Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach

Abstract

Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure-activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle-nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(ii) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model.

Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles

“…In our previous studies, we showed that computational models could help forecasting various biological effects such as protein binding or cellular uptake for diverse sets of MNPs with a reasonable prediction accuracy evaluated by the means of external validation using MNPs that were not employed for model development. Similar retrospective studies have been reported by our colleagues (Gajewicz et al, 2014; Kar et al, 2014; Luan et al, 2014; Puzyn et al, 2011; Singh & Gupta, 2014; Toropova et al, 2014) for different sets of MNPs. However, so far there has not been any published report where molecular modeling was employed prospectively as part of an experimental and iterative design of MNPs with the desired biological activity.…”

“…Similarly, it would be highly desirable to develop computer-based approaches to ( i ) predict the biological (including toxicological) effects of MNPs solely from their physical, geometrical, and chemical properties (Carbó-Dorca & Besalú, 2011), and ( ii ) guide experimental investigations by focusing costly toxicological studies on a small number of selected or rationally designed MNPs. The need to develop such approaches has been well articulated in recent scientific literature (Burello & Worth, 2011; Kar et al, 2014; Luan et al, 2014; Puzyn et al, 2009; Singh & Gupta, 2014; Winkler et al, 2012). …”

Toxicity of engineered metal oxide nanomaterials mediated by nano–bio–eco–interactions: a review and perspective

“…Nowadays, a growing body of data shows the potential of QSAR as an alternative to interpret and model the physicochemical properties of nanomaterials and their toxicities. The majority of laboratory studies on biological endpoints are mainly in vitro studies, including linear/log-linear regression models of EC 50 /LC 50 cytotoxicity, 29,36,133,134,136,137 the concentration of nanoparticles leading to 50% reduction in cell viability (TC 50 ), 143 damage to cellular membranes (units L −1 ) via lactate dehydrogenase (LDH) release, 130,135,140,141 oxidative stress, 121 intracellular calcium flux, 121 mitochondrial membrane potential, 121,132,138 surface membrane permeability, 121 cytotoxic inhibition ratio with MTT assay, 139 cell apoptosis, 131,132 ATP content, 132,138 apoptosis, 138 reducing equivalents, 132,138 plasma membrane leakage, 128 and cell membrane damage via propidium iodide uptake. For other related computational modeling studies (e.g.…”

Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota

“…In the context of Cheminformatics, the effect of these deviations has been quantified using Moving Average (MA) operators. MA operators have been used to quantify perturbations in complex systems (Duardo-Sanchez et al, 2014), and nanoparticles (Luan et al, 2014; Messina et al, 2015). …”