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The availability of reference data is a key requirement for the development of new approach methods (NAM), i.e., in vitro, in chemico and in silico methods and integrated approaches, like defined approaches (DA), which combine these data sources. Reference data are of even greater importance for regulatory acceptance. In contrast to most other adverse effects, human skin sensitization data on many chemicals are available, next to data from animal studies, such as the local lymph node assay (LLNA). Skin sensitization NAM data can therefore be compared to different reference datasets. Recent publications and validation at the OECD focused on human and LLNA reference data. The “2 out of 3” DA (2o3 DA) is the first DA for skin sensitization solely based on experimental data from validated tests and was recently adopted as an OECD test guideline. Here we review the predictivity of the 2o3 DA on multiple human and LLNA reference datasets. Concomitantly, we compare the predictivity of the LLNA for human data within the same datasets. Comparing predictivity of methods not only bilaterally (NAM or DA vs. animal method) but including human data in a triangle “NAM data – animal data – human data” offers a comprehensive assessment of the NAM’s and DA’s predictivity. In all these assessments, the 2o3 DA was superior to the LLNA in predicting human skin sensitization hazard. This highlights the importance of a holistic view of reference data instead of limiting validation of NAMs and DAs to data from a single animal test only.

Several in vitro OECD test guidelines address key events 1-3 of the adverse outcome pathway for skin sensitization, but none are validated for sensitizer potency assessment. The reaction of sensitizing molecules with skin proteins is the molecular initiating event and appears to be rate-limiting, as chemical reactivity strongly correlates with sensitizer potency. The kinetic direct peptide reactivity assay (kDPRA), a modification of the DPRA (OECD TG 442C), allows derivation of rate constants of the depletion of the cysteine-containing model peptide upon reaction with the test item. Its reproducibility was demonstrated in an inter-laboratory study. Here, we present a database of rate constants, expressed as log kmax, for 180 chemicals to define the prediction threshold to identify strong sensitizers (classified as GHS 1A). A threshold of log kmax -2 offers a balanced accuracy of 85% for predicting GHS 1A sensitizers according to the local lymph node assay. The kDPRA is proposed as a stand-alone assay for identification of GHS 1A sensitizers among chemicals identified as sensitizers by other tests or defined approaches. It may also be used for the prediction of sensitizer potency on a continuous scale, ideally in combination with continuous parameters from other in vitro assays. We show how the rate constant could be combined with read-outs of other in vitro assays in a defined approach. A decision model based on log kmax alone has, however, a high predictivity and can be used as stand-alone model for identification of GHS 1A sensitizers among chemicals predicted as sensitizers.

While the skin sensitization hazard of substances can be identified using non-animal methods, the classification of potency into UN GHS sub-categories 1A and 1B remains challenging. The kinetic direct peptide reactivity assay (kDPRA) is a modification of the DPRA wherein the reaction kinetics of a test substance towards a synthetic cysteine-containing peptide are evaluated. For this purpose, several concentrations of the test substance are incubated with the synthetic peptide for several incubation times. The reaction is stopped by addition of monobromobimane, which forms a fluorescent complex with the free cysteine of the model peptide. The relative remaining non-depleted amount of peptide is determined. Kinetic rate constants are derived from the depletion vs concentration and time matrix and used to distinguish between UN GHS sub-category 1A sensitizers and test substances in sub-category 1B/not classified test substances. In this study, we present a ring trial of the kDPRA with 24 blind-coded test substances in seven laboratories. The intra- and inter-laboratory reproducibility were 96% and 88%, respectively (both for differentiating GHS Cat 1A sensitizers from GHS Cat 1B/not classified). Following an independent peer review, the kDPRA was considered to be acceptable for the identification of GHS Cat 1A skin sensitizers. Besides GHS Cat 1A identification, the kDPRA can be used as part of a defined approach(es) with a quantitative data integration procedure for skin sensitization potency assessment. For this aim, next to reproducibility of classification, the quantitative reproducibility and variability of the rate constants were quantified in this study.

Test methods to assess the skin sensitization potential of a substance usually use threshold criteria to dichotomize continuous experimental read-outs into yes/no conclusions. The threshold criteria are prescribed in the respective OECD test guidelines and the conclusion is used for regulatory hazard assessment, i.e., classification and labelling of the substance. We can identify a borderline range (BR) around the classification threshold within which test results are inconclusive due to a test method's biological and technical variability. We quantified BRs in the prediction models of the non-animal test methods DPRA, LuSens and h-CLAT, and of the animal test LLNA, respectively. Depending on the size of the BR, we found that between 6% and 28% of the substances in the sets tested with these methods were considered borderline. When the results of individual non-animal test methods were combined into integrated testing strategies (ITS), borderline test results of individual tests also affected the overall assessment of the skin sensitization potential of the testing strategy. This was analyzed for the 2-out-of-3 ITS: Four out of 40 substances (10%) were considered borderline. Based on our findings we propose expanding the standard binary classification of substances into "positive"/"negative" or "hazardous"/"non-hazardous" by adding a "borderline" or "inconclusive" alert for cases where test results fall within the borderline range.