Bio: Dr. El Yamani received her Ph.D. in Genetics from the Autonomous University of Barcelona. She began her work in nanotoxicology as a visiting researcher at the University of Oslo and later joined the Climate and Environmental Research Institute–NILU. She has contributed to multiple EU projects on nanosafety, focusing on the risks of nanomaterials to human health and the environment. Her research involves developing experimental models with regulatory relevance. Dr. El Yamani is also dedicated to the EU "Safe and Sustainable by Design" (SSbD) framework, supporting industries in developing safer, more sustainable nanomaterials.

Abstract: Engineered nanomaterials (NM) offer significant advantages in various applications due to their unique physicochemical features, such as surface chemistry, reactivity, and size-dependent behavior. However, these same properties also pose challenges for environmental and health safety. Reliable safety assessment of NM relies on accurate hazard characterization, which requires validated test methods and a comprehensive understanding of NM behaviors. Conventional in vitro and in-chemical methods often struggle to assess NM toxicity due to interactions with the test system or testing procedures. Such interferences can result in inaccurate toxicity predictions, hindering the risk assessment of NM. Despite progress in standardizing in vitro assays, systematic investigations of NM interference remain limited. To support optimizing the experimental design and minimize potential NM’s interferences, implementing a decision tree can be highly beneficial. The decision tree would serve as a practical tool to guide researchers in identifying and investigating potential NM’ s interferences. Offering step-by-step guidance, can help researchers pinpoint specific sources of interference and provide tailored strategies and best practices for mitigating them. Such a structured approach can significantly enhance the reliability, and reproducibility of research findings, promote the integration of in silico hazard predictions into risk assessments, and support science-based governance across industries.