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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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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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Pharmacokinetic Models: Overview01:20

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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.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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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.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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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.
A recent model describes pravastatin's hepatobiliary excretion,...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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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: Sep 2, 2025

Modeling Brain Metastasis by Internal Carotid Artery Injection of Cancer Cells
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Multiphysics pharmacokinetic model for targeted nanoparticles.

Emma M Glass1,2, Sahil Kulkarni3, Christina Eng3

  • 1Department of Computational Applied Mathematics and Statistics, College of William and Mary, Williamsburg, VA, United States.

Frontiers in Medical Technology
|August 1, 2022
PubMed
Summary
This summary is machine-generated.

A new physics-based model predicts nanoparticle biodistribution for drug delivery, improving translation from animal studies to humans. This computational tool accounts for nanoparticle size, surface chemistry, and immune responses.

Keywords:
biodistributionneural networkphysiologically based pharmacokineticquasi steady-state approximationvascular branching

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Area of Science:

  • Biomedical Engineering
  • Pharmacokinetics
  • Computational Modeling

Background:

  • Nanoparticles (NP) show promise for targeted drug delivery, enhancing solubility and cellular transport.
  • A significant translational gap exists between animal and human studies for NP therapeutics, limiting FDA approvals.
  • Current physiologically based pharmacokinetic (PBPK) models often lack universality due to empirical frameworks.

Purpose of the Study:

  • To develop a physics-based, multiscale PBPK compartmental model for predicting continuous nanoparticle biodistribution.
  • To create a modular model adaptable to various NP constructs and experimental conditions.

Main Methods:

  • Developed and validated two physics-based compartmental models (A and B) against experimental data.
  • Implemented a branched model to incorporate varying NP sizes and assess vascular branching effects on NP uptake.
  • Compared computational performance of MATLAB and Julia (DifferentialEquations.jl) for solving stiff ordinary differential equations (ODEs).
  • Explored neural network solutions for ODEs using Julia's Flux.jl package, leveraging quasi-steady-state approximation (QSSA).

Main Results:

  • Model B demonstrated higher physiological relevance and closer agreement with experimental data (NRMSD analysis).
  • Vascular branching was shown to enhance NP uptake in organ tissues via the branched model.
  • Julia's DifferentialEquations.jl significantly outperformed MATLAB in solving stiff ODE systems.
  • Neural networks successfully solved ODEs when made non-stiff via QSSA.

Conclusions:

  • The developed physics-based PBPK model provides a more comprehensive and modular approach to predicting NP biodistribution.
  • The model's modularity allows incorporation of NP surface chemistry, vascular hydrodynamics, and immune system effects.
  • Computational advancements, particularly in Julia, enhance the efficiency of solving complex PBPK models.