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Extrapolation of Drug Clearance in Children ≤ 2 Years of Age from Empirical Models Using Data from Children
1Office of Tissue and Advanced Therapies (OTAT), Center for Biologics Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993-0002, USA. Iftekharmahmood@aol.com.
Insights
Simple empirical models accurately predict drug clearance in young children, outperforming complex models. This research aids in optimizing pediatric drug dosing and safety for children aged two years and younger.
Area of Science:
- Pharmacology
- Pediatric Drug Development
- Biostatistics
Background:
- Modeling and simulation are increasingly vital in clinical pharmacology.
- Accurate prediction of drug clearance in pediatric populations is crucial for safe and effective medication use.
- Existing models often struggle to predict drug clearance in very young children.
Purpose of the Study:
- To develop and evaluate six empirical models for predicting drug clearance in children aged ≤2 years.
- Models were derived from clearance data of children aged >2 years and adults.
- To assess model suitability for preterm infants, term infants, and infants.
Main Methods:
- Ten drugs with known elimination routes (renal, hepatic, or both) were analyzed.
- Six empirical models were developed, including age/weight-dependent, allometric, and semi-physiological approaches.
- Model predictions were compared against observed clearance data in children ≤2 years, with ≤50% prediction error considered acceptable.
Main Results:
- Three models (body weight-dependent sigmoidal Emax, age-dependent allometric, and semi-physiological) showed acceptable prediction accuracy (>80% observations with ≤50% error).
- Individual predicted clearance values were often erratic and inconsistent with observed data across most models.
- Data from 282 children across ten drugs were used for comparison.
Conclusions:
- Simple empirical models demonstrated superior accuracy in predicting drug clearance in young children compared to complex models.
- The findings highlight the importance of selecting appropriate modeling strategies for pediatric drug development.
- Further refinement of predictive models for pediatric drug clearance is warranted.
Background:
The application of modeling and simulation approaches in clinical pharmacology studies has gained momentum over the last 20 years.
Objectives:
The objective of this study was to develop six empirical models from clearance data obtained from children aged > 2 years and adults to evaluate the suitability of the models to predict drug clearance in children aged ≤ 2 years (preterm, term, and infants).
Methods:
Ten drugs were included in this study and administered intravenously: alfentanil, amikacin, busulfan, cefetamet, meperidine, oxycodone, propofol, sufentanil, theophylline, and tobramycin. These drugs were selected according to the availability of individual subjects' weight, age, and clearance data (concentration-time data for these drugs were not available to the author). The chosen drugs are eliminated by extensive metabolism by either the renal route or both the renal and hepatic routes. The six empirical models were (1) age and body weight-dependent sigmoidal maximum possible effect (Emax) maturation model, (2) body weight-dependent sigmoidal Emax model, (3) uridine 5'-diphospho [body weight-dependent allometric exponent model (BDE)], (4) age-dependent allometric exponent model (ADE), (5) a semi-physiological model, and (6) an allometric model developed from children aged > 2 years to adults. The model-predicted clearance values were compared with observed clearance values in an individual child. In this analysis, a prediction error of ≤ 50% for mean or individual clearance values was considered acceptable.
Results:
Across all age groups and the ten drugs, data for 282 children were compared between observed and model-predicted clearance values. The validation data consisted of 33 observations (sum of different age groups for ten drugs). Only three of the six models (body weight-dependent sigmoidal Emax model, ADE, and semi-physiological model) provided reasonably accurate predictions of clearance (> 80% observation with ≤ 50% prediction error) in children aged ≤ 2 years. In most instances, individual predicted clearance values were erratic (as indicated by % error) and were not in agreement with the observed clearance values.
Conclusions:
The study indicated that simple empirical models can provide more accurate results than complex empirical models.
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