Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Pharmacokinetic–Pharmacodynamic Relationship: Problems

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...

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...

Pharmacodynamic Models: Overview

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

Modeling with Differential Equations

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

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

Sort by

Same author

Genetic variation and historical breeding patterns in common bean (Phaseolus vulgaris L.) affect fermentation patterns by the human gut microbiome.

Communications biology·2025

Same author

The association of oxaliplatin-induced peripheral neuropathy and falls with intercycle delays and early discontinuation of chemotherapy in older colorectal cancer patients.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2025

Same author

Traumatic brain injury heterogeneity affects cell death and autophagy.

Experimental brain research·2024

Same author

The associations of oxaliplatin-induced peripheral neuropathy, sociodemographic characteristics, and clinical characteristics with time to fall in older adults with colorectal cancer.

American journal of epidemiology·2024

Same author

The Association of Supplemental Nutrition Assistance Program Participation and Food Insufficiency among Households with Children in the United States during COVID-19.

The Journal of nutrition·2023

Same author

The association between sociodemographic, clinical, and potentially preventive therapies with oxaliplatin-induced peripheral neuropathy in colorectal cancer patients.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2023

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Published on: December 13, 2012

Semiparametric mixed-effects analysis of PK/PD models using differential equations.

Yi Wang¹, Kent M Eskridge, Shunpu Zhang

¹Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583-0963, USA.

Journal of Pharmacokinetics and Pharmacodynamics

|September 11, 2008

Summary

This study introduces a novel semiparametric modeling approach for population pharmacokinetic/pharmacodynamic (PK/PD) analysis, enhancing accuracy by addressing structural model misspecification using penalized splines.

More Related Videos

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jun 28, 2026

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Published on: December 13, 2012

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Pharmacometrics
Statistical Modeling
Longitudinal Data Analysis

Background:

Population pharmacokinetic/pharmacodynamic (PK/PD) analyses often face challenges with structural model misspecification.
Existing semiparametric nonlinear mixed-effects models offer some flexibility but can be further improved.

Purpose of the Study:

To develop a new semiparametric modeling approach to address structural model misspecification in population PK/PD analysis.
To enhance the reliability and flexibility of PK/PD models by incorporating nonparametric functions within ordinary differential equations (ODEs).

Main Methods:

Utilized ordinary differential equations (ODEs) with a nonparametric function B(t) estimated via penalized splines.
Integrated this nonparametric component into a nonlinear mixed-effects modeling framework for population analysis.
Applied the method to cefamandole data and assessed performance through simulations.

Main Results:

The proposed method allows for feasible identification of structural model misspecification by quantifying model uncertainty.
Demonstrated flexibility in accommodating potential structural model deficiencies.
Successfully illustrated the approach with real-world cefamandole data.

Conclusions:

The developed semiparametric modeling approach offers a robust solution for structural model misspecification in population PK/PD analysis.
This method enhances model interpretability and predictive performance by flexibly accounting for model deficiencies.