Structure-activity Relationship Models for Hazard Assessment and Risk Management of Engineered Nanomaterials

Abstract

The widespread use of engineered nanomaterials (ENMs) for commercial purposes made human exposure to these materials almost inevitable. Moreover, the number of in vivo and in vitro studies reporting the potential adverse effects of exposure to ENMs is growing rapidly. Consequently, there is an urgent need to understand the interactions between ENMs and biological/environmental systems. Although the need to improve our understanding of the adverse health effects of ENMs has been recognised for some time, it has not been fully met to date. There are many reasons that have caused the hazard assessment of ENMs to fall behind the innovations in nanotechnology such as knowledge gaps exist in the field of nanotoxicology, difficulties in categorization of ENMs for toxicological considerations and uncertainties regarding the evaluation and regulation of potential risks of nanoparticles. The presence of a large number of ENMs with unknown risks has led to increased interest in the use of fast, cost-effective and efficient computational methods for predicting the toxic potential of ENMs. To that end, the potential use of in silico techniques, such as quantitative structure-activity relationship (QSAR), to model the relationship between biological activities and physicochemical characteristics of ENMs is investigated in this paper. The focus of this paper is on defining the current level of knowledge in (Q)SAR modeling of potential hazards of ENMs and demonstrating the use of (Q)SAR to predict the potential risks specific to ENMs with a case study. Moreover, it presents an overview of the (1) existing barriers currently limiting the development of robust nano-(Q)SAR models, (2) the current obstacles to regulatory acceptance of these models and (3) the integration of (Q)SARs into the risk assessment process. The result of this study demonstrated that the use of (Q)SAR modeling approach to model the toxicity of ENMs based on specific structural and compositional features greatly facilitates (1) filling knowledge gaps regarding the effect of specific parameters on the biological activities of ENMs, (2) predicting the potential risks associated with the exposure to ENMs, (3) classifying the ENMs according to their physicochemical properties and potential hazard degree and (4) reducing the risk by modifying ENMs based on the observed correlations between structural features and biological responses.