Strategy for Identification of Nanomaterials’ Critical Properties Linked to Biological Impacts: Interlinking of Experimental and Computational Approaches

Abstract

Significant progress has been made over the last 10 years towards understanding those characteristics of nanoscale particles which correlate with enhanced biological activity and/or toxicity, as the basis for development of predictive tools for risk assessment and safer-by-design strategies. However, there are still a number of disconnects in the nanosafety workflow that hamper rapid progress towards full understanding of nano-specific mechanisms of action and nanomaterials (NMs)-induced adverse outcome pathways. One such disconnect is between physico-chemical characteristics determined experimentally as part of routine NMs characterisation, and the ability to predict a NM’s uptake and impacts on biological systems based on its pristine physico-chemical characteristics. Identification of critical properties (physico-chemical descriptors) that confer the ability to induce harm in biological systems under the relevant exposure conditions is central, in order to enable both prediction of impacts from related NMs [via quantitative property-activity or structure-activity relationshipsQuantitative (nano)structure-activity relationships (QnSAR) (QPARs/QSARs)] and development of strategies to ensure that these features are avoided in NM production in the future (“safety by design”). For this purpose, we have launched the Enalos InSilico platform, which is dedicated to the dissemination of our developed in silico workflows for NM risk assessment. So far, two predictive models have been made available online. The first tool is a Quantitative Nanostructure-Activity Relationship (QNAR) model for the prediction of the cellular uptake of NMs in pancreatic cancer cells and the second is an online tool for in silico screening of iron oxide NMs with a predictive classification model for their toxicological assessment.