What is known and objectives Gentamicin is often used for the treatment of Gram‐negative infections. Due to pharmacokinetic variability in paediatric patients, appropriate dosing of gentamicin in the paediatric population is challenging. This article reviews published population pharmacokinetic models of gentamicin in paediatric patients, identifies covariates that significantly influence gentamicin pharmacokinetics, and determines whether there is a consensus on proposed dosing for intravenous gentamicin in this population. Methods The PubMed database was searched for articles published until the end of 2017. If the articles described population pharmacokinetic models of gentamicin in the paediatric population (after intravenous administration of gentamicin), the following data were extracted: type of study, year of publication, population characteristics and number of patients, gentamicin dosing, total number of gentamicin (serum and/or plasma) concentrations, type of population modelling approach, developed model with pharmacokinetic parameters and covariates included. Results and discussion In most of the studies, one‐ or two‐compartment modelling was applied. The mean estimated gentamicin clearance for newborns, infants and the complete paediatric population was 0.048, 0.13 and 0.067 L/h/kg, respectively, and the mean predicted volume of distribution was 0.475, 0.35 and 0.33 L/kg, respectively. The values reflect differences in body composition and kidney maturation within the different paediatric populations. Gentamicin pharmacokinetics were most influenced by age, body size and renal function. What is new and conclusion Based on our review, the authors agree on a prolonged dosing interval for preterm and term newborns (up to 48 hours). However, there was no agreement on proposed dosing with respect to gestational age. In general, the proposed daily doses were lower compared to those initially applied for preterm newborns and comparable to those for term newborns. For infants and children, the dosing interval remained unchanged (24 hours), but the proposed daily doses were higher than actually applied. When differences in the paediatric population are considered and an appropriate population PK model with applicable covariates is applied, dosing can be individualized. In the future, studies of gentamicin pharmacokinetics in paediatric patients should focus on currently underestimated covariates, such as fat‐free mass, concomitantly administered drugs, body temperature and critical illness because these can change gentamicin PK considerably. Consequently, different dosing is required and TDM becomes even more important.

The aim of this study was to externally validate the predictive performance of published population pharmacokinetic models of gentamicin in all paediatric age groups, from preterm newborns to adolescents. We first selected published population pharmacokinetic models of gentamicin developed in the paediatric population with a wide age range. The parameters of the literature models were then re-estimated using the PRIOR subroutine in NONMEM®. The predictive ability of the literature and the tweaked models was evaluated. Retrospectively collected data from a routine clinical practice (512 concentrations from 308 patients) were used for validation. The models with covariates characterising developmental changes in clearance and volume of distribution had better predictive performance, which improved further after re-estimation. The tweaked model by Wang 2019 performed best, with suitable accuracy and precision across the complete paediatric population. For patients treated in the intensive care unit, a lower proportion of patients would be expected to reach the target trough concentration at standard dosing. The selected model could be used for model-informed precision dosing in clinical settings where the entire paediatric population is treated. However, for use in clinical practice, the next step should include additional analysis of the impact of intensive care treatment on gentamicin pharmacokinetics, followed by prospective validation.