CYP2C19 Genotyping May Provide a Better Treatment Strategy when Administering Escitalopram in Chinese Population

Escitalopram population pharmacokinetics and remedial strategies based on CYP2C19 phenotype

BACKGROUND AND PURPOSE

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.

METHODS

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

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.

LIMITATION

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.

CONCLUSION

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.

Escitalopram Personalized Dosing: A Population Pharmacokinetics Repository Method

Escitalopram (SCIT) represents a first-line antidepressant and antianxiety medication. Pharmacokinetic studies of SCIT have demonstrated considerable interindividual variability, emphasizing the need for personalized dosing. Accordingly, we aimed to create a repository of parametric population pharmacokinetic (PPK) models of SCIT to facilitate model-informed precision dosing. In November 2022, we searched PubMed, Embase, and Web of Science for published PPK models and identified eight models. All the structural models reported in the literature were either one- or two-compartment models. In order to investigate the variances in model performance, the parameters of all PPK models were derived from the literature published. A representative virtual population, characterized by an age of 30, a body weight of 70 kg, and a BMI of 23 kg/m2, was generated for the purpose of replicating these models. To accomplish this, the rxode2 package in the R programming language was employed. Subsequently, we compared simulated concentration-time profiles and evaluated the impact of covariates on clearance. The most significant covariates were CYP2C19 phenotype, weight, and age, indicating that dosing regimens should be tailored accordingly. Additionally, among Chinese psychiatric patients, SCIT showed nearly double the exposure compared to other populations, specifically when considering the same CYP2C19 population restriction, which is a knowledge gap that needs further investigation. Furthermore, this repository of parametric PPK models for SCIT has a wide range of potential applications, like design miss or delay dose remedy strategies and external PPK model validation.

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age

Objective: To establish a population pharmacokinetic model in Chinese psychiatric patients to characterize escitalopram pharmacokinetic profile to identify factors influencing drug exposure, and through simulation to compare the results with the established therapeutic reference range.

Methods: Demographic information, dosing regimen, CYP2C19 genotype, concomitant medications, and liver and kidney function indicators were retrospectively collected for inpatients taking escitalopram with therapeutic drug monitoring from 2018 to 2021. Nonlinear mixed-effects modeling was used to model the pharmacokinetic characteristics of escitalopram. Goodness-of-fit plots, bootstrapping, and normalized prediction distribution errors were used to evaluate the model. Simulation for different dosing regimens was based on the final estimations.

Results: The study comprised 106 patients and 337 measurements of serum sample. A structural model with one compartment with first-order absorption and elimination described the data adequately. The population-estimated apparent volume of distribution and apparent clearance were 815 and 16.3 L/h, respectively. Age and CYP2C19 phenotype had a significant effect on the apparent clearance (CL/F). CL/F of escitalopram decreased with increased age, and CL/F of poor metabolizer patients was significantly lower than in extensive and immediate metabolizer patients. The final model-based simulation showed that the daily dose of adolescents with poor metabolizer might be as high as 15 mg or 20 mg and referring to the therapeutic range for adults may result in overdose and a high risk of adverse effects in older patients.

Conclusion: A population pharmacokinetics model of escitalopram was successfully created for the Chinese population. Depending on the age of the patients, CYP2C19 genotype and serum drug concentrations throughout treatment are required for adequate individualization of dosing regimens. When developing a regimen for older patients, especially those who are poor metabolizers, vigilance is required.