RESUMEN
To develop and evaluate scientifically robust and innovative approaches for the safety assessment of chemicals across multiple regulatory sectors, the EU Reference Laboratory for alternatives to animal testing (EURL ECVAM) has started a project to explore how to better use the available information, including that from existing animal studies. The aim is to minimize reliance on in vivo testing to avoid redundancy and to facilitate the integration of novel non-animal methods in the regulatory setting with the ultimate goal of designing sustainable testing strategies. In this thought-starter paper, we present a number of examples to illustrate and trigger further discussions within the scientific and regulatory communities on ways to extrapolate useful information for predicting toxicity from one toxicity endpoint to another or across endpoints based on mechanistic information.
RESUMEN
The knowledge of the biological mechanisms leading to the induction of skin sensitisation has favoured in recent years the development of alternative non-animal methods. During the formal validation process, results from the Local Lymph Node Assay (LLNA) are generally used as reference data to assess the predictive capacity of the non-animal tests. This study reports an analysis of the variability of the LLNA for a set of chemicals for which multiple studies are available and considers three hazard classification schemes: POS/NEG, GHS/CLP and ECETOC. As the type of vehicle used in a LLNA study is known to influence to some extent the results, two analyses were performed: considering the solvent used to test the chemicals and without considering the solvent. The results show that the number of discordant classifications increases when a chemical is tested in more than one solvent. Moreover, it can be concluded that study results leading to classification in the strongest classes (1A and EXT) seem to be more reliable than those in the weakest classes. This study highlights the importance of considering the variability of the reference data when evaluating non-animal tests.