Predicting skin sensitization potency for Michael acceptors in the LLNA using quantum mechanics calculations

Area of Science:

• Computational toxicology
• Chemical reactivity modeling
• Dermal toxicology

Background:

• Skin sensitization is a key endpoint in chemical safety.
• Predicting sensitization potency often relies on animal tests like the Local Lymph Node Assay (LLNA).
• Michael acceptors are a chemical class known to cause skin sensitization.

Purpose of the Study:

• To develop quantitative mechanistic models for predicting skin sensitization potency.
• To establish a mechanistically driven applicability domain for these models.
• To support the reduction of animal testing in risk assessment.

Main Methods:

• Utilized computational descriptors based on the Michael addition reaction mechanism.
• Employed density functional theory to calculate the stability of reaction intermediates.
• Incorporated a descriptor for the available surface area at the reaction site.

Main Results:

• Developed quantitative mechanistic models for predicting LLNA skin sensitization potency.
• Identified key descriptors: reaction intermediate stability and surface area.
• Established a well-defined, mechanistically driven applicability domain by analyzing poorly predicted compounds.

Conclusions:

• In silico quantitative mechanistic models can reliably predict skin sensitization potency.
• The developed models and applicability domain offer a pathway for non-animal testing strategies.
• This approach can be integrated into a weight of evidence framework for chemical risk assessment.