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Toxicity Testing in Animals

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...

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Related Experiment Video

Updated: Feb 28, 2026

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Published on: June 13, 2025

Metabolomics‑driven, data‑augmented machine learning for predicting toxicity of microplastic mixtures.

Beilei Yuan¹, Chengzhi Liu², Shuang Chen¹

¹College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu 210009, China.

Ecotoxicology and Environmental Safety

|February 25, 2026

Summary

This summary is machine-generated.

Predicting microplastic (MP) toxicity is challenging due to complex mixtures. A metabolomics-driven machine learning model effectively predicted MP cytotoxicity, offering insights into cellular energy metabolism reprogramming.

Keywords:

Cytotoxicity Data augmentation Machine learning Microplastics QSAR

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Last Updated: Feb 28, 2026

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

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Published on: June 13, 2025

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Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

Published on: July 28, 2018

Area of Science:

Environmental Science
Toxicology
Computational Chemistry

Background:

Microplastics (MPs) exist as complex mixtures in the environment, hindering individual toxicity assessments.
Developing rapid and effective methods to evaluate the toxicity of MP mixtures is crucial for risk assessment.

Purpose of the Study:

To develop predictive models for evaluating the toxicity of microplastic mixtures.
To compare the performance of quantitative structure-activity relationship (QSAR), quantitative bioactivity relationship (QBAR), and quantitative structure-bioactivity relationship (QSBAR) models.

Main Methods:

Explored three model frameworks: QSAR, QBAR, and QSBAR.
Employed six machine learning algorithms with data augmentation strategies.
Utilized metabolomics data to screen biodescriptors for the QBAR model.

Main Results:

The QBAR-based eXtreme Gradient Boosting (XGB-qbar) model demonstrated superior performance (R²test = 0.8923).
Key biological descriptors influencing toxicity were identified.
Metabolomics analysis revealed MP mixture exposure may reprogram cellular energy metabolism.

Conclusions:

A metabolomics-driven, data-augmented machine learning approach efficiently predicts microplastic toxicity in complex mixtures.
This approach provides mechanistic insights and a feasible pathway for environmental risk assessment.