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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Updated: Apr 29, 2026

Determination of the Transport Rate of Xenobiotics and Nanomaterials Across the Placenta using the ex vivo Human Placental Perfusion Model
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Predicting passive and active tissue:plasma partition coefficients: interindividual and interspecies variability.

Christopher D Ruark1, C Eric Hack2, Peter J Robinson2

  • 1HJF, Molecular Bioeffects Branch, Bioeffects Division, Human Effectiveness Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Dayton, Ohio 45433; Department of Biomedical Sciences, Wright State University, Dayton, Ohio 45435.

Journal of Pharmaceutical Sciences
|May 17, 2014
PubMed
Summary
This summary is machine-generated.

A new model predicts chemical partitioning in tissues across species, accounting for active transport. This helps understand variability and aids in scaling animal data for human drug development.

Keywords:
Monte CarloQSPRactive transportcomputational ADMEcomputational biologydrug transportin silico modelingphysicochemical propertiespopulation pharmacokinetic/pharmacodynamic modelstissue partition

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Area of Science:

  • Pharmacokinetics and Toxicokinetics
  • Physiologically Based Pharmacokinetic (PBPK) Modeling
  • Interspecies Extrapolation

Background:

  • Predicting chemical distribution in tissues is crucial for risk assessment and drug development.
  • Understanding interspecies and interindividual variability in tissue:plasma partition coefficients (K(t:pl)) is essential for accurate extrapolation.
  • Existing models often lack comprehensive incorporation of active transport mechanisms and species-specific biological data.

Purpose of the Study:

  • To develop and validate a mechanistic tissue composition model for predicting K(t:pl) across multiple mammalian species.
  • To assess interindividual and interspecies variability in K(t:pl) using a Monte Carlo analysis.
  • To evaluate the role of active transport in determining chemical partitioning and its concentration-dependent effects.

Main Methods:

  • Developed a mechanistic model integrating chemical properties (lipophilicity, pKa, binding) and tissue composition (lipids, proteins, water, pH).
  • Incorporated passive and active transport mechanisms, with active transport quantified using Michaelis-Menten parameters.
  • Compiled species-specific biological data from 126 articles for eight species (mouse, rat, guinea pig, rabbit, dog, pig, monkey, human) and performed Monte Carlo simulations.

Main Results:

  • The model successfully predicted K(t:pl) for organic chemicals across 10 tissues and eight species.
  • Significant interspecies variability in K(t:pl) was observed for several tissues, though some differences were obscured by uncertainty.
  • Chemicals undergoing active transport exhibited concentration-dependent K(t:pl), highlighting the importance of this mechanism.

Conclusions:

  • The tissue composition-based mechanistic model provides a robust framework for predicting K(t:pl) across species.
  • This model is valuable for drug development, particularly for scaling K(t:pl) from animal models to humans.
  • The model's ability to incorporate active transport improves the accuracy of chemical distribution predictions and risk assessments.