Purpose: To explain the high inter-individual variability (IIV) and the frequency of exceeding the therapeutic reference range and the laboratory alert level of amisulpride, a population pharmacokinetic (PPK) model in Chinese patients with schizophrenia was built based on therapeutic drug monitoring (TDM) data to guide individualized therapy.
Patients and Methods: Plasma concentration data (330 measurements from 121 patients) were analyzed using a nonlinear mixed-effects modeling (NONMEM) approach with first-order conditional estimation with interaction (FOCE I). The concentrations of amisulpride were detected by HPLC-MS/MS. Age, weight, sex, combination medication history and renal function status were evaluated as main covariates. The model was internally validated using goodness-of-fit, bootstrap and normalized prediction distribution error (NPDE). Recommended dosage regimens for patients with key covariates were estimated on the basis of Monte Carlo simulations and the established model.
Results: A one-compartment model with first-order absorption and elimination was found to adequately characterize amisulpride concentration in Chinese patients with schizophrenia. The population estimates of the apparent volume of distribution (V/F) and apparent clearance (CL/F) were 12.7 L and 1.12 L/h, respectively. Age significantly affected the clearance of amisulpride and the final model was as follows: CL/F=1.04×(AGE/32)^{− 0.624} (L/h). To avoid exceeding the laboratory alert level (640 ng/mL), the model-based simulation results showed that the recommended dose of amisulpride was no more than 600 mg/d for patients aged 60 years, 800 mg/d for those aged 40 years and 1200 mg/d for those aged 20 years, respectively.
Conclusion: Dosage optimization of amisulpride can be carried out according to age to reduce the risk of adverse reactions. The model can be used as a suitable tool for designing individualized therapy for Chinese patients with schizophrenia.
Keywords: amisulpride, population pharmacokinetics, therapeutic drug monitoring, modeling and simulation, individualized therapy