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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

150
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.
150
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

127
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...
127
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

387
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
387
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

126
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
126
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
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...
87
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.1K
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...
1.1K

You might also read

Related Articles

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

Sort by
Same author

MCSimMod: An R Package for Working with Ordinary Differential Equation Models Encoded in the MCSim Model Specification Language.

Journal of open source software·2026
Same author

Modeling BK Virus Infection in Renal Transplant Recipients.

Viruses·2025
Same author

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models.

Toxicological sciences : an official journal of the Society of Toxicology·2024
Same author

A Generic Pharmacokinetic Model for Quantifying Mother-to-Offspring Transfer of Lipophilic Persistent Environmental Chemicals.

Toxicological sciences : an official journal of the Society of Toxicology·2022
Same author

Uncertainty estimation strategies for quantitative non-targeted analysis.

Analytical and bioanalytical chemistry·2022
Same author

A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances.

Toxicological sciences : an official journal of the Society of Toxicology·2021
Same journal

MEHP exposure damages the basement membrane by suppressing the expression of its constituent proteins in peritubular myoid cells of the rat testis.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

Words Matter in Toxicology and Risk Assessment as They Impact Risk Communication.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

Tetrabromobisphenol S (TBBPS)-induced developmental toxicity in zebrafish embryos: impacts on cytoskeletal proteins during gastrulation.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

Hepatic inflammation after exposure to a mixture of low-dose arsenic and cadmium in a murine model of fatty liver disease.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

Pulmonary vascular remodeling in rats following methamphetamine self-administration: toward a model of methamphetamine-associated pulmonary arterial hypertension.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

UVB light activates an S phase-dependent DNA damage response in human keratinocytes independent of oxidative stress.

Toxicological sciences : an official journal of the Society of Toxicology·2026
See all related articles
  1. Home
  3. Characterizing Variability And Uncertainty For Parameter Subset Selection In Pbpk Models.
  1. Home
  3. Characterizing Variability And Uncertainty For Parameter Subset Selection In Pbpk Models.

Related Experiment Video

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Characterizing variability and uncertainty for parameter subset selection in PBPK models.

Celia M Schacht1, Dustin F Kapraun1, Annabel E Meade2

  • 1Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States.

Toxicological Sciences : an Official Journal of the Society of Toxicology
|July 30, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Physiologically based pharmacokinetic (PBPK) models estimate human equivalent doses (HEDs). Global sensitivity analysis (GSA) helps identify key parameters influencing extreme HED percentiles for improved risk assessment.

Keywords:
human risk assessmentmathematical modelingphysiologically based pharmacokineticssensitivity analysisuncertainty quantification

More Related Videos

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K

Related Experiment Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.0K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K

Area of Science:

  • Pharmacokinetics and toxicological modeling
  • Computational toxicology and risk assessment
  • Environmental health sciences

Background:

  • Physiologically based pharmacokinetic (PBPK) models simulate chemical absorption, distribution, metabolism, and excretion.
  • Probabilistic PBPK models generate human equivalent dose (HED) distributions using Monte Carlo sampling.
  • Extreme HED percentiles are critical for evaluating risks in sensitive populations.

Purpose of the Study:

  • To develop methods for identifying influential parameters in PBPK models affecting extreme HED percentiles.
  • To assess the impact of parameter distribution uncertainty on HED estimates.
  • To enhance the reliability of risk evaluations using PBPK modeling.

Main Methods:

  • Global sensitivity analysis (GSA) was applied to published PBPK models for dichloromethane and chloroform.
  • Analysis included inhalation and oral exposure scenarios across different internal target levels.
  • A novel method was employed to evaluate the sensitivity of extreme HED percentiles to parameter distributions.

    • Main Results:

    • GSA identified parameter subsets that significantly influence the 1st and 99th HED percentiles.
    • The specific influential parameters varied across different models and exposure conditions.
    • Characterizing uncertainty in influential parameters can increase confidence in extreme HED percentile estimates.

    Conclusions:

    • Global sensitivity analysis is effective in pinpointing key PBPK model parameters for extreme HED percentiles.
    • Precise distributional data for parameters identified by GSA can improve the accuracy of risk assessments.
    • Refining PBPK models with sensitivity-informed parameterization enhances confidence in evaluating chemical exposure risks.