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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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Area of Science:

  • Computational toxicology
  • Environmental health
  • Machine learning applications in chemical safety

Background:

  • Animal testing is insufficient for evaluating the health risks of numerous environmental chemicals.
  • Computational approaches utilizing high-throughput experimental data offer a more efficient alternative for predicting chemical toxicity.
  • Predicting in vivo repeat-dose toxicity at the organ-level is crucial for chemical risk assessment.

Purpose of the Study:

  • To systematically investigate the importance of study type, machine learning algorithm, and descriptor type in predicting in vivo repeat-dose organ-level toxicity.
  • To evaluate the predictive performance of various computational models using diverse chemical and bioactivity descriptors.
  • To identify optimal strategies for enhancing the accuracy of toxicity predictions.

Main Methods:

  • Utilized a supervised machine learning strategy on 985 compounds, represented by chemical structural, ToxPrint chemotype, and ToxCast bioactivity descriptors.
  • Identified 35 target organ outcomes from ToxRefDB with sufficient positive and negative chemical data.
  • Employed Naïve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification models, assessed using F1 scores and 5-fold cross-validation.

Main Results:

  • Target organ outcome was the primary driver of predictive model variance, followed by descriptor type and machine learning algorithm.
  • Combinations of bioactivity and chemical structure/chemotype descriptors yielded the most predictive models.
  • Model performance increased with the number of chemicals, showing a strong correlation (ρ = 0.92) with dataset size.

Conclusions:

  • A combination of bioactivity and chemical descriptors can accurately predict various target organ toxicity outcomes in repeat-dose studies.
  • The findings demonstrate the potential of computational approaches to supplement or replace traditional animal testing for chemical hazard assessment.
  • Further methodological improvements may enhance the predictivity of these computational toxicity models.