| Title |
A Mechanistic Framework for In Vitro–In Vivo Extrapolation of Liver Membrane Transporters: Prediction of Drug–Drug Interaction Between Rosuvastatin and Cyclosporine
|
|---|---|
| Published in |
Clinical Pharmacokinetics, July 2013
|
| DOI | 10.1007/s40262-013-0097-y |
| Pubmed ID | |
| Authors |
M. Jamei, F. Bajot, S. Neuhoff, Z. Barter, J. Yang, A. Rostami-Hodjegan, K. Rowland-Yeo |
| Abstract |
The interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug-drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug-drug interactions becomes very challenging. To address this, an in vitro-in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator(®). |
Mendeley readers
The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Austria | 1 | <1% |
| Switzerland | 1 | <1% |
| Canada | 1 | <1% |
| South Africa | 1 | <1% |
| Unknown | 133 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 38 | 27% |
| Student > Ph. D. Student | 34 | 24% |
| Student > Master | 13 | 9% |
| Other | 10 | 7% |
| Student > Bachelor | 9 | 6% |
| Other | 14 | 10% |
| Unknown | 21 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 41 | 29% |
| Medicine and Dentistry | 28 | 20% |
| Agricultural and Biological Sciences | 17 | 12% |
| Biochemistry, Genetics and Molecular Biology | 6 | 4% |
| Computer Science | 4 | 3% |
| Other | 17 | 12% |
| Unknown | 26 | 19% |
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 21 January 2014.
All research outputs
#20,265,771
of 22,796,179 outputs
Outputs from Clinical Pharmacokinetics
#1,403
of 1,482 outputs
Outputs of similar age
#174,188
of 198,157 outputs
Outputs of similar age from Clinical Pharmacokinetics
#11
of 11 outputs
Altmetric has tracked 22,796,179 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 1,482 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. 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 198,157 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 11 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.