↓ Skip to main content

Prediction of Pharmacokinetics and Drug–Drug Interactions When Hepatic Transporters are Involved

Overview of attention for article published in Clinical Pharmacokinetics, July 2014
Altmetric Badge

Mentioned by

facebook
1 Facebook page

Citations

dimensions_citation
92 Dimensions

Readers on

mendeley
71 Mendeley
Title
Prediction of Pharmacokinetics and Drug–Drug Interactions When Hepatic Transporters are Involved
Published in
Clinical Pharmacokinetics, July 2014
DOI 10.1007/s40262-014-0156-z
Pubmed ID
Authors

Rui Li, Hugh A. Barton, Manthena V. Varma

Abstract

Hepatobiliary transport mechanisms have been identified to play a significant role in determining the systemic clearance for a number of widely prescribed drugs and an increasing number of new molecular entities (NMEs). While determining the pharmacokinetics, drug transporters also regulate the target tissue exposure and play a key role in regulating the pharmacological and/or toxicological responses. Consequently, it is of great relevance in drug discovery and development to assess hepatic transporter activity in regard to pharmacokinetic and dose predictions and to evaluate pharmacokinetic variability associated with drug-drug interactions (DDIs) and genetic variants. Mechanistic predictions utilizing physiological-based pharmacokinetic modeling are increasingly used to evaluate transporter contribution and delineate the transporter-enzyme interplay on the basis of hypothesis-driven functional in vitro findings. Significant strides were made in the development of in vitro techniques to facilitate characterization of hepatobiliary transport. However, challenges exist in the quantitative in vitro-in vivo extrapolation of transporter kinetics due to the lack of information on absolute abundance of the transporter in both in vitro and in vivo situations, and/or differential function in the holistic in vitro reagents such as suspended and plated hepatocytes systems, and lack of complete mechanistic understanding of liver model structure. On the other hand, models to predict transporter-mediated DDIs range from basic models to mechanistic static and dynamic models. While basic models provide conservative estimates and are useful upfront in avoiding false negative predictions, mechanistic models integrate multiple victim and perpetrator drugs parameters and are expected to provide quantitative predictions. The aim of this paper is to review the current state of the model-based approaches to predict clinical pharmacokinetics and DDIs of drugs or NMEs that are substrates of hepatic transporters.

Mendeley readers

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 %
United States 2 3%
France 1 1%
Unknown 68 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 25%
Student > Ph. D. Student 13 18%
Student > Master 7 10%
Other 4 6%
Student > Bachelor 4 6%
Other 6 8%
Unknown 19 27%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 17 24%
Medicine and Dentistry 13 18%
Agricultural and Biological Sciences 8 11%
Biochemistry, Genetics and Molecular Biology 3 4%
Engineering 3 4%
Other 7 10%
Unknown 20 28%
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 07 September 2014.
All research outputs
#20,236,620
of 22,763,032 outputs
Outputs from Clinical Pharmacokinetics
#1,402
of 1,481 outputs
Outputs of similar age
#192,700
of 228,869 outputs
Outputs of similar age from Clinical Pharmacokinetics
#14
of 15 outputs
Altmetric has tracked 22,763,032 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 1,481 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 228,869 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 15 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.