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Purpose <p>This study aimed to establish a physiologically based pharmacokinetic (PBPK) model of TQB3909 and predict the CYP3A4-mediated drug-drug interactions (DDIs) of TQB3909 to inform dosing recommendations in patients.</p> Methods <p>PBPK model was developed by integrating the physicochemical properties, in vivo and in vitro metabolic parameters of TQB3909, along with phase I clinical pharmacokinetic data and DDI clinical trial data.</p> Results <p>Model verification showed that the simulated drug concentrations by established PBPK model were fitted well with clinical data, with a mean fold error (MFE) ≤ 2 and most of predicted C<sub>max</sub> and AUC were within the range of 2-fold boundaries compared to the clinical data. Predicted C<sub>max</sub> and AUC geometric mean ratios (GMRs) of TQB3909 with strong CYP3A4 inhibitor itraconazole or inducer rifampicin were within 0.8 to 1.25-fold range of the observed data. DDI simulations showed that co-administration with verapamil, a moderate CYP3A4 inhibitor, increased the exposure of TQB3909 by 26%, whereas co-administration with efavirenz, a moderate CYP3A4 inducer, decreased TQB3909 exposure to 53.8% of baseline. No obvious effect of fluvoxamine (a weak CYP3A4 inhibitor) was found.</p> Conclusion <p>This developed PBPK model effectively simulated the impact of CYP3A4 perpetrators on the pharmacokinetic behavior of TQB3909 in humans, informing clinical dose adjustment of TQB3909.</p>

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Prediction of CYP3A4-mediated drug-drug interactions of a novel Bcl-2 inhibitor TQB3909 using physiologically based pharmacokinetic modeling

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Hui Chen,
  • Shixing Zhu,
  • Anqi Yang,
  • Xu Li,
  • Xunqiang Wang,
  • Wei Zhao,
  • Xin Wang,
  • Ding Yu

摘要

Purpose

This study aimed to establish a physiologically based pharmacokinetic (PBPK) model of TQB3909 and predict the CYP3A4-mediated drug-drug interactions (DDIs) of TQB3909 to inform dosing recommendations in patients.

Methods

PBPK model was developed by integrating the physicochemical properties, in vivo and in vitro metabolic parameters of TQB3909, along with phase I clinical pharmacokinetic data and DDI clinical trial data.

Results

Model verification showed that the simulated drug concentrations by established PBPK model were fitted well with clinical data, with a mean fold error (MFE) ≤ 2 and most of predicted Cmax and AUC were within the range of 2-fold boundaries compared to the clinical data. Predicted Cmax and AUC geometric mean ratios (GMRs) of TQB3909 with strong CYP3A4 inhibitor itraconazole or inducer rifampicin were within 0.8 to 1.25-fold range of the observed data. DDI simulations showed that co-administration with verapamil, a moderate CYP3A4 inhibitor, increased the exposure of TQB3909 by 26%, whereas co-administration with efavirenz, a moderate CYP3A4 inducer, decreased TQB3909 exposure to 53.8% of baseline. No obvious effect of fluvoxamine (a weak CYP3A4 inhibitor) was found.

Conclusion

This developed PBPK model effectively simulated the impact of CYP3A4 perpetrators on the pharmacokinetic behavior of TQB3909 in humans, informing clinical dose adjustment of TQB3909.