Computer-aided knowledge generation for understanding skin sensitization mechanisms: the TOPS-MODE approach

Area of Science:

• Computational chemistry
• Toxicology
• Dermatology

Background:

• Skin sensitization is a significant adverse effect of chemical exposure.
• Understanding molecular structural contributions is key to predicting sensitization.
• Existing methods for identifying structural alerts can be time-consuming and data-intensive.

Purpose of the Study:

• To develop predictive models for skin sensitization using the TOPS-MODE approach.
• To identify novel structural alerts for skin sensitization.
• To elucidate the molecular structural contributions to skin sensitization.

Main Methods:

• Utilized the topological substructural molecular descriptors (TOPS-MODE) approach.
• Developed predictive models based on a dataset of 93 compounds.
• Validated models using cross-validation and external validation sets.
• Analyzed bond contributions to understand structural influences.

Main Results:

• Achieved high predictivity in skin sensitization models.
• Reported 29 new skin sensitization (EC3) values.
• Successfully identified potential new structural alerts more rapidly than traditional methods.
• Presented structural contributions to sensitization for various chemical classes.
• Identified structural alerts for chemicals requiring metabolic activation.

Conclusions:

• The TOPS-MODE approach provides a powerful tool for understanding and predicting skin sensitization.
• The developed models offer high accuracy and have been rigorously validated.
• This methodology accelerates the identification of structural alerts, aiding in chemical safety assessment.
• The findings contribute to a deeper understanding of chemical structure-activity relationships in skin sensitization.