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The Quantitative Structure-Activity Relationship (QSAR) has been used to investigate organic mixtures but QSAR in the nanomaterial field (QNAR) is still new. Toxicity is a result of the interaction of many substances. QNAR research focuses on a single nanomaterial in the long-term. It is difficult to find an appropriate descriptor to build a model due to the complexity of the mixture. Here, we attempt to build a QNAR model to predict cell viability for HK-2 cells exposed to a mixture containing nano-TiO 2 and heavy metals. HK-2 cells were exposed to four groups of mixtures containing heavy-metals and nanomaterials and CCK8 was added to obtain the number of living cells. At the same time, ROS was investigated to study this mechanism. Each descriptor of the components and mixtures were obtained using the formula D mix = [Formula: see text] respectively. We used the Multiple Partial Least Squares Regression (PLS) and Random Forest Regression (RF) to build a QNAR model. Both models reliably predict and assess viability of HK-2 cells exposed to the mixture. The RF model showed greater stability and higher precision in toxicity predictability and can be applied to environmental nano-toxicology., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The use of silver nanoparticles (NPs) in medical, industrial and agricultural fields is becoming more widespread every year. This leads to an increasing number of experimental toxicological and microbiological studies of silver NPs aimed at establishing the risk-benefit ratio for their application. The following key parameters affecting the biological activity of silver dispersions are traditionally taken into consideration: mean diameter of NPs, surface potential of NPs and equilibrium concentration of Ag + . These characteristics are mainly predetermined by the chemical nature of the capping agent used for stabilization. However, the extent to which they influence the biological activity and the toxicity of silver NPs varies greatly. In this work, dispersions of silver NPs stabilized with a wide array of substances of different chemical nature were used for quantitative evaluation of whether the various measurable properties of silver NPs fit as descriptors of linear QNAR (quantitative nanostructure-activity relationship) models for silver NP toxicity evaluation with respect to a model eukaryotic microorganism- Saccharomyces cerevisiae yeast cells. It was shown that among the factors that determine silver NP toxicity, the charge of particles, their colloidal stability and the ability to generate Ag + ions carry more importance than the descriptors related to the particle size. A significant synergistic effect between the ζ-potential and the colloidal stability of silver NPs on their toxicity was also discovered. Following this, a new descriptor has been proposed for the integral characterization of the silver dispersion colloidal stability. According to the obtained data, it can be considered applicable for building QNAR models of higher efficacy. The validity testing of the proposed model for theoretical prediction of silver NP toxicity using a wide range of living organisms has shown that this new descriptor correlates with toxicity much better compared to most traditionally used descriptors. Consequently, it seems promising in terms of being used not only in situations involving the rather narrow array of the objects tested, but also for the construction of silver NP toxicity models with respect to other living organisms.

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The development and implementation of safe-by-design strategies is key for the safe development of future generations of nanotechnology enabled products. The safety testing of the huge variety of nanomaterials that can be synthetized is unfeasible due to time and cost constraints. Computational modeling facilitates the implementation of alternative testing strategies in a time and cost effective way. The development of predictive nanotoxicology models requires the use of high quality experimental data on the structure, physicochemical properties and bioactivity of nanomaterials. The FP7 Project MODERN has developed and evaluated the main components of a computational framework for the evaluation of the environmental and health impacts of nanoparticles. This chapter describes each of the elements of the framework including aspects related to data generation, management and integration; development of nanodescriptors; establishment of nanostructure-activity relationships; identification of nanoparticle categories; hazard ranking and risk assessment.