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: 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 Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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.
Dose-Response Relationship: Selectivity and Specificity01:25

Dose-Response Relationship: Selectivity and Specificity

Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and β2-adrenergic receptors...
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...
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Dose Response Curve: Conventional Versus Nonmonotonic01:21

Dose Response Curve: Conventional Versus Nonmonotonic

The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response relationships...

You might also read

Related Articles

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

Sort by
Same author

Causal Directed Acyclic Graphs to Mitigate Confounding Bias in Exposure-Response Analyses.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Latent Variable Indirect Response Modeling of Cendakimab Exposure-Response for Longitudinal Dysphagia Days Using a Combined Uniform-Binomial Likelihood Framework.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Integrating MBMA and QSP to Identify Key Covariates and Predict Treatment Outcomes in Relapsed/Refractory Multiple Myeloma.

CPT: pharmacometrics & systems pharmacology·2025
Same author

Company Sponsored Platform Trials: Recommendations and Lessons Learned from Cross-Industry Interviews.

Therapeutic innovation & regulatory science·2025
Same author

Novel endpoints based on tumor size ratio to support early clinical decision-making in oncology drug-development.

Journal of pharmacokinetics and pharmacodynamics·2024
Same author

Visual predictive check of longitudinal models and dropout.

Journal of pharmacokinetics and pharmacodynamics·2024

Related Experiment Video

Updated: Jul 17, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Estimating the predictive quality of dose-response after model selection.

Chuanpu Hu1, Yingwen Dong

  • 1Biostatistics, Sanofi-aventis, 9 Great Valley Parkway, Malvern, PA 19355, USA. Chuanpu.Hu@sanofi-aventis.com

Statistics in Medicine
|January 9, 2007
PubMed
Summary

Data perturbation is a new method that accurately estimates prediction errors in dose-response modeling. This approach helps address uncertainties from model selection, improving drug dose selection.

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Related Experiment Videos

Last Updated: Jul 17, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Area of Science:

  • Pharmacometrics
  • Statistical modeling
  • Drug development

Background:

  • Dose-response prediction is crucial for selecting optimal drug doses.
  • Model selection introduces uncertainty in predictions, complicating accurate assessment.
  • Existing methods struggle to account for model selection uncertainties.

Purpose of the Study:

  • To evaluate the performance of data perturbation in estimating standard errors and prediction errors in dose-response modeling.
  • To assess the impact of model selection on estimation bias.
  • To explore how data perturbation can improve dose selection in drug development.

Main Methods:

  • Simulation studies were conducted to assess data perturbation.
  • Performance was evaluated for estimating standard error of parameter estimates and prediction errors.
  • Influence of model selection on estimation bias was analyzed.

Main Results:

  • Data perturbation provided excellent estimates of prediction errors.
  • Accurate standard error estimates sometimes required large Monte Carlo sample sizes.
  • Model selection significantly influences estimation bias, guiding candidate model choices.

Conclusions:

  • Data perturbation is a valuable tool for quantifying uncertainties in dose-response predictions.
  • This method has the potential to enhance the accuracy of dose selection in drug development.
  • Understanding model selection's influence aids in choosing models for robust predictions.