Aneugen Molecular Mechanism Assay: Proof-of-Concept With 27 Reference Chemicals

Derek T Bernacki1, Steven M Bryce1, Jeffrey C Bemis1

  • 1Litron Laboratories, Rochester, New York, 14623.

Insights

This study developed a tiered bioassay to identify molecular targets of chemical-induced aneugenicity. A machine learning approach using specific biomarkers reliably predicted these targets in TK6 cells.

Area of Science:

  • Toxicology and Pharmacology
  • Molecular Biology
  • Computational Biology

Background:

  • Chemical-induced aneugenicity poses a significant risk, yet its molecular targets are not always clear.
  • Understanding these targets is crucial for accurate risk assessment and the development of safer chemicals.
  • Existing methods may not comprehensively identify the primary molecular mechanisms of aneugenicity.

Purpose of the Study:

  • To establish and evaluate a tiered bioassay strategy for identifying molecular targets of chemical-induced aneugenicity.
  • To investigate tubulin dynamics and mitotic kinase inhibition as key mechanisms.
  • To develop a predictive model using machine learning for classifying aneugenic molecular targets.

Main Methods:

  • TK6 cells were exposed to 27 presumed aneugens, with biomarkers like γH2AX, p53, phospho-histone H3 (p-H3), and polyploidization assessed.
  • A follow-up assay involved exposing cells to 26 chemicals with 488 Taxol, analyzing p-H3 and Ki-67 ratios via flow cytometry.
  • Unsupervised hierarchical clustering and an artificial neural network classification algorithm were used for data analysis and prediction.

Main Results:

  • The initial assay identified 26 of 27 chemicals as genotoxic, with 25 showing aneugenic signatures.
  • Follow-up analyses distinguished tubulin binders (stabilizers/destabilizers) and mitotic kinase inhibitors based on 488 Taxol fluorescence and p-H3:Ki-67 ratios.
  • The artificial neural network achieved 25/26 accuracy in predicting molecular targets using leave-one-out cross-validation.

Conclusions:

  • The tiered bioassay effectively identifies common molecular targets of chemical-induced aneugenicity.
  • The combination of 488 Taxol, p-H3, and Ki-67 responses, analyzed by machine learning, reliably predicts these targets.
  • This strategy offers a promising approach for elucidating aneugenic mechanisms and improving chemical safety assessments.

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