Abstract
Amikacin is commonly used for probabilistic antimicrobial therapy in critically ill patients with sepsis. Its narrow therapeutic margin makes it challenging to determine the right individual dose that ensures the highest efficacy target attainment rate (TAR) in this setting. This study aims to develop a new initial dosing approach for amikacin by optimizing the a priori TAR in this population. A population pharmacokinetic model was built with a learning data set from critically ill patients who received amikacin. It was then used to design an initial dosing approach maximizing a priori TAR for a target ratio of ≥8 for the peak concentration to the MIC (Cmax/MIC) or of ≥75 for the ratio of the area under the concentration-time curve from 0 to 24 h to the MIC (AUC0-24/MIC). In the 166 patients included, 53% had amikacin Cmax of ≥64 mg/liter with a median dose of 23.4 mg/kg. A two-compartment model with creatinine clearance and body surface area as covariates best described the data and showed good predictive performance. Our dosing approach was successful in optimizing TAR for Cmax/MIC, with a rate of 92.9% versus 67.9% using a 30-mg/kg regimen, based on an external subset of data and assuming a MIC of 8 mg/liter. Mean optimal doses were higher (3.5 ± 0.5 g) than with the 30-mg/kg regimen (2.1 ± 0.3 g). Suggested doses varied with the MIC, the target index, and desired TAR threshold. A dosing algorithm based on the method is proposed for a large range of patient covariates. Clinical studies are necessary to confirm efficacy and safety of this optimized dosing approach.