Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Toxic Reactions: Overview

Toxic Reactions: Overview

When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
Toxicity falls into two primary categories: local and systemic.
Local toxicity appears at the exposure site, such as protein denaturation caused by caustic substances.
In contrast, systemic toxicity requires the toxic agent's absorption and distribution,...

Mutagenicity and Carcinogenicity

Mutagenicity and Carcinogenicity

Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...

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

Application of read-across to data-poor N-nitrosamines: N-nitroso-ulifloxacin, a case study.

Regulatory toxicology and pharmacology : RTP·2026

Same author

Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models.

Journal of chemical information and modeling·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

Search research articles

Related Experiment Video

Updated: Aug 9, 2025

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

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

Published on: August 28, 2019

Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods.

Craig M Zwickl¹, Jessica Graham², Robert Jolly³

¹Transendix LLC, Indianapolis, IN 46229, USA.

Computational Toxicology (Amsterdam, Netherlands)

|February 23, 2023

Summary

In silico models now reliably predict acute toxicity (LD50) for GHS classification. A new framework integrates these with in vitro data for comprehensive hazard assessment.

More Related Videos

Lethality Bioassay Using Artemia salina L.

Lethality Bioassay Using Artemia salina L.

Published on: October 11, 2022

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Related Experiment Videos

Last Updated: Aug 9, 2025

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

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

Published on: August 28, 2019

Lethality Bioassay Using Artemia salina L.

Lethality Bioassay Using Artemia salina L.

Published on: October 11, 2022

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Area of Science:

Toxicology
Computational Chemistry
Regulatory Science

Background:

Acute toxicity assessment is crucial for chemical safety across R&D, registration, and handling.
Current adoption of in silico models is limited by a lack of standardized guidance for analysis and documentation.
Existing methods often rely on LD50, prompting a need for more advanced, mechanism-based approaches.

Purpose of the Study:

To propose a framework for acute toxicity hazard assessment integrating in silico and in vitro/in vivo data.
To analyze the suitability of in silico methods for predicting in vivo outcomes like LD50.
To support the shift towards mechanism-based toxicity endpoints.

Main Methods:

Analysis of in silico methods for predicting LD50.
Overview of in vitro assay endpoints relevant to acute toxicity.
Development of a framework combining diverse data sources.
Application of weight-of-evidence considerations.

Main Results:

In silico predictions are well-suited for reliable LD50 assessment and GHS classification.
In vitro data and mechanistic understanding enable a move beyond LD50-centric evaluations.
The proposed framework effectively integrates various data types for hazard assessment.

Conclusions:

In silico models are valuable tools for acute toxicity assessment, particularly for GHS classification.
A comprehensive framework combining in silico, in vitro, and in vivo data, with expert review, enhances hazard assessment reliability.
The future of acute toxicity testing involves mechanism-based endpoints supported by computational and in vitro methods.