Genetic-Based Population Pharmacokinetic Study of Risperidone in Patients with Schizophrenia

Background: Individual differences in the blood concentrations of risperidone and its active metabolites affect efficacy and adverse reactions in patients with schizophrenia. Beyond the CYP2D6 gene, the comprehensive effects of other metabolic enzyme genes and clinical baseline characteristics on pharmacokinetic variability remain unclear. Further investigation is necessary to optimize treatment regimens. Objective: This study was aimed at constructing a population pharmacokinetic model of risperidone and 9-hydroxyrisperidone in patients with schizophrenia, to clarify the effects of genetic factors such as CYP2D6 and non-genetic factors, including age, and liver and kidney function, on pharmacokinetic parameters including clearance (CL) and volume of distribution (V). Methods: Patients with schizophrenia who met ICD-10 diagnostic criteria and were treated with risperidone alone or in combination with other medications were included. Their baseline data and laboratory indicators were collected. After reaching steady state, the trough concentrations of risperidone and 9-hydroxyrisperidone were determined with UPLC-MS/MS. The DR MassARRAY was used to detect CYP2D6 , CYP2C19 , and CYP2C9 gene polymorphisms, and determine metabolic phenotypes. The population pharmacokinetic model was constructed with the NONMEM method. Results: We established a compartmental pharmacokinetic model of risperidone and its active metabolite, 9-hydroxyrisperidone, to quantify core pharmacokinetic processes, according to parameters including the absorption rate constant (Ka), clearance (CL and CL 2 ), and metabolic rate constant (K 12 ). Goodness-of-fit verification indicated the model’s favorable predictive performance: population and individual predicted concentrations were highly consistent with the observed concentrations. Conditional weighted residuals were evenly distributed within the ±2 threshold and showed no concentration- or time-dependent trends. Covariate analysis indicated that the CYP2D6 intermediate metabolizer phenotype decreased the clearance rate of risperidone and the rate of metabolism to 9-hydroxyrisperidone. Body weight positively correlated with risperidone clearance. Among the 94 participants, 88.2% were men, the average age was 57.3 ± 1.8 years, and the participants had normal liver and kidney function. The main genotypes were CYP2D6 normal metabolizers (60.6%), CYP2C19 intermediate metabolizers (47.9%), and CYP2C9 normal metabolizers (87.2%). Key model parameters included a risperidone CL of 3.790 L/h, apparent V of 14.500 L, K 12 of 0.026 h −1 , and 9-hydroxyrisperidone CL 2 of 0.117 L/h. The variability estimates were reasonable for each parameter. Conclusion: We constructed and validated a population compartmental pharmacokinetic model of risperidone and 9-hydroxyrisperidone, which had favorable and stable predictive performance. CYP2D6 metabolic phenotype and body weight are key factors affecting risperidone pharmacokinetics. The core model parameters may provide a basis for clinical individualized dose adjustment.