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Clin Pharmacokinet. 2018 Aug 24. doi: 10.1007/s40262-018-0707-9. [Epub ahead of print]

Monitoring of Tobramycin Exposure: What is the Best Estimation Method and Sampling Time for Clinical Practice?

Author information

1
School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall Street, Woolloongabba, Brisbane, QLD, 4102, Australia.
2
School of Pharmacy, Pharmacy Australia Centre of Excellence, University of Queensland, 20 Cornwall Street, Woolloongabba, Brisbane, QLD, 4102, Australia. s.hennig@uq.edu.au.
3
Princess Alexandra Hospital, Brisbane, QLD, Australia.

Abstract

OBJECTIVES:

The objective of this article is to investigate the influence of blood sampling times on tobramycin exposure estimation and clinical decisions and to determine the best sampling times for two estimation methods used for therapeutic drug monitoring.

METHODS:

Adult patients with cystic fibrosis, treated with once-daily intravenous tobramycin, were intensively sampled over one 24-h dosing interval to determine true exposure (AUC0-24). The AUC0-24s were then estimated using both log-linear regression and Bayesian forecasting methods for 21 different sampling time combinations. These were compared to true exposure using relative prediction errors. The differences in subsequent dose recommendations were calculated.

RESULTS:

Twelve patients, with a median (range) age of 25 years (18-36) and weight of 66.5 kg (50.6-76.4) contributed 96 tobramycin concentrations. Five hundred and eighty-eight estimated AUC0-24s were compared to 12 measured true AUC0-24 values. Median relative prediction errors ranged from - 34.7 to 45.5% for the log-linear regression method and from - 14.46 to 11.23% for the Bayesian forecasting method across the 21 sampling combinations. The most unbiased exposure estimation was provided from concentrations sampled at 100/640 min after the start of the infusion using log-linear regression and at 70/160 min using Bayesian forecasting. Subsequent dosing recommendations varied greatly depending on the estimation method and the sampling times used.

CONCLUSION:

Sampling times markedly influence bias in AUC0-24 estimation, leading to greatly varied dose adjustments. The impact of blood sampling times on dosing decisions is reduced when using Bayesian forecasting.

PMID:
30140975
DOI:
10.1007/s40262-018-0707-9

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