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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Related Experiment Video

Updated: Sep 18, 2025

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
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Understanding Mechanisms of PFAS Absorption, Distribution, and Elimination Using a Physiologically Based

Fabian C Fischer1,2, Colin Thackray1, Nicholas Ferguson2

  • 1Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.

Environmental Science & Technology
|June 24, 2025
PubMed
Summary

A new physiologically based toxicokinetic (PBTK) model helps understand how per- and polyfluoroalkyl substances (PFAS) move through the body. The model reveals key factors influencing PFAS absorption, distribution, and elimination in mice.

Keywords:
PFASeliminationmouse modeltissue distributiontoxicokinetic modeling

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Area of Science:

  • Environmental Science
  • Toxicology
  • Computational Biology

Background:

  • Per- and polyfluoroalkyl substances (PFAS) are linked to adverse health outcomes.
  • Understanding PFAS toxicokinetics is crucial for risk assessment.
  • Existing mechanistic models for PFAS toxicokinetics are limited.

Purpose of the Study:

  • To develop and evaluate a physiologically based toxicokinetic (PBTK) model for PFAS.
  • To simulate PFAS tissue concentrations in mice using *in vitro* and *in vivo* data.
  • To identify key drivers of PFAS absorption, distribution, and elimination.

Main Methods:

  • Developed a PBTK model parameterized with *in vitro* data for *in vivo* mouse studies.
  • Simulated tissue concentrations of 9 perfluoroalkyl acids (PFAA) with varying chain lengths (4-10).
  • Quantified parameters including blood flow, protein/phospholipid binding, membrane permeability, transporters, and excretion.

Main Results:

  • Model evaluation showed good agreement between simulated and experimental mouse blood/tissue concentrations (*R*2 ≥ 0.65).
  • Sensitivity analyses revealed that permeability and phospholipid binding are critical for long-chain PFAS (ηpfc ≥ 7) elimination and distribution.
  • Renal transporters and albumin binding significantly impact short-chain PFAS (ηpfc ≤ 6) elimination.

Conclusions:

  • Integrating *in vitro* data into PBTK models provides mechanistic insights into PFAS toxicokinetics.
  • The developed PBTK model accurately simulates PFAS behavior across different chain lengths and exposure routes.
  • This approach facilitates a better understanding of PFAS health risks by elucidating compound-specific toxicokinetic differences.