Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms

IF 3.5 3区医学 Q2 CHEMISTRY, MULTIDISCIPLINARY

Pharmaceutical Research Pub Date : 2024-03-05 DOI:10.1007/s11095-024-03680-8

Chaozhuang Shen, Hongyi Yang, Wenxin Shao, Liang Zheng, Wei Zhang, Haitang Xie, Xuehua Jiang, Ling Wang

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Abstract

Background

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.

Purpose

A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).

Methods

The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim^® and MoBi^®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model’s performance was evaluated by comparing predicted and observed values of plasma concentration–time (PCT) curves and PK parameters values.

Results

In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC_0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.

Conclusions

In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

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基于生理学的药代动力学模型揭示文拉法辛的药物基因相互作用：基于 CYP2D6 和 CYP2C19 多态性的活性评分依赖性代谢作用

背景文拉法辛（VEN）是缓解抑郁和焦虑症的常用药物。由于存在基因多态性，不同表型的血浆浓度差异很大。目的建立 VEN 及其代谢物 O-去甲文拉法辛（ODV）的生理学药代动力学（PBPK）模型，预测 CYP2D6 和 CYP2C19 基因多态性对 VEN 药代动力学（PK）的影响。利用先前的研究，推导并实现了 CYP2D6 和 CYP2C19 活性评分 (AS) 依赖性代谢，以模拟药物基因相互作用 (DGI) 情景下的暴露。结果在基础模型中，VEN、ODV 和 VEN + ODV 的预测血浆浓度分别有 91.1%、94.8% 和 94.6% 在相应观察浓度的两倍误差范围内。对于 DGI 方案，VEN 和 ODV 的预测值分别为 81.4% 和 85%。将 CYP2C19 AS = 2 组中的 CYP2D6 AS = 2（正常代谢者，NM）人群与 AS = 0（贫代谢者，PM）、0.25、0.5、0.75、1.0（中等代谢者，IM）、1.25、1.5（NM）和 3.0（超快速代谢者，UM）人群进行比较，发现 CYP2C19 AS = 2.在 CYP2C19 AS = 2.0 组中，VEN 的预测 DGI AUC0-96 h 比率分别为 3.65、3.09、2.60、2.18、1.84、1.56、1.34、0.61，ODV 的预测 DGI AUC0-96 h 比率分别为 0.17、0.35、0.51、0.64、0.75、0.83、0.90、1.11，其他 CYP2C19 组的结果类似。结论在临床实践中，应考虑基因分型对 VEN 体内处置过程的影响，以确保治疗的安全性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Pharmaceutical Research 医学-化学综合

CiteScore

6.60

自引率

5.40%

发文量

276

审稿时长

3.4 months

期刊介绍： Pharmaceutical Research, an official journal of the American Association of Pharmaceutical Scientists, is committed to publishing novel research that is mechanism-based, hypothesis-driven and addresses significant issues in drug discovery, development and regulation. Current areas of interest include, but are not limited to: -(pre)formulation engineering and processing- computational biopharmaceutics- drug delivery and targeting- molecular biopharmaceutics and drug disposition (including cellular and molecular pharmacology)- pharmacokinetics, pharmacodynamics and pharmacogenetics. Research may involve nonclinical and clinical studies, and utilize both in vitro and in vivo approaches. Studies on small drug molecules, pharmaceutical solid materials (including biomaterials, polymers and nanoparticles) biotechnology products (including genes, peptides, proteins and vaccines), and genetically engineered cells are welcome.