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Aim: To assess the ability of model-based personalised dosing tools to estimate busulfan exposure (i) in comparison to clinically used intensive sampling exposure estimation procedure, (ii) using limited sampling strategies and (iii) to predict changes in busulfan clearance during busulfan treatment. Methods: Data on intravenous busulfan dosing for patients with 4 consecutive days were entered into Bayesian forecasting software, InsightRX and NextDose. Prediction of busulfan cumulative exposure was compared to current clinical practice estimation, aiming for pre-defined individualised target of cumulative exposure. Estimation performance was tested given several limited sampling strategies. Results: Thirty-two paediatric patients (0.2–16.5 years) provided a total of 103 daily exposure measurements estimated using 7 samples taken per day (full sampling), with 19 patients having sampling following all doses administered. Both software tools utilising Bayesian methods provided acceptable relative bias and precision of cumulative exposure estimations under the tested sampling scenarios. Relative bias ranged from median RE of 0.1–14.6% using InsightRX and from 3.4–7.8% using NextDose. Precision ranged from median RMSE of 0.19–0.32 mg·h·L−1 for InsightRX and 0.08–0.1 mg·h·L−1 for NextDose. A median reduction in busulfan clearance from day 1 to day 4 was observed in the clinical data (−10.9%), when using InsightRX (−18.6%) and with NextDose (−14.7%). Conclusion: Bayesian methods were shown to have relatively low bias and precisely estimate busulfan exposure using intensive sampling and several limited sampling strategies, which provides evidence for prospective studies to evaluate these tools in clinical practice. A trend to overestimation of exposure using Bayesian methods was observed compared to clinical practice. Reduction of busulfan clearance from day 1 to 4 of once daily dosing was confirmed and should be considered when adjusting doses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background and Objectives: Bayesian forecasting (BF) methods for tobramycin dose individualisation has not seen widespread clinical adoption, despite being endorsed by clinical practice guidelines. Several freeware and commercial programmes using BF methods are available to support personalised dosing. This study evaluated exposure estimates, dose recommendations, and predictive performance compared with current clinical practice.Methods: Data from 105 patients (50 adults and 55 children) with cystic fibrosis who received intravenous tobramycin treatment and had paired concentration-time measurements were analysed using (1) log-linear regression analysis, and (2) three BF programmes: TDMx, InsightRX, and DoseMe. Exposure estimates and dose recommendations were compared using the Wilcoxon signed-rank test and Bland-Altman analysis. Predictive performance of BF programmes was compared based on bias and imprecision.Results: Median estimated tobramycin exposure with current clinical practice was significantly lower (87.8 vs. 92.5, 94.0 and 90.3 mg h l-1; p ≤ 0.01), hence median subsequent dose recommendations were significantly higher (10.1 vs. 9.4, 9.4 and 9.2 mg kg-1; p ≤ 0.01) compared with BF programmes. Furthermore, median relative dose-adjustment differences were higher in adults (> 10%) compared with children (4.4-7.8%), and differences in individual dose recommendations were > 20% on 19.1-27.4% of occasions. BF programmes showed low bias (< 7%) and imprecision (< 20%), and none of the programmes made consistently significantly different recommendations compared with each other.Conclusions: On average, the predictions made by the BF programmes were similar, however substantial individual differences were observed for some patients. This suggests the need for detailed investigations of true tobramycin exposure. [ABSTRACT FROM AUTHOR]

When a dataset is imbalanced, the prediction of the scarcely-sampled subpopulation can be over-influenced by the population contributing to the majority of the data. The aim of this study was to develop a Bayesian modelling approach with balancing informative prior so that the influence of imbalance to the overall prediction could be minimised. The new approach was developed in order to weigh the data in favour of the smaller subset(s). The method was assessed in terms of bias and precision in predicting model parameter estimates of simulated datasets. Moreover, the method was evaluated in predicting optimal dose levels of tobramycin for various age groups in a motivating example. The bias estimates using the balancing informative prior approach were smaller than those generated using the conventional approach which was without the consideration for the imbalance in the datasets. The precision estimates were also superior. The method was further evaluated in a motivating example of optimal dosage prediction of tobramycin. The resulting predictions also agreed well with what had been reported in the literature. The proposed Bayesian balancing informative prior approach has shown a real potential to adequately weigh the data in favour of smaller subset(s) of data to generate robust prediction models. [ABSTRACT FROM AUTHOR]

Background and Objectives: Several dosage adjustment methods are currently available to individualize intravenous tobramycin dosing. This study compared different methods in terms of their recommendations for dosage adjustment, their estimation of patients' pharmacokinetic parameter values and their ability to predict subsequent observed tobramycin concentrations following once-daily tobramycin treatment in children and adolescents with cystic fibrosis. Methods: Retrospective data from 172 patients treated at the Royal Children's Hospital (Brisbane, QLD, Australia) were analysed. To be included in the analysis, each patient had to have at least one pair of tobramycin plasma concentration-time measurements recorded over a dosing interval. One or both of the concentrations in the paired set were applied in each of the following dosage adjustment methods: (i) the Therapeutic Guidelines Aminoglycoside nomogram; (ii) the Massie nomogram; (iii) log-linear regression analysis; and two Bayesian forecasting software programs: (iv) TCIWorks and (v) DoseMe. All methods were compared in regard to their recommendations for tobramycin dosage adjustment. The latter three methods were also examined in terms of estimated pharmacokinetic parameter values and their ability to predict subsequent observed tobramycin concentrations. Results: The Therapeutic Guidelines nomogram recommended significantly greater mean doses for dosage adjustment (27.0 mg/kg) compared with all other methods ( p ≤ 0.01), which gave similar mean dose recommendations (11.6-14.6 mg/kg); however, >20 % differences in doses on an individual level were seen on 20-35 % of occasions across all methods. The log-linear regression analysis method and the two Bayesian forecasting methods (TCIWorks and DoseMe) showed negligible bias but imprecision of around 20 % in predicting subsequent observed tobramycin concentrations. The Bayesian forecasting methods showed no significant difference in mean dose recommendations when using either one or two concentration measurements but increased imprecision in predicting subsequent observed tobramycin concentrations. Conclusion: The log-linear regression method and Massie nomogram are likely to be suitable alternative methods for tobramycin dosage adjustment when Bayesian forecasting software is unavailable. The Therapeutic Guidelines nomogram should not be used to aid dose adjustment of tobramycin therapy in children with cystic fibrosis. [ABSTRACT FROM AUTHOR]

Background and Objectives: While several studies have examined the pharmacokinetics of tobramycin in patients with cystic fibrosis (CF), there is no consensus on whether they differ in patients with and without CF. The objectives of this study were to identify covariates which explain pharmacokinetic variability and to examine whether having the disease CF in itself alters these relationships and drug dose requirements. Methods: To investigate this issue, a population pharmacokinetic meta-analysis of data from eight centres was undertaken. NONMEM 7.2 was used to analyse the data, which comprised 4,514 concentration-time measurements from 465 adults and children with CF and 1,095 concentration-time measurements from 267 adults and children without CF. Results: Tobramycin disposition was well described by a two-compartment model with first-order elimination. Patient age, fat-free mass, serum creatinine concentration and sex were identified as significant covariates in the final model. Fat-free mass was superior to total bodyweight as a descriptor of clearance, volume of distribution of the central and peripheral compartments and inter-compartmental clearance. CF as an independent disease-specific factor had no significant influence on the pharmacokinetics of tobramycin at any stage during covariate model building. An optimal dose of 11 mg/kg every 24 h was defined for CF patients using a utility function approach. Conclusion: The pharmacokinetics of tobramycin do not differ significantly in CF patients compared with patients without CF when subject age, fat-free mass, sex and renal function are taken into consideration. Variations in tobramycin dosing between CF and non-CF patients should therefore reflect target concentrations or exposures based on differences in expected pathogen sensitivity and not the presence of CF. [ABSTRACT FROM AUTHOR]

A simulation-based comparison study on the relative merits of dose-control trials (DCTs) with exposure–response analysis versus concentration-control trials (CCTs) for drugs with narrow therapeutic index showed that DCT designs are more informative about the exposure–response relationship. The authors revisit the question employing optimal design methodology and propose strategies for designing ethically attractive trials for these drugs, balancing between individual–collective risk and informativeness. An optimal study was performed considering a hypothetical immunosuppressant agent with 2 clinical end points. Different scenarios were optimized applying cost-based designs (unwanted events vs number of subjects/trial or maximal individual risk). Dose/exposure targets and number of subjects per trial/arm were optimized. Prior information inclusion on baseline risks was evaluated. DCTs were more informative, needing smaller studies to provide the same information as CCTs. Using the number of unwanted events—rather than subjects—as cost resulted in ethically more attractive designs. Including prior baseline risk information reduced the number of subject/events and allowed the use of targets closer to the optimal. Designing dose-finding trials for some narrow therapeutic index drugs may be improved by using DCTs with exposure–response analysis, cost-based designs, prior information, and optimal design analysis providing information on the ethical trade-off between individual risk and information gain. [ABSTRACT FROM PUBLISHER]