Wisdom of crowds for supporting the safety evaluation of nanomaterials

Saarimäki, L.A.; Fratello, M.; del Giudice, G.; Di Lieto, E.; Afantitis, A.; Alenius, H.; Chiavazzo, E.; Gulumian, M.; Karisola, P.; Lynch, I.; Mancardi, G.; Melagraki, G.; Netti, P.; Papadiamantis, A.G.; Peijnenburg, W.J.G.M.; Santos, H.A.; Serchi, T.; Shahbazi, M.-A.; Stoeger, T.; Valsami-Jones, E.; Vivo, P.; Vrček, I.V.; Vogel, U.; Wick, P.; Winkler, D.A.; Serra, A.; Greco, D.

doi:doi:10.1021/acs.est.5c00841

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...Show moreThe 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.
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Wisdom of crowds for supporting the safety evaluation of nanomaterials