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 Experiment Video

Updated: Jun 26, 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

In Silico Prediction of Chronic Oral Reference Doses for PIANO Target Analytes.

Paul D Rockswold1, Gregory J Joseph1,2, Elaine A Merrill3

  • 1Defense Centers for Public Health, Portsmouth, VA 23708, USA.

Toxics
|June 25, 2026
PubMed
Summary

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...

You might also read

Related Articles

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

Sort by
Same author

Development of Preliminary Candidate Surface Guidelines for Air Force-Relevant Dermal Sensitizers Using New Approach Methodologies.

Toxics·2025
See all related articles

This study developed Quantitative Structure-Activity Relationship (QSAR) models to predict human health risks from drinking water contaminants. The new approach accurately estimates toxicity values for compounds lacking data, reducing uncertainty in risk assessments.

Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Computational Chemistry

Background:

  • Assessing human health risks from drinking water contaminants is difficult due to missing toxicity data.
  • The PIANO (Paraffin, Isoparaffin, Aromatic, Naphthene, and Olefin) method identifies hundreds of compounds in jet fuel, but few have established reference doses (RfDs).

Purpose of the Study:

  • To predict oral reference doses (RfDs) for 290 PIANO compounds lacking toxicity data using Quantitative Structure-Activity Relationship (QSAR) models.
  • To develop a novel alternative methodology (NAM) for reducing uncertainty in human health risk assessment.

Main Methods:

  • Developed five QSAR models using stepwise linear regression with 2-dimensional molecular descriptors and published toxicity data.
  • Trained models on a master dataset of 1113 compounds, including 43 PIANO compounds with known RfDs.
Keywords:
chronic oral reference doses (RfDs)hydrocarbonsmolecular descriptorspetroleum compoundsquantitative structure–activity relationship (QSAR) modelsstepwise multivariable linear regression

Related Experiment Videos

Last Updated: Jun 26, 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

  • Predicted RfDs using the geometric means of four high-quality QSAR model results for compounds without toxicity information.

    • Main Results:

    • Successfully predicted RfDs for 290 PIANO compounds.
    • For compounds with known RfDs, 79% of predictions were within an 8-fold margin of published values, demonstrating model accuracy.
    • The developed QSAR models showed high agreement with existing toxicity data, validating the approach.

    Conclusions:

    • The QSAR-based approach provides a reliable method for estimating toxicity values for compounds lacking data.
    • This new alternative methodology (NAM) can significantly reduce uncertainty in human health risk assessments.
    • The findings support regulatory decision-making and improve the characterization of health risks associated with complex mixtures in drinking water.