Our study aims to explore the pharmacokinetics of valproic acid (VPA) in Chinese patients with epilepsy or after neurosurgery and establish a robust population pharmacokinetics (PPK) model. The PPK model was developed using nonlinear mixed-effects modeling, incorporating a total of 615 VPA plasma concentration data points from 443 Chinese epilepsy or after neurosurgery patients. A one-compartment model with an additive residual model was established. Forward addition and backward elimination strategies were used to assess the impact of covariates on the model parameters. Goodness-of-fit plots, bootstrap, visual predict check and normalized prediction distribution errors were used for model validation. In the final model, the apparent clearance (CL) was estimated using the following formula: CL L/h=0.430ĂBW/600.787ĂCr/50.3â0.253ĂALB/39â0.873ĂegenderĂeCBPĂeIND2 ĂeηCL (gender = 0.121 when is female, otherwise = 0; CBP = 1.50 when combined with carbapenems, otherwise = 0; IND2 = 0.15 when combined with oxcarbazepine, carbamazepine, phenobarbital, or phenytoin, otherwise = 0). The volume of distribution (Vd) was estimated using the formula: Vd L=8.66ĂBW/600.751. Comedication with carbapenems could increase VPA clearance by 4.48 times, and comedication with oxcarbazepine could enhance VPA clearance by 116%. Besides, creatinine and albumin could affect VPA clearance. Goodness-of-fit plots, bootstrap, visual predict check and normalized prediction distribution showed acceptable data fit, stability, and predictability of the model. In our study, a PPK model was utilized to attain a more comprehensive insight into these variables, improving the accuracy and individualization of VPA therapy in Chinese patients with epilepsy or after neurosurgery.
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