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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

368
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 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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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

327
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: Overview01:20

Pharmacokinetic Models: Overview

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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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Related Experiment Video

Updated: Apr 22, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
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Mathematical Modeling of Photoplethysmography: Model Assessment and Validation.

Adrien Lefieux1, Justine Daraize2, Fabien Vergnet1

  • 1Sorbonne Université, CNRS, LJLL, Inria, Paris, France.

Cardiovascular Engineering and Technology
|April 20, 2026
PubMed
Summary

This study introduces a new mathematical model for photoplethysmography (PPG) signals. The model accurately simulates PPG data and can estimate vital tissue perfusion parameters like arterial pulse pressure.

Keywords:
BiomarkerDiffusion bio-opticsFinite element methodMathematical modelingNumerical simulationPhotoplethysmography (PPG) signalPoroelasticity

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Area of Science:

  • Biomedical Engineering
  • Mathematical Modeling
  • Optical Physiology

Background:

  • Photoplethysmography (PPG) non-invasively measures optically perfused bio-tissue volume changes using light emitters and receptors.
  • Analyzing received light infers tissue properties, but accurate modeling remains a challenge.

Purpose of the Study:

  • To present a novel distributed mathematical model for PPG signals.
  • To combine poroelastic tissue perfusion with light diffusion models for enhanced accuracy.

Main Methods:

  • A linear poroelastic model for tissue perfusion under small deformations was developed.
  • The finite element method was used for numerical discretization, focusing on fingertip vascularization.
  • Extensive simulations (216,000) were performed using a quasi-Monte Carlo approach.

Main Results:

  • The model successfully reproduced AC/DC PPG amplitude biomarkers for red and infrared wavelengths.
  • Simulated pulse pressures closely matched experimental measurements (<1 mmHg difference).

Conclusions:

  • The proposed mathematical model is relevant for simulating PPG signals.
  • It shows potential for estimating tissue perfusion parameters, particularly arterial pulse pressure.