Individualising Gentamicin Dosage Regimens

Summary: Population pharmacokinetic parameter estimates were calculated from 725 routine plasma gentamicin concentrations obtained in 177 neonates of 24 to 42 weeks' gestational age in their first week of life. K el increases and V/W decreases with increasing gestational age. Almost identical results were obtained with iterative twostage Bayesian fitting (MW\PHARM 3.30) as with a non-parametric maximization algorithm (NPEM2). The effect of various covariates on drug disposition was investigated retrospectively using multiple regression analysis. Predictive power for K el increases with rising gestational age. For neonates Յ28.5 weeks and neonates >28.5 weeks and Յ30.9 weeks, the predictive power of the regression equation for K el was relatively low (r 2 respectively 0.270 and 0.364). Better predictivity was found for K el at gestational ages >30.9 weeks (r 2 ‫ס‬ 0.482), with gestational age, postnatal age, and Apgar score at 5 minutes being predictors. A very strong correlation existed between volume of distribution and weight (r 2 ‫ס‬ 0.83). Volume as a function of weight could be described with low predictivity by gestational age and to a lesser degree by Apgar score at 5 minutes (r 2 ‫ס‬ 0 .298)The developed models need appropriate prospective clinical validation.

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confidence: 99%

Population Pharmacokinetics and Relationship Between Demographic and Clinical Variables and Pharmacokinetics of Gentamicin in Neonates

Stolk¹,

Degraeuwe²,

Nieman³

et al. 2002

“…This time strategy provides information on both distribution and elimination parameters, whereas sampling at peak only or trough only provides information that is more restricted to distribution and elimination parameters, respectively. 12,13 Therefore, for a single-sample strategy, sampling at T6 would be recommended to optimize AUC estimation. However, this is the least convenient sampling time in clinical practice because patients have to stay at the hospital up to this moment.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Limited Sampling Strategies for Bayesian Estimation of Daptomycin Area Under the Concentration–Time Curve: A Short Communication

Tuloup

Millet

Taricco

et al. 2023

Increasing evidence supports daptomycin therapeutic drug monitoring. The author's reference center used to perform therapeutic drug monitoring in patients who receive high-dose daptomycin for bone and joint infections, with a three-sample strategy to estimate the daptomycin daily area under the concentration-time curve (AUC). The objective of this study was to evaluate simpler strategies based on only 2 or 1 sample(s). Methods:The authors used the BestDose software to estimate the daptomycin AUC after Bayesian posterior estimation of individual pharmacokinetic (PK) parameters at steady state. The reference AUC (AUC full ) was based on 3 samples obtained predose (T0) and approximately 1 hour (T1) and 6 hours (T6) after the start of a 30-minute infusion of IV daptomycin. It was compared with the AUC based on all possible 2-sample and 1-sample strategies. Bias, imprecision, regression, and Bland-Altman plots were used to assess the performance of the alternative strategies.

“…However, little information is available in the literature. A previous study compared a Bayesian approach with other regression techniques, 27 whereas others have comparatively examined the performance of the MM (also known as stochastic control) and MAP. 4,10,28 In 2008, MacDonald et al 23 compared the ability of the MM and IMM to fit past concentrations of gentamicin and vancomycin.…”

Section: Discussionmentioning

confidence: 99%

To Estimate or to Forecast? Lessons From a Comparative Analysis of Four Bayesian Fitting Methods Based on Nonparametric Models

Goutelle

Alloux

Bourguignon

et al. 2021

Using pharmacokinetic (PK) models and Bayesian methods in dosing software facilitates the analysis of individual PK data and precision dosing. Several Bayesian methods are available for computing Bayesian posterior distributions using nonparametric population models. The objective of this study was to compare the performance of the maximum a posteriori (MAP) model, multiple model (MM), interacting MM (IMM), and novel hybrid MM(HMM) in estimating past concentrations and predicting future concentrations during therapy. Amikacin and vancomycin PK data were analyzed in older hospitalized patients using 2 strategies. First, the entire data set of each patient was fitted using each of the 4 methods implemented in BestDose software. Then, the 4 methods were used in each therapeutic drug monitoring occasion to estimate the past concentrations available at this time and to predict the subsequent concentrations to be observed on the next occasion. The bias and precision of the model predictions were compared among the methods. A total of 406 amikacin concentrations from 96 patients and 718 vancomycin concentrations from 133 patients were available for analysis. Overall, significant differences were observed in the predictive performance of the 4 Bayesian methods. The IMM method showed the best fit to past concentration data of amikacin and vancomycin, whereas the MM method was the least precise. However, MM best predicted the future concentrations of amikacin. The MAP and HMM methods showed a similar predictive performance and seemed to be more appropriate for the prediction of future vancomycin concentrations than the other models were. The richness of the prior distribution may explain the discrepancies between the results of the 2 drugs. Although further research with other drugs and models is necessary to confirm our findings, these results challenge the widely accepted assumption in PK modeling that a better data fit indicates better forecasting of future observations.