Population pharmacokinetics of vancomycin and prediction of pharmacodynamics in the Chinese people.
- Author:
Xiao-Rongl HE
;
Zhi-He LIU
;
Shuang-Min JI
;
Tao-Tao LIU
;
Liang LI
;
Tian-Yan ZHOU
;
Wei LU
- Publication Type:Journal Article
- MeSH:
Aged;
Asian Continental Ancestry Group;
Female;
Humans;
Male;
Models, Biological;
Nonlinear Dynamics;
Vancomycin;
pharmacokinetics
- From:
Acta Pharmaceutica Sinica
2014;49(11):1528-1535
- CountryChina
- Language:Chinese
-
Abstract:
Population pharmacokinetics of vancomycin (VAN) in the Chinese patients was described by using nonlinear mixed-effects modeling (NONMEM). 619 VAN serum concentrations data from 260 patients including 177 males and 83 females were collected separately from two centers. A one-compartment model was used to describe this sparse data. No significant difference was observed between two center datasets by introducing SID covariate. The final model was as CL= (θ (base0+ θ(max) x(1 -e(-θ(Age)(Age/72) and V = θ x θ (Age)(Age/72). The creatinine clearance (CL(Cr)) and Age were identified as the most significant covariate in the final model. Typical values of clearance (CL) and volume of distribution (V) in the final model were 2.91 L x h(-1) and 54.76 L, respectively. Internal model validation by Bootstrap and NPDE were performed to evaluate the robustness and prediction of the final model. The median and 95% confidence intervals for the final model parameters were based on 1000 Bootstraps. External model evaluation was conducted using an independent dataset that consisted of 34 patients to predict model performance. Pharmacodynamic assessment for VAN by AUC (0-24 h) to MIC ratios of over 400 was considered to be the best to predict treatment outcomes for patients. AUC (0-24 h) was calculated by clearance based on the above population model. The results indicate that the conventional dosing regimen probably being suboptimal concentrations in aged patients. The approach via population pharmacokinetic of VAN combined with the relationship of MIC, Age, CL(Cr) and AUC(0-24 h)/MIC can predict the rational dose for attaining efficacy.
