Suchergebnisse

Plastics are used ubiquitously and have become part of our everyday life. The global production of plastics is rising, which in consequence is leading to increasing amounts of plastics being released into the environment. Recently, the issue of human exposure to micro‐ and nanoplastic particles and potentially resulting toxicological consequences has been broached, triggered by the discovery of microplastics in foodstuff. In addition to dietary exposure via contaminated food and beverages, other exposure paths such as via air and cosmetics, have to be considered. Currently there is no legislation for microplastics and nanoplastics as contaminants in food. Substantial data gaps with respect to exposure as well as toxicity of such particles impede the risk assessment. Within this EU‐FORA fellowship project, a comprehensive data mining approach was followed, focusing on up‐to‐date knowledge on the occurrence and possible toxic effects associated with micro‐ and nanoplastics after oral exposure, especially via food products and beverages, in order to provide a basis for risk assessment and to identify important research gaps. The fellowship project was further complemented by practical work aimed at the determination of in vitro toxicity of micro‐sized polylactic acid particles.

In vivo skin sensitization assays have to be provided by applicants to the competent authorities in the European Union for the approval of active substances (AS) in pesticides. This study aimed to test the practicability of in silico predictions for AS by freely available (Q)SAR tools to evaluate their use as a time- and cost-effective alternative to animal testing in the context of the 3R concept. Predictions of skin sensitization for 48 selected sensitizing and non-sensitizing AS by the software programs CAESAR, Toxtree, OECD (Q)SAR Toolbox, CASE Ultra, Leadscope and SciQSAR were collected and compared. Different data evaluation methodologies (score definition, mean, weighted mean, threshold score definition) were applied to optimize the predictions. The calculation methods were internally cross-validated and further validated with an additional validation set of 80 AS. Although the presented calculation methodologies are not suitable as a stand-alone method, this study has shown weaknesses and strengths of some prominent (Q)SAR programs and diverse combinatorial options in the prediction of skin sensitization by pesticidal AS. The present study will help to foster discussions on in silico alternatives to animal testing in the pesticide area.