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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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Pharmacokinetic Models: Comparison and Selection Criterion01:26

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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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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.
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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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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Updated: Sep 15, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
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Machine learning enabled multiscale model for nanoparticle margination and physiology based pharmacokinetics.

Sahil Kulkarni1, Benjamin Lin2,3, Ravi Radhakrishnan1,3

  • 1University of Pennsylvania, Chemical and Biomolecular Engineering, Philadelphia, 19104, PA, USA.

Computers & Chemical Engineering
|July 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a multiscale model to predict nanoparticle behavior and biodistribution for targeted drug delivery. It combines fluid dynamics with pharmacokinetic modeling for enhanced therapeutic applications.

Keywords:
DeepONetFokker–PlanckHematocritMarginationNanoparticlesPBPK

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Nanotechnology

Background:

  • Nanoparticle (NP) behavior and biodistribution are critical for targeted drug delivery.
  • Accurate prediction requires integrating blood flow dynamics with whole-body pharmacokinetics.
  • Existing models often lack the multiscale integration needed for complex biological systems.

Purpose of the Study:

  • To develop and validate a multiscale modeling framework for simulating nanoparticle behavior and biodistribution.
  • To predict nanoparticle margination and concentration profiles within the red-blood cell-free layer (RBCFL).
  • To inform nanoparticle biodistribution across organs using physiologically based pharmacokinetic (PBPK) modeling.

Main Methods:

  • Coupling a DeepONet-enabled Fokker-Planck equation with a hemorheological model to simulate NP drift-diffusion in the RBCFL.
  • Utilizing hematocrit and vessel radius as inputs for NP margination and concentration profile prediction.
  • Integrating predicted microvasculature NP concentrations into a PBPK model for whole-body biodistribution assessment.

Main Results:

  • The framework successfully simulates NP drift-diffusion and margination within the RBCFL.
  • Predicted NP concentration profiles accurately inform the PBPK model.
  • The multiscale approach provides a comprehensive prediction of NP biodistribution.

Conclusions:

  • The developed multiscale modeling framework enables accurate simulation and prediction of nanoparticle behavior and biodistribution.
  • This approach enhances the design and efficacy of nanoparticle-based targeted drug delivery systems.
  • The integrated modeling strategy offers a powerful tool for preclinical assessment of nanomedicines.