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Related Experiment Video

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Engineered 3D Silk-collagen-based Model of Polarized Neural Tissue
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A physiologically based modeling strategy during preclinical CNS drug development.

Kathryn Ball1, François Bouzom, Jean-Michel Scherrmann

  • 1Centre de Pharmacocinétique et Métabolisme, Groupe de Recherche Servier , Orléans, France.

Molecular Pharmaceutics
|January 23, 2014
PubMed
Summary
This summary is machine-generated.

Physiologically based pharmacokinetic (PBPK) modeling aids in predicting drug concentrations in the central nervous system (CNS). This study proposes a decision tree for robust PBPK model development in rats, enhancing CNS drug development.

Keywords:
CNSIVIVEKp,brainKp,uu,brainPBPK modelblood-cerebrospinal fluid barrierblood−brain barrierdrug developmentmembranepermeability

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Area of Science:

  • Pharmacokinetics and Drug Metabolism
  • Neuroscience and Neurology
  • Computational Biology and Bioinformatics

Background:

  • Physiologically based pharmacokinetic (PBPK) modeling is crucial for predicting drug concentrations at CNS therapeutic targets during development.
  • Existing PBPK models for CNS drugs exhibit structural variability, complicating parameter interpretation and hindering reliable human predictions.
  • A standardized, adaptable PBPK modeling approach is needed for CNS drug development, especially considering data availability.

Purpose of the Study:

  • To develop a coherent and adaptable PBPK modeling strategy for the rat central nervous system (CNS).
  • To propose a decision tree for PBPK model parametrization and structure selection based on available in vivo data.
  • To evaluate the utility of in vitro permeability data for predicting blood-brain barrier (BBB) passive permeability in the absence of in vivo measurements.

Main Methods:

  • Sensitivity analysis of PBPK model parameters.
  • Case studies using three CNS drugs: atomoxetine, acetaminophen, and S 18986.
  • Evaluation of bottom-up scaling of Caco-2 cell line permeability data to predict BBB passive permeability.

Main Results:

  • A decision tree was proposed to guide PBPK model structure and parametrization based on data availability.
  • Comparison of proposed parameter estimates with published values revealed differences in mechanistic interpretation.
  • Bottom-up scaling of in vitro permeability data successfully predicted BBB passive permeability, offering an alternative to microdialysis.

Conclusions:

  • The developed PBPK modeling strategy provides a robust framework for CNS drug development.
  • The proposed decision tree enhances the reliability and interpretability of PBPK models for CNS-targeting drugs.
  • PBPK modeling, particularly with integrated in vitro-in vivo data scaling, is a valuable predictive tool throughout CNS drug development.