Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients

Bloomfield, C.; Staatz, C. E.; Unwin, Sean; Hennig, Stefanie

doi:10.1128/aac.02654-15

Cited by 27 publications

(27 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is the most robust form of assessment of predictive performance of models and has already been used to assess models for other drugs. 22,23 The aims of this study were to review all published valproate population pharmacokinetic models developed for children and assess them by external validation to determine their predictive performance. The final goal was to identify which model could be used for simulations to evaluate an appropriate dosing regimen in our population and for Bayesian forecasting.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Simulations of Valproate Doses Based on an External Evaluation of Pediatric Population Pharmacokinetic Models

Tauzin

Tréluyer

Nabbout

et al. 2018

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Valproate is an old-generation antiepileptic drug often used in children. The pharmacokinetics of valproate are noteworthy for a large and difficult to predict interindividual variability in measured serum concentrations and for saturable protein binding. A model-based approach to personalize valproate treatment could be relevant in pediatric patients. The aims of this study were to review all published valproate population pharmacokinetic models in children and assess them by external validation to determine their predictive performance. Through simulations with the best model, we evaluated dosing regimen. A validation data set included valproate serum concentrations assayed during routine therapeutic drug monitoring of epileptic children. We applied to our population 11 published pediatric population pharmacokinetic models. For each model, predictive performance was assessed by external validation, using bias and precision calculations as well as goodness-of-fit plots. Dose simulations were conducted with the best predictive model to evaluate dosing regimen. The validation data set contained 178 valproate concentrations ranging from 13.4 to 128 mg/L from 114 patients. The best model exhibited a mean prediction error of 6.6 mg/L and a root mean squared error of 25.1 mg/L, with no model misspecification evidenced by visual predictive check. In our cohort, half the patients had a trough concentration <50 mg/L. Simulations suggested increasing doses, especially for children ࣘ40 kg. External evaluation of published valproate pharmacokinetic models enabled us to identify a suitable model for simulations and Bayesian forecasting. Dosing regimen should be adjusted to weight, with decreasing doses with increasing weight.

show abstract

mentioning

confidence: 99%

“…External validation of PPK models uses data collected from a new patient cohort receiving a drug. This is the most robust form of assessment of predictive performance of models and has already been used to assess models for other drugs …”

mentioning

confidence: 99%

Simulations of Valproate Doses Based on an External Evaluation of Pediatric Population Pharmacokinetic Models

Tauzin

Tréluyer

Nabbout

et al. 2018

The Journal of Clinical Pharma

View full text Add to dashboard Cite

show abstract

“…External validation was performed in the validation data set that matched the data used for model development. The predictive performance of the model was assessed numerically through calculation of the mean error (ME), the mean relative error (MRE), and the relative root mean squared error (RMSE), as previously reported …”

Section: Methodsmentioning

confidence: 99%

Identification of Factors Affecting Tacrolimus Trough Levels in Latin American Pediatric Liver Transplant Patients

et al. 2019

View full text Add to dashboard Cite

Tacrolimus is the cornerstone in pediatric liver transplant immunosuppression. Despite close monitoring, fluctuations in tacrolimus blood levels affect safety and efficacy of immunosuppressive treatments. Identifying the factors related to the variability in tacrolimus exposure may be helpful in tailoring the dose. The aim of the present study was to characterize the clinical, pharmacological, and genetic variables associated with systemic tacrolimus exposure in pediatric liver transplant patients. De novo transplant patients with a survival of more than 1 month were considered for inclusion and were genotyped for cytochrome P450 3A5 (CYP3A5). Peritransplant clinical factors and laboratory covariates were recorded retrospectively between 1 month and 2 years after transplant, including alanine aminotransferase (ALT), aspartate aminotransferase, hematocrit, and tacrolimus predose steady‐state blood concentrations collected 12 hours after tacrolimus dosing. A linear mixed effect (LME) model was used to assess the association of these factors and the log‐transformed tacrolimus dose‐normalized trough concentration (logC0/D) levels. Bootstrapping was used to internally validate the final model. External validation was performed in an independent group of patients who matched the original population. The developed LME model described that logC0/D increases with increases in time after transplant (β = 0.019, 95% confidence interval [CI], 0.010‐0.028) and ALT values (β = 0.00030, 95% CI, 0.00002‐0.00056), whereas logC0/D is significantly lower in graft CYP3A5 expressers compared with nonexpressers (β = −0.349, 95% CI, −0.631 to −0.062). In conclusion, donor CYP3A5 genotype, time after transplant, and ALT values are associated with tacrolimus disposition between 1 month and 2 years after transplant. A better understanding of tacrolimus exposure is essential to minimize the occurrence of an out‐of‐range therapeutic window that may lead to adverse drug reactions or acute rejection.

show abstract

“…Bayesian monitoring methods have gained importance in the CF population due to the narrow TR and the sufficient unexplained interpatient variability and low intrapatient variability in PK parameters (and response) (11). In addition, tobramycin distribution may be better described using two-compartmental PK (30). As such, fixed-dosing, traditional nomograms (31) and PK software that use noncompartmental models should be used with caution if only one or two serum concentrations are available (17).…”

Section: Discussionmentioning

confidence: 99%

Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis

Barras

Serisier

Hennig

et al. 2016

Antimicrob Agents Chemother

Self Cite

View full text Add to dashboard Cite

cFixed 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 (AUC 1 ); and (iii) a Bayesian estimate using two concentrations (AUC 2 ). 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 (AUC 1 ), and 1.0 (AUC 2 ). 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.

show abstract

Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients

Cited by 27 publications

References 42 publications

Simulations of Valproate Doses Based on an External Evaluation of Pediatric Population Pharmacokinetic Models

Simulations of Valproate Doses Based on an External Evaluation of Pediatric Population Pharmacokinetic Models

Identification of Factors Affecting Tacrolimus Trough Levels in Latin American Pediatric Liver Transplant Patients

Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis

Contact Info

Product

Resources

About