OBJECTIVE: This study was conducted to develop a population pharmacokinetic (PopPK) model for vancomycin and to detect the significant covariates that influence the PopPKs to facilitate individualized therapy for Chinese pediatric patients. METHODS: Patients ≤ 10 years old who received vancomycin for ≥ 72 hours between 2007 and 2010 were analyzed using a nonlinear mixed-effects modeling approach (-NONMEM). A one-compartment model with first-order elimination was chosen to depict the data. Stepwise covariate modeling (SCM) was employed to detect significant covariates and obtain a final model. Internal validation methods, including bootstrapping and visual predictive checks (VPC), were applied to evaluate the robustness and predictive power of the final model. RESULTS: The analysis included 54 pediatric inpatients with 128 serum concentration samples. The mean age (range) was 124.30 (1.29 - 541.4) weeks, the mean weight was 10.36 (1.4 - 33.5) kg, and the mean baseline serum creatinine (Scr) level was 0.39 (0.15 - 1.32) mg/dL. Pneumonia was the most common indication for vancomycin therapy (33.33%), followed by bacteremia (25.93%), and meningitis (22.22%). A PopPK model of vancomycin in Chinese pediatric patients was developed. Postnatal age (PNA) significantly affected the vancomycin clearance (CL) of pediatric patients, and the influence was described using the sigmoid maximum effect (E<sub>max</sub>) model. The effect of body weight (WT) on CL and volume of distribution (V) was investigated using an allometric scaling equation. The final model parameters were CL(L/h) = 11.75×[PNA<sup>0.4672</sup>/(PNA<sup>0.4672</sup>+33.3<sup>0.4672</sup>)]×(WT/70)<sup>0.75</sup>×e<sup>0.362</sup> and V(L) = 54.49×WT/70×e<sup>0.6711</sup>. The model evaluation results suggested robustness and good predictability of the final model. CONCLUSION: A PopPK model of vancomycin for Chinese pediatric patients was developed in this study. The significant covariates distinguished in the final model can provide helpful information to facilitate individualized therapy for Chinese pediatric patients. .
OBJECTIVE: This study was conducted to develop a population pharmacokinetic (PopPK) model for vancomycin and to detect the significant covariates that influence the PopPKs to facilitate individualized therapy for Chinese pediatric patients. METHODS:Patients ≤ 10 years old who received vancomycin for ≥ 72 hours between 2007 and 2010 were analyzed using a nonlinear mixed-effects modeling approach (-NONMEM). A one-compartment model with first-order elimination was chosen to depict the data. Stepwise covariate modeling (SCM) was employed to detect significant covariates and obtain a final model. Internal validation methods, including bootstrapping and visual predictive checks (VPC), were applied to evaluate the robustness and predictive power of the final model. RESULTS: The analysis included 54 pediatric inpatients with 128 serum concentration samples. The mean age (range) was 124.30 (1.29 - 541.4) weeks, the mean weight was 10.36 (1.4 - 33.5) kg, and the mean baseline serum creatinine (Scr) level was 0.39 (0.15 - 1.32) mg/dL. Pneumonia was the most common indication for vancomycin therapy (33.33%), followed by bacteremia (25.93%), and meningitis (22.22%). A PopPK model of vancomycin in Chinese pediatric patients was developed. Postnatal age (PNA) significantly affected the vancomycin clearance (CL) of pediatric patients, and the influence was described using the sigmoid maximum effect (E<sub>max</sub>) model. The effect of body weight (WT) on CL and volume of distribution (V) was investigated using an allometric scaling equation. The final model parameters were CL(L/h) = 11.75×[PNA<sup>0.4672</sup>/(PNA<sup>0.4672</sup>+33.3<sup>0.4672</sup>)]×(WT/70)<sup>0.75</sup>×e<sup>0.362</sup> and V(L) = 54.49×WT/70×e<sup>0.6711</sup>. The model evaluation results suggested robustness and good predictability of the final model. CONCLUSION: A PopPK model of vancomycin for Chinese pediatric patients was developed in this study. The significant covariates distinguished in the final model can provide helpful information to facilitate individualized therapy for Chinese pediatric patients. .