Development of computational models to predict the toxicity of advanced materials

The aim of this study is to harness computational power to enhance existing knowledge on NM safety and to optimize the use of existing nanotoxicity data. The primary goal is to support the safe(r)-by-design concept, necessitating early integration of safety considerations into NM design through structural manipulation strategies. This thesis focuses on three case studies: zinc oxide, silver, and gold NP, using data manually collected from the literature. Analyses with zinc oxide and silver NP revealed a correlation between their toxicity and both internal (intrinsic properties, size, shape, surface charge) and external (cell and analysis-related properties) factors. For zinc oxide, it was found that coating had significant influence on cell viability, with a critical threshold identified at 20 µg/ml concentration and 10 nm size. Similarly, for silver NPs, concentration, size, and exposure time were significant factors. Coating with organic macromolecules increased cell viability, whereas green-synthesized NPs (using bacteria, plant extracts, algae) decreased it. The gold NP study highlighted that ensemble methods were more effective in elucidating complex relationships, with cellular uptake linked to particle size, zeta potential, concentration, and exposure time. Overall, this thesis contributes to safer-by-design strategies, crucial for developing commercially viable and safe NMs. The findings advocate for a broader toxicity evaluation approach, considering various physicochemical aspects and experimental procedures. The complex interactions observed suggest that advanced algorithms are necessary for accurate modeling, supporting the optimization of experimental parameters in NP engineering for biomedical applications.