 6%
Professor > Associate Professor 1 6%
Other 1 6%
Unknown 1 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 35%
Chemistry 4 24%
Pharmacology, Toxicology and Pharmaceutical Science 2 12%
Medicine and Dentistry 2 12%
Computer Science 1 6%
Other 0 0%
Unknown 2 12%
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 15 December 2017.
All research outputs
#18,577,751
of 23,009,818 outputs
Outputs from BMC Systems Biology
#836
of 1,144 outputs
Outputs of similar age
#325,454
of 437,742 outputs
Outputs of similar age from BMC Systems Biology
#26
of 43 outputs
Altmetric has tracked 23,009,818 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.