Precision dosing of amikacin in term neonates using pharmacometric approach

Background: Maintaining amikacin concentrations within a specific therapeutic window is crucial to avoid sub-therapeutic or toxic levels. This study aimed to design a dosing nomogram for amikacin in neonates using a Population Pharmacokinetic (PopPK) modeling approach. Methods: PopPK model was developed using 101 amikacin concentrations from 80 neonates and validated using model diagnostics, and empirical Bayesian forecasting was performed. Pharmacokinetic profiles were simulated for virtual subjects with a range of covariates to identify suitable dosage regimens. Dosage regimens with the highest probability for the target group were selected to design the dosing nomogram. Results: A two-compartment PK model best described the study data. Body weight (WT), serum creatinine (SCR), and post-natal age (PNA) affected the clearance of amikacin. The model predictions are with less than 15% absolute prediction error. WT and SCR were divided into five groups each, with each group repeated for every week of PNA for four weeks for dosing nomogram development. Conclusion: A PopPK model was developed and successfully-predicted concentrations in the study population. This model was used to develop a nomogram considering significant covariates like WT, SCR, and PNA. The proposed dosing nomogram can assist clinicians in developing individualized dosage regimens. Impact: Population pharmacokinetic (PopPK) models for amikacin in term neonates were developed using clinical data from an Indian clinical setting and successfully-predicted the amikacin concentrations for the study population. Pharmacokinetic simulations with virtual subjects were used to calculate the probability of target attainment for different dosing regimens. The proposed dosing nomogram can potentially assist clinicians in designing optimal amikacin dosage regimens for neonates.