Age influence on clearance of phenobarbital in paediatric patients
JS Pérez-Blanco (1,2), P Teixeira (1), MJ Otero (2,3), E Laso Lucas (3), MJ García (1,2), D Santos Buelga (1,2)
(1) Department of Pharmacy and Pharmaceutical Technology, University of Salamanca, Salamanca, Spain; (2) Institute for Biomedical Research of Salamanca (IBSAL), Spain. (3) University Hospital of Salamanca, Spain.
Objectives: To describe the influence of AGE on phenobarbital clearance (CL/FPBT) developing a pharmacokinetic (PK) model in a paediatric population.
Methods: The study has been conducted in 39 paediatric patients, aged 0-14 years old and treated with oral administration of PBT. A total of 71 PBT steady state serum concentrations were fitted to a PK model using non-linear mixed-effects modelling implemented in NONMEM V7.2 (FOCEI). Covariates were evaluated against CL/F by a stepwise forward inclusion (p = 0.05) and backward exclusion (p = 0.001) process to the base model. The covariates analysed were: AGE, WGT, HGT, BSA, BMI, LBM, SEX and concomitant treatments (phenytoin, lamotrigine, valproic acid, others). Missing data of covariates were handled considering as missing completely at random (MCAR) [1]. Evaluation of the final model was performed using bootstraps and VPC.
Results: A one compartment model with first order absorption and elimination processes (ADVAN 2 TRANS 2) has been selected as the best structural model. The values of the volume of distribution and absorption rate constant were fixed to those proposed in literature [2-3]. Missing HGT data (MCAR) were inputted following a simple linear regression performed with the available information of HGT and AGE (r2=0.84) in the population studied. AGE and valproic acid treatment were included in the final model as covariates on the CL/FPBT, those account for a 48% of its variability and a 59% of the residual variability (proportional error):
CL/F= (0.179-0.129·e-AGE*0.24)·0.647VLP
V= 0.9 L/kg
Ka=1.33 h-1
CVCL/F= 23.4%
CVRES= 10.9%
The relative standard error (RSE) for the fixed parameters was lower than 25%, except for the exponent of the AGE covariate (43%). RSE and estimated shrinkages for random parameters were lower than 30%. The proposed model was satisfactorily evaluated with a bootstrap (n=200) and VPC.
Conclusions: A suitable population PK model of PBT in paediatric patients has successfully been developed. The final model showed an important influence of AGE on the CL/FPBT. Valproic acid treatment was included following statistics criteria (?OFV=-13.5) despite the fact that there were only 10% of the population concomitantly treated with this drug. Consequently, its real influence should be evaluated again with a more representative set of data of this covariate. The model proposed is useful for raising awareness of the PBT PK in childhood and could be helpful for TDM.
References:
[1] Johansson A and Karlsson M. Comparison of Methods for Handling Missing Covariate Data. The AAPS Journal (2013) 15(4): 1232-1241.
[2] Patsalos PN. Antiepileptic drug interactions. A clinical guide. 2nd ed. Springer; London. 2013.
[3] Walt JS, Wilmhurst JM, Karlsson MO. Population pharmacokinetics of phenobarbitone administered as oral rescue therapy in children with refractory status epilepticus. 2008; Available at: http://www.paganz.org/abstracts/population-pharmacokinetics-of-phenobarbitone-administered-as-oral-rescue-therapy-in-children-with-refractory-status-epilepticus/. Accessed January/22, 2015.
