Monitoring of a single post-infusion blood sample to estimate the actual peak and trough concentration of tobramycin in critically ill patients

Bayesian Forecasting for Intravenous Tobramycin Dosing in Adults With Cystic Fibrosis Using One Versus Two Serum Concentrations in a Dosing Interval

BACKGROUND

Intravenous tobramycin treatment requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of cystic fibrosis. The 24-hour area under the concentration-time curve (AUC24) is widely used to guide dosing; however, there remains variability in practice around methods for its estimation. The objective of this study was to determine the potential for a sparse-sampling strategy using a single postinfusion tobramycin concentration and Bayesian forecasting to assess the AUC24 in routine practice.

METHODS

Adults with cystic fibrosis receiving once-daily tobramycin had paired concentrations measured 2 hours (c1) and 6 hours (c2) after the end of infusion as routine monitoring. AUC24 exposures were estimated using Tucuxi, a Bayesian forecasting application that incorporates a validated population pharmacokinetic model. Simulations were performed to estimate AUC24 using the full data set using c1 and c2, compared with estimates using depleted data sets (c1 or c2 only), with and without concentration data from earlier in the course. The agreement between each simulation condition and the reference was assessed graphically and numerically using the median difference (∆) AUC24 and (relative) root mean square error (rRMSE) as measures of bias and accuracy, respectively.

RESULTS

A total of 55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median ∆AUC24 <2 mg·h·L-1 and rRMSE of <15% for sequential c1 and c2 conditions.

CONCLUSIONS

Bayesian forecasting implemented in Tucuxi, using single postinfusion concentrations taken 2-6 hours after tobramycin administration, yield similar exposure estimates to more intensive (two-sample) methods and are suitable for routine TDM practice.

Approaches for dosage individualisation in critically ill patients

INTRODUCTION

Pharmacokinetic variability in critically ill patients is the result of the overlapping of multiple pathophysiological and clinical factors. Unpredictable exposure from standard dosage regimens may influence the outcome of treatment. Therefore, strategies for dosage individualisation are recommended in this setting.

AREAS COVERED

The authors focus on several approaches for dosage individualisation that have been developed, ranging from the well-established therapeutic drug monitoring (TDM) up to the innovative application of pharmacogenomics criteria. Furthermore, the authors summarise the specific population pharmacokinetic models for different drugs developed for critically ill patients to improve the initial dosage selection and the Bayesian forecasting of serum concentrations. The authors also consider the use of Monte Carlo simulation for the selection of dosage strategies.

EXPERT OPINION

Pharmacokinetic/pharmacodynamics (PK/PD) modelling and dosage individualisation methods based on mathematical and statistical criteria will contribute in improving pharmacologic treatment in critically ill patients. Moreover, substantial effort will be necessary to integrate pharmacogenomics criteria into critical care practice. The lack of availability of target biomarkers for dosage adjustment emphasizes the value of TDM which allows a large part of treatment outcome variability to be controlled.