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

Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

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...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...

You might also read

Related Articles

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

Sort by

Same author

Transitional initiatives for advancing the phasing out of the use of animals for drug and chemical safety testing: The IHI VICT3R project for reducing the use of animals by implementing virtual control groups.

NAM journal·2026

Same author

TOXTRUST: a tool leveraging the Dempster-Shafer Theory for robust integration of NAM results in decision-making considering uncertainty.

NAM journal·2026

Same author

Development of Acute-to-Chronic Ratios (ACRs) to Support Ecotoxicity Prediction for Surfactants.

Environmental toxicology and chemistry·2026

Same author

PM<sub>2.5</sub> air pollution inequities in the US by sector and state: Past trajectories and future directions.

Science advances·2026

Same author

Integrating Genetic Variants and Expression Profiles of Pharmacogenes to Investigate Resistance to Antidepressant Treatment.

Medicina (Kaunas, Lithuania)·2026

Same author

Towards a decentralized future for open-science databases.

Nature genetics·2026

Search research articles

Related Experiment Video

Updated: Aug 3, 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

Making in silico predictive models for toxicology FAIR.

Mark T D Cronin¹, Samuel J Belfield¹, Katharine A Briggs²

¹School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK.

Regulatory Toxicology and Pharmacology : RTP

|April 10, 2023

Summary

This summary is machine-generated.

Applying FAIR principles to in silico toxicology models enhances their reproducibility and regulatory acceptance. This ensures reliable predictions for chemical risk assessment and fills crucial data gaps.

Keywords:

FAIR In silico model New approach methodologies Next generation risk assessment PBK QSAR Toxicology

More Related Videos

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

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Related Experiment Videos

Last Updated: Aug 3, 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

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

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Area of Science:

Toxicology
Computational Chemistry
Regulatory Science

Background:

In silico predictive models, such as quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) models, are crucial for predicting chemical properties and toxicological effects.
These models are essential for addressing data gaps in chemical risk assessment.
Ensuring the availability and reproducibility of these in silico toxicology models is a growing necessity.

Purpose of the Study:

To describe the application of FAIR (Findable, Accessible, Interoperable, Reusable) principles to in silico predictive models for toxicology.
To investigate how FAIR principles can improve the regulatory acceptance of predictions from these models.

Main Methods:

Development of eighteen specific principles derived from the FAIR data sharing framework.
Application of these principles to various aspects of in silico toxicology models.

Main Results:

The study outlines a comprehensive set of FAIR principles tailored for in silico toxicology models.
These principles address findability, accessibility, interoperability, and reusability of predictive models and their outputs.

Conclusions:

FAIRification of in silico toxicology models is expected to increase their utilization in regulatory processes.
Adherence to FAIR principles will enhance the trustworthiness and acceptance of computational toxicology predictions.