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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.
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Predictive Toxicity Models for Chemically Related Substances: A Case Study with Nonionic Alcohol Ethoxylate

Adriana C Bejarano1, James R Wheeler2

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Environmental Toxicology and Chemistry
|April 5, 2021
PubMed
Summary

Interspecies correlation estimation (ICE) models offer a reliable alternative to animal toxicity testing for predicting aquatic surfactant toxicity. These models demonstrate good accuracy and can aid in hazard assessments.

Keywords:
Aquatic toxicityComputational toxicologyHazard assessmentInterspecies correlation estimation modelsSpecies sensitivity distributions

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Area of Science:

  • Environmental Toxicology
  • Chemical Risk Assessment
  • Ecotoxicology

Background:

  • Animal toxicity testing is being replaced by predictive models like Interspecies Correlation Estimation (ICE).
  • ICE models establish mathematical relationships to predict toxicity between species.
  • Previous ICE models exist for various chemical groups.

Purpose of the Study:

  • To develop and validate ICE models for alcohol ethoxylate (AE) surfactants.
  • To assess the predictive accuracy of AE-ICE models compared to existing models.
  • To estimate acute-to-chronic ratios and develop predictive relationships for AE substances.

Main Methods:

  • Developed ICE models using curated aquatic toxicity data for 19 AE surfactants.
  • Validated AE-ICE predictions against external datasets.
  • Calculated acute and chronic hazard concentrations (HC5s) and liposome-water partitioning coefficients (log K_lipw).
  • Performed regression analysis to establish HC5-log K_lipw relationships.

Main Results:

  • AE-ICE models showed reasonable predictive accuracy, outperforming previous ICE models.
  • An acute-to-chronic ratio of 5 was determined for AE substances.
  • Established HC5-log K_lipw relationships for estimating toxicity data.

Conclusions:

  • ICE models are viable alternatives to traditional animal toxicity testing.
  • AE-ICE models can be effectively used in environmental hazard assessments.
  • The developed relationships facilitate toxicity prediction for AE substances with limited data.