Amélie Marsot1, Romain Guilhaumou2, Camille Riff2, Olivier Blin2. 1. Service de Pharmacologie Clinique et Pharmacovigilance, AP-HM, Pharmacologie intégrée et interface clinique et industrielle, Institut des Neurosciences Timone, AMU-CNRS 7289, Aix Marseille Université, 13385, Marseille, France. amelie.marsot@ap-hm.fr. 2. Service de Pharmacologie Clinique et Pharmacovigilance, AP-HM, Pharmacologie intégrée et interface clinique et industrielle, Institut des Neurosciences Timone, AMU-CNRS 7289, Aix Marseille Université, 13385, Marseille, France.
Abstract
BACKGROUND: Amikacin is an aminoglycoside commonly used in intensive care units for the treatment of patients with life-threatening Gram-negative infections. Although aminoglycosides are extensively used, the accurate determination of their optimal dosage is complicated by marked intra- and interindividual variability in intensive care unit patients. Amikacin pharmacokinetics have been described in numerous studies over the past 25 years. OBJECTIVE: This review presents a synthesis of the population pharmacokinetic models for amikacin described in critically ill patients. The objective was to determine whether there was a consensus on a structural model and which covariates had been identified. METHODS: A literature search was conducted from the PubMed database, from its inception up until December 2015, using the following terms: 'amikacin', 'pharmacokinetic(s)', 'population', 'model(ling)' and 'nonlinear mixed effect'. Articles were excluded if they were not pertinent. The reference lists of all selected articles were also evaluated. RESULTS: Ten articles were included in this review: pharmacokinetics of amikacin were described by a one-compartment or a two-compartment model. Various covariates were tested, but only two (creatinine clearance and total body weight) were included in almost all of the described models. After inclusion of these covariates, the interindividual variability (range) in clearance and the volume of distribution were 44.4 % (28.2-69.4 %) and 31.3 % (8.1-44.7 %), respectively. The residual variability (range) was around 21.0 % (9.0-31.0 %), using a proportional model, and for a combined model (proportional/additive), the median (range) values were 0.615 mg/L (0.2-1.03 mg/L) and 29.2 % (26.8-31.6 %). CONCLUSION: This review highlights the different population pharmacokinetic models for amikacin developed in critically ill patients over the past decades and proposes relevant information for clinicians and researchers. To optimize amikacin dosage, this review points out the relevant covariates according to the target population. In a population of critically ill patients, dose optimization mainly depends on creatinine clearance and total body weight. New pharmacokinetic population studies could be considered, with new covariates of interest to be tested in model building and to further explain variability. Another future perspective could be external evaluation of previously published models.
BACKGROUND:Amikacin is an aminoglycoside commonly used in intensive care units for the treatment of patients with life-threatening Gram-negative infections. Although aminoglycosides are extensively used, the accurate determination of their optimal dosage is complicated by marked intra- and interindividual variability in intensive care unit patients. Amikacin pharmacokinetics have been described in numerous studies over the past 25 years. OBJECTIVE: This review presents a synthesis of the population pharmacokinetic models for amikacin described in critically illpatients. The objective was to determine whether there was a consensus on a structural model and which covariates had been identified. METHODS: A literature search was conducted from the PubMed database, from its inception up until December 2015, using the following terms: 'amikacin', 'pharmacokinetic(s)', 'population', 'model(ling)' and 'nonlinear mixed effect'. Articles were excluded if they were not pertinent. The reference lists of all selected articles were also evaluated. RESULTS: Ten articles were included in this review: pharmacokinetics of amikacin were described by a one-compartment or a two-compartment model. Various covariates were tested, but only two (creatinine clearance and total body weight) were included in almost all of the described models. After inclusion of these covariates, the interindividual variability (range) in clearance and the volume of distribution were 44.4 % (28.2-69.4 %) and 31.3 % (8.1-44.7 %), respectively. The residual variability (range) was around 21.0 % (9.0-31.0 %), using a proportional model, and for a combined model (proportional/additive), the median (range) values were 0.615 mg/L (0.2-1.03 mg/L) and 29.2 % (26.8-31.6 %). CONCLUSION: This review highlights the different population pharmacokinetic models for amikacin developed in critically illpatients over the past decades and proposes relevant information for clinicians and researchers. To optimize amikacin dosage, this review points out the relevant covariates according to the target population. In a population of critically illpatients, dose optimization mainly depends on creatinine clearance and total body weight. New pharmacokinetic population studies could be considered, with new covariates of interest to be tested in model building and to further explain variability. Another future perspective could be external evaluation of previously published models.
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