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Prediction of skin sensitization potency using machine learning approaches.

Qingda Zang¹, Michael Paris¹, David M Lehmann²

¹ILS, Research Triangle Park, NC, 27709, USA.

Journal of Applied Toxicology : JAT

|January 12, 2017

Summary

Computational models using non-animal data accurately predict skin sensitization potency. These machine learning approaches offer a promising alternative to animal testing for regulatory classification and potency categorization.

Keywords:

KeratinoSens Skin sensitization potency allergic contact dermatitis (ACD)direct peptide reactivity assay (DPRA)h-CLAT (human cell line activation test)integrated decision strategy (IDS)machine learning murine local lymph node assay (LLNA)

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Area of Science:

Toxicology
Computational Chemistry
Dermatology

Background:

Replacing animal testing for skin sensitizer classification is a priority for US federal agencies.
Machine learning models have been developed to classify sensitizers without animal data.
Regulatory agencies require further classification of sensitizers into potency categories.

Purpose of the Study:

To develop statistical models for predicting skin sensitization potency.
To evaluate non-animal test methods and physicochemical properties for predicting potency.
To compare one-tiered and two-tiered modeling strategies for potency classification.

Main Methods:

Developed statistical models using six physicochemical properties and data from three non-animal tests (DPRA, h-CLAT, KeratinoSens™).
Employed four machine learning approaches to predict three potency categories.
Validated models using external test sets and leave-one-out cross-validation.

Main Results:

The best two-tiered support vector machine model achieved 88% accuracy for murine local lymph node assay (LLNA) outcomes and 81% for human outcomes.
The best one-tiered model achieved 78% accuracy for LLNA and 75% for human outcomes.
The LLNA itself predicts human potency categories with 69% accuracy.

Conclusions:

Computational models utilizing non-animal methods show significant potential for assessing skin sensitization potency.
These models can provide valuable data for regulatory classification, reducing reliance on animal testing.
The developed models offer improved accuracy compared to traditional animal tests for potency prediction.