Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, People's Republic of China.
Institute of Clinical Pharmacology, Central South University, Changsha, People's Republic of China.
Drug Des Devel Ther. 2023 Sep 27;17:2955-2967. doi: 10.2147/DDDT.S425654. eCollection 2023.
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/m, 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.
依他普仑（SCIT）是一种一线抗抑郁和抗焦虑药物。SCIT 的药代动力学研究表明个体间存在很大的变异性，强调需要进行个体化给药。因此，我们旨在创建一个依他普仑参数群体药代动力学（PPK）模型的存储库，以促进模型指导的精准给药。2022 年 11 月，我们在 PubMed、Embase 和 Web of Science 上搜索了已发表的 PPK 模型，并确定了 8 个模型。文献中报告的所有结构模型均为单室或双室模型。为了研究模型性能的变异性，所有 PPK 模型的参数均从文献中已发表的参数中得出。为了复制这些模型，我们生成了一个具有 30 岁年龄、70kg 体重和 23kg/m BMI 的代表性虚拟人群。为了实现这一目标，我们使用了 R 编程语言中的 rxode2 包。随后，我们比较了模拟的浓度-时间曲线，并评估了协变量对清除率的影响。最重要的协变量是 CYP2C19 表型、体重和年龄，表明应相应调整给药方案。此外，在中国精神科患者中，与其他人群相比，SCIT 的暴露量几乎增加了一倍，特别是在考虑相同 CYP2C19 人群限制时，这是一个需要进一步研究的知识空白。此外，这个依他普仑参数 PPK 模型存储库具有广泛的潜在应用，例如设计缺失或延迟剂量补救策略和外部 PPK 模型验证。
