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A probabilistic model of human variability in physiology for future application to dose reconstruction and QIVIVE

Overview of attention for article published in Frontiers in Pharmacology, October 2015
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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Title
A probabilistic model of human variability in physiology for future application to dose reconstruction and QIVIVE
Published in
Frontiers in Pharmacology, October 2015
DOI 10.3389/fphar.2015.00213
Pubmed ID
Authors

McNally, Kevin, Loizou, George D, Loizou, George D.

Abstract

The risk assessment of environmental chemicals and drugs is undergoing a paradigm shift in approach which seeks the full replacement of animal testing with high throughput, mechanistic, in vitro systems. This new approach will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated by in vitro, in silico, and in chemico systems can be integrated and utilized for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited to this and are needed to translate in vitro concentration- response relationships to an exposure or dose, route and duration regime in human populations. Thus, a realistic description of the variation in the physiology of the human population being modeled is critical. Whilst various studies in the past decade have made progress in describing human variability, the algorithms are typically coded in computer programs and as such are unsuitable for reverse dosimetry. In this report we overcome this limitation by developing a hierarchical statistical model using standard probability distributions for the specification of a virtual US and UK human population. The work draws on information from both population databases and cadaver studies.

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The data shown below were collected from the profiles of 5 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 19%
Student > Bachelor 4 15%
Student > Ph. D. Student 4 15%
Student > Doctoral Student 3 12%
Student > Master 3 12%
Other 3 12%
Unknown 4 15%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 8 31%
Agricultural and Biological Sciences 4 15%
Engineering 3 12%
Environmental Science 2 8%
Nursing and Health Professions 1 4%
Other 2 8%
Unknown 6 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 10 April 2017.
All research outputs
#7,979,533
of 24,163,421 outputs
Outputs from Frontiers in Pharmacology
#3,656
of 18,028 outputs
Outputs of similar age
#95,728
of 283,526 outputs
Outputs of similar age from Frontiers in Pharmacology
#35
of 104 outputs
Altmetric has tracked 24,163,421 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 18,028 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 79% 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 283,526 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 65% of its contemporaries.
We're also able to compare this research output to 104 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 67% of its contemporaries.