The role of machine learning in predicting titanium dioxide nanoparticles induced pulmonary pathology using...

This study explores the application of machine learning (ML) in identifying transcriptomic changes associated with pulmonary pathologies induced by titanium dioxide nanoparticles (TiO₂-NPs). Such an approach significantly contributes to understanding the underlying mode-of-action of TiO₂-NP inhalation and follows the European Chemicals Agency's recommendations on applying Novel Approach Methodologies designed for reducing animal studies. The lung gene expression profiles from mice exposed via single intratracheal instillations to TiO₂-NPs with varying physicochemical properties on day 1, and day 28 post-exposure were analyzed to develop computational models for predicting the lung pathologies of rutile TiO₂-NPs. More than 600 random forest models were generated and rigorously validated, leading to the identification of 17 high-quality models with an average accuracy of 0.95. These models link nanoparticle-deposited surface area, charge, and post-exposure sampling time with dysregulation in key genes, including serum amyloid Saa1 (59.7-fold increase), Saa3 (253.7-fold increase), and the cytokine Ccl2 (3.4-fold increase). These genes are strongly associated with lung inflammation and fibrosis, key pathological responses to nanomaterial exposure. The study highlights critical nanoparticle features that drive transcriptomic changes. Hierarchical clustering confirmed the mechanistic links between nanoparticle properties and transcriptomic changes. This study demonstrates ML's potential to integrate omics data for nanosafety, offering a robust framework for early detection of adverse effects. The models enable the prediction of gene expression changes based on nanoparticle features, aiding in potential Safe and Sustainable-by-design of nanomaterials.