(Q)SAR For Chronic Toxicity Of Metal Oxide Nanomaterials To Danio Rerio

With the deepening of the experimental research and increasing of scientific achievements on nanotoxicology, it is imperative to develop the rapid, predictive, and high-throughput models or systems to evaluate of effects of manufactured nanoparticles on the environment as well as animals and humans. Recently, Quantitative Structure-Activity Relationship (QSAR) models as a promising method to evaluate the safety of nanomaterials begin to play their roles in predicting the potential toxicity of nanomaterials. As a supplement to the traditional nanotoxicological test methods, QSAR models for nanomaterials (nano-QSAR), combining the classic QSAR methods with the special physochemical properties of nanomaterials, offered a new way to screen nanomatreials rapidly and priotitize testing. Recently, however, the building of nano-QSAR models is facing great challenges, because the lack of the unified test standard make the desire to build nano-QSAR models according the data from the published literature not realistic. Therefore, the model building in the early stages of nano-QSAR research need rely on the data obtained from systematic studies under the same experimental conditions.The systematic, chronic exposure experiments were employed to investigate the long-term impacts of metal oxide nanomaterials on zebrafish (Danio rerio). The10kinds of metal oxide nanomaterials were nano-ZnO (zinc oxide), nano-CuO (copper oxide), nano-Fe2O3(ferric oxide), nano-Fe3O4(ferroferric oxide), nano-A-TiO2(anatase titanium dioxide), nano-R-TiO2(rutile titanium dioxide), nano-α-Al2O3(α-aluminum oxide), nano-γ-AlO3(γ-aluminum oxide), nano-M-ZrO2(monoclinic zirconia) and nano-T-ZrO2(tetragonal zirconia), respectively. Chronic exposure experiments included accumulation and elimination of nannomaterials in zebrafis, and oxidative stress of nanomatrials to zebrafish. We obtained the data from three aspects:(1) the parameters of structure and properties of nanomaterials,(2) the endpionts of accumulation and elimination of nanomaterials, and (3) the indicators of zebrafish under oxidative stress. Then the (Q)SAR models for chronic toxicity of metal oxide nanomaterials to zebrafish were build. 1. Structure characterization and property determination of nanomaterialsBased on the characterization of size distribution and ratio surface area, we obtained the structure parameters. A semi-static waterborne exposure (test media renewed daily) regimen was employed to perform the aggregation and sedimentation experiment in nanomaterial suspensions. The initial concentrations of nanomaterial suspensions were4.0and10.0mg/L, respectively. Due to the difficulty of sample digestion and determination of the last4kinds of nanomaterials, the results were discussed according to the reliable data of the first6kinds of nanmaterials. The results of3cycles showed that:6kinds of nanomaterials in the exposure period appeared sedimentation in varying degrees, the order of average sedimentation rates for24h is nano-CuO> nano-Fe3O4> nano-R-Ti02> nano-A-TiO2>nano-Fe203> nano-ZnO. The average sedimentation rate under4.0mg/L treatment groups was higher than that under10.0mg/L treatment groups. The average sedimentation rate of different crystal nanomaterials with the same chemical composition were different.2. Accumulation and eliminationSemi-static (test media renewed daily) experiments including a28-day uptake period and a24-day elimination period were performed to measure the profile of accumulation and elimination in zebrafish exposed to nanomaterials. The concentrations of the experimental suspensions were set to4.0and10.0mg/L. Through measuring the content of nanomaterials in the zebrafish at different time point, the maximum accumulation amount (Cmax), maximum accumulation factor (BAFmax), elimination rate and depuration rate coefficient were calculated, respectively. According these chronic toxicity endpoints, we assessed the profiles of accumulation and elimination of nanomaterials in zebrafish. Due to the difficulty of sample digestion and determination of the last4kinds of nanomaterials, the results were discussed according to the reliable data of the first6kinds of nanmaterials. The results showed that:(1) The varying accumulations of6kinds of nanomaterials were found in zebrafish. Among them, the accumulation amounts of nano-Fe2O3were the largest with Cmax of1.89mg/g for4.0mg/L treatment group and1.64mg/g for10.0mg/L treatment group, respectively. The accumulation amount of nano-R-TiO2was the least, the Cmax were0.13mg/g for4.0mg/L treatment group and0.28mg/g for10.0mg/L treatment group, respectively.(2) BAFmax of6kinds of nanomaterials were45.0～716.7, which spanned two orders of magnitude. Among them, the BAFmax of two iron oxide nanomaterials were both higher, and that of two crystal types of nano-TiO2were both lower. According to international general standard for classification, neither of them is considered as bioaccumulative substances.(3) During the elimination period, the decrease of body burden of nanomaterials conformed to the first-order exponential decay equation. Accumulated nanoparticles in zebrafish could be eliminated efficiently with the elimination rates ranged from86%to100%by24days post-exposure. Among them, the elimination rate of nano-CuO was the highest. During the experiment, the elimination of nano-ZnO from zebrafish was incompleted until24days post-exposure.(4) The accumulation amounts of two kinds of nano-TiO2in zebrafish were different. The values of Cmax and BAFmax of nano-A-TiO2were both higher than that of nano-R-TiO2.3. Oxidative StressAn exposure experiment for28days was performed under the semi-static waterborne exposure (test media renewed daily) regimen for all10kinds of nanomaterials in zebrafish. The concentrations of the experimental suspensions were set to4.0and10.0mg/L. Two biochemical indicators (MDA and GSH/GSSG) of zebrafish were measured at exposure time points of1day and28days in zebrafish. The results showed that:(1) At exposure for1day and28days, the MDA contents of nanomaterial treatment groups were higher than that in the control group overall. Among them, the MDA contents in treatment groups of nano-ZnO、nano-Fe2O3、nano-Fe3O4、 nano-α-AlO3、nano-γ-Al2O3and nano-M-ZrO2were significantly higher than control. The MDA content of nano-CuO treatment groups were similar with control.(2) At exposure for1day and28days, the values of GSH/GSSG of nanomaterial treatment groups were higher than that in the control group overall. At two time points, the treatment groups, which significantly promote GSH/GSSG, were different. In addition, for the nanomaterials that toxicity were higher (such as nano-γ-Al2O3), the increasing GSH/GSSG was instead of death after toxicity reached a certain level.(3) The nanomaterials with the same chemical composition and different crystal structures had the different induction effect on the MDA and GSH/GSSG.(4) The MDA and GSH/GSSG were not significantly different between the two exposure concentrations.(5) At28days of exposure, two biochemical indicators and accumulation amouts were positive correlation, and the correlation between MDA content and the accumulation amount reached a significant level.4. Building (Q)SAR modelBased on the study aboved, the relationship between structural parameters and biological activity of nanomaterials were analyzed qualitatively and quantitatively. And then the nano-(Q)S AR was build. Finally, the goodness of fit was tested, and the external validation of model was conducted according to literature data.The qualitative analysis showed that mortality and oxidative damage of zebrafish were related to average particle size, surface area and sedimentation rate of metal oxide nanomaterials. The oxidative damage or mortality can occur when some structure parameter reach certain level.The quantitative analysis showed that:there were significant negative correlations between the average particle sizes and Cmax, BAFmax.This result suggests that the particle size plays an important role on uptake and accumulation of nanomaterials in zebrafish. The six simple but statistically significant nano-QSAR models were as follows:(1) The relationship between Cmax(4.0mg/L)(y1) and average particle size (x) is y1=-0.009x+2.400(R2=0.581, P=0.004);(2) The relationship between Cmax(10.0mg/L)(y2) and average particle size (x) is y2=-0.006x+1.994(R2=0.505, P=0.010);(3) The relationship between BAFmax(4.0mg/L)(y3) and average particle size (x) is y3=-3.663x+965.900(R2=0.674, P=0.001);(4) The relationship between BAFmax(10.0mg/L)(y4) and average particle size (x) is y4=-1.209x+383.028(R2=0.615, P=0.003);(5) The relationship between Cmax (y5) and average particle size (x) is y3=y5=-0.007x+2.197(R2=0.522, P<0.001);(6) The relationship between BAFmax (y6) and average particle size (x) is y6=-2.436x+674.464(R2=0.448, P<0.001).The internal and external test of models shows that they have the certain goodness-of-fit, robustness and the ability to predict.In conclusion, this study describes a methodology for developing (Q)SAR models to identify structure features of metal oxide nanomaterials that can induce potential accumulation effects and oxidative damages to zebrafish. The exploratory attempt for building (Q)SAR for chronic toxicity effect of metal oxide nanomaterials to zebraflsh confirmed the correlation between the structure and their chronic toxicity, and select a structural parameter——particle size that could influence the accumulation of metal oxide nanomaterials in zebrafish. The models develop here successfully give possibility to use structure features to build up the predictive model for accumulation effect of metal oxide nanoparticles.