Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories

Area of Science:

• Toxicology
• Computational Chemistry
• Risk Assessment

Background:

• Repeated dose toxicity (RDT) is crucial for chemical risk assessment.
• Mechanistically transparent structure-activity models for RDT are challenging due to endpoint complexity.
• The category approach using mechanistic information is effective for RDT data gap filling via read-across.

Purpose of the Study:

• To develop a toxicological category library for RDT.
• To integrate this library into a computational platform for predicting RDT values.
• To facilitate mechanistically reasonable chemical grouping and read-across.

Main Methods:

• Compilation of experimental RDT data for 500 chemicals.
• Integration of mechanistic knowledge on chemical effects on organs.
• Development of 33 categories for 14 toxicity types.
• Incorporation into the Hazard Evaluation Support System (HESS).

Main Results:

• A library of 33 toxicological categories was established.
• The HESS platform now provides mechanistically reasonable RDT predictions for untested chemicals.
• The system facilitates grouping of chemicals and read-across based on toxicity mechanisms.

Conclusions:

• The developed category library and HESS integration offer a robust method for RDT assessment.
• This approach enhances the reliability of read-across for chemical safety evaluations.
• Mechanistically informed categorization improves predictions for data-poor chemicals.