A simulation study on model‐informed precision dosing of amikacin for achieving target area under the concentration–time curve

Abstract

AimsAmikacin requires therapeutic drug monitoring for optimum efficacy; however, the optimal model‐informed precision dosing strategy for the area under the concentration–time curve (AUC) of amikacin is uncertain. This simulation study aimed to determine the efficient blood sampling points using the Bayesian forecasting approach for early achievement of the target AUC range for amikacin in critically ill patients.MethodsWe generated a virtual population of 3000 individuals using 2 validated population pharmacokinetic models identified using a systematic literature search. AUC for each blood sampling point was evaluated using the probability of achieving a ratio of estimated/reference AUC at steady state in the 0.8–1.2 range.ResultsOn day 1, the 1‐point samplings for population pharmacokinetic models showed a priori probabilities of 26.3 and 45.6%, which increased to 47.3 and 94.4% at 23 and 15 h, respectively. Using 2‐point sampling at the peak (3 and 4 h) and trough (24 h) on day 1, these probabilities further increased to 72.3 and 99.5%, respectively. These probabilities were comparable on days 2 and 3, regardless of 3 and 6 sampling points or estimated glomerular filtration rate. These results indicated the higher predictive accuracy of 2‐point sampling than 1‐point sampling on day 1 for amikacin AUC estimation. Moreover, 2‐point sampling was a more reasonable approach than rich sampling.ConclusionsThis study contributes to the development of an efficient model‐informed precision dosing strategy for early targeting of amikacin AUC in critically ill patients.