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Non-Animal Methods to Predict Skin Sensitization (II): An Assessment of Defined Approaches

Nicole C. Kleinstreuer, Sebastian Hoffmann, Nathalie Alépée, David Allen, Takao Ashikaga, Warren Casey, Elodie Clouet, Magalie Cluzel, Bertrand Desprez, Nichola Gellatly, Carsten Göbel, Petra S. Kern, Martina Klaric, Jochen Kühnl, Silvia Martinozzi-Teissier, Karsten Mewes, Masaaki Miyazawa, Judy Strickland, Erwin van Vliet, Qingda Zang, Dirk Petersohn.
Critical Reviews in Toxicology (2018) DOI: PMID: 29474122



Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.


Figure 1. The heatmap shows defined approach (DA) predictions and LLNA/Human hazard data.

The heatmap shows defined approach (DA) predictions and LLNA/Human hazard data for the 68 substances with some degree of discordance across the results. Orange or red indicates sensitizer based on DA predictions or in vivo data, respectively, and tan cells are non-sensitizer predictions/data. White indicates that not all required features were present to run the model for the DA. The dendrogram on the left shows clusters of substances (complete linkage method) and the color coding above the plot indicates which features were used by each model. Abbreviations for each DA name are defined in the text.


Table 1. Twelve defined and/or integrated approaches to testing and assessment.

Twelve defined and/or integrated approaches to testing and assessment for assessing skin sensitization potential.

Table 2. Qualitative evaluation categories and criteria.

Table 3. Defined Approach (DA) performance in predicting human hazard (sensitizer/non-sensitizer).

Table 4. Defined approach (DA) performance in predicting LLNA hazard (sensitizer/non-sensitizer).

Table 5. Defined Approach (DA) performance in predicting human sensitizing potency.

Table 6. Defined Approach (DA) performance in predicting LLNA sensitizing potency.

Supplemental Materials

Supplemental Material

Associated data via Datacite

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