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Updated: Jul 10, 2026

Whole-Body Nanoparticle Aerosol Inhalation Exposures

Whole-Body Nanoparticle Aerosol Inhalation Exposures

Published on: May 7, 2013

An integrative omics-machine learning framework for predicting pulmonary responses to titanium dioxide nanoparticles.

Viacheslav Muratov¹, Karolina Jagiello^1,2, Tomasz Puzyn^1,2

¹University of Gdansk, Faculty of Chemistry, Laboratory of Environmental Chemoinformatics, Wita Stwosza 63, 80-308 Gdansk, Poland. karolina.jagiello@ug.edu.pl.

|February 26, 2026

Summary

We optimized the Transcriptomic Response Index (TRI) for nanosafety testing. A compact TRI variant effectively predicts lung responses to titanium dioxide nanoparticles (TiO2-NPs), streamlining omics-driven safety assessments.

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Area of Science:

Toxicogenomics
Nanomaterial Safety Assessment
Computational Biology

Background:

Lung transcriptomic data complexity poses challenges for nanosafety evaluation.
Existing methods require analyzing numerous genes, hindering efficient screening.
New Approach Methodologies (NAMs) need integrated, omics-ready endpoints.

Purpose of the Study:

To re-optimize the Transcriptomic Response Index (TRI) as a single, NAM-ready omics endpoint.
To assess TRI's utility for predicting lung responses to instilled titanium dioxide nanoparticles (TiO2-NPs).
To balance explained transcriptomic variance with predictive performance for TiO2-NP exposure.

Main Methods:

Utilized mouse lung gene-expression profiles from intratracheal instillation of five TiO2-NPs across multiple doses and time points.
Compressed 621 differentially expressed genes (DEGs) into a single TRI variable using Principal Component Analysis (PCA).
Evaluated TRI variants (TRI2-TRI29) and linked compact TRI2 to exposure predictors via ridge regression with genetic algorithm feature selection.

Main Results:

The full TRI (TRI29) reconstructed ~99.9% of transcriptomic variance.
A compact TRI2 variant captured ~44% of variance but maximized predictive performance (R2 = 0.79, Q2CV = 0.70, Q2 = 0.79).
TRI demonstrated portability across different nanomaterials and reversibility to gene expression.

Conclusions:

Re-optimized TRI provides a compact, interpretable, and validated endpoint for TiO2-NP nanosafety assessment.
TRI streamlines omics-driven screening, potency ranking, and extrapolation in regulatory NAM pipelines.
TRI balances explained variance and predictive performance, offering a bridge from NP attributes to transcriptomic response.