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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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...
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...

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Related Experiment Video

Updated: May 29, 2026

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody
07:36

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody

Published on: May 16, 2020

A systems approach for tumor pharmacokinetics.

Greg Michael Thurber1, Ralph Weissleder

  • 1Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America. gthurber@alum.mit.edu

Plos One
|September 22, 2011
PubMed
Summary

This study introduces a new framework to categorize drug pharmacokinetics in tumors based on delivery limitations. Understanding these categories aids in designing effective combination therapies and novel drug delivery systems.

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Last Updated: May 29, 2026

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Measuring Real-time Drug Response in Organotypic Tumor Tissue Slices

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Area of Science:

  • Pharmacology
  • Biomedical Engineering
  • Mathematical Biology

Background:

  • Advancements in drug discovery yield diverse agents (small molecules, biologics).
  • Drug delivery differences impact combination therapies and tumor physiology modification (e.g., anti-angiogenic treatment).
  • Existing pharmacokinetic models are often molecule-specific, hindering comparative analysis.

Purpose of the Study:

  • Develop a universal framework for categorizing primary pharmacokinetics of drugs within tumors.
  • Classify drugs based on their rate-limiting delivery step, not mechanism of action.
  • Facilitate understanding of drug distribution for improved therapeutic strategies.

Main Methods:

  • Developed a mathematical modeling framework for drug delivery in tumors.
  • Simulations incorporated perfusion, vascularization, interstitial transport, and local binding/metabolism.
  • Classified drugs into four categories based on uptake-limiting factors: blood flow, extravasation, interstitial diffusion, or local binding/metabolism.

Main Results:

  • A novel categorization framework for tumor drug pharmacokinetics was established.
  • Simulations revealed significant differences in distribution between small molecule and macromolecular drugs.
  • Demonstrated distinct in vivo behavior using antibody delivery in mouse xenografts.

Conclusions:

  • The proposed categorization aids in understanding drug transport and distribution in tumors.
  • Recognizing distinct distribution patterns of small vs. large molecule drugs is crucial for multi-drug therapy design.
  • This transport analysis framework supports model development, experimental data interpretation, and therapeutic agent design.