Bridging the gap: Innovative human-based in vitro approaches for nanomaterials hazard assessment and their role in safe and sustainable by design, risk assessment, and life cycle assessment.

The application of nanomaterials in industry and consumer products is growing exponentially, which has pressed the development and use of predictive human in vitro models in pre-clinical analysis to closely extrapolate potential toxic effects in vivo. The conventional cytotoxicity investigation of nanomaterials using cell lines from cancer origin and culturing them two-dimensionally in a monolayer without mimicking the proper pathophysiological microenvironment may affect a precise prediction of in vitro effects at in vivo level. In recent years, complex in vitro models (also belonging to the new approach methodologies, NAMs) have been established in unicellular to multicellular cultures either by using cell lines, primary cells or induced pluripotent stem cells (iPSCs), and reconstituted into relevant biological dimensions mimicking in vivo conditions.

Predicting chemical hazard across taxa through machine learning.

We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in the classification performance. We quantified the gain obtained throught the introduction of taxonomic and experimental information, compared to classification based on chemical information alone.