Monitoring of Tobramycin Exposure: What is the Best Estimation Method and Sampling Time for Clinical Practice?
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.
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.
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.
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.
The authors acknowledge the support of the staff of the Mater Health Services Pharmacy Department and the medical and nursing staff of the Mater Health Services Adult Respiratory Unit as well as the reviewers of the manuscript for their comments.
All authors meet the criteria of authorship. YG was responsible for collecting and analysing the data and drafting the manuscript. SH and MB developed the study concept, supported data collection, reviewed and supported the analyses, and reviewed and edited the manuscript.
Compliance with Ethical Standards
No external funding was received for the preparation of this article.
Conflict of interest
Yanhua Gao, Stefanie Hennig and Michael Barras have no conflicts of interest directly relevant to the contents of this article.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Ethics approval was obtained from the Mater Health Services Human Research Ethics Committee.
Consent to participate
Informed consent was obtained from all participants included in the study.
- 15.Antibiotic Expert Group. Therapeutic guidelines: antibiotic. Version 15. Melbourne: Therapeutic Guidelines Ltd; 2014.Google Scholar
- 27.Riviere JE. Comparative pharmacokinetics principles, techniques, and applications. North Carolina State University, Raleigh, North Carolina. 2nd ed. Chichester: Wiley-Blackwell; 2011.Google Scholar
- 30.Foundation CF. Cystic Fibrosis Foundation patient registry. 2015 annual data report. Bethesda; 2016. https://www.cff.org/Our-Research/CF-Patient-Registry/2015-Patient-Registry-Annual-Data-Report.pdf. Accessed 31 Aug 2018.