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

Obedience01:08

Obedience

35.7K
According to obedience research, we may harm others under the forceful pressures of an authority figure (Milgram, 1974). How about if the inappropriate orders were delivered with less force? The increasing interdependence between nurses and physicians compelled Hofling and his colleagues to explore nurses’ reactions to a potentially harmful medical request made by the perceived authority figure, the doctor (Hofling, Brotzman, Dalrymple, Graves, & Pierce, 1966). In this situation,...
35.7K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K

You might also read

Related Articles

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

Sort by
Same author

SARM1 base-exchange inhibitors induce SARM1 activation and neurodegeneration at low doses.

npj drug discovery·2026
Same author

Exploring the chemical space of pharmaceutical extractables and leachables.

Regulatory toxicology and pharmacology : RTP·2026
Same author

Transforming animal study toxicology reports into structured, harmonized data using large language models.

Archives of toxicology·2026
Same author

Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.

Archives of toxicology·2026
Same author

Ames concordance with the in vivo transgenic rodent (TGR) gene mutation assay for NDSRIs and relative in vivo TGR potency with nitrosamines with robust dose-response carcinogenicity data.

Regulatory toxicology and pharmacology : RTP·2026
Same author

Bridging science and curriculum: preparing future leaders in computational toxicology.

Frontiers in toxicology·2026
Same journal

The NTP Chronic Inhalation Study Does Not Support an Inherent Lung Cancer Hazard of Talc: Implications of Lung Particle Overload and Maximum Tolerated Dose.

Regulatory toxicology and pharmacology : RTP·2026
Same journal

Consideration of Carcinogenicity and Mode of Action Information by an Independent Expert Panel to Support Derivation of No-Significant-Risk-Level Values for Vinyl Acetate Monomer.

Regulatory toxicology and pharmacology : RTP·2026
Same journal

Which carcinogenicity study should I use? Automated identification of reliable studies.

Regulatory toxicology and pharmacology : RTP·2026
Same journal

Adoption of artificial intelligence in drug review across the lifecycle: Transformation of regulatory decision-making.

Regulatory toxicology and pharmacology : RTP·2026
Same journal

Cardiovascular outcomes following intrauterine and lactational exposure to cyantraniliprole in male Wistar rats.

Regulatory toxicology and pharmacology : RTP·2026
Same journal

Pesticide residue and mycotoxin occurrence in apples and their impact on human health in Morocco.

Regulatory toxicology and pharmacology : RTP·2026
See all related articles

Related Experiment Video

Updated: Feb 11, 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

14.7K

In silico toxicology protocols.

Glenn J Myatt1, Ernst Ahlberg2, Yumi Akahori3

  • 1Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA.

Regulatory Toxicology and Pharmacology : RTP
|April 22, 2018
PubMed
Summary
This summary is machine-generated.

Standardized computational toxicology (in silico) protocols are needed for consistent and reproducible toxicity predictions. This study outlines information for developing these novel in silico toxicology (IST) protocols to enhance hazard identification.

Keywords:
Computational toxicologyExpert alertExpert reviewIn silicoIn silico toxicologyPredictive toxicologyQSAR

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.3K
An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
08:59

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment

Published on: December 3, 2020

8.7K

Related Experiment Videos

Last Updated: Feb 11, 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

14.7K
In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.3K
An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
08:59

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment

Published on: December 3, 2020

8.7K

Area of Science:

  • Toxicology
  • Computational Science
  • Regulatory Science

Background:

  • In silico toxicology approaches are increasingly applied across industries.
  • A lack of standardized protocols hinders consistent and reproducible toxicity predictions.
  • Existing methods require harmonization for wider regulatory acceptance.

Purpose of the Study:

  • To survey applications of in silico toxicology.
  • To articulate the necessary information for developing standardized in silico toxicology (IST) protocols.
  • To propose a novel approach for assessing the reliability and confidence of in silico predictions.

Main Methods:

  • Review of current in silico toxicology applications and needs.
  • Development of a framework for IST protocol creation.
  • Integration of in silico predictions with experimental data for reliability assessment.

Main Results:

  • Identification of key toxicological endpoints requiring standardized protocols (e.g., genetic toxicity, carcinogenicity).
  • A general outline for developing IST protocols is provided.
  • A novel method for evaluating the reliability and confidence of in silico predictions is presented.

Conclusions:

  • Standardized IST protocols are crucial for consistent, reproducible, and well-documented toxicological assessments.
  • These protocols will facilitate broader adoption and acceptance of in silico methods in regulatory settings.
  • The proposed approach enhances the reliability and confidence in computational toxicology assessments.