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Physiologically Based Pharmacokinetic Modeling to Simulate CYP3A4-Mediated Drug-Drug Interactions for Pyrotinib

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Abstract

Introduction

Pyrotinib is a newly developed tyrosine kinase inhibitor whose in vivo clearance relies heavily on cytochrome P450 3A4 (CYP3A4) activity. Clinical trials are ongoing to explore the effects of coadministration with CYP3A4 perpetrators on pyrotinib exposure. The present study aims to utilize physiologically based pharmacokinetic (PBPK) modeling to predict CYP3A4-based drug interactions of pyrotinib.

Methods

Pyrotinib PBPK model was developed in the PK-Sim® multicompartmental physiology structure. Physiochemical parameters were obtained from the literature, and clearance-related parameters were optimized by fitting clinical single-dose pharmacokinetic data. Pharmacokinetic parameters from the model output were compared with the observed data to validate the model predictive performance. Using validated CYP3A4 perpetrator models, we conducted PBPK simulations for drug interactions in a virtual population to explore the impacts of comedication with these perpetrators.

Results

The PBPK model accurately describes pyrotinib single- and multi-dose pharmacokinetics. The model also predicts dramatic exposure change of pyrotinib in the presence of itraconazole and rifampicin, though the impact of rifampicin is somewhat underestimated. According to model predictions, coadministration with typical potent or moderate CYP3A4 perpetrators increases pyrotinib concentration by over sixfold, extinguishing the possibility of dose adjustment for pyrotinib. A weak CYP3A4 inhibitor has minimal influence on pyrotinib pharmacokinetics.

Conclusion

PBPK modeling provides valuable information to avoid irrational medication when receiving pyrotinib chemotherapy.

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References

  1. Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Pineros M, Znaor A, et al. Cancer statistics for the year 2020: an overview. Int J Cancer. 2021;149:778–89.

    Article  CAS  Google Scholar 

  2. Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). 2021;134(7):783–91.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Oh DY, Bang YJ. HER2-targeted therapies—a role beyond breast cancer. Nat Rev Clin Oncol. 2020;17(1):33–48.

    Article  CAS  PubMed  Google Scholar 

  4. Blair HA. Pyrotinib: first global approval. Drugs. 2018;78(16):1751–5.

    Article  PubMed  Google Scholar 

  5. Ma F, Li Q, Chen S, Zhu W, Fan Y, Wang J, et al. Phase I study and biomarker analysis of pyrotinib, a novel irreversible Pan-ErbB receptor tyrosine kinase inhibitor, in patients with human epidermal growth factor receptor 2-positive metastatic breast cancer. J Clin Oncol. 2017;35(27):3105–12.

    Article  CAS  PubMed  Google Scholar 

  6. Zhu Y, Li L, Zhang G, Wan H, Yang C, Diao X, et al. Metabolic characterization of pyrotinib in humans by ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2016;1033–1034:117–27.

    Article  PubMed  Google Scholar 

  7. Meng J, Liu XY, Ma S, Zhang H, Yu SD, Zhang YF, et al. Metabolism and disposition of pyrotinib in healthy male volunteers: covalent binding with human plasma protein. Acta Pharmacol Sin. 2019;40(7):980–8.

    Article  CAS  PubMed  Google Scholar 

  8. Liu Y, Zhang Q, Lu C, Hu W. Multiple administrations of itraconazole increase plasma exposure to pyrotinib in chinese healthy adults. Drug Des Dev Ther. 2021;15:2485–93.

    Article  Google Scholar 

  9. Cai MM, Dou T, Tang L, Sun QY, Zhai ZH, Wang HP, et al. Effects of rifampicin on antineoplastic drug pyrotinib maleate pharmacokinetics in healthy subjects. Invest New Drugs. 2022;40(4):756–61.

    Article  CAS  PubMed  Google Scholar 

  10. Zhang X, Yang Y, Grimstein M, Fan J, Grillo JA, Huang SM, et al. Application of PBPK modeling and simulation for regulatory decision making and its impact on US prescribing information: an update on the 2018–2019 submissions to the US FDA’s office of clinical pharmacology. J Clin Pharmacol. 2020;60(Suppl 1):S160–78.

    CAS  PubMed  Google Scholar 

  11. Wendl T, Frechen S, Gerisch M, Heinig R, Eissing T. Physiologically-based pharmacokinetic modeling to predict CYP3A4-mediated drug-drug interactions of finerenone. CPT Pharmacometrics Syst Pharmacol. 2022;11(2):199–211.

    Article  CAS  PubMed  Google Scholar 

  12. Pilla Reddy V, Walker M, Sharma P, Ballard P, Vishwanathan K. Development, verification, and prediction of osimertinib drug-drug interactions using PBPK modeling approach to inform drug label. CPT Pharmacometrics Syst Pharmacol. 2018;7(5):321–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hanke N, Frechen S, Moj D, Britz H, Eissing T, Wendl T, et al. PBPK models for CYP3A4 and P-gp DDI prediction: a modeling network of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin. CPT Pharmacometrics Syst Pharmacol. 2018;7(10):647–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kanacher T, Lindauer A, Mezzalana E, Michon I, Veau C, Mantilla JDG, et al. A physiologically-based pharmacokinetic (PBPK) model network for the prediction of CYP1A2 and CYP2C19 drug-drug-gene interactions with fluvoxamine, omeprazole, S-mephenytoin, moclobemide, tizanidine, mexiletine, ethinylestradiol, and caffeine. Pharmaceutics. 2020;12(12):1191.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Frechen S, Solodenko J, Wendl T, Dallmann A, Ince I, Lehr T, et al. A generic framework for the physiologically-based pharmacokinetic platform qualification of PK-Sim and its application to predicting cytochrome P450 3A4-mediated drug-drug interactions. CPT Pharmacometrics Syst Pharmacol. 2021;10(6):633–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Nishimura M, Yaguti H, Yoshitsugu H, Naito S, Satoh T. Tissue distribution of mRNA expression of human cytochrome P450 isoforms assessed by high-sensitivity real-time reverse transcription PCR. Yakugaku Zasshi. 2003;123(5):369–75.

    Article  CAS  PubMed  Google Scholar 

  17. Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci. 2006;95(6):1238–57.

    Article  CAS  PubMed  Google Scholar 

  18. Britz H, Hanke N, Volz AK, Spigset O, Schwab M, Eissing T, et al. Physiologically-based pharmacokinetic models for CYP1A2 drug-drug interaction prediction: a modeling network of fluvoxamine, theophylline, caffeine, rifampicin, and midazolam. CPT Pharmacometrics Syst Pharmacol. 2019;8(5):296–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. FDA U. Drug Interactions & Labeling last update 2020. https://www.fda.gov/drugs/development-resources/drug-interactions-labeling.

  20. PRESCRIBING INFORMATION for INLYTA® (axitinib) tablets 2012. https://www.accessdata.fda.gov/drugsatfda_docs/label/2012/202324lbl.pdf.

  21. Li X, Wang Y, Zhu X, Zheng L. Clinical study of drug-drug interaction between omeprazole and pyrotinib after meal. Br J Clin Pharmacol. 2022;88(5):2349–58.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank Jiangsu Hengrui Pharmaceuticals Co., Ltd., for providing clinical data of pyrotinib and Dr. Bin Ye from Shenzhen Institute of Advanced Technology for providing graphing support.

Funding

This study is funded by the Research Fund of Anhui Institute of Translational Medicine (project no. 2022zhyx-B10) and the Natural Science Foundation of Anhui Province (no. 2108085QH383). The Rapid Service Fee was funded by the authors.

Author Contributions

Liang Ni, Liang Zheng, Yueyue Liu, Wenwen Xu, Yingjie Zhao, Ling Wang, Qian Zhang, Wei Hu, and Xijing Chen contributed to the study concept and design, modeling, data analysis, drafting of the manuscript, and revision of the manuscript.

Disclosures

Liang Ni, Liang Zheng, Yueyue Liu, Wenwen Xu, Yingjie Zhao, Ling Wang, Qian Zhang, Wei Hu, and Xijing Chen have no conflicts of interest to declare in this work.

Compliance with Ethics Guidelines

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Data Availability

The model file will be publicly available in the Open Systems Pharmacology Community (https://github.com/Open-Systems-Pharmacology).

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Author notes

  1. Liang Ni and Liang Zheng contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Clinical Pharmacokinetics Laboratory, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China

    Liang Ni & Xijing Chen

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

    Liang Zheng, Yueyue Liu, Yingjie Zhao, Qian Zhang & Wei Hu

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

    Wenwen Xu & Ling Wang

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  1. Liang Ni
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Corresponding authors

Correspondence to Wei Hu or Xijing Chen.

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Ni, L., Zheng, L., Liu, Y. et al. Physiologically Based Pharmacokinetic Modeling to Simulate CYP3A4-Mediated Drug-Drug Interactions for Pyrotinib. Adv Ther 40, 4310–4320 (2023). https://doi.org/10.1007/s12325-023-02602-1

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  • DOI: https://doi.org/10.1007/s12325-023-02602-1

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