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Predicting the Drug–Drug Interaction Mediated by CYP3A4 Inhibition: Method Development and Performance Evaluation

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Abstract

The prediction of drug–drug interactions (DDIs) plays critical roles for the estimation of DDI risk caused by inhibition of CYP3A4. The aim of this paper is to develop a physiologically based pharmacokinetic (PBPK)-DDI model for prediction of the DDI co-administrated with ketoconazole in humans and evaluate the predictive performance of the model. The pharmacokinetic and biopharmaceutical properties of 35 approved drugs, as victims, were collected for the development of a PBPK model, which were linked to the PBPK model of ketoconazole for the DDI prediction. The PBPK model of victims and ketoconazole were validated by matching actual in vivo pharmacokinetic data. The predicted results of DDI were compared with actual data to evaluate the predictive performance. The percentage of predicted ratio of AUC (AUCR), Cmax (CmaxR), and Tmax (TmaxR) was 75%, 69%, and 91%, respectively, which were within the twofold threshold (range, 0.5–2.0×) of the observed values. Only 3% of the predicted AUCRs are obviously underestimated. After integration of the reported fraction of metabolism (fm) into the PBPK-DDI model for limited four cases, the model-predicted AUCRs were improved from the twofold range of the observed AUCRs to the 90% confidence interval. The developed method could reasonably predict drug–drug interaction with a low risk of underestimation. The present accuracy of the prediction was improved compared with that of static mechanistic models. The evaluation of predictive performance increases the confidence using the model to evaluate the risk of DDIs co-administrated with ketoconazole before the in vivo DDI study.

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Abbreviations

ACAT:

Advanced compartmental absorption and transit

AUCR:

Ratio of area under the concentration–time curve of victim after co-administration with ketoconazole over area under the concentration–time curve of victim with dosing alone

C maxR:

Ratio of victim Cmax after co-administration with ketoconazole over victim Cmax with dosing alone

F a :

Cumulative intestinal absorption, %

FDA:

Food and Drug Administration

f m :

Fraction of metabolism

f u :

Fraction unbound in the plasma

GMR:

Geometric mean ratio

K 12 :

Transfer rate constant from central compartment to peripheral compartment

K 13 :

Transfer rate constant from central compartment to peripheral compartment 2

K 31 :

Transfer rate constant from peripheral compartment 2 to peripheral central compartment

k i :

Inhibition constant of ketoconazole

MPT:

Mean precipitation time

PC1:

Peripheral compartment 1

PC2:

Peripheral compartment 2

R bp :

Ratio of concentration in whole blood versus plasma

T maxR:

Ratio of victim Tmax after co-administration with ketoconazole over victim Tmax with dosing alone

UDF:

Excretion of unchanged drugs in feces

UDU:

Excretion of unchanged drugs in urine

V c :

Distribution volume in central compartment

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Acknowledgements

The authors thank Simulations Plus, Inc. (Lancaster, CA, USA), for authorizing the use of the optimization module in this study. The authors would also like to thank Dr. Xiao Zhu (School of Pharmacy, University of Otago) for his proofreading of the manuscript.

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Authors and Affiliations

  1. Department of Clinical Pharmacology and DMPK, Hutchison MediPharma Ltd., Building 4, 720 Cailun Road, Zhang-Jiang Hi-Tech Park, Shanghai, 201203, China

    Hong-can Ren, Yang Sai & Chun Zhang

  2. Department of Biology, GenFleet Therapeutics (Shanghai) Inc., 1206 Zhangjiang Road, Suite A, Shanghai, China

    Hong-can Ren & Lily Tang

  3. Shanghai PharmoGo Co., Ltd., 3F, Block B, Weitai Building, No. 58, Lane 91, Shanghai, 200127, People’s Republic of China

    Tao Chen

  4. Department of General Surgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, 1111 XianXia Road, Shanghai, 200336, China

    Cheng-guang Yang

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  1. Hong-can Ren
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Contributions

Study design: Hong-can Ren and Yang Sai. Data collection: Hong-can Ren and Chun Zhang. Data analysis and integrity: Hong-can Ren, Tao Chen, Chun Zhang, and Cheng-guang Yang. Drafting the manuscript: Hong-can Ren and Tao Chen. Revising the manuscript: Yang Sai, Lily Tang, and Cheng-guang Yang. Approving the final version of the manuscript: all authors.

Corresponding authors

Correspondence to Hong-can Ren, Yang Sai or Cheng-guang Yang.

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Conflict of Interest

Tao Chen is an employee of Shanghai PharmoGo Co., Ltd., an authorized distributor of Simulations Plus, the developer of GastroPlus™, in China.

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Ren, Hc., Sai, Y., Chen, T. et al. Predicting the Drug–Drug Interaction Mediated by CYP3A4 Inhibition: Method Development and Performance Evaluation. AAPS J 24, 12 (2022). https://doi.org/10.1208/s12248-021-00659-w

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