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Related Concept Videos

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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...
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Updated: Jun 26, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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QSAR Modeling to Predict Aquatic Toxicity Across Multiple Species.

Iglika Lessigiarska1, Petko Alov1, Maria Angelova1

  • 1Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str., Bl. 21, 1113 Sofia, Bulgaria.

Toxics
|June 25, 2026
PubMed
Summary

New Approach Methodologies (NAMs), including quantitative structure-activity relationship (QSAR) models, efficiently assess chemical aquatic toxicity. These in silico models accurately predict toxicity across species, aiding environmental hazard assessment.

Keywords:
Daphnia magnaQSAARQSARRaphidocelis subcapitatafathead minnowrandom forestzebrafish embryo

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Area of Science:

  • Environmental Chemistry
  • Toxicology
  • Computational Chemistry

Background:

  • Increasing chemical contamination necessitates advanced methods for aquatic toxicity assessment.
  • Regulatory bodies require reliable alternatives to traditional animal testing for chemical safety evaluations.
  • In silico methods, such as quantitative structure-activity relationship (QSAR) modeling, offer promising avenues for predicting chemical toxicity.

Purpose of the Study:

  • To develop and validate robust in silico models for predicting the aquatic toxicity of chemicals.
  • To establish quantitative structure-activity relationship (QSAR) and interspecies quantitative structure-activity-activity relationship (QSAAR) models for key aquatic organisms.
  • To support regulatory decision-making and environmental hazard assessment through reliable predictive toxicology.

Main Methods:

  • Compilation of curated, structurally diverse aquatic toxicity datasets for algae (Raphidocelis subcapitata), crustaceans (Daphnia magna), and fish (zebrafish, fathead minnow).
  • Development of QSAR models utilizing interpretable molecular descriptors related to lipophilicity, polarity, and reactivity.
  • Construction of QSAAR models to enable cross-species extrapolation of toxicity data from lower to higher trophic levels.

Main Results:

  • Developed QSAR and QSAAR models exhibited strong and consistent predictive performance across various aquatic toxicity assays.
  • Identified key molecular descriptors (lipophilicity, polarity, reactivity) driving aquatic toxicity predictions.
  • Demonstrated the efficacy of using lower trophic level data and structural features to predict fish toxicity.

Conclusions:

  • In silico NAMs, specifically QSAR and QSAAR, provide efficient and reliable tools for aquatic chemical toxicity assessment.
  • These models facilitate cross-species extrapolation, enhancing environmental hazard assessment capabilities.
  • The developed models contribute to reducing reliance on traditional testing methods and support informed regulatory decisions.