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

Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and the T...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Noncompartmental Analysis: Mean Residence Time

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...

Estimation of k and VD of Aminoglycosides

Estimation of k and V_D of Aminoglycosides

Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...

Pharmacodynamic Models: Linear Concentration–Effect Model

Pharmacodynamic Models: Linear Concentration–Effect Model

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 drug...

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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...

You might also read

Related Articles

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

Sort by

Same author

Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy.

Journal of pharmacokinetics and pharmacodynamics·2023

Same author

The application of extreme value theory to pharmacometrics.

Journal of pharmacokinetics and pharmacodynamics·2020

Same author

Editorial to the themed issue on application of pharmacometrics to the development of drugs for rare diseases.

Journal of pharmacokinetics and pharmacodynamics·2019

Same author

Editorial to themed issue: Recent advances in cardiovascular pharmacokinetic-pharmacodynamic modeling and simulation.

Journal of pharmacokinetics and pharmacodynamics·2018

Same author

Editorial to the themed issue on focus on infectious disease.

Journal of pharmacokinetics and pharmacodynamics·2017

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

Estimation of QT interval prolongation through model-averaging.

Peter L Bonate¹

¹Astellas, 1 Astellas Way, N2.184, Northbrook, IL, 60062, USA. peter.bonate@astellas.com.

Journal of Pharmacokinetics and Pharmacodynamics

|April 20, 2017

Summary

The best model approach for concentration-QT interval analysis is reliable, but model averaging offers greater validity. Both methods showed similar confidence interval coverage in simulations, with no significant performance differences.

Keywords:

AIC AICc BIC Concentration–response E14 Linear mixed effect models Modeling TQT

More Related Videos

Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes (hPSC-CMs) Using Multi-electrode Arrays (MEAs)

Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes (hPSC-CMs) Using Multi-electrode Arrays (MEAs)

Published on: May 12, 2017

In Vivo Surface Electrocardiography for Adult Zebrafish

In Vivo Surface Electrocardiography for Adult Zebrafish

Published on: August 1, 2019

Related Experiment Videos

Last Updated: Jun 28, 2026

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes (hPSC-CMs) Using Multi-electrode Arrays (MEAs)

Electrophysiological Analysis of human Pluripotent Stem Cell-derived Cardiomyocytes (hPSC-CMs) Using Multi-electrode Arrays (MEAs)

Published on: May 12, 2017

In Vivo Surface Electrocardiography for Adult Zebrafish

In Vivo Surface Electrocardiography for Adult Zebrafish

Published on: August 1, 2019

Area of Science:

Pharmacology
Biostatistics
Clinical Trial Design

Background:

Current concentration-QT interval analysis relies on a best model approach, neglecting model uncertainty.
This omission can lead to overly optimistic and narrow confidence intervals.
Model averaging, weighting candidate models by Akaike weights, theoretically offers more realistic estimates.

Purpose of the Study:

To evaluate the narrowness of confidence intervals in concentration-QT prolongation analysis.
To compare the performance of model averaging versus the best model approach using Monte Carlo simulations.
To assess the impact of different information criteria (AIC, AICc, BIC) on confidence interval coverage.

Main Methods:

Conducted Monte Carlo simulations for single ascending dose and thorough QT trial designs.
Compared concentration-QT interval analysis using a best model approach versus model averaging.
Utilized Akaike Information Criterion (AIC), AICc, and Bayesian Information Criterion (BIC) for model selection and weighting.

Main Results:

Model averaging performed comparably to the best model approach across most simulated conditions.
No significant numerical advantage was found for model averaging over the best model approach.
Confidence interval coverage showed no difference between methods when using AIC or AICc; BIC occasionally resulted in narrower intervals.

Conclusions:

The best model approach can be confidently used for concentration-QT modeling.
Model averaging provides enhanced face validity and may be beneficial when model misspecification is suspected.
Both approaches are statistically sound, but model averaging may be more easily understood by non-technical stakeholders.