Escitalopram Personalized Dosing: A Population Pharmacokinetics Repository Method

Area of Science:

• Pharmacology
• Pharmacometrics
• Computational Biology

Background:

• Escitalopram (SCIT) is a widely used antidepressant and antianxiety medication.
• Significant interindividual variability in SCIT pharmacokinetics necessitates personalized dosing strategies.
• Existing population pharmacokinetic (PPK) models for SCIT require consolidation and evaluation.

Purpose of the Study:

• To develop a comprehensive repository of parametric population pharmacokinetic (PPK) models for escitalopram (SCIT).
• To facilitate model-informed precision dosing of SCIT.
• To evaluate the performance and covariate impacts of existing SCIT PPK models.

Main Methods:

• Systematic literature search for published SCIT PPK models (PubMed, Embase, Web of Science).
• Replication and simulation of identified one- and two-compartment models using the rxode2 package in R.
• Analysis of simulated concentration-time profiles and covariate effects on clearance.

Main Results:

• Eight parametric PPK models for SCIT were identified and characterized.
• CYP2C19 phenotype, body weight, and age were identified as significant covariates influencing SCIT clearance.
• A notable finding was nearly double the SCIT exposure in Chinese psychiatric patients compared to other populations under similar CYP2C19 restrictions.

Conclusions:

• A curated repository of SCIT PPK models is established, supporting precision dosing.
• Dosing regimens for SCIT should be individualized based on CYP2C19 phenotype, weight, and age.
• Further investigation is required to understand the increased SCIT exposure observed in Chinese psychiatric patients.