Transcriptomics-Based and AOP-Informed Structure-Activity Relationships to Predict Pulmonary Pathology Induced by...

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