Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and
Chunle Lv1, Jiejiu Lu1, Li Jing2
1Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Insights
Existing vancomycin population pharmacokinetic (PopPK) models for children showed poor predictability when applied to new clinical centers. Bayesian forecasting with therapeutic drug monitoring, particularly using Model 3, can improve vancomycin dosing adjustments.
Area of Science:
- Pharmacology
- Clinical Pharmacy
- Pediatric Medicine
Background:
- Population pharmacokinetic (PopPK) models are crucial for optimizing vancomycin therapy in pediatric patients.
- External validation of these models across different clinical centers is essential but underexplored.
Purpose of the Study:
- To externally validate the predictability of existing vancomycin PopPK models in Chinese pediatric patients.
- To assess the applicability of these models in an independent dataset from different clinical centers.
Main Methods:
- Screening of 10 published vancomycin PopPK models from the literature.
- Evaluation using an external dataset of 451 vancomycin blood concentrations from 220 pediatric patients.
- Application of prediction-based diagnostics, simulation-based diagnostics (VPC, NPDE), and Bayesian forecasting.
Main Results:
- None of the 10 evaluated PopPK models met all diagnostic standards for satisfactory predictability.
- Both visual predictive checks (VPC) and normalized prediction distribution error (NPDE) indicated model misspecification.
- Bayesian forecasting improved prediction accuracy with initial observations; Model 3 demonstrated superior predictability.
Conclusions:
- Published vancomycin PopPK models demonstrated unsatisfactory performance for initial dosing regimen design in pediatric patients.
- Consideration of pharmacokinetic characteristics and covariates (weight, renal function, age, disease) is vital for model extrapolation.
- Bayesian forecasting combined with therapeutic drug monitoring, utilizing Model 3, can effectively adjust vancomycin dosing regimens.
What Is Known And Objectives:
Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set.
Methods:
The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction- and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models.
Results:
Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F20 ) ≥35% and PE% within ±30% (F30 ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models.
What Is New And Conclusion:
The 10 published models performed unsatisfactorily in prediction- and simulation-based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.
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