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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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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.

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Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 15%
Student > Bachelor 9 11%
Student > Ph. D. Student 9 11%
Student > Postgraduate 7 9%
Student > Master 7 9%
Other 12 15%
Unknown 23 29%
Readers by discipline Count As %
Medicine and Dentistry 11 14%
Biochemistry, Genetics and Molecular Biology 10 13%
Pharmacology, Toxicology and Pharmaceutical Science 9 11%
Agricultural and Biological Sciences 5 6%
Nursing and Health Professions 4 5%
Other 15 19%
Unknown 25 32%