ArticleDrugs in R&D2020
Prediction of Clearance in Children from Adults Following Drug-Drug Interaction Studies: Application of Age-Dependent Exponent Model.
Article in Drugs in R&D, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 10 citations in OpenAlex.
- Pediatric pharmacokinetics and pharmacodynamics of guanabenz for the treatment of vanishing white matter.Scientific reports · 2026Article
- A comparison of different methods for the first-in-pediatric dose selection.Journal of clinical and translational research · 2022Article
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Authors and funding
1 author at 1 institution in 2 countries.
Funding
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Abstract
BACKGROUND AND
objectivePharmacokinetic drug-drug interaction (DDI) studies are conducted in adult subjects during drug development but there are limited studies that have characterized pharmacokinetic DDI studies in children. The objective of this study was to evaluate if the DDI clearance values from adults can be allometrically extrapolated from adults to children.
methodsFifteen drugs were included in this study and the age of the children ranged from premature neonates to adolescents (30 observations across the age groups). The age-dependent exponent (ADE) model was used to predict the clearance of drugs in children from adults following DDI studies.
resultsThe prediction error of drug clearances following DDIs in children ranged from 4 to 67%. Of 30 observations, 17 (57%) and 27 (90%) observations had a prediction error ≤ 30% and ≤ 50%, respectively.
conclusionThis study indicates that it is possible to predict the clearance of drugs with reasonable accuracy in children from adults following DDI studies using an ADE model. The method is simple, robust, and reliable and can replace other complex empirical models.
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