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Related Concept Videos

Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

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The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
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Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

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The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
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Two-Compartment Open Model: Overview01:05

Two-Compartment Open Model: Overview

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Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
The...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Three-Compartment Open Model01:06

Three-Compartment Open Model

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The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
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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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Related Experiment Video

Updated: Sep 2, 2025

Visualization and Analysis of Blood Flow and Oxygen Consumption in Hepatic Microcirculation: Application to an Acute Hepatitis Model
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Compartmental model describing the physiological basis for the HepQuant SHUNT test.

Michael P McRae1, Steve M Helmke2, James R Burton3

  • 1Custom Diagnostic Solutions LLC, Houston, Texas.

Translational Research : the Journal of Laboratory and Clinical Medicine
|August 10, 2022
PubMed
Summary

A new compartmental model (CM) for the HepQuant SHUNT test shows improved reproducibility and reliability in assessing liver function compared to the minimal model (MM). This advanced model better quantifies hepatic impairment and aids in monitoring treatment effects for liver diseases like NASH and HCV.

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Area of Science:

  • Hepatology and Clinical Biochemistry
  • Physiologically Based Modeling
  • Biomarker Development

Background:

  • The HepQuant SHUNT test measures hepatic functional impairment using cholate clearance.
  • Noncompartmental analysis (minimal model, MM) previously established reliable liver function measures.
  • Compartmental models (CM) offer more detailed physiological parameter estimation.

Purpose of the Study:

  • To compare the reproducibility and reliability of a new physiologically based compartmental model (CM) against the established minimal model (MM).
  • To evaluate the performance of both models in assessing key indices of hepatic disease, including nonalcoholic steatohepatitis (NASH) and hepatitis C virus (HCV) infection.

Main Methods:

  • Analyzed data from 16 control, 16 NASH, and 16 HCV subjects with 3 replicate tests each.
  • Developed a CM describing cholate transfer between systemic, portal, and liver compartments.
  • Compared CM and MM using intraclass correlation coefficients (ICC) for 6 hepatic disease indices.

Main Results:

  • The CM demonstrated high correlation with the MM for disease severity index (R²=0.96).
  • The CM achieved acceptable reproducibility (ICC > 0.7) for all 6 hepatic disease indices, outperforming the MM (5/6 indices).
  • The CM showed significantly improved ICC for SHUNT (absolute bioavailability) compared to the MM (0.84 vs 0.73).

Conclusions:

  • The physiologically based compartmental model (CM) offers superior reproducibility and reliability for assessing liver function via the SHUNT test.
  • The CM enables determination of anatomic shunt and hepatic extraction, providing deeper insights than the MM.
  • This advanced CM is a valuable tool for monitoring treatment effects and predicting clinical outcomes in patients with liver disease.