Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium. Issue 47 (19th October 2020) - Kokot, Hana; Kokot, Boštjan; Sebastijanović, Aleksandar; Voss, Carola; Podlipec, Rok; Zawilska, Patrycja; Berthing, Trine; Ballester‐López, Carolina; Danielsen, Pernille Høgh; Contini, Claudia; Ivanov, Mikhail; Krišelj, Ana; et al.
Abstract: On a daily basis, people are exposed to a multitude of health‐hazardous airborne particulate matter with notable deposition in the fragile alveolar region of the lungs. Hence, there is a great need for identification and prediction of material‐associated diseases, currently hindered due to the lack of in‐depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modeling, it is determined herein that the long‐lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows prediction of the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modeling, potentially relating outcomes to material properties for a large number of materials, and thus boosting safe‐by‐design‐based material development. Because of its profound implications for animal‐free predictive toxicology, this work paves the way to a more efficient and hazard‐free introduction of numerous new advanced materials into our lives. Abstract : The prediction of material‐associated diseases is currently hindered by the lack of understanding of underlying mechanisms. Counteracting of nanomaterial quarantining and nanomaterial cycling are identified as the main modes of cellular response in a living nanomaterial‐exposed lung epithelium. This enables animal‐free prediction of material‐induced chronic inflammation using only in vitro measurements and in silico modeling.