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Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.

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This study introduces a novel computational approach using quantitative structure-activity relationship (QSAR) models to predict chemical carcinogenicity, identifying potential new human carcinogens efficiently.

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Area of Science:

  • Computational toxicology
  • cheminformatics
  • predictive modeling

Background:

  • Traditional chemical toxicity assessments are costly and time-consuming.
  • Quantitative structure-activity relationship (QSAR) models offer a cost-effective alternative but often suffer from limited training data and low predictivity.
  • Accurate prediction of carcinogenicity is crucial for public health and regulatory purposes.

Purpose of the Study:

  • To develop and validate a data-driven QSAR modeling approach for predicting chemical carcinogenicity.
  • To identify potential new human carcinogens using the developed models.
  • To establish an automated technique for prioritizing toxicants based on validated QSAR models.

Main Methods:

  • Utilized a US Environmental Protection Agency Integrated Risk Information System (IRIS) carcinogen dataset to identify relevant PubChem bioassays.
  • Selected eight PubChem assays demonstrating significant carcinogenicity predictivity for QSAR model training.
  • Developed 15 QSAR models using 5 machine learning algorithms and 3 chemical fingerprint types for each selected assay.

Main Results:

  • The developed QSAR models demonstrated acceptable predictivity, with an average cross-validation classification accuracy of 0.71.
  • The models accurately predicted and ranked the carcinogenic potentials of 342 IRIS compounds, achieving a positive predictive value of 0.72.
  • Potential new carcinogens were identified and subsequently validated through a literature search.

Conclusions:

  • The data-driven QSAR approach provides a reliable and efficient method for predicting chemical carcinogenicity.
  • This methodology enables the identification and prioritization of potential new human carcinogens.
  • The study highlights the potential for automated toxicant prioritization using validated QSAR models trained on extensive public data resources.