Population Pharmacokinetic Modeling and Optimal Sampling Strategy for Bayesian Estimation of Amikacin Exposure in Critically Ill Septic Patients

Abstract

Because the sepsis-induced pharmacokinetic (PK) modifications need to be considered in aminoglycoside dosing, the present study aimed to develop a population PK model for amikacin (AMK) in severe sepsis and to subsequently propose an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters. Concentration-time profiles for AMK were obtained from 88 critically ill septic patients during the first 24 hours of antibiotic treatment. The population PK model was developed using a nonlinear mixed effects modeling approach. Covariate analysis included demographic data, pathophysiological characteristics, and comedication. Optimal sampling times were selected based on a robust Bayesian design criterion. Taking into account clinical constraints, a two-point sampling approach was investigated. A two-compartment model with first-order elimination best fitted the AMK concentrations. Population PK estimates were 19.2 and 9.34 L for the central and peripheral volume of distribution and 4.31 and 2.21 L/h for the intercompartmental and total body clearance. Creatinine clearance estimated using the Cockcroft-Gault equation was retained in the final model. The two optimal sampling times were 1 hour and 6 hours after onset of the drug infusion. Predictive performance of individual Bayes estimates computed using the proposed optimal sampling strategy was reported: mean prediction errors were less than 5% and root mean square errors were less than 30%. The present study confirmed the significant influence of the creatinine clearance on the PK disposition of AMK during the first hours of treatment in critically ill septic patients. Based on the population estimates, an optimal sampling strategy suitable for Bayesian estimation of the drug PK parameters was developed, meeting the need of clinical practice.

The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis

“…The pharmacokinetic parameters for standard first-line drugs and for drugs used in both MDR and XDR tuberculosis are shown in table 9, mainly based on publications from South Africa, India, and the USA. [404][405][406][407][408][409][410][411][412][413][414][415][416] Predicting what concentration a patient will achieve is difficult, given the multiple determinants of pharmacokinetic variability. 191 Therefore, to identify the specific concentration-time profile, the drug concentrations should be measured in the patient directly.…”

Amikacin Pharmacokinetics/Pharmacodynamics in a Novel Hollow-Fiber Mycobacterium abscessus Disease Model

“…The amikacin concentrations achieved in all the HFS were validated by sampling each central compartment during the last 2 days at 0, 0.5, 2.7, 5. 4, 18.7, 23.5, 24.5, 26.7, 29.4, 42.7, and 47.5 h after the drug administration; the sampling times were chosen based on optimal sampling theory for the pharmacokinetics of amikacin (16)(17)(18). In order to quantify the M. abscessus CFU per milliliter, 1 ml of the peripheral compartment contents was removed from each system on days 0, 1, 2, 3, 5, 7, 10, and 14.…”

“…In order to translate from the HFS to the bedside, we performed computeraided clinical trial simulations based on Monte Carlo experiments, according to the steps outlined in the recent recommendations for academia and industry (24). The population pharmacokinetic parameter estimates we used and the covariance were from a study by Delattre et al (18) of 88 Belgian patients. This study was chosen because of the rigorous use of optimal sampling theory, which minimizes bias and inaccuracies in identifying pharmacokinetic parameter estimates.…”

Bayesian Network to Optimize the First Dose of Antibiotics: Application to Amikacin

“…The infection severity may influence the pharmacokinetic of aminoglycosides, as observed in patients with septic shocks [34,35]. However, in this situation, a large variability of the volume of distribution has been described [36,37], so the existence of a stable relationship between this parameter and body weight, and the relevance of a dosage calculation based on this descriptor are uncertain. Similarly, as information on possible fluid overload or dehydration was not available in the dataset used for this study, it was not possible to include this information in the BN, although it could affect the volume of distribution of amikacine.…”