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

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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 AUC0-96 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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Data Availability

Upon reasonable request, the corresponding author can provide the available data.

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Acknowledgements

We express gratitude to all contributing authors for their participation and valuable discussions.

Funding

This work was supported by the Key Research and Development Program of Science and Technology Department of the Sichuan Province 111 Project (B18035).

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Authors and Affiliations

  1. Department of Clinical Pharmacy and Pharmacy Administration, School of Pharmacy, Sichuan University, Chengdu, 610064, West China, China

    Chaozhuang Shen, Hongyi Yang, Xuehua Jiang & Ling Wang

  2. Anhui Provincial Center for Drug Clinical Evaluation, Yijishan Hospital of Wannan Medical College, Wuhu, 241001, Anhui, China

    Wenxin Shao & Haitang Xie

  3. Department of Clinical Pharmacology, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, Anhui, China

    Liang Zheng & Wei Zhang

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  1. Chaozhuang Shen
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Contributions

Ling Wang, Xuehua Jiang, Haitang Xie, and Chaozhuang Shen participated in the study's conceptualization. Chaozhuang Shen and Ling Wang drafted the manuscript. Hongyi Yang, Wenxin Shao, Liang Zheng, and Wei Zhang enriched the study through insightful discussions. All co-authors listed actively contributed to the modeling endeavors.

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Correspondence to Ling Wang.

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Shen, C., Yang, H., Shao, W. et al. Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms. Pharm Res 41, 731–749 (2024). https://doi.org/10.1007/s11095-024-03680-8

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