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Physiologically-based pharmacokinetic (PBPK) modeling networks can predict complex drug-drug-gene interactions (DDGIs), aiding clinical dose recommendations. This study developed a PBPK network for CYP2D6, demonstrating its potential for precision dosing.

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Area of Science:

  • Pharmacology and Clinical Pharmacology
  • Computational Biology and Bioinformatics
  • Drug Metabolism and Pharmacokinetics

Background:

  • Drug-drug-gene interactions (DDGIs) pose challenges for clinical dose recommendations due to their complexity.
  • Physiologically-based pharmacokinetic (PBPK) modeling offers a promising approach to navigate these complex interactions and inform clinical guidelines.
  • The cytochrome P450 (CYP) 2D6 enzyme is a key player in DDGIs due to genetic variability and drug interactions.

Purpose of the Study:

  • To develop and validate a comprehensive PBPK network for modeling CYP2D6-mediated drug-gene interactions (DGIs), drug-drug interactions (DDIs), and DDGIs.
  • To assess the predictive performance of the PBPK network for various interaction types involving CYP2D6 substrates and inhibitors.
  • To demonstrate the potential clinical utility of the PBPK network for personalized dose adjustments in untested DDGI scenarios.

Main Methods:

  • Construction of a PBPK network incorporating 23 compounds, including CYP2D6 substrates and inhibitors (sensitive, moderate, strong, weak) based on FDA guidance.
  • Inclusion of interactions mediated by CYP3A4 and P-glycoprotein alongside CYP2D6.
  • Development based on data from 30 DGI, 45 DDI, and 7 DDGI studies, covering 32 unique drug combinations.

Main Results:

  • The PBPK network demonstrated good predictive performance across all interaction types, with mean geometric mean fold errors for AUC ratios (1.40, 1.38, 1.56) and Cmax ratios (1.29, 1.43, 1.60).
  • The model successfully predicted interactions involving sensitive and moderate CYP2D6 substrates and inhibitors.
  • The network was applied to calculate dose adaptations for atomoxetine and metoprolol in simulated DDGI scenarios.

Conclusions:

  • PBPK modeling networks provide a robust framework for exploring complex DDGIs, particularly for the highly polymorphic CYP2D6 enzyme.
  • The developed PBPK network shows strong predictive accuracy, supporting its use in regulatory submissions and clinical practice.
  • This approach facilitates model-informed precision dosing, enabling patient-specific dose adjustments for challenging DDGI scenarios.