Assessment of Pre- and Pro-haptens Using Nonanimal Test Methods for Skin Sensitization

Because of ethical and regulatory reasons, several nonanimal test methods to assess the skin sensitization potential of chemicals have been developed and validated. In contrast to <i>in vivo</i> methods, they lack or provide limited metabolic capacity. For this reason, identification of pro-haptens but also pre-haptens, which require molecular transformations to gain peptide reactivity, is a challenge for these methods. In this study, 27 pre- and pro-haptens were tested using nonanimal test methods. Of these, 18 provided true positive results in the direct peptide reactivity assay (DPRA; sensitivity of 67%), although lacking structural alerts for direct peptide reactivity. The reaction mechanisms leading to peptide depletion in the DPRA were therefore elucidated using mass spectrometry. Hapten–peptide adducts were identified for 13 of the 18 chemicals indicating that these pre-haptens were activated and that peptide binding occurred. Positive results for five of the 18 chemicals can be explained by dipeptide formations or the oxidation of the sulfhydryl group of the peptide. Nine of the 27 chemicals were tested negative in the DPRA. Of these, four yielded true positive results in the keratinocyte and dendritic cell based assays. Likewise, 16 of the 18 chemicals tested positive in the DPRA were also positive in either one or both of the cell-based assays. A combination of DPRA, KeratinoSens, and h-CLAT used in a 2 out of 3 weight of evidence (WoE) approach identified 22 of the 27 pre- and pro-haptens correctly (sensitivity of 81%), exhibiting a similar sensitivity as for directly acting haptens. This analysis shows that the combination of <i>in chemico</i> and <i>in vitro</i> test methods is suitable to identify pre-haptens and the majority of pro-haptens

Decision tree models to classify nanomaterials according to the <i>DF4nanoGrouping</i> scheme

<p>To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is <i>DF4nanoGrouping</i> scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property ‘lowest unoccupied molecular orbital’) need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low– one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the <i>DF4nanoGrouping</i> scheme that has been proposed before.</p