Intravenous aminoglycoside usage and monitoring of patients with cystic fibrosis in Australia. What's new?

Comparing two-sample log-linear exposure estimation with Bayesian model-informed precision dosing of tobramycin in adult patients with cystic fibrosis

Tobramycin dosing in patients with cystic fibrosis (CF) is challenged by its high pharmacokinetic (PK) variability and narrow therapeutic window. Doses are typically individualized using two-sample log-linear regression (LLR) to quantify the area under the concentration-time curve (AUC). Bayesian model-informed precision dosing (MIPD) may allow dose individualization with fewer samples; however, the relative performance of these methods is unknown. This single-center retrospective analysis included adult patients with CF receiving tobramycin from 2015 to 2022. Tobramycin concentrations were predicted using LLR or Bayesian estimation with two population PK models (Hennig and Alghanem). Then, both methods were used to estimate the AUC for simulated patients. For Bayesian estimation, AUC estimation with flattened priors and limited sampling strategies were also assessed. Predictions were evaluated using normalized root mean square error (nRMSE), mean percent error (MPE), and accuracy. The data set included 70 treatment courses, with 32 not evaluable by LLR due to detection limits or timing issues. Bayesian estimation demonstrated worse accuracy (47.1%-50.7% vs 75.7%), higher MPE (24.2%-32.4% vs -2.4%), and higher nRMSE (35.0%-39.4% vs 24.8%) than LLR for peak concentrations but performed better on troughs (accuracy: 92.0%-92.9% vs 84.6%). Bayesian estimation with flattened priors and a single sample at 4 h was comparable to LLR performance, with better accuracy (42.9%-68.0% vs 41.1% LLR), comparable MPE (-2.3% to -3.7% vs -0.5%) and nRMSE (11.3%-21.6% vs 17.3%). Bayesian estimation with one concentration and flattened priors can match LLR prediction accuracy. However, popPK models must be improved to better estimate peak samples.

Is there a multinational consensus of tobramycin prescribing and monitoring for cystic fibrosis? Survey of current therapeutic drug monitoring practices in USA/Canada, UK/Ireland, and Australia/New Zealand

OBJECTIVES

Sophisticated scientific methods have facilitated dose individualisation with substantial advancements in therapeutic drug monitoring (TDM) practice. It is unclear whether these methods have translated to the clinical setting. This study aimed to determine current TDM practice for tobramycin monitoring in cystic fibrosis (CF) centres in the USA and Canada, UK and Ireland, and Australia and New Zealand due to a high prevalence of CF.

METHODS

A web-based survey was developed and circulated via CF specialist groups within the targeted geographical regions. Themes included centre demographics, tobramycin usage, dosing and infusion practices, TDM practices, and blood sampling methods.

RESULTS

In total 77 responses were received from 75 different CF centres over the 3-month evaluation period (October 2019-January 2020). Respondents were from the USA and Canada (60%), Australia and New Zealand (25%), and the UK and Ireland (15%). Tobramycin was used in 97% of sites, with an international variation in practice across all survey aspects including dosing and infusion practice. TDM-based dose adjustment in the UK and Ireland was most commonly based only on trough sample collection for avoidance of toxicity, where use of computer programs for targeting both efficacy and toxicity endpoints were most common in Australia and New Zealand. The underlying pharmacokinetic basis of that program was not known by 33% of sites who utilised a computer program for tobramycin dose individualisation.

CONCLUSION

There remains substantial heterogeneity in tobramycin management worldwide. Despite two decades of research into TDM of tobramycin, there has been a slow uptake of new technologies and evolution of practice. An improved understanding of TDM processes is required for translation of evidence-based research into clinical practice. International guidelines require updating due to the advances in research to support confidence in the changes in clinical practice.

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.

Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis

Fixed tobramycin (mg/kg) dosing is often inappropriate in patients with cystic fibrosis (CF), as pharmacokinetics are highly variable. The area under the concentration-time curve (AUC) is an exposure metric suited to monitoring in this population. Bayesian strategies to estimate AUC have been available for over 20 years but are not standard practice in the clinical setting. To assess their suitability for use in clinical practice, three AUC estimation methods using limited sampling were compared to measured true exposure by using intensive sampling tobramycin data. Adults prescribed once daily intravenous tobramycin had eight concentrations taken over 24 h. An estimate of true exposure within one dosing interval was calculated using the trapezoidal method and compared to three alternate estimates determined using (i) a two-sample log-linear regression (LLR) method (local hospital practice); (ii) a Bayesian estimate using one concentration (AUC₁); and (iii) a Bayesian estimate using two concentrations (AUC₂). Each method was evaluated against the true measured exposure by a Bland-Altman analysis. Twelve patients with a median (range) age and weight of 25 (18 to 36) years and 66.5 (51 to 76) kg, respectively, were recruited. There was good agreement between the true exposure and the three alternate estimates of AUC, with a mean AUC bias of <10 mg/liter · h in each case, i.e., -8.2 (LLR), 3.8 (AUC₁), and 1.0 (AUC₂). Bayesian analysis-based and LLR estimation methods of tobramycin AUC are equivalent to true exposure estimation. All three methods may be suitable for use in the clinical setting; however, a one-sample Bayesian method may be most useful in ambulatory patients for which coordinating blood samples is difficult. Suitably powered, randomized clinical trials are required to assess patient outcomes.

Glucose tolerance during pulmonary exacerbations in children with cystic fibrosis

Background

Patients with Cystic Fibrosis (CF) are relatively insulinopenic and are at risk of diabetes, especially during times of stress. There is a paucity of data in the literature describing glucose tolerance during CF pulmonary exacerbations. We hypothesised that glucose tolerance would be worse during pulmonary exacerbations in children with CF than during clinical stability.

Methods

Patients with CF, 10 years or older, admitted with a pulmonary exacerbation underwent an OGTT within 48 hours of admission. A repeat OGTT was performed 4 to 6 weeks post discharge when the patients were well.

Results

Nine patients completed the study. Four patients were found to have normal glucose tolerance, 3 with impaired and 2 with CF related diabetes during the exacerbation. Mean change in 2-hour glucose was 1.1 mmol (SD = 0.77). At the follow up OGTT, 8 of 9 (89%) remained within their respective glucose tolerance status groupings.

Conclusion

The findings of this study show that there is little difference in glucose tolerance during CF exacerbations compared to clinical stability in the majority of patients.