Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials

The development of
new approach methodologies (NAMs) to replace
current in vivo 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.