Improved defined approaches for predicting skin sensitization hazard and potency in humans_suppl

Haojian Li

doi:10.14573/altex.1809191s

Abstract

Since the EU banned animal testing for cosmetic products and ingredients in 2013, many defined approaches (DA) for skin sensitization assessment have been developed. Machine learning models were shown to be effective in DAs, but the predictivity might be affected by data imbalance (i.e., more sensitizers than non-sensitizers) and limited information in the databases. To improve the predictivity of DAs, we attempted to apply data-rebalancing ensemble learning (bagging with support vector machine (SVM)) and a novel and comprehensive Cosmetics Europe database. For predicting human hazard and three-class potency, 12 models were built for each using a training set of 96 substances and a test set of 32 substances from the database. The model that predicted hazard with the highest accuracy (90.63% for the test set and 88.54% for the training set, named hazard-DA) used SVM-bagging with combinations of all variables (V6), while the model that predicted potency with the highest accuracy (68.75% for the test set and 82.29% for the training set, named potency-DA) used SVM alone. Both DAs showed better performance than LLNA and other machine learning-based DAs, and the potency-DA provided more in-depth assessment. These findings indicate that SVM-bagging-based DAs provide enhanced predictivity for hazard assessment by further data rebalancing. Meanwhile, the effect of imbalanced data might be offset by more detailed categorization of sensitizers for potency assessment, thus SVM-based DA without bagging could provide sufficient predictivity. The improved DAs in this study could be promising tools for skin sensitization assessment without animal testing.

Highlights

Allergic contact dermatitis (ACD) is a clinically relevant condition induced by contact with an allergen. 15%-20% of the population suffers from ACD at some point in their life (Thyssen et al, 2007)
The bagging method mainly improved the accuracy by improving the corresponding specificity, which increased either in the training set or the test set for 3 of the 6 SVM models (i.e., SVM V1, V5, and V6)
The hazard-defined approaches (DA) was generated by the combination of the bagging method and the SVM model, while the potency-DA was generated by the SVM model alone

Summary

Introduction

Allergic contact dermatitis (ACD) is a clinically relevant condition induced by contact with an allergen. 15%-20% of the population suffers from ACD at some point in their life (Thyssen et al, 2007). Substantial progress has been made in this regard by developing in vitro assays addressing different key events (KEs) in the skin sensitization adverse outcome pathway (AOP) (OECD, 2015, 2018a,b). The KEs in the AOP include the molecular initiating event (covalent binding to skin proteins) and the cellular response (activation of keratinocytes and dendritic cells) to the sensitizers. To evaluate these KEs, in vitro methods such as the direct peptide reactivity assay (DPRA), KeratinoSensTM, and h-CLAT have been developed and accepted by the Organisation for Economic Co-operation and Development (OECD) (Emter et al, 2010; Gerberick et al, 2004; Sakaguchi et al, 2006). As the components of the integrated approach to testing and assessment (IATA), many defined approaches (DA), which cover complementary characteristics of the in vitro methods and further take physicochemical properties and structure into consideration, have greatly improved the predictivity of skin sensitization (Worth and Patlewicz, 2016)

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Results

Conclusion