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Evaluating Skin Sensitization Via Soft and Hard Multivariate Modeling.

Filipa A L S Silva¹, Gonçalo Brites^2,3, Isabel Ferreira^2,3

¹Department of Chemistry, Faculty of Sciences and Technology, Coimbra Chemistry Centre, 56069University of Coimbra, Coimbra, Portugal.

International Journal of Toxicology

|August 8, 2020

Summary

This summary is machine-generated.

This study developed accurate models for predicting skin sensitization, reducing the need for animal testing. Combining in vitro, in chemico, and in silico data achieved high accuracy in classifying sensitizers and their potency.

Keywords:

MOLS PLS cross-validation model selection multivariate model skin sensitization

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Published on: December 19, 2019

Area of Science:

Toxicology
Computational Chemistry
Dermatology

Background:

Allergic contact dermatitis affects 15-20% of the population, historically relying on animal testing for assessment.
Significant efforts are underway to replace animal testing for skin sensitization endpoints.
Integrating in vitro, in chemico, and in silico data offers a promising alternative but requires optimized data utilization strategies.

Purpose of the Study:

To develop and validate predictive models for skin sensitization using integrated data approaches.
To assess the accuracy of models in discriminating sensitizers, classifying according to GHS, and determining potency levels.
To explore efficient methods for maximizing information gain from combined experimental and computational data.

Main Methods:

Utilized a dataset of known human skin allergens with available in vitro, in chemico, and in silico descriptors.
Developed three distinct classification models: binary (sensitizer/non-sensitizer), GHS-based (3-level), and potency-based (6-level).
Employed soft and hard multivariate modeling techniques for classifier construction, optimization, and refinement.

Main Results:

Achieved 100% accuracy in distinguishing between skin sensitizers and non-sensitizers.
Demonstrated high accuracy in 3-level (98.8%) and 6-level (97.5%) potency characterization.
Confirmed that combining in vitro, in chemico, and in silico data is feasible and effective, aligning with adverse outcome pathways.

Conclusions:

Integrated data from in vitro, in chemico, and in silico methods provide a highly accurate and efficient approach to skin sensitization assessment.
The developed models significantly reduce reliance on animal testing for evaluating skin sensitization potential and potency.
This approach supports the development of safer chemicals and aligns with the principles of the adverse outcome pathway for skin sensitization.