Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis

Area of Science:

• Nanotechnology Safety
• Computational Toxicology
• Pulmonary Medicine

Background:

• Nanotechnology advancements necessitate predictive models for assessing nanoparticle safety, especially concerning irreversible lung fibrosis.
• Identifying specific descriptors for predicting nanoparticle-induced lung fibrosis is critical for in silico model development.

Purpose of the Study:

• To uncover essential predictive descriptors for nanoparticle-induced pulmonary fibrosis.
• To develop in silico models for assessing the safety of metal oxide nanoparticles (MeONPs) in the context of lung fibrosis.

Main Methods:

• Analyzed metal oxide nanoparticle (MeONP) interactions with biological media and cell lines (macrophages, epithelial cells).
• Evaluated physicochemical properties, nano-bio interactions, and cellular responses (membrane, lysosome, mitochondria).
• Assessed fibrogenic potential in vivo and employed random forest classification to identify predictive descriptors from in chemico, in vitro, and in vivo data.

Main Results:

• Seven key features from 18 quantitative descriptors were identified for predicting MeONP pro-fibrogenic potential.
• Interleukin-1 beta (IL-1β) was the most significant predictor (27.8%), followed by mitochondrial activity (NADH levels) in macrophages (17.6%).
• Other key features include TGF-β1 release, epithelial cell NADH levels, dissolution, zeta potential, and hydrodynamic size.

Conclusions:

• A combination of seven in chemico and in vitro descriptors, reflecting nano-bio interactions, can predict nanoparticle-induced lung fibrosis.
• These findings provide crucial insights for developing in silico predictive models for nano-induced pulmonary fibrosis.
• This research supports the advancement of nanotechnology safety assessments through computational approaches.