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

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: 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...
Dose-Response Relationship: Potency and Efficacy01:22

Dose-Response Relationship: Potency and Efficacy

The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it produces...
Pharmacokinetic–Pharmacodynamic Relationship: Dose to Pharmacological Effect01:28

Pharmacokinetic–Pharmacodynamic Relationship: Dose to Pharmacological Effect

A drug’s dosage and pharmacokinetic properties determine how quickly it acts, how intense its effects are, and how long it lasts. Higher doses increase drug concentration at receptor sites, producing a hyperbolic curve when pharmacologic response is plotted against drug dose. Converting this scale to a log-linear format results in a sigmoidal curve, better representing dose–response relationships.For drugs following a one-compartment model, the pharmacologic response is directly proportional to...
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...

You might also read

Related Articles

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

Sort by
Same author

<i>HK2</i> knockdown is associated with context-dependent inflammatory and angiogenesis-related transcriptional programmes in glioblastoma cells.

Frontiers in immunology·2026
Same author

Language models do not yet achieve explanatory adequacy because language is more than incremental prediction.

The Behavioral and brain sciences·2026
Same author

Children's narrow learning bottleneck accelerates the emergence of statistical properties of language.

Cognition·2026
Same author

Using objective measures of physical activity, sleep, and breathing for disease profiling of patients with systemic lupus erythematosus and Sjögren's disease.

Frontiers in digital health·2026
Same author

Neurocognitive Aging Following Acute Illness: Pathobiology and a Framework for Developing Neurotherapeutic Agents.

Brain and behavior·2026
Same author

Cultural Transmission Promotes the Emergence of Statistical Properties That Support Language Learning.

Cognitive science·2025

Related Experiment Video

Updated: Jun 25, 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

Adaptive modelling of dose-response relationships using smoothing splines.

Simon Kirby1, Peter Colman, Mark Morris

  • 1Statistics, Pfizer Global Research and Development, Sandwich, Kent, UK. simon.kirby@pfizer.com

Pharmaceutical Statistics
|February 11, 2009
PubMed
Summary

Smoothing splines offer a flexible, adaptive approach to modeling dose-response relationships. This method, using generalized cross-validation and bootstrap percentiles, shows comparable results to existing adaptive methods for dose-ranging studies.

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Related Experiment Videos

Last Updated: Jun 25, 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

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Biostatistics
  • Pharmacometrics
  • Clinical Trial Design

Background:

  • Dose-response modeling is crucial for drug development.
  • Adaptive methods allow for flexible adjustments during clinical trials.
  • Existing methods like GADA provide a benchmark for adaptive dose-ranging.

Purpose of the Study:

  • To evaluate smoothing splines for adaptive dose-response modeling.
  • To compare smoothing splines with adaptive analysis of variance.
  • To assess the utility of smoothing splines in identifying optimal doses.

Main Methods:

  • Nonparametric smoothing splines were employed for dose-response modeling.
  • Generalized cross-validation determined the degree of smoothness.
  • Bootstrap percentiles guided dose selection after interim analysis.
  • Simulations compared smoothing splines to adaptive ANOVA and GADA.

Main Results:

  • Smoothing spline adaptive modeling yielded results comparable to the GADA procedure.
  • The approach demonstrated flexibility in adapting dose-ranging studies.
  • Performance was evaluated against established adaptive methods using simulated scenarios.

Conclusions:

  • Smoothing splines provide a viable, adaptive method for dose-response modeling.
  • This approach aids in detecting dose-response relationships and identifying clinically relevant doses.
  • The methods are beneficial for adaptive dose-ranging study designs.