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Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug–Drug Interactions Involving Modafinil

Overview of attention for article published in Frontiers in Pharmacology, December 2016
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Title
Optimized Cocktail Phenotyping Study Protocol Using Physiological Based Pharmacokinetic Modeling and In silico Assessment of Metabolic Drug–Drug Interactions Involving Modafinil
Published in
Frontiers in Pharmacology, December 2016
DOI 10.3389/fphar.2016.00517
Pubmed ID
Authors

Angela Rowland, Arduino A. Mangoni, Ashley Hopkins, Michael J. Sorich, Andrew Rowland

Abstract

In vivo cocktail pathway phenotyping (ICPP) is routinely used to assess the metabolic drug-drug interaction (mDDI) potential of new drug candidates (NDC) during drug development. However, there are a number of potential limitations to this approach and the use of validated drug cocktails and study protocols is essential. Typically ICPP mDDI studies assess only the impact of interactions following multiple postulated perpetrator doses and hence the emphasis in terms of validation of these studies has been ensuring that there are no interactions between probe substrates. Studies assessing the comparative impact of single and multiple doses of the postulated perpetrator have the potential to provide richer information regarding both the clinical impact and mechanism of mDDIs. Using modafinil as a model compound, we sought to develop an optimized ICPP mDDI study protocol to evaluate the potential magnitude and clinical relevance of mDDIs using a physiologically based pharmacokinetic modeling approach.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 21%
Student > Ph. D. Student 3 16%
Other 1 5%
Student > Doctoral Student 1 5%
Professor 1 5%
Other 2 11%
Unknown 7 37%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 8 42%
Medicine and Dentistry 2 11%
Agricultural and Biological Sciences 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Unknown 7 37%
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 27 December 2016.
All research outputs
#20,376,559
of 22,925,760 outputs
Outputs from Frontiers in Pharmacology
#10,136
of 16,211 outputs
Outputs of similar age
#355,573
of 420,925 outputs
Outputs of similar age from Frontiers in Pharmacology
#102
of 170 outputs
Altmetric has tracked 22,925,760 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 16,211 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 420,925 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 170 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.