Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials
Saarimäki, Laura Aliisa Fratello, Michele Del Giudice, Giusy Di Lieto, Emanuele Afantitis, Antreas Alenius, Harri Chiavazzo, Eliodoro Gulumian, Mary Karisola, Piia Lynch, Iseult
Saarimäki, Laura Aliisa
Fratello, Michele
Del Giudice, Giusy
Di Lieto, Emanuele
Afantitis, Antreas
Alenius, Harri
Chiavazzo, Eliodoro
Gulumian, Mary
Karisola, Piia
Lynch, Iseult
Series / Report no.
Open Access
Type
Journal Article
Article
Article
Language
en
Date of publication
2025-07-17
Year of publication
Research Projects
Organizational Units
Journal Issue
Title
Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials
Translated Title
Published in
Environ Sci Technol 2025; 59(29):14969-14980
Abstract
The development of new approach methodologies (NAMs) to replace current testing for the safety assessment of engineered nanomaterials (ENMs) is hindered by the scarcity of validated experimental data for many ENMs. We introduce a framework to address this challenge by harnessing the collective expertise of professionals from multiple complementary and related fields ("wisdom of crowds" or WoC). By integrating expert insights, we aim to fill data gaps and generate consensus concern scores for diverse ENMs, thereby enhancing the predictive power of nanosafety computational models. Our investigation reveals an alignment between expert opinion and experimental data, providing robust estimations of concern levels. Building upon these findings, we employ predictive machine learning models trained on the newly defined concern scores, ENM descriptors, and gene expression profiles, to quantify potential harm across various toxicity end points. These models further reveal key genes potentially involved in underlying toxicity mechanisms. Notably, genes associated with metal ion homeostasis, inflammation, and oxidative stress emerge as predictors of ENM toxicity across diverse end points. This study showcases the value of integrating expert knowledge and computational modeling to support more efficient, mechanism-informed, and scalable safety assessment of nanomaterials in the rapidly evolving landscape of nanotechnology.