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ABCB1 genetic variants in leukemias: current insights into treatment outcomes

Overview of attention for article published in Pharmacogenomics and Personalized Medicine, May 2017
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3 X users
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1 Facebook page

Citations

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32 Mendeley
Title
ABCB1 genetic variants in leukemias: current insights into treatment outcomes
Published in
Pharmacogenomics and Personalized Medicine, May 2017
DOI 10.2147/pgpm.s105208
Pubmed ID
Authors

Ravindran Ankathil

Abstract

Despite improvements in treatment of different types of leukemia, not all patients respond optimally for a particular treatment. Some treatments will work better for some, while being harmful or ineffective for others. This is due to genetic variation in the form of single-nucleotide polymorphisms (SNPs) that affect gene expression or function and cause inherited interindividual differences in the metabolism and disposition of drugs. Drug transporters are one of the determinants governing the pharmacokinetic profile of chemotherapeutic drugs. The ABCB1 transporter gene transports a wide range of drugs, including drugs used in leukemia treatment. Polymorphisms in the ABCB1 gene do affect intrinsic resistance and pharmacokinetics of several drugs used in leukemia treatment protocols and thereby affect the efficacy of treatment and event-free survival. This review focuses on the impact of three commonly occurring SNPs (1236C>T, 2677G>T/A, and 3435C>T) of ABCB1 on treatment response of various types of leukemia. From the literature available, some of the genotypes and haplotypes of these SNPs have been found to be potential determinants of interindividual variability in drug disposition and pharmacologic response in different types of leukemia. However, due to inconsistencies in the results observed across the studies, additional studies, considering novel genomic methodologies, comprehensive definition of clinical phenotypes, adequate sample size, and uniformity in all the confounding factors, are warranted.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Bachelor 5 16%
Student > Doctoral Student 4 13%
Student > Ph. D. Student 3 9%
Student > Master 3 9%
Other 3 9%
Unknown 8 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 25%
Pharmacology, Toxicology and Pharmaceutical Science 6 19%
Agricultural and Biological Sciences 3 9%
Medicine and Dentistry 3 9%
Neuroscience 2 6%
Other 2 6%
Unknown 8 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 June 2017.
All research outputs
#15,995,084
of 25,748,735 outputs
Outputs from Pharmacogenomics and Personalized Medicine
#1
of 1 outputs
Outputs of similar age
#179,833
of 325,543 outputs
Outputs of similar age from Pharmacogenomics and Personalized Medicine
#1
of 1 outputs
Altmetric has tracked 25,748,735 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1 research outputs from this source. They receive a mean Attention Score of 1.8. This one scored the same or higher as 0 of them.
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