Million A Tegenge1, Iftekhar Mahmood2. 1. Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration. 2. Office of Tissue and Advanced Therapeutics (OTAT), Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD.
Abstract
BACKGROUND: In population pharmacokinetic modeling, bodyweight is often incorporated as an important covariate using fixed (0.75) or single-exponent model. In recent years, several variations of allometric models have been suggested for the prediction of drug clearance across a wide age range. The objective of this study is to develop and evaluate single-exponent, bodyweight-dependent allometric exponent (BDE), age-dependent exponent (ADE), and segmented regression models for predicting clearance and maintenance dose of theophylline. METHODS: The BDE model was described by the following equation: (Equation is included in full-text article.), where L × BW defines the BDE for clearance. The coefficient and the exponents L and M were estimated. The ADE model consisted of several empirical exponents based on age and ranged from 0.75 (children >5 years and adults) to 1.2 (premature neonates). Data for model development and validation were based on 52 subjects each. RESULTS: All structural and statistical parameters were estimated with acceptable precision for single-exponent and BDE models (<30%); however, the BDE model was superior in describing theophylline clearance across a wide age range for the training data. The segmented regression model on log-transformed data also adequately described theophylline clearance. When models were evaluated with validation data, a single-exponent model overpredicted clearance and dosing rate in premature neonates and adults with a mean prediction error of ≥50%. For premature neonates and adults, mean clearance and dosing rate were predicted within a 30% prediction error using the BDE, ADE, and segmented models. CONCLUSIONS: This study demonstrates that the BDE, ADE, and segmented models performed better than a single-exponent model for predicting clearance and dose of theophylline across a wide age range.
BACKGROUND: In population pharmacokinetic modeling, bodyweight is often incorporated as an important covariate using fixed (0.75) or single-exponent model. In recent years, several variations of allometric models have been suggested for the prediction of drug clearance across a wide age range. The objective of this study is to develop and evaluate single-exponent, bodyweight-dependent allometric exponent (BDE), age-dependent exponent (ADE), and segmented regression models for predicting clearance and maintenance dose of theophylline. METHODS: The BDE model was described by the following equation: (Equation is included in full-text article.), where L × BW defines the BDE for clearance. The coefficient and the exponents L and M were estimated. The ADE model consisted of several empirical exponents based on age and ranged from 0.75 (children >5 years and adults) to 1.2 (premature neonates). Data for model development and validation were based on 52 subjects each. RESULTS: All structural and statistical parameters were estimated with acceptable precision for single-exponent and BDE models (<30%); however, the BDE model was superior in describing theophylline clearance across a wide age range for the training data. The segmented regression model on log-transformed data also adequately described theophylline clearance. When models were evaluated with validation data, a single-exponent model overpredicted clearance and dosing rate in premature neonates and adults with a mean prediction error of ≥50%. For premature neonates and adults, mean clearance and dosing rate were predicted within a 30% prediction error using the BDE, ADE, and segmented models. CONCLUSIONS: This study demonstrates that the BDE, ADE, and segmented models performed better than a single-exponent model for predicting clearance and dose of theophylline across a wide age range.
Authors: Anil R Maharaj; Huali Wu; Christoph P Hornik; Stephen J Balevic; Chi D Hornik; P Brian Smith; Daniel Gonzalez; Kanecia O Zimmerman; Daniel K Benjamin; Michael Cohen-Wolkowiez Journal: JAMA Pediatr Date: 2020-10-05 Impact factor: 16.193
Authors: E A M Calvier; E H J Krekels; T N Johnson; A Rostami-Hodjegan; D Tibboel; Catherijne A J Knibbe Journal: AAPS J Date: 2019-03-08 Impact factor: 4.009