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Prediction of Omeprazole Pharmacokinetics and its Inhibition on Gastric Acid Secretion in Humans Using Physiologically Based Pharmacokinetic-Pharmacodynamic Model Characterizing CYP2C19 Polymorphisms

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Abstract

Purpose

To develop a whole physiologically based pharmacokinetic-pharmacodynamic (PBPK-PD) model to describe the pharmacokinetics and anti-gastric acid secretion of omeprazole in CYP2C19 extensive metabolizers (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs) and ultrarapid metabolizers (UMs) following oral or intravenous administration.

Methods

A PBPK/PD model was built using Phoenix WinNolin software. Omeprazole was mainly metabolized by CYP2C19 and CYP3A4 and the CYP2C19 polymorphism was incorporated using in vitro data. We described the PD by using a turn-over model with parameter estimates from dogs and the effect of a meal on the acid secretion was also implemented. The model predictions were compared to 53 sets of clinical data.

Results

Predictions of omeprazole plasma concentration (72.2%) and 24 h stomach pH after administration (85%) were within 0.5–2.0-fold of the observed values, indicating that the PBPK-PD model was successfully developed. Sensitivity analysis demonstrated that the contributions of the tested factors to the plasma concentration of omeprazole were Vmax,2C19 ≈ Papp > Vmax,3A4 > Kti, and contributions to its pharmacodynamic were Vmax,2C19 > kome > kms > Papp > Vmax,3A4. The simulations showed that while the initial omeprazole dose in UMs, EMs, and IMs increased 7.5-, 3- and 1.25-fold compared to those of PMs, the therapeutic effect was similar.

Conclusions

The successful establishment of this PBPK-PD model highlights that pharmacokinetic and pharmacodynamic profiles of drugs can be predicted using preclinical data. The PBPK-PD model also provided a feasible alternative to empirical guidance for the recommended doses of omeprazole.

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Data Availability

All data selected during this study are included in this published article and its electronic supplementary information files, and further inquiries can be directed to the corresponding authors.

Abbreviations

AUC:

Area under the curve

Cmax :

Peak plasma concentrations

EMs:

Extensive metabolizers

IMs:

Intermediate metabolizers

IVIVE:

In vitro to in vivo extrapolation

Papp :

Apparent permeability

Peff :

Effective permeability coefficient

PBPK-PD:

Physiologically based pharmacokinetic-pharmacodynamic

PMs:

Poor metabolizers

PPI:

Proton pump inhibitor

UMs:

Ultrarapid metabolizers

VPCs:

Visual predictive checks

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Acknowledgments

We would like to thank all the authors for their participation and helpful discussions. Due to space constraints, we are aware that there is far more research associated with this field and regret that we could not cite every available report.

Funding

This study was supported by funding from the National Natural Science Foundation of China (N0.82073922; 82173884; 82204511), the “Double First-Class” university project (No. CPU2022QZ21) and the Jiangsu Funding Program for Excellent Postdoctoral Talent (No. 1412200067).

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Author notes

  1. Cailing Jiang and Kang Yu have contributed equally to this work.

Authors and Affiliations

  1. Center of Pharmacokinetics and Metabolism, School of Pharmacy, China Pharmaceutical University, Nanjing, China

    Shuai Li, Lei Xie, Lu Yang, Ling Jiang, Yiting Yang, Hao Zhi, Xiaodong Liu, Hanyu Yang & Li Liu

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  1. Shuai Li
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  2. Lei Xie
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  3. Lu Yang
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  4. Ling Jiang
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  6. Hao Zhi
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  7. Xiaodong Liu
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  8. Hanyu Yang
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  9. Li Liu
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Contributions

SL, HY, LL, and XL designed the paper frame. LX, LJ, and YY selected the data. LY and HZ prepared the figures and tables. SL, LL, and XL wrote the manuscript. All authors contributed to the paper and approved the submitted version.

Corresponding authors

Correspondence to Xiaodong Liu, Hanyu Yang or Li Liu.

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Li, S., Xie, L., Yang, L. et al. Prediction of Omeprazole Pharmacokinetics and its Inhibition on Gastric Acid Secretion in Humans Using Physiologically Based Pharmacokinetic-Pharmacodynamic Model Characterizing CYP2C19 Polymorphisms. Pharm Res 40, 1735–1750 (2023). https://doi.org/10.1007/s11095-023-03531-y

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