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

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
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...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

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...
Pharmacokinetic–Pharmacodynamic Relationship: Model Components01:14

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: Overview01:27

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...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

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...

You might also read

Related Articles

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

Sort by
Same author

MBMA Bridging Models as a Tool for Exploration of Clinical Endpoints in Unstudied Indications.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Population pharmacokinetics and pharmacodynamics of intraperitoneal aerosolized nanoparticle albumin-bound paclitaxel (nab-PTX) and metabolites.

British journal of clinical pharmacology·2026
Same author

A Phase 1 Bioequivalence Study to Assess the Pharmacokinetics, Safety and Tolerability of Guselkumab After a Single-Dose Administration via Two Subcutaneous Injection Devices in Healthy Volunteers.

Clinical pharmacology in drug development·2026
Same author

Second-Generation AURKA-Targeting PROTACs: Structural Optimization toward in Vivo Degradation in Neuroblastoma.

Journal of medicinal chemistry·2025
Same author

A Model-Based Meta-Analysis Framework Quantifying Drivers of Placebo Response in Atopic Dermatitis Trials.

CPT: pharmacometrics & systems pharmacology·2025
Same author

Building Sub-Saharan African PBPK Populations Reveals Critical Data Gaps: A Case Study on Aflatoxin B1.

Toxins·2025

Related Experiment Video

Updated: Jun 3, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Modeling delayed drug effect using discrete-time nonlinear autoregressive models: a connection with indirect response

Xu Steven Xu1, Hui Wang, An Vermeulen

  • 1Clinical Pharmacology, Advanced PK-PD Modeling and Simulation, Johnson and Johnson Pharmaceutical R&D, Raritan, NJ, USA. sxu26@its.jnj.com

Journal of Pharmacokinetics and Pharmacodynamics
|April 1, 2011
PubMed
Summary

This study introduces nonlinear autoregressive (AR) models as an alternative to indirect response (IDR) models for pharmacodynamic (PD) analysis. These AR models effectively capture delayed responses, especially with intensive sampling, offering a comparable approach to IDR modeling.

More Related Videos

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

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

Published on: June 5, 2017

Related Experiment Videos

Last Updated: Jun 3, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

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

Published on: June 5, 2017

Area of Science:

  • Pharmacometrics
  • Pharmacokinetics/Pharmacodynamics (PK/PD) modeling
  • Systems Biology

Background:

  • Indirect response (IDR) models are standard for pharmacodynamic (PD) analysis, especially for delayed or hysteretic drug effects.
  • Existing IDR models may have limitations in describing complex temporal dynamics.
  • Alternative modeling strategies are needed for robust PK/PD analysis.

Purpose of the Study:

  • To propose and evaluate a class of nonlinear discrete-time autoregressive (AR) models as an alternative to IDR models for analyzing delayed response data.
  • To mathematically derive the relationship between IDR and nonlinear AR models.
  • To assess the performance of AR models in simulating and estimating parameters for PK/PD data.

Main Methods:

  • Developed nonlinear discrete-time autoregressive (AR) models incorporating drug concentration as a covariate.
  • Performed mathematical derivations to establish the connection between AR and IDR models.
  • Conducted simulations to compare temporal response profiles and parameter estimates between AR and IDR models under varying sampling densities.
  • Applied mixed-effects modeling to analyze simulated longitudinal PK/PD data using the proposed AR models.

Main Results:

  • Nonlinear AR models were shown to approximate IDR models, particularly with small time intervals between data points.
  • Simulations demonstrated comparable temporal response profiles between IDR and AR models.
  • AR model parameter estimates became more comparable to IDR estimates with decreased time intervals (increased sampling intensity).
  • The proposed nonlinear AR models successfully described simulated longitudinal PK/PD data within a mixed-effects framework.

Conclusions:

  • Nonlinear discrete-time AR models offer a viable alternative to IDR models for analyzing delayed pharmacodynamic responses.
  • The AR modeling approach shows promise, especially under intensive sampling conditions.
  • Further research is recommended to extend these AR models for irregular and sparse PK/PD data analysis.