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Sampling Optimization in Pharmacokinetic Bridging Studies: Example of the Use of Deferiprone in Children With β ‐Thalassemia
Author(s) -
Bellanti Francesco,
Di Iorio Vincenzo Luca,
Danhof Meindert,
Della Pasqua Oscar
Publication year - 2016
Publication title -
the journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 116
eISSN - 1552-4604
pISSN - 0091-2700
DOI - 10.1002/jcph.708
Subject(s) - deferiprone , pharmacokinetics , bridging (networking) , sample size determination , medicine , covariate , population , sampling (signal processing) , mathematics , statistics , thalassemia , pharmacology , computer science , computer network , environmental health , filter (signal processing) , computer vision
Abstract Despite wide clinical experience with deferiprone, the optimum dosage in children younger than 6 years remains to be established. This analysis aimed to optimize the design of a prospective clinical study for the evaluation of deferiprone pharmacokinetics in children. A 1‐compartment model with first‐order oral absorption was used for the purposes of the analysis. Different sampling schemes were evaluated under the assumption of a constrained population size. A sampling scheme with 5 samples per subject was found to be sufficient to ensure accurate characterization of the pharmacokinetics of deferiprone. Whereas the accuracy of parameters estimates was high, precision was slightly reduced because of the small sample size (CV% >30% for Vd/F and KA). Mean AUC ± SD was found to be 33.4 ± 19.2 and 35.6 ± 20.2 mg · h/mL, and mean C max ± SD was found to be 10.2 ± 6.1 and 10.9 ± 6.7 mg/L based on sparse and frequent sampling, respectively. The results showed that typical frequent sampling schemes and sample sizes do not warrant accurate model and parameter identifiability. Expectation of the determinant (ED) optimality and simulation‐based optimization concepts can be used to support pharmacokinetic bridging studies. Of importance is the accurate estimation of the magnitude of the covariate effects, as they partly determine the dose recommendation for the population of interest.