Updating the Dermal Sensitisation Thresholds using an expanded dataset and an in silico expert system

Area of Science:

• Toxicology
• Dermatology
• Computational Chemistry

Background:

• Dermal Sensitisation Thresholds (DST) are established as Thresholds of Toxicological Concern (TTC) for skin sensitisation risk assessment.
• Exposure-based waiving in risk assessments relies on the accurate determination of DST values.
• Previous DST values were derived from a limited Local Lymph Node Assay (LLNA) dataset.

Purpose of the Study:

• To update existing Dermal Sensitisation Thresholds (DST) by expanding the underlying Local Lymph Node Assay (LLNA) dataset.
• To assign chemical reactivity using an in silico expert system (Derek Nexus) for improved classification.
• To enhance the robustness and applicability of DSTs in skin sensitisation risk assessments.

Main Methods:

• Expanded the LLNA dataset for sensitisation potency values.
• Utilized Derek Nexus, an in silico expert system, to classify chemical sensitisation activity and identify High Potency Categories (HPC).
• Calculated updated DSTs for non-reactive, reactive (non-HPC), and HPC chemical categories.

Main Results:

• The expanded dataset, comprising 1152 chemicals, maintained a similar gamma distribution for potency values.
• Three updated DSTs were determined: 710 μg/cm² (non-reactive), 73 μg/cm² (reactive, non-HPC), and 1.0 μg/cm² (HPC).
• The updated thresholds demonstrated robustness and similarity to previously published values despite dataset expansion.

Conclusions:

• The updated DSTs, derived from a larger dataset and in silico reactivity assessment, offer reliable values for skin sensitisation risk assessment.
• In silico classification of reactivity ensures reproducible, scalable, and accessible application of DSTs.
• The findings increase confidence in the utility of DSTs for regulatory safety evaluations.