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Related Concept Videos

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

63
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...
63

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Read-Across Structural Analysis of PFAS Acute Oral Toxicity in Rats Powered by the Isalos Analytics Platform's

Aikaterini Theodori1, Konstantinos D Papavasileiou1,2, Andreas Tsoumanis1,2,3

  • 1Department of ChemInformatics, NovaMechanics MIKE, 18545 Piraeus, Greece.

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|February 26, 2026
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Summary
This summary is machine-generated.

This study developed a machine learning model to predict the acute oral toxicity of per- and polyfluoroalkyl substances (PFASs). The model accurately identifies high-toxicity PFAS structures, aiding in the development of safer chemical alternatives.

Keywords:
Enalos Cloud PlatformLD50PFASacute oral toxicitymachine learningper- and polyfluoroalkyl substancesratsread-acrossstructural analysis

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Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Computational Chemistry

Background:

  • Per- and polyfluoroalkyl substances (PFASs) are persistent environmental contaminants with known human health risks.
  • Understanding PFAS toxicity mechanisms is crucial for developing safer alternatives.
  • In silico methods offer a promising approach for efficient toxicity assessment.

Purpose of the Study:

  • To develop and validate a robust computational model for predicting the acute oral toxicity of PFASs in rats.
  • To identify structural features associated with high PFAS toxicity.
  • To provide a tool for high-throughput screening of PFAS compounds.

Main Methods:

  • Utilized automated machine learning (autoML) on the Isalos Analytics Platform to optimize four machine learning models (kNN, RF, XGBoost, NN).
  • Employed Mold2 molecular descriptors and five-fold cross-validation to select the best performing model.
  • Conducted Shapley values analysis and nearest-neighbor-based read-across for structural interpretation.

Main Results:

  • The k-nearest neighbors (kNN) model (k=3) achieved 81.5% accuracy in predicting PFAS acute oral toxicity.
  • Identified polyaromatic and heterocyclic structural features as consistently associated with high acute oral toxicity.
  • The validated model is accessible via the INSIGHT RatTox web application and Enalos Cloud.

Conclusions:

  • The developed in silico model provides an accurate and efficient method for assessing PFAS acute oral toxicity.
  • The model facilitates the identification of structural alerts for high toxicity, guiding the design of safer PFAS alternatives.
  • This tool supports regulatory efforts and the development of environmentally benign chemicals.