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Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax

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Abstract

Purpose

Venetoclax (VEN), an anti-tumor drug that is a substrate of cytochrome P450 3A enzyme (CYP3A4), is used to treat leukemia. Voriconazole (VCZ) is an antifungal medication that inhibits CYP3A4. The goal of this study is to predict the effect of VCZ on VEN exposure.

Method

Two physiological based pharmacokinetics (PBPK) models were developed for VCZ and VEN using the bottom-up and top-down method. VCZ model was also developed to describe the effect of CYP2C19 polymorphism on its pharmacokinetics (PK). The reversible inhibition constant (Ki) of VCZ for CYP3A4 was calibrated using drug-drug interaction (DDI) data of midazolam and VCZ. The clinical verified VCZ and VEN model were used to predict the DDI of VCZ and VEN at clinical dosing scenario.

Result

VCZ model predicted VCZ exposure in the subjects of different CYP2C19 genotype and DDI related fold changes of sensitive CYP3A substrate with acceptable prediction error. VEN model can capture PK of VEN with acceptable prediction error. The DDI PBPK model predicted that VCZ increased the exposure of VEN by 4.5–9.6 fold. The increase in VEN exposure by VCZ was influenced by subject’s CYP2C19 genotype. According to the therapeutic window, VEN dose should be reduced to 100 mg when co-administered with VCZ.

Conclusion

The PBPK model developed here could support individual dose adjustment of VEN and DDI risk assessment. Predictions using the robust PBPK model confirmed that the 100 mg dose adjustment is still applicable in the presence of VCZ with high inter-individual viability.

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Abbreviations

AML:

Acute myeloid leukemia

AUC:

The area under the concencention-time curve

bid:

Twice daily

CLL:

Chronic lymphocytic leukemia

CYP:

Cytochrome P450

CLint :

The enzymatic intrinsic clearance

DDI:

Drug drug interaction

EM:

Extensive metabolizer

FMO:

Flavin-containing monooxygenase

Fm :

Fraction of drugs metabolized

IM:

Intermediate metabolizers

Kcat :

Catalytic rate constant

Ki :

Reversible inhibition constant

Km :

Michaelis–Menten constant

PBPK:

Physiologically based pharmacokinetics

PM:

Poor metabolizer

qd:

Once a day

UM:

Ultrarapid metabolizer

VCZ:

Voriconazole

VEN:

Venetoclax

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Acknowledgments

The authors would like to thank Jae-Gook Shin, Zi-teng Wang for helpful discussion on topics related to the work.

Funding

This work was supported by National Clinical Research Center for Hemotologic Diseases, the First Affiliated Hospital of Soochow Univerisity(2020WSC07).

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Authors and Affiliations

  1. Department of Clinical Pharmacology, The First Affiliated Hospital of Soochow University, Suzhou, China

    Ji Dong, Chen-rong Huang & Li-yan Miao

  2. Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Shuai-bing Liu

  3. Pharmacogenomics Research Center, Inje University, Busan, Republic of Korea

    Jony Md Rasheduzzaman

  4. Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China

    Ji Dong, Chen-rong Huang & Li-yan Miao

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  1. Ji Dong
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Correspondence to Chen-rong Huang or Li-yan Miao.

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Dong, J., Liu, Sb., Rasheduzzaman, J.M. et al. Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax. Pharm Res 39, 1921–1933 (2022). https://doi.org/10.1007/s11095-022-03289-9

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  • DOI: https://doi.org/10.1007/s11095-022-03289-9

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