Population pharmacokinetic parameters for Bayesian monitoring of amikacin therapy in intensive care unit patients

Evaluation of Current Amikacin Dosing Recommendations and Development of an Interactive Nomogram: The Role of Albumin

This study aimed to evaluate the potential efficacy and safety of the amikacin dosage proposed by the main guidelines and to develop an interactive nomogram, especially focused on the potential impact of albumin on initial dosage recommendation. The probability of target attainment (PTA) for each of the different dosing recommendations was calculated through stochastic simulations based on pharmacokinetic/pharmacodynamic (PKPD) criteria. Large efficacy and safety differences were observed for the evaluated amikacin dosing guidelines together with a significant impact of albumin concentrations on efficacy and safety. For all recommended dosages evaluated, efficacy and safety criteria of amikacin dosage proposed were not achieved simultaneously in most of the clinical scenarios evaluated. Furthermore, a significant impact of albumin was identified: The higher is the albumin, (i) the higher will be the PTA for maximum concentration/minimum inhibitory concentration (Cmax/MIC), (ii) the lower will be the PTA for the time period with drug concentration exceeding MIC (T_>MIC) and (iii) the lower will be the PTA for toxicity (minimum concentration). Thus, accounting for albumin effect might be of interest for future amikacin dosing guidelines updates. In addition, AMKnom, an amikacin nomogram builder based on PKPD criteria, has been developed and is freely available to help evaluating dosing recommendations.

Amikacin population pharmacokinetics in critically ill Kuwaiti patients

Amikacin pharmacokinetic data in Kuwaiti (Arab) intensive care unit (ICU) patients are lacking. Fairly sparse serum amikacin peak and trough concentrations data were obtained from adult Kuwaiti ICU patients. The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorithm. The estimations of the developed model were assessed using mean error (ME) as a measure of bias and mean squared error (MSE) as a measure of precision. A total of 331 serum amikacin concentrations were obtained from 56 patients. The mean (± SD) model parameter values found were Vc = 0.2302 ± 0.0866 L/kg, kslope = 0.004045 ± 0.00705 min per unit of creatinine clearance, k12 = 2.2121 ± 5.506 h(-1), and k21 = 1.431 ± 2.796 h(-1). The serum concentration data were estimated with little bias (ME = -0.88) and good precision (MSE = 13.08). The present study suggests that amikacin pharmacokinetics in adult Kuwaiti ICU patients are generally rather similar to those found in other patients. This population model would provide useful guidance in developing initial amikacin dosage regimens for such patients, especially using multiple model (MM) dosage design, followed by appropriate Bayesian adaptive control, to optimize amikacin dosage regimens for each individual patient.

Evaluation of population pharmacokinetic models for amikacin dosage individualization in critically ill patients

Objectives

The aim of this study was to evaluate the reliability for dosage individualization and Bayesian adaptive control of several literature-retrieved amikacin population pharmacokinetic models in patients who were critically ill.

Methods

Four population pharmacokinetic models, three of them customized for critically-ill patients, were applied using pharmacokinetic software to fifty-one adult patients on conventional amikacin therapy admitted to the intensive care unit. An estimation of patient-specific pharmacokinetic parameters for each model was obtained by retrospective analysis of the amikacin serum concentrations measured (n = 162) and different clinical covariates. The model performance for a priori estimation of the area under the serum concentration-time curve (AUC) and maximum serum drug concentration (Cmax) targets was obtained.

Key findings

Our results provided valuable confirmation of the clinical importance of the choice of population pharmacokinetic models when selecting amikacin dosages for patients who are critically ill. Significant differences in model performance were especially evident when only information concerning clinical covariates was used for dosage individualization and over the two most critical determinants of clinical efficacy of amikacin i.e. the AUC and Cmax values.

Conclusions

Only a single amikacin serum level seemed necessary to diminish the influence of population model on dosage individualization.

Pharmacokinetics of amikacin in intensive care unit patients

OBJECTIVE

To characterize the population pharmacokinetics of amikacin in intensive care unit (ICU) patients and to analyse whether these patients show different kinetic behaviour on the basis of their clinical diagnoses.

METHOD

The patient population comprised 104 medical ICU patients on amikacin treatment for several presumed or documented Gram-negative infections. Four study groups were defined according to patients' clinical diagnosis: sepsis group (n = 39), trauma group (n = 20), pneumonia group (n = 21) and 'other diagnosis' group (n = 24). The pharmacokinetic parameters for amikacin in these patients were then compared.

RESULTS

The ICU patients were found to have increased values for the amikacin volume of distribution (0.52 +/- 0.21 litres/kg), whereas total amikacin clearance expressed as a linear function of creatinine clearance was Cl (ml/min/kg) = 0.13 +/- 0.86 ClCR, which is not significantly different from other estimations reported in the literature. However, this relationship revealed statistically significant differences among the four groups of ICU patients. Moreover, the septic and trauma patients showed higher (but not statistically significant) values for the amikacin volume of distribution.

CONCLUSION

The amikacin pharmacokinetic parameters obtained should allow Bayesian individualization of amikacin doses in patients admitted to medical ICUs, on the basis of their clinical diagnoses.

Development and evaluation of a Bayesian pharmacokinetic estimator and optimal, sparse sampling strategies for ceftazidime

Data were gathered during an activity-controlled trial in which seriously ill, elderly patients were randomized to receive intravenous ceftazidime or ciprofloxacin and for which adaptive feedback control of drug concentrations in plasma and activity profiles was prospectively performed. The adaptive feedback control algorithm for ceftazidime used an initial population model, a maximum a posteriori (MAP)-Bayesian pharmacokinetic parameter value estimator, and an optimal, sparse sampling strategy for ceftazidime that had been derived from data in the literature obtained from volunteers. Iterative two-stage population pharmacokinetic analysis was performed to develop an unbiased MAP-Bayesian estimator and updated optimal, sparse sampling strategies. The final median values of the population parameters were follows: the volume of distribution of the central compartment was equal to 0.249 liter/kg, the volume of distribution of the peripheral compartment was equal to 0.173 liter/kg, the distributional clearance between the central and peripheral compartments was equal to 0.2251 liter/h/kg, the slope of the total clearance (CL) versus the creatinine clearance (CLCR) was equal to 0.000736 liter/h/kg of CL/1 ml/min/1.73 m2 of CLCR, and nonrenal clearance was equal to + 0.00527 liter/h/kg. Optimal sampling times were dependent on CLCR; for CLCR of > or = 30 ml/min/1.73 m2, the optimal sampling times were 0.583, 3.0, 7.0, and 16.0 h and, for CLCR of < 30 ml/min/1.73 m2, optimal sampling times were 0.583, 4.15, 11.5, and 24.0 h. The study demonstrates that because pharmacokinetic information from volunteers may often not be reflective of specialty populations such as critically ill elderly individuals, iterative two-stage population pharmacokinetic analysis, MAP-Bayesian parameter estimation, and optimal, sparse sampling strategy can be important tools in characterizing their pharmacokinetics.

Population pharmacokinetics of amikacin in intensive care unit patients studied by NPEM algorithm

The population pharmacokinetics of amikacin was studied in 40 intensive care unit patients (212 plasma concentrations) by NPEM algorithm using a one-compartment model. The population was best characterized by the following pharmacokinetic parameters: renal clearance relative to creatinine clearance (Cs = 0.96 +/- 0.33), and either the total volume of distribution (Vd = 23.9 +/- 7.0 l) or the volume of distribution relative to body weight (Vs = 0.36 +/- 0.10 l.kg-1. The volume of distribution was increased with respect to the usual value of 0.25 l.kg-1. The statistical distribution of these pharmacokinetic parameters was approximately gaussian, with no significant correlation between volume of distribution and clearance. The medians and standard deviations of Cs and Vs were used as reference population values to estimate the pharmacokinetics of amikacin in a second group of 29 patients by the bayesian method, with two blood samples per patient. For each patient, the fitted parameters were able to predict the plasma concentrations of amikacin during the next 72 h with no significant bias and good precision (2.9 mg.l-1 for peaks and 0.5 mg.l-1 for troughs). This study confirms the ability of the NPEM algorithm to provide reference population values for use in bayesian monitoring of aminoglycoside therapy.

Pharmacokinetics and dosage regimens of amikacin in intensive care unit patients

The pharmacokinetics of amikacin have been studied in 40 intensive care unit (ICU) patients using a two-compartment model and the Bayesian estimation method implemented in the USC PC-PACK program of Jelliffe et al. The volume of the central compartment was significantly higher in these patients (0.36 l.kg-1) than in the reference population (0.20 l.kg-1). A method has been designed to compute dosage regimens in order to maintain a constant steady-state average plasma concentration of 8 mg.l-1 for repeated i.v. infusions. The regimen calculated for the 'average' ICU patient varies between 11 mg.kg-1 three times per day for the patient with normal renal function and 6 mg.kg-1 every 2 days for the anuric patient. This regimen is intended to begin amikacin therapy in an ICU patient, while the population pharmacokinetic parameters would allow the individualization of the regimen by means of the Bayesian method.