Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

320
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...
320
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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

470
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...
470
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

94
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
94
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

73
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
73
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

467
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.
467
Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

195
The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
195

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response.

NPJ systems biology and applications·2025
Same author

Human nerve growth factor delivery to the retina: Quantitative methodology and mathematical modeling in preclinical settings.

PNAS nexus·2025
Same author

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning.

ACS nano·2025
Same author

Author Correction: Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion.

Communications engineering·2025
Same author

Development of a Multiscale Mechanistic Model for Predicting Tumor Response to Anti-miR-155.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-informed Machine Learning.

Research square·2025

Related Experiment Video

Updated: Apr 18, 2026

Modeling Chemotherapy Resistant Leukemia In Vitro
08:41

Modeling Chemotherapy Resistant Leukemia In Vitro

Published on: February 9, 2016

9.7K

Development of a diffusion-based mathematical model for predicting chemotherapy effects.

Zhihui Wang, Romica Kerketta, Yao-Li Chuang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Mathematical modeling aids chemotherapy by predicting outcomes and optimizing drug delivery. A larger blood vessel radius relative to drug penetration enhances tumor cell kill, improving treatment effectiveness for personalized medicine.

    More Related Videos

    Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
    10:23

    Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

    Published on: December 1, 2023

    1.2K
    Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
    10:24

    Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

    Published on: September 19, 2019

    6.9K

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    Modeling Chemotherapy Resistant Leukemia In Vitro
    08:41

    Modeling Chemotherapy Resistant Leukemia In Vitro

    Published on: February 9, 2016

    9.7K
    Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
    10:23

    Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

    Published on: December 1, 2023

    1.2K
    Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
    10:24

    Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

    Published on: September 19, 2019

    6.9K

    Area of Science:

    • Oncology
    • Mathematical Biology
    • Pharmacokinetics

    Background:

    • Drug resistance is a major challenge in chemotherapy.
    • Mathematical modeling offers a complementary approach to experimental and clinical studies.
    • Understanding drug transport is crucial for developing effective cancer treatments.

    Purpose of the Study:

    • To present a mathematical model for predicting chemotherapy outcomes.
    • To investigate the impact of key parameters on treatment efficacy.
    • To provide a framework for patient-specific chemotherapy treatment strategies.

    Main Methods:

    • Developed a time- and space-dependent mathematical model.
    • The model is based on diffusion theory.
    • Analyzed the influence of blood vessel characteristics and drug diffusion parameters.

    Main Results:

    • A higher ratio of blood vessel radius to drug diffusion penetration length leads to increased tumor cell kill.
    • This parameter significantly influences the fraction of tumor killed.
    • The model predicts a better overall treatment outcome with optimized parameters.

    Conclusions:

    • Mathematical modeling can predict chemotherapy outcomes.
    • Key parameters, like the vessel radius to diffusion length ratio, are critical for treatment success.
    • Clinical application of this model can personalize chemotherapy dosage and frequency.