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

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Toxicokinetics: Overview01:21

Toxicokinetics: Overview

Studies that assess how a drug is absorbed, distributed, metabolized, and excreted (ADME) at toxic doses are termed toxicokinetics. Understanding toxicokinetics helps predict adverse drug reactions (ADRs) and manage toxicity in humans.Toxicokinetics differs from pharmacokinetics mainly in the dose levels studied, with toxicokinetics focusing on higher toxic doses. The kinetics at these levels can be non-linear due to altered physiological processes. Toxicodynamics examines the relationship...
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).
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...
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

Scientific Opinion on Benzophenone - 4 (CAS No. 4065-45-6, EC No. 223-772-2) used in cosmetics products - SCCS/1660/23.

NAM journal·2026
Same author

SCCS opinion on biphenyl-2-ol and sodium 2-biphenylolate used in cosmetic products (CAS/EC No. 90-43-7/201-993-5 and 132-27-4/205-055-6)- SCCS/1669/24.

NAM journal·2026
Same author

In silico prediction of Ames mutagenicity for organosilicon compounds: Exploring and enhancing chemical space boundaries.

Regulatory toxicology and pharmacology : RTP·2026
Same author

Simulation of fish chronic toxicity using the Las Vegas algorithm and the vector of ideality of correlation.

Environmental toxicology and chemistry·2026
Same author

QSAR in the AI Era: Reflections for Advancing Chemical Safety Assessment.

Chemical research in toxicology·2026
Same author

A framework for chemical hazard assessments under 'Safe and Sustainable by Design' using multiple in silico tools.

Integrated environmental assessment and management·2026
Same journal

New approach methodologies as first tier in an integrated approach to testing and assessment for non-genotoxic carcinogens.

ALTEX·2026
Same journal

Assessing the FDA's Year One Progress Report on Reducing Animal Testing.

ALTEX·2026
Same journal

Europe's roadmap finally arrives: Long on rigor, short on a clock.

ALTEX·2026
Same journal

Towards advanced in vitro models of testicular steroidogenesis for endocrine disruption testing.

ALTEX·2026
Same journal

Evaluation of EndoSens: A reliable skin sensitization assessment model.

ALTEX·2026
Same journal

Serum-free in vitro assessment of receptor-mediated endocrine activity including Phase-1 metabolism.

ALTEX·2026
See all related articles

Related Experiment Video

Updated: May 15, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Using toxicological evidence from QSAR models in practice.

Emilio Benfenati1, Simon Pardoe, Todd Martin

  • 1Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy.

ALTEX
|January 23, 2013
PubMed
Summary
This summary is machine-generated.

Quantitative structure-activity relationship (QSAR) models offer supporting data to enhance toxicity prediction reliability. This study demonstrates practical use of QSAR documentation with expert toxicologist review for bioconcentration factor (BCF) assessments.

More Related Videos

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)
11:38

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)

Published on: May 10, 2016

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Related Experiment Videos

Last Updated: May 15, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)
11:38

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)

Published on: May 10, 2016

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Area of Science:

  • Computational toxicology
  • Environmental chemistry
  • Predictive modeling

Background:

  • Quantitative structure-activity relationship (QSAR) models generate toxicological predictions.
  • Supporting documentation from QSAR models aids in evaluating prediction reliability.
  • Effective use of this information is crucial for toxicologists.

Purpose of the Study:

  • To explore the practical application of supporting documentation from QSAR platforms.
  • To assess the reliability of bioconcentration factor (BCF) predictions for three compounds.
  • To evaluate toxicologist judgments on QSAR reliability using provided evidence.

Main Methods:

  • Evaluation of supporting documentation from EPISuite, T.E.S.T., and VEGA QSAR platforms.
  • Case studies of three compounds with varying reliability challenges for BCF assessment.
  • Analysis of reliability judgments from 28 toxicologists using QSAR supporting information.

Main Results:

  • Demonstrated how supporting documentation aids in understanding chemical properties and prediction reliability.
  • Highlighted the distinct challenges in recognizing high reliability, complex evidence, and uncertainty in QSAR predictions.
  • Showcased variability in toxicologist judgments based on the interpretation of supporting data.

Conclusions:

  • QSAR models are valuable tools for reducing or replacing in vivo testing in toxicology.
  • Scientific expertise and rigorous interpretation are essential for the effective use of QSAR predictions.
  • Supporting documentation significantly enhances the utility and trustworthiness of QSAR toxicological assessments.