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Updated: Oct 27, 2025

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Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose.

Yue Wu1, Jieqiang Zhu1, Peter Fu2

  • 1National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.

International Journal of Environmental Research and Public Health
|July 20, 2021
PubMed
Summary

Identifying specific structural alerts in drugs can predict the risk of developing autoimmune diseases (ADs). Nitrogen-containing benzene substituents are linked to increased AD risk, aiding in drug safety prescreening.

Keywords:
drug-induced autoimmune diseasesmachine learningquantum chemistrystructural alerts

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Area of Science:

  • Drug Development
  • Toxicology
  • Immunology

Background:

  • Assessing drug-induced autoimmune diseases (ADs) is critical in drug development.
  • Reactive metabolite (RM) formation is a known mechanism for drug-induced ADs.

Purpose of the Study:

  • To develop a workflow for assessing the association between structural alerts and drug-induced ADs.
  • To improve toxicological prescreening by identifying predictive structural alerts for AD risk.

Main Methods:

  • Constructed a database of 171 RM-related structural alerts.
  • Compiled a dataset of 407 AD- and non-AD-associated drugs.
  • Performed statistical analysis and developed a machine-learning predictive model.

Main Results:

  • Nitrogen-containing benzene substituent alerts were significantly associated with drug-induced AD risk (OR=2.95, p=0.0036).
  • A machine-learning model using daily dose and these alerts achieved 70% AUC.
  • Quantum chemistry confirmed the reactivity of aniline and related metabolites.

Conclusions:

  • Identified structural alerts, particularly nitrogen-containing benzene substituents, can predict drug-induced AD risk.
  • These findings can enhance drug safety profiles by identifying potential AD risks early.
  • The workflow improves toxicological prescreening for drug candidates.