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Aims: Glomerular filtration rate (GFR) is estimated daily in paediatric oncology patients; however, few equations, particularly ones that do not include serum creatinine, have been evaluated in this population. We aimed to compare the predictive performance of different equations available to estimate GFR in paediatric oncology patients., Methods: GFR was measured (mGFR) in paediatric oncology patients based on a chromium 51-labeled ethylene diamine tetraacetic acid excretion test. GFR was estimated (eGFR) in these same patients using equations identified from the literature. mGFR and eGFR values were compared, and the predictive performance of various eGFR equations was assessed in terms of their bias, precision and accuracy., Results: In total, 124 mGFR values ranging from 7 to 146 mL/min were available for analysis from 73 children. Twenty-two equations were identified from the literature. The Flanders metadata equation displayed the lowest absolute bias (mean error of 0.9 mL/min) and the greatest precision (root mean square error of 13.1 mL/min). The univariate Schwartz equation predicted the highest percentage (81.5%) of eGFR values within 30% of mGFR values, and the Rhodin fat-free mass equation predicted the highest percentage (37.1%) of eGFR values within 10% of mGFR values., Conclusions: A number of equations were identified that could be used to estimate renal function in paediatric oncology patients; however, none was found to be highly accurate. The Flanders metadata equation and univariate Schwartz performed the best in this study, and we would suggest that these two equations may be used cautiously in paediatric oncology patients for clinical decision making, understanding their limitations., (© 2017 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).)

Objectives: Glycaemic control in children and adolescents with type 1 diabetes mellitus can be challenging, complex and influenced by many factors. This study aimed to identify patient characteristics that were predictive of satisfactory glycaemic control in the paediatric population using a logistic regression mixed-effects (population) modelling approach. Methods: The data were obtained from 288 patients aged between 1 and 22 years old recorded retrospectively over 3 years (1852 HbA1c observations). HbA1c status was categorised as ‘satisfactory’ or ‘unsatisfactory’ glycaemic control, using an a priori cut-off value of HbA1c ≥ 9% (75 mmol/mol), as used routinely by the hospital’s endocrine paediatricians. Patients’ characteristics were tested as covariates in the model as potential predictors of glycaemic control. Results: There were three patient characteristics identified as having a significant influence on glycaemic control: HbA1c measurement at the beginning of the observation period (Odds Ratio (OR) = 0.30 per 1% HbA1c increase, 95% confidence interval (CI) = 0.20–0.41); Age (OR = 0.88 per year increase, 95% CI = 0.80–0.94), and fractional disease duration (disease duration/age, OR = 0.80 per 0.10 increase, 95% CI = 0.66–0.93) were collectively identified as factors contributing significantly to lower the probability of satisfactory glycaemic control. Conclusions: The study outcomes may prove useful for identifying paediatric patients at risk of having unsatisfactory glycaemic control, and who could require more extensive monitoring, support, or targeted interventions. [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]

The objective was to study the population pharmacokinetics of bound and unbound phenytoin in critically ill children, including influences on the protein binding profile. A population pharmacokinetic approach was used to analyze paired protein-unbound and total phenytoin plasma concentrations (n = 146 each) from 32 critically ill children (0.08-17 years of age) who were admitted to a pediatric hospital, primarily intensive care unit. The pharmacokinetics of unbound and bound phenytoin and the influence of possible influential covariates were modeled and evaluated using visual predictive checks and bootstrapping. The pharmacokinetics of protein-unbound phenytoin was described satisfactorily by a 1-compartment model with first-order absorption in conjunction with a linear partition coefficient parameter to describe the binding of phenytoin to albumin. The partitioning coefficient describing protein binding and distribution to bound phenytoin was estimated to be 8.22. Nonlinear elimination of unbound phenytoin was not supported in this patient group. Weight, allometrically scaled for clearance and volume of distribution for the unbound and bound compartments, and albumin concentration significantly influenced the partition coefficient for protein binding of phenytoin. The population model can be applied to estimate the fraction of unbound phenytoin in critically ill children given an individual's albumin concentration. [ABSTRACT FROM AUTHOR]

What is already known about this subject • Two recent papers have been published which have attempted to build a full population pharmacokinetic model for tobramycin in children with cystic fibrosis. • However, neither study was able to provide any information about between-subject variability (BSV) and between-occasion variability (BOV), which is necessary to justify and draw conclusions about the use of target concentration intervention (TCI). • In the publications no simulations were provided to show any new directions or evaluate current therapy. What this study adds • This study provides sound evidence that TCI must be undertaken in this patient group, as the BOV is significantly less than the BSV. • The simulations from this model clearly show that current dosing and monitoring methods will not achieve the necessary targets to maximize the pharmacokinetic–pharmacodynamic relationships of aminoglycosides in this patient group. • The model presented is able to be easily incorporated into Bayesian dose individualization software, e.g. Abbottbase or TCIworks, to achieve dosing targets more accurately. Aim The primary aim was to estimate the population pharmacokinetic parameters of once-daily intravenous (i.v.) tobramycin in paediatric cystic fibrosis (CF) patients and to investigate the influence of covariates. The second aim was to assess the need for target concentration intervention (TCI) for tobramycin in this patient group. Methods Retrospective demographic, dosing and concentration data were collected from 35 CF patients (21 female, 14 male) aged 0.5–17.8 years, from whom 318 tobramycin plasma concentrations were available. NONMEM was used to estimate the population pharmacokinetics of tobramycin. Simulations were performed using weight-based dosing with a weight from a covariate distribution model to evaluated current dosing schedules and monitoring practices. Results A two-compartment model best described the data with population parameter estimates for clearance of central compartment (CL) of 6.37 l h−1 per 70 kg; volume of central compartment ( Vc) of 18.7 l per 70 kg; intercompartmental clearance ( Q) of 0.393 l h−1; and volume of peripheral compartment ( Vper) of 1.32 l. The inclusion of total body weight as covariate reduced the random component of between-subject variability in CL from 50.1% to 11.7% and in Vc from 62.2% to 11.6%. The between-occasion variability on CL was estimated in the final model as 6.5%. Simulations show that one dose does not fit all and TCI and dose adjustment are required. Conclusions This study provides the first pharmacokinetic model of once-daily i.v. tobramycin for the use of target concentration intervention in paediatric CF patients. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The objective of the study was to characterise the population pharmacokinetic  properties  of  itraconazole  and  its  active metabolite hydroxy-itraconazole in a representative paediatric population of cystic fibrosis and bone marrow transplant (BMT) patients and to identify patient characteristics influencing the pharmacokinetics of itraconazole. The ultimate goals were to determine the relative bioavailability between the two oral formulations (capsules vs oral solution) and to optimise dosing regimens in these patients. METHODS: All paediatric patients with cystic fibrosis or patients undergoing BMT at The Royal Children’s Hospital, Brisbane, QLD, Australia, who were prescribed oral itraconazole for the treatment of allergic bronchopulmonary aspergillosis (cystic fibrosis patients) or for prophylaxis of any fungal infection (BMT patients) were eligible for the study. Blood samples were taken from the recruited patients as per an empirical sampling design either during hospitalisation or during outpatient clinic visits. Itraconazole and hydroxy-itraconazole plasma concentrations were determined by a validated high-performance liquid chromatography assay with fluorometric detection. A nonlinear mixed-effect modelling approach using the NONMEM software to simultaneously describe the pharmacokinetics of itraconazole and its metabolite. RESULTS: A one-compartment model with first-order absorption described the itraconazole data, and the metabolism of the parent drug to hydroxy-itraconazole was described by a first-order rate constant. The metabolite data also showed one-compartment characteristics with linear elimination. For itraconazole the apparent clearance (CLitraconazole) was 35.5 L/hour, the apparent volume of distribution (Vd(itraconazole)) was 672L, the absorption rate constant for the capsule formulation was 0.0901 h and for the oral solution formulation was 0.96 h. The lag time was estimated to be 19.1 minutes and the relative bioavailability between capsules and oral solution (Frel) was 0.55. For the metabolite, volume of distribution, Vm/(F · fm), and clearance, CL/(F · fm), were 10.6L and 5.28 L/h, respectively. The influence of total bodyweight was significant, added as a covariate on CLitraconazole/F and Vd(itraconazole)/F (standardised to a 70kg person) using allometric three-quarter power scaling on CLitraconazole/F, which therefore reflected adult values. The unexplained between-subject variability (coefficient of variation %) was 68.7%, 75.8%, 73.4% and 61.1% for CLitraconazole/F, Vd(itraconazole)/F, CLm/(F · fm) and Frel, respectively. The correlation between random effects of CLitraconazole and Vd(itraconazole) was 0.69. CONCLUSION: The developed population pharmacokinetic model adequately described  the  pharmacokinetics  of  itraconazole and its active metabolite, hydroxy-itraconazole, in paediatric patients with either cystic fibrosis or undergoing BMT. More appropriate dosing schedules have been developed for the oral solution and the capsules to secure a minimum therapeutic trough plasma concentration of 0.5 mg/L for these patients. [ABSTRACT FROM AUTHOR]