Computational approach for collection and prediction of molecular initiating events in developmental toxicity

Area of Science:

• Toxicology
• Computational Biology
• Drug Development

Background:

• Developmental toxicity poses risks to developing organisms, with current assessment methods having limitations.
• Existing in vitro and in silico models struggle with the complex mechanisms underlying developmental toxicity.
• Understanding these mechanisms is crucial for accurate toxicity prediction and risk assessment.

Purpose of the Study:

• To compile a dataset (DVTOX) of compounds with developmental toxicity and annotated mechanisms of action.
• To identify potential Molecular Initiating Events (MIEs) by selecting a panel of protein targets.
• To validate candidate MIEs and identify a suitable animal model for developmental toxicity assessment.

Main Methods:

• Compiled the DVTOX dataset linking compounds, toxicity, and mechanisms.
• Selected protein targets potentially involved in MIEs of developmental toxicity.
• Validated MIEs using new drug-target relationships and performed orthology analysis for animal model selection.

Main Results:

• Successfully identified a panel of candidate protein targets for MIEs.
• Validated the predictive power of these targets through new drug-target relationship analysis.
• Demonstrated the utility of the approach using the zebrafish embryo toxicity test with positive outcomes.

Conclusions:

• The study presents a novel, data-driven approach to identify MIEs for developmental toxicity.
• The validated protein targets and selected animal model offer a promising strategy for improved toxicity testing.
• This work contributes to more objective and efficient assessment of developmental toxicity risks.