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A Model for Predicting the Interindividual Variability of Drug-Drug Interactions

Overview of attention for article published in The AAPS Journal, December 2016
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Title
A Model for Predicting the Interindividual Variability of Drug-Drug Interactions
Published in
The AAPS Journal, December 2016
DOI 10.1208/s12248-016-0021-0
Pubmed ID
Authors

M. Tod, L. Bourguignon, N. Bleyzac, S. Goutelle

Abstract

Pharmacokinetic drug-drug interactions are frequently characterized and quantified by an AUC ratio (Rauc). The typical value of the AUC ratio in case of cytochrome-mediated interactions may be predicted by several approaches, based on in vitro or in vivo data. Prediction of the interindividual variability of Rauc would help to anticipate more completely the consequences of a drug-drug interaction. We propose and evaluate a simple approach for predicting the standard deviation (sd) of Ln(Rauc), a metric close to the interindividual coefficient of variation of Rauc. First, a model was derived to link sd(Ln Rauc) with the substrate fraction metabolized by each cytochrome and the potency of the interactors, in case of induction or inhibition. Second, the parameters involved in these equations were estimated by a Bayesian hierarchical model, using the data from 56 interaction studies retrieved from the literature. Third, the model was evaluated by several metrics based on the fold prediction error (PE) of sd(Ln Rauc). The median PE was 0.998 (the ideal value is 1) and the interquartile range was 0.96-1.03. The PE was in the acceptable interval (0.5 to 2) in 52 cases out of 56. Fourth, a surface plot of sd(Ln Rauc) as a function of the characteristics of the substrate and the interactor has been built. The minimal value of sd(Ln Rauc) was about 0.08 (obtained for Rauc = 1) while the maximal value, 0.7, was obtained for interactions involving highly metabolized substrates with strong interactors.

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Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 20%
Researcher 2 20%
Student > Ph. D. Student 1 10%
Unspecified 1 10%
Professor > Associate Professor 1 10%
Other 1 10%
Unknown 2 20%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 3 30%
Medicine and Dentistry 3 30%
Agricultural and Biological Sciences 1 10%
Unspecified 1 10%
Unknown 2 20%
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 23 April 2017.
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#17,887,790
of 22,965,074 outputs
Outputs from The AAPS Journal
#1,050
of 1,293 outputs
Outputs of similar age
#291,795
of 420,255 outputs
Outputs of similar age from The AAPS Journal
#23
of 36 outputs
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