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DeTox: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential.

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This study developed Quantitative Structure-Activity Relationship (QSAR) models to predict prenatal developmental toxicity, aiding the creation of safer medications for pregnant women. The models are accessible via a user-friendly web tool, supporting regulatory assessments and reducing animal testing.

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Area of Science:

  • * Computational toxicology
  • * Cheminformatics
  • * Reproductive toxicology

Background:

  • * Medication use during pregnancy is common, but fetal safety data is limited.
  • * Quantitative Structure-Activity Relationship (QSAR) models offer a predictive approach to assess developmental toxicity.
  • * QSAR aids in developing safer medications and aligns with the 3Rs (refining, reducing, replacing animal testing).

Purpose of the Study:

  • * To create and validate QSAR models for predicting prenatal developmental toxicity.
  • * To build a curated database of compounds with known developmental toxicity.
  • * To implement predictive models in an accessible online platform for regulatory use.

Main Methods:

  • * Compiled and curated data from FDA and Teratogen Information System (TERIS) databases.
  • * Developed QSAR models using machine learning algorithms (RF, SVM, LightGBM) with Bayesian hyperparameter optimization.
  • * Implemented validated models into a user-friendly web tool named DeTox.

Main Results:

  • * Developed QSAR models for overall and trimester-specific developmental toxicity.
  • * Achieved correct classification rates of 76% (overall), 80% (1st trimester), 95% (2nd trimester), and 95% (3rd trimester).
  • * Established a publicly accessible web portal (https://detox.mml.unc.edu/) for toxicity predictions.

Conclusions:

  • * The DeTox tool supports regulatory assessment of pharmaceuticals and cosmetics, aligning with the 3Rs.
  • * Publicly available dataset and user-friendly web tool facilitate the development of safer drugs for pregnant populations.
  • * DeTox promotes safer medication development and regulatory compliance through predictive toxicology.