Model-based clinical dose optimization for phenobarbital in neonates: An illustration of the importance of data sharing and external validation

Background: Particularly in the pediatric clinical pharmacology field, data-sharing offers the possibility of making the most of all available data. In this study, we utilize previously collected therapeutic drug monitoring (TDM) data of term and preterm newborns to develop a population pharmacokinetic model for phenobarbital. We externally validate the model using prospective phenobarbital data from an ongoing pharmacokinetic study in preterm neonates.

Methods: TDM data from 53 neonates (gestational age (GA): 37 (24-42) weeks, bodyweight: 2.7 (0.45-4.5) kg; postnatal age (PNA): 4.5 (0 - 22) days) contained information on dosage histories, concentration and covariate data (including birth weight, actual weight, post-natal age (PNA), postmenstrual age, GA, sex, liver and kidney function, APGAR-score). Model development was carried out using NONMEM (R) 7.3. After assessment of model fit, the model was validated using data of 17 neonates included in the DINO (Drug dosage Improvement in NeOnates)-study.

Results: Modelling of 229 plasma concentrations, ranging from 3.2 to 75.2 mg/L, resulted in a one compartment model for phenobarbital. Clearance (CL) and volume (V-d) for a child with a birthweight of 2.6 kg at PNA day 4.5 was 0.0091 L/h (9%) and 2.38 L (5%), respectively. Birthweight and PNA were the best predictors for CL maturation, increasing CL by 36.7% per kg birthweight and 5.3% per postnatal day of living, respectively. The best predictor for the increase in Vd was actual bodyweight (0.31 L/kg). External validation showed that the model can adequately predict the pharmacokinetics in a prospective study.

Conclusion: Data-sharing can help to successfully develop and validate population pharmacokinetic models in neonates. From the results it seems that both PNA and bodyweight are required to guide dosing of phenobarbital in term and preterm neonates.