Progress in data interoperability to support computational toxicology and chemical safety evaluation.

Area of Science:

• Toxicology
• Computational Biology
• Data Science

Background:

• Chemical safety evaluation faces challenges due to limited data on environmental exposures.
• The
• Toxicity Testing in the 21st Century
• initiative spurred data generation for computational toxicology.
• Projects like Tox21 and ToxCast have produced extensive chemical bioactivity data.

Purpose of the Study:

• To review efforts in bioactivity and toxicological data interoperability.
• To address challenges in integrating New Approach Methodologies (NAMs) with traditional toxicology data.
• To promote data management solutions aligned with FAIR Data Principles.

Main Methods:

• Review of toxicology-related initiatives focusing on data interoperability.
• Analysis of strategies for identifying and linking bioactivity and hazard information.
• Examination of approaches for integrating NAM and traditional data for computational modeling.

Main Results:

• Progress has been made in generating and analyzing large datasets for chemical safety.
• Key questions remain regarding semantic interoperability for integrating diverse data sources.
• Development of computational models using integrated data shows promise for regulatory applications.

Conclusions:

• Integrating New Approach Methodologies (NAMs) and traditional toxicology data is essential for advancing data-driven toxicology.
• Adherence to Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles is critical for effective data integration.
• Improved data interoperability will support more accurate risk assessments and safety evaluations.