Escitalopram population pharmacokinetics and remedial strategies based on CYP2C19 phenotype

CYP2C19 is a key factor influencing escitalopram (SCIT) exposure. However, different studies reported various results. This study aims to develop a population pharmacokinetic (popPK) model characterizes the disposition of SCIT in the Chinese population. Based on the popPK model, the study simulates non-adherence scenarios and proposes remedial strategies to facilitate SCIT personalized therapy. Nonlinear mixed-effects modeling using data from two Chinese bioequivalence studies was employed. Monte-Carlo simulation was used to explore non-adherence scenarios and propose remedial strategies based on the proportion of time within the therapeutic window. Results showed that a one-compartment model with transit absorption and linear elimination described the data well, CYP2C19 phenotypes and weight were identified as significant covariates impacting SCIT exposure. Patients were recommended to take the entire delayed dose immediately if the delay time was no >12 h, followed by the regular regimen at the next scheduled time. When there is one or two doses missed, taking a double dose immediately was recommended to the CYP2C19 intermediate and extensive population, and a 1.5-fold dose was recommended to the CYP2C19 poor metabolizers with the consideration of adverse effects. All samples were derived from the homogenized Chinese healthy population for model building, which may pose certain constraints on the ability to identify significant covariates, such as age. The study highlights the importance of considering patient characteristics for personalized medication and offers a unique perspective on utilizing the popPK repository in precision dosing.