Sampling Optimization in Pharmacokinetic Bridging Studies: Example of the Use of Deferiprone in Children With ß-Thalassemia.

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 Cmax ± 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.

Translation of drug effects from experimental models of neuropathic pain and analgesia to humans.

Neuropathic pain research remains a challenging undertaking owing to: (i) the lack of understanding about the underlying disease processes; and (ii) poor predictive validity of the current models of evoked pain used for the screening of novel compounds. Common consensus is that experimental models replicate symptoms (i.e. have face validity but no construct validity). Another issue that requires attention is the sensitivity of endpoints to discriminate drug effects that are relevant to the disease in humans. In this paper we provide an overview of the pre-clinical models that can be used in conjunction with a model-based approach to facilitate the prediction of drug effects in humans. Our review strongly suggests that evidence of the concentration-effect relationship is necessary for translational purposes.