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Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model

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Abstract

Aims

To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions.

Methods

A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration–time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation.

Results

Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure.

Conclusions

The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

JL thanks the sponsorship from the Innovation Fund of the Graduate School of Wuhan Institute of Technology (CX2022066). The authors also thank Dr Hongyun Wang at Peking Union Medical College Hospital for providing guidance and scientific suggestions.

Author information

Authors and Affiliations

  1. School of Chemical Engineering & Pharmacy, Wuhan Institute of Technology, Wuhan, China

    Jingxi Li, Sihui Long, Xiong Jin, Mengjun Zhang & Bo Liu

  2. Yinghan Pharmaceutical Technology (Shanghai) Co., Ltd, Shanghai, 200063, China

    Xue Li, Keheng Wu, Youni Zhao, Xinyi Wu, Zhijun Huang, Zhou Zhou & Jack Liu

Authors
  1. Jingxi Li
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Contributions

Conceptualization, Bo Liu; Data curation, Jingxi Li, Xiong Jin, Mengjun Zhang; Formal analysis, Keheng Wu Xinyi, Wu, Zhijun Huang; Methodology, Keheng Wu; Project administration, Bo Liu; Software, Keheng Wu & Youni Zhao; Validation, Jingxi Li; Writing-original draft, Jingxi Li; Writing review & editing, Zhou Zhou, Xue Li, Sihui Long and Jack Liu.

Corresponding author

Correspondence to Bo Liu.

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Conflict of Interests

Xinyi Wu, Zhijun Huang, Zhou Zhou, Xue Li, Keheng Wu, Youni Zhao, Jack Liu were employees of Yinghan Pharmaceutical Technology (Shanghai) at the time of study conducted.

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Li, J., Li, X., Wu, K. et al. Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model. Pharm Res 41, 699–709 (2024). https://doi.org/10.1007/s11095-024-03691-5

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  • DOI: https://doi.org/10.1007/s11095-024-03691-5

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