Abstract Early evaluation of long-term health hazards is crucial for safe introduction of new advanced materials. We here present an animal-free new approach method (NAM) for assessment of lung inflammation triggered by inhaled particles. First, early in vitro cellular responses to particulates are monitored for 24 h by fluorescence and scattering microscopy, from which the dynamics of mechanistically-relevant observables are extracted, including six inflammation-related key events (KEs). A patented algorithm then identifies temporal relations between these in vitro responses, representing the early Mode-of-Action (eMoA) for each material, ie material-specific triggering of material agnostic AOP. Finally, an in vitro-learnt in silico model, InFinite lung digital twin, generates predictions of long-term outcomes. Within the EU project nanoPASS, the predictions have been calibrated and validated against in vivo data for neutrophils influx into the lungs, as early systemic inflammation marker (KE1497), using a database of 44 benchmark materials including metal oxides, nanoclays, engineered carbon particles, and combustion products. For all doses and post-exposure timepoints form 1 d to 9 mo, the in vivo database comprises over 300 datapoints. Using 15 of these benchmark materials for calibration, predictions on 29 other benchmark materials reach around 82% accuracy across all doses and timepoints. Importantly, the developed testing approach does not rely on measuring any physico-chemical property of the material, making it suitable also for complex industrial and environmental samples. We demonstrate the applicability of the method to real-world materials by five industrial cases covering different compositions, applications and life-cycle stages: advanced nanomaterials, cement, electronic waste, dental fillings, and 3D printed plastics.
Urbančič et al. (Thu,) studied this question.