A Novel Population Pharmacokinetic Model for Gentamicin in Septic Neonates at Nigerian NICUs with Bayesian Adaptive Dosing Algorithm

Gentamicin is a first-line antibiotic for neonatal sepsis in Nigeria, but its narrow therapeutic index and highly variable pharmacokinetics in septic neonates necessitate personalised dosing that is currently unavailable due to the absence of Nigerian population pharmacokinetic models. This study developed a novel population pharmacokinetic model for gentamicin in septic neonates at NICUs in four teaching hospitals across the South East, South West, North Central, and North West zones of Nigeria. A prospective sparse sampling pharmacokinetic study design was employed in 120 neonates with confirmed or suspected sepsis. Gentamicin plasma concentrations were quantified by validated EMIT immunoassay. Population pharmacokinetic modelling was conducted using NONMEM 7.5 with first-order conditional estimation. Covariate analysis investigated weight, gestational age, postnatal age, Apgar score, serum creatinine, and concurrent medications as pharmacokinetic variability determinants. The base model was a one-compartment model. Final covariate model identified current weight (CL allometric exponent 0.75) and postmenstrual age (on Vd) as the strongest predictors. A Bayesian adaptive dosing algorithm using the validated model was developed and evaluated in an external validation dataset of 30 neonates. Bayesian dose individualisation achieved target attainment (AUC/MIC 70 to 120 mg.h/L) in 81.7 percent of cases, compared to 48.3 percent with extended-interval flat dosing. This novel model constitutes an original contribution to neonatal clinical pharmacokinetics in Nigeria and provides the infrastructure for a clinically deployable neonatal gentamicin dosing tool.

Keywords: gentamicin, population pharmacokinetics, neonatal sepsis, Bayesian dosing, NONMEM