Journal of Shanghai Jiao Tong University (Medical Science) >

2024 , Vol. 44 >Issue 11: 1414 - 1421

DOI: https://doi.org/10.3969/j.issn.1674-8115.2024.11.008

Clinical research

Prediction of drug-drug interactions in clozapine combination therapy based on physiologically based pharmacokinetic model

  • Fan MOU ,
  • Zhiwei HUANG ,
  • Yu CHENG ,
  • Xue ZHAO ,
  • Huafang LI ,
  • Shunying YU
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  • 1.Genetic Biochemistry Laboratory, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
    2.Drug Clinical Trial Institution, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
    3.Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
YU Shunying, E-mail: yushunying@smhc.org.cn.

Received date: 2024-05-22

  Accepted date: 2024-06-24

  Online published: 2024-11-28

Supported by

Clinical Research Center at Shanghai Mental Health Center(CRC2021ZD02);Medical Engineering Cross Research Fund(YG2023LC14)

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Abstract

Objective ·To develop physiologically based pharmacokinetic (PBPK) models specifically designed for the Chinese population by utilizing the combination of clozapine and fluvoxamine as a case, and predict the drug-drug interaction (DDI) associated with the combination medication of clozapine, ultimately optimizing the dosage of clozapine. Methods ·By obtaining the physicochemical parameters, absorption, distribution, metabolism, excretion (ADME)-related parameters, and physiologically relevant parameters of the Chinese population through literature and pharmacology-related databases, PBPK models for the clozapine and fluvoxamine were constructed by using PK-Sim? software. The models′ accuracy was evaluated by comparing predicted values of the area under the curve (AUC) and peak concentration (Cmax) to observed data, using the mean percentage error (MPE) and mean absolute percentage error (MAPE) as evaluation indicators. The models were validated against real-world plasma drug concentration data. Additionally, combining the inhibitory effect of fluvoxamine on clozapine, models for the combination therapy of clozapine and fluvoxamine were developed to predict the pharmacokinetic changes of clozapine. The presence of clinically significant DDI was determined by using the 90% confidence interval of the AUC ratio (AUCR) or Cmax ratio (CmaxR) as evaluation metrics, with a non-effect boundary set at 80%?125%. The pharmacokinetic changes of clozapine upon co-administration with fluvoxamine based on PBPK models were quantified, and a dosage optimization for clozapine was developed. Results ·The constructed model of clozapine and fluvoxamine was considered accurate if the absolute value of the MPE was ≤10% and the MAPE was <25% during validation, indicating that the predicted concentration-time curves were accurate. The PBPK model for the co-administration of clozapine and fluvoxamine was able to accurately predict pharmacokinetic parameters if the ratio of predicted AUC to observed AUC was within 1.25. The prediction of PBPK model for the co-administration showed that the 90% confidence intervals for AUCR and CmaxR of the combination therapy of clozapine and fluvoxamine were not entirely within the ineffective effect boundary, indicating a clinically significant DDI when these two drugs were used concomitantly. Moreover, the dose optimization according to the PBPK models indicated that when subjects were co-administered with clozapine and fluvoxamine, reducing the dose of clozapine to 50% of the original dose could maintain the exposure levels of clozapine consistent with monotherapy. Conclusion ·The established PBPK model can effectively simulate the impact of combination therapy on pharmacokinetic changes of clozapine, providing valuable insights for predicting potential DDI and optimizing dosage regimens. If clozapine needs to be co-administered with fluvoxamine during the treatment, clinicians should remain vigilant for clinically significant DDI and contemplate optimizing the dosage of clozapine accordingly.

Key words： clozapine; combination medication; drug-drug interaction; physiologically based pharmacokinetic model

Cite this article

Fan MOU , Zhiwei HUANG , Yu CHENG , Xue ZHAO , Huafang LI , Shunying YU . Prediction of drug-drug interactions in clozapine combination therapy based on physiologically based pharmacokinetic model[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024 , 44(11) : 1414 -1421 . DOI: 10.3969/j.issn.1674-8115.2024.11.008

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