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Covariate effects and population pharmacokinetics of lamivudine in HIV ‐infected children
Author(s) -
Piana Chiara,
Zhao Wei,
Adkison Kimberly,
Burger David,
JacqzAigrain Evelyne,
Danhof Meindert,
Della Pasqua Oscar
Publication year - 2014
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.12247
Subject(s) - covariate , pharmacokinetics , lamivudine , dosing , population , medicine , volume of distribution , cmax , population pharmacokinetics , pharmacology , statistics , mathematics , immunology , environmental health , hepatitis b virus , virus
Aim Lamivudine is used as first line therapy in HIV ‐infected children. Yet, like many other paediatric drugs, its dose rationale has been based on limited clinical data, without thorough understanding of the effects of growth on drug disposition. Here we use lamivudine to show how a comprehensive population pharmacokinetic model can account for the influence of demographic covariates on exposure (i.e. AUC and C max ). Methods Data from three paediatric trials were used to describe the pharmacokinetics across the overall population. Modelling was based on a non‐linear mixed effects approach. A stepwise procedure was used for covariate model building. Results A one compartment model with first order elimination best described the pharmacokinetics of lamivudine in children. The effect of weight on clearance ( CL ) and volume of distribution ( V ) was characterized by an exponential function, with exponents of 0.705 and 0.635, respectively. For a child with median body weight (17.6 kg), CL and V were 16.5 (95% CI 15.2, 17.7) l h −1 and 46.0 (95% CI 42.4, 49.5) l, respectively. There were no differences between formulations (tablet and solution). The predicted AUC (0,12 h) after twice daily doses of 4 mg kg −1 ranged from 4.44 mg l −1 h for children <14 kg to 7.25 mg l −1 h for children >30 kg. Conclusions The use of meta‐analysis is critical to identify the correct covariate‐parameter relationships, which must be assessed before a model is applied for predictive purposes (e.g. defining dosing recommendations for children). In contrast to prior modelling efforts, we show that the covariate distribution in the target population must be considered.