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: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
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...
Dosage Regimens: Designs and Approaches01:28

Dosage Regimens: Designs and Approaches

Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
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...
Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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

Informative Futility Rules Based on Conditional Assurance.

Statistics in medicine·2026
Same author

Joint modeling of longitudinal endpoints and its applications to trial planning, monitoring and analysis.

Journal of biopharmaceutical statistics·2025
Same author

Bayesian efficient safety monitoring: a simple and well-performing framework to continuous safety monitoring of adverse events in randomized clinical trials.

Journal of biopharmaceutical statistics·2025
Same author

Views on Emerging Issues Pertaining to Data Monitoring Committees for Adaptive Trials.

Therapeutic innovation & regulatory science·2018
Same author

Adaptive Clinical Trials: Overview of Phase III Designs and Challenges.

Therapeutic innovation & regulatory science·2018
Same author

Adaptive Design Applied to Identification of the Minimum Effective Dose in Schizophrenia: Simulations of Scientific and Commercial Value.

Therapeutic innovation & regulatory science·2018

Related Experiment Video

Updated: Jul 10, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Adaptive designs for dose-finding studies based on sigmoid Emax model.

Vladimir Dragalin1, Francis Hsuan, S Krishna Padmanabhan

  • 1Wyeth Research, Collegeville, Pennsylvania 19426, USA.

Journal of Biopharmaceutical Statistics
|November 21, 2007
PubMed
Summary

This study introduces an adaptive dose-finding method for continuous efficacy endpoints in clinical trials. It uses a logistic model and optimal designs for efficient treatment selection.

More Related Videos

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

Related Experiment Videos

Last Updated: Jul 10, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

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:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Dose-finding studies are crucial for determining optimal drug dosages.
  • Continuous efficacy endpoints require specialized statistical methods.
  • Existing methods may lack adaptability in dose selection.

Purpose of the Study:

  • To develop an adaptive procedure for dose-finding in clinical trials.
  • To accommodate continuous efficacy endpoints.
  • To enhance the efficiency of optimal design selection.

Main Methods:

  • Modeling the continuous efficacy endpoint mean using a four-parameter logistic function.
  • Assuming normal or gamma distribution for the efficacy endpoint.
  • Deriving analytic formulae for the Fisher information matrix.
  • Constructing locally and adaptive D-optimal designs.

Main Results:

  • The proposed adaptive procedure allows for flexible dose-finding.
  • The methodology accounts for various variance assumptions (fixed, known/unknown).
  • Analytic Fisher information matrix formulae facilitate optimal design construction.

Conclusions:

  • The adaptive procedure offers an efficient approach for dose-finding with continuous endpoints.
  • The developed optimal designs can improve the precision of dose selection in clinical trials.
  • This method provides a robust framework for adaptive clinical trial design.