134 Designing out the next asbestos: high-throughput NAMs, AOPs, FAIR data and the future of advanced material safety

Abstract The history of particle toxicology reminds us that innovation without foresight can have devastating consequences. Occupational exposure to asbestos, quartz and coal has resulted in global epidemics of lung disease—lessons learned too late and at enormous human cost. As development of advanced materials accelerates, often with the ambition of improving safety and sustainability, we must ensure that innovation does not once again outpace understanding. Designing out the next asbestos requires moving from reactive risk assessment to predictive, knowledge-driven design. This keynote will argue that operationalizing Safe and Sustainable by Design (SSbD), in alignment with emerging regulatory approaches, demands strategic integration of the collective knowledge gathered over decades in biology and toxicology. New Approach Methodologies (NAMs), Adverse Outcome Pathways (AOPs), and FAIR (Findable, Accessible, Interoperable, Reusable) data infrastructures are central to this transformation. Inhalation toxicology provides a critical testing ground. High-throughput screening, advanced in vitro lung models, and omics technologies generate unprecedented volumes of mechanistic data. Yet without structure, interoperability, and curation, their value for decision-making and design remains limited. When embedded within AOP frameworks and FAIR data ecosystems, these approaches can define applicability domains, support read-across and enable earlier identification of hazardous material properties. Advanced AI systems can further amplify this potential—but only if data and models are transparent, explainable, and mechanistically anchored. Achieving predictive and trusted advanced material safety requires coordinated action from researchers, regulators, and industry to ensure that the next generation of particles is designed for safety from the outset, rather than regulated after harm emerges.