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Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology

Overview of attention for article published in BMC Systems Biology, December 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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2 tweeters

Citations

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10 Dimensions

Readers on

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71 Mendeley
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Title
Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0520-3
Pubmed ID
Authors

Joanna H. Sier, Alfred E. Thumser, Nick J. Plant

Abstract

Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. In addition, endogenous (e.g. genetic variation) and external (e.g. exposure to estrogen-like chemicals) factors are known to impact estrogen biology. To understand how these multiple factors interact to determine an individual's response to therapy is complex, and may be best approached through a systems approach. We present a physiologically-based pharmacokinetic model (PBPK) of estradiol, and validate it against plasma kinetics in humans following intravenous and oral exposure. We extend this model by replacing the intrinsic clearance term with: a detailed kinetic model of estrogen metabolism in the liver; or, a genome-scale model of liver metabolism. Both models were validated by their ability to reproduce clinical data on estradiol exposure. We hypothesise that the enhanced mechanistic information contained within these models will lead to more robust predictions of the biological phenotype that emerges from the complex interactions between estrogens and the body. To demonstrate the utility of these models we examine the known drug-drug interactions between phenytoin and oral estradiol. We are able to reproduce the approximate 50% reduction in area under the concentration-time curve for estradiol associated with this interaction. Importantly, the inclusion of a genome-scale metabolic model allows the prediction of this interaction without directly specifying it within the model. In addition, we predict that PXR activation by drugs results in an enhanced ability of the liver to excrete glucose. This has important implications for the relationship between drug treatment and metabolic syndrome. We demonstrate how the novel coupling of PBPK models with genome-scale metabolic networks has the potential to aid prediction of drug action, including both drug-drug interactions and changes to the metabolic landscape that may predispose an individual to disease development.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 17%
Student > Bachelor 9 13%
Student > Ph. D. Student 7 10%
Student > Postgraduate 7 10%
Student > Master 7 10%
Other 11 15%
Unknown 18 25%
Readers by discipline Count As %
Medicine and Dentistry 10 14%
Biochemistry, Genetics and Molecular Biology 9 13%
Pharmacology, Toxicology and Pharmaceutical Science 8 11%
Agricultural and Biological Sciences 5 7%
Nursing and Health Professions 3 4%
Other 16 23%
Unknown 20 28%

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 18 December 2017.
All research outputs
#6,806,451
of 12,312,517 outputs
Outputs from BMC Systems Biology
#425
of 1,022 outputs
Outputs of similar age
#151,759
of 345,835 outputs
Outputs of similar age from BMC Systems Biology
#20
of 53 outputs
Altmetric has tracked 12,312,517 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,022 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 57% of its peers.
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 345,835 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.