Transcriptomics?Based and AOP?Informed Structure–Activity Relationships to Predict Pulmonary Pathology Induced by Multiwalled Carbon Nanotubes

This study presents a novel strategy that employs quantitative structure–activity relationship models for nanomaterials (Nano-QSAR) predicting transcriptomic pathway level response using lung tissue inflammation, an essential key event (KEs) in the existing adverse outcome (AOP) fibrosis, as model response. Transcriptomic profiles of mouse lungs exposed to ten different multiwalled carbon nanotubes (MWCNTs) are analyzed statistical and bioinformatics tools. Three pathways “agranulocyte adhesion diapedesis,” “granulocyte “acute phase signaling,” (1) commonly perturbed across MWCNTs panel, (2) show dose (Benchmark dose, BMDs), (3) anchored KEs identified fibrosis AOP, considered modelling. The three associated with inflammation. results aspect ratio (?) is directly correlated BMDs. establishes methodology QSAR construction based on canonical proposes grouping ?-values specific genes. Finally, shows how AOP framework can help guide modelling efforts; conversely, aid refining certain aspects question (here, fibrosis).