Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

A review on the structural characterization of nanomaterials for nano-QSAR models

Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.

A structure-activity approach towards the toxicity assessment of multicomponent metal oxide nanomaterials

The increase of human and environmental exposure to engineered nanomaterials (ENMs) due to the emergence of nanotechnology has raised concerns over their safety. The challenging nature of in vivo and in vitro toxicity assessment methods for ENMs, has led to emerging in silico techniques for ENM toxicity assessment, such as structure-activity relationship (SAR) models. Although such approaches have been extensively developed for the case of single-component nanomaterials, the case of multicomponent nanomaterials (MCNMs) has not been thoroughly addressed. In this paper, we present a SAR approach for the case metal and metal oxide MCNMs. The developed SAR framework is built using a dataset of 796 individual toxicity measurements for 340 different MCNMs, towards human cells, mammalian cells, and bacteria. The novelty of the approach lies in the multicomponent nature of the nanomaterials, as well as the size, diversity and heterogeneous nature of the dataset used. Furthermore, the approach used to calculate descriptors for surface loaded MCNMs, and the mechanistic insight provided by the model results can assist the understanding of MCNM toxicity. The developed models are able to correctly predict the toxic class of the MCNMs in the heterogeneous dataset, towards a wide range of human cells, mammalian cells and bacteria. Using the abovementioned approach, the principal toxicity pathways and mechanisms are identified, allowing a more holistic understanding of metal oxide MCNM toxicity.

An Insight into the Combined Toxicity of 3,4-Dichloroaniline with Two-Dimensional Nanomaterials: From Classical Mixture Theory to Structure-Activity Relationship

The assessment and prediction of the toxicity of engineered nanomaterials (NMs) present in mixtures is a challenging research issue. Herein, the toxicity of three advanced two-dimensional nanomaterials (TDNMs), in combination with an organic chemical (3,4-dichloroaniline, DCA) to two freshwater microalgae (Scenedesmus obliquus and Chlorella pyrenoidosa), was assessed and predicted not only from classical mixture theory but also from structure-activity relationships. The TDNMs included two layered double hydroxides (Mg-Al-LDH and Zn-Al-LDH) and a graphene nanoplatelet (GNP). The toxicity of DCA varied with the type and concentration of TDNMs, as well as the species. The combination of DCA and TDNMs exhibited additive, antagonistic, and synergistic effects. There is a linear relationship between the different levels (10, 50, and 90%) of effect concentrations and a Freundlich adsorption coefficient (K_F) calculated by isotherm models and adsorption energy (E_a) obtained in molecular simulations, respectively. The prediction model incorporating both parameters K_F and E_a had a higher predictive power for the combined toxicity than the classical mixture model. Our findings provide new insights for the development of strategies aimed at evaluating the ecotoxicological risk of NMs towards combined pollution situations.

Toxicity of Silver Nanoparticles in the Presence of Zinc Oxide Nanoparticles Differs for Acute and Chronic Exposures in Zebrafish

We assessed the acute toxicity effects (96 h) of silver nanoparticles (Ag NPs) and zinc oxide nanoparticles (ZnO NPs) and chronic (28 d) exposure to Ag NPs, including in combination with ZnO NPs. In the chronic studies, we further assessed the toxicokinetics and bioaccumulation of Ag and the resulting histopathological effects in the gill, intestine, and liver of zebrafish. Co-exposures with ZnO NPs reduced the toxicity of Ag NPs for acute (lethality) but enhanced the toxicity effects (tissue histopathology) for chronic exposures. The histological lesions for both NPs exposures in the gill included necrosis and fusion of lamellae, for the intestine necrosis and degeneration, and in the liver, mainly necrosis. The severity of the histological lesions induced by the Ag NPs was related to the amount of accumulated Ag in the zebrafish organs. The Ag accumulation in different organs was higher in the presence of ZnO NPs in the order of the gill > intestine > liver. Depuration kinetics illustrated the lowest half-life for Ag occurred in the gill and for the combined exposure of Ag with ZnO NPs. Our findings illustrate that in addition to tissue, time, and exposure concentration dependencies, the Ag NPs toxicity can also be influenced by the co-exposure to other NPs (here ZnO NPs), emphasizing the need for more combination exposure effects studies for NPs to more fully understand their potential environmental health risks.

Review and Prospects on the Ecotoxicity of Mixtures of Nanoparticles and Hybrid Nanomaterials

The rapid development of nanomaterials (NMs) and the emergence of new multicomponent NMs will inevitably lead to simultaneous exposure of organisms to multiple engineered nanoparticles (ENPs) at varying exposure levels. Understanding the joint impacts of multiple ENPs and predicting the toxicity of mixtures of ENPs are therefore evidently of importance. We reviewed the toxicity of mixtures of ENPs to a variety of different species, covering algae, bacteria, daphnia, fish, fungi, insects, and plants. Most studies used the independent-action (IA)-based model to assess the type of joint effects. Using co-occurrence networks, it was revealed that 53% of the cases with specific joint response showed antagonistic, 25% synergistic, and 22% additive effects. The combination of nCuO and nZnO exhibited the strongest interactions in each type of joint interaction. Compared with other species, plants exposed to multiple ENPs were more likely to experience antagonistic effects. The main factors influencing the joint response type of the mixtures were (1) the chemical composition of individual components in mixtures, (2) the stability of suspensions of mixed ENPs, (3) the type and trophic level of the individual organisms tested, (4) the biological level of organization (population, communities, ecosystems), (5) the exposure concentrations and time, (6) the endpoint of toxicity, and (7) the abiotic field conditions (e.g., pH, ionic strength, natural organic matter). This knowledge is critical in developing efficient strategies for the assessment of the hazards induced by combined exposure to multiple ENPs in complex environments. In addition, this knowledge of the joint effects of multiple ENPs assists in the effective prediction of hybrid NMs.

Nanomaterials: Quasi-SMILES as a flexible basis for regulation and environmental risk assessment

Basic principles and problems of the systematization of data on nanomaterials are discussed. The eclectic character of nanomaterials is defined as the key difference between nanomaterials and traditional substances. The quasi-SMILES technique is described and discussed. The possible role of the approach is bridging between experimentalists and developers of models for endpoints related to nanomaterials. The use of models on the possible impact of nanomaterials on the environment and human health has been collected and compared. The new criteria of the predictive potential for the above models are discussed. The advantage of the statistical criteria sensitive simultaneously to both the correlation coefficient and the root mean square error noted. The rejection of the border between the effect of the biochemical reality of substances at a molecular level and the effect of experiment conditions at the macro level gives the possibility to develop models that are epistemologically more reliable in the comparison with traditional models based exclusively on the molecular structure-biological activity interdependence (without taking into account experimental conditions). Models of the physicochemical and biochemical behaviour of nanomaterials are necessary in order to develop and apply new industrial achievements, everyday comfort species, medicine, cosmetics, and foods without negative effects on ecology and human health. The CORAL (abbreviation CORrelation And Logic) software provides the user with the possibility to build up nano-QSAR models as a mathematical function of so-called correlation weights of fragments of quasi-SMILES. These models are built up via the Monte Carlo method. Apparently, the quasi-SMILES is a universal representation of nano-reality since there is no limitation to choose the list of eclectic data able to have an impact on nano-phenomena. This paradigm is a convenient language to the conversation of experimentalists and developers of models for nano-phenomena.

New methods of top-to-down mixture toxicity prediction: A case study of eliminating of the effects of cosolvent from binary mixtures

At present, the toxicity prediction of mixtures mainly focuses on the concentration addition (CA) and independent action (IA) based on individual toxicants to predict the toxicity of multicomponent mixtures. This process of predicting the toxicity of multicomponent mixtures based on single substances or low component mixtures is called down-to-top method in this study. However, due to the particularity of some toxicants, we have to use the top-to-down idea to obtain or eliminate the toxicity of some components from mixtures. For example, the toxicity of toxicants is obtained from the toxicity of a mixture with, especially toxic, cosolvent added. In the study, two top-to-down methods, the inverse CA (ICA) and inverse IA (IIA) models, were proposed to eliminate the effects of a certain component from multicomponent mixtures. Furthermore, taking the eight binary mixtures consisting of different shapes of cosolvents (isopropyl alcohol (IPA) having hormesis and dimethyl sulfoxide (DMSO)) and toxicants (two ionic liquids and two pesticides) as an example, combined with the interaction evaluated by CA and IA model, the influence of different shapes of components on top-to-down toxicity prediction was explored. The results showed that cosolvent IPA having hormesis may cause unpredictable effects, even at low concentrations, and should be used with caution. For DMSO, most of the toxicant's toxicity obtained by ICA and IIA models were almost in accordance with those observed experimentally, which showed that ICA and IIA could effectively eliminate the effects of cosolvent, even if toxic cosolvent, from the mixture. Ultimately, a frame of cosolvent use and toxicity correction for the hydrophobic toxicant were suggested based on the top-to-down toxicity prediction method. The proposed methods improve the existing framework of mixture toxicity prediction and provide a new idea for mixture toxicity evaluation and risk assessment.

Performance of TiO2/UV-LED-Based Processes for Degradation of Pharmaceuticals: Effect of Matrix Composition and Process Variables

Ultra-violet light-emitting diode (UV-LED)-based processes for water treatment have shown the potential to surpass the hurdles that prevent the adoption of photocatalysis at a large scale due to UV-LEDs' unique features and design flexibility. In this work, the degradation of five EU Watch List 2020/1161 pharmaceutical compounds was comprehensively investigated. Initially, the UV-A and UV-C photolytic and photocatalytic degradation of individual compounds and their mixtures were explored. A design of experiments (DoE) approach was used to quantify the effects of numerous variables on the compounds' degradation rate constant, total organic carbon abatement, and toxicity. The reaction mechanisms of UV-A photocatalysis were investigated by adding different radical scavengers to the mix. The influence of the initial pH was tested and a second DoE helped evaluate the impact of matrix constituents on degradation rates during UV-A photocatalysis. The results showed that each compound had widely different responses to each treatment/scenario, meaning that the optimized design will depend on matrix composition, target pollutant reactivity, and required effluent standards. Each situation should be analyzed individually with care. The levels of the electrical energy per order are still unfeasible for practical applications, but LEDs of lower wavelengths (UV-C) are now approaching UV-A performance levels.

Developing random forest based QSAR models for predicting the mixture toxicity of TiO2 based nano-mixtures to Daphnia magna

During emission, TiO₂ nanoparticles (NPs) might meet various chemicals, including metal ions and organic compounds in aquatic environments (e.g., surface water, sediments). At environmentally safe concentrations, combinations of both TiO₂ NPs and those chemicals might cause cocktail effects (i.e., mixture toxicity) to aquatic organisms. Previous models such as concentration addition and independent action require dose-response curves of single components in the mixtures to predict the mixture toxicity. Structure-activity relationship (QSAR) models might predict the toxicity of nano-mixtures without dose-response curves of single components in the mixtures. However, current quantitative structure-activity relationship (QSAR) models are mainly focused on predicting cytotoxicity (i.e., cell viability) of heterogeneous metallic TiO₂ nanoparticles (NPs) or mixtures of TiO₂ NPs and four metal ions (Cu²⁺, Cd²⁺, Ni^2+, and Zn²⁺). To minimize the experimental cost of nano-mixture risk assessment, in this study, we developed novel nano-mixture QSAR models to predict i) EC₅₀ of 76 nano-mixtures containing TiO₂ NPs and one of eight inorganic/organic compounds (i.e., AgNO₃, Cd(NO₃)₂, Cu(NO₃)₂, CuSO₄, Na₂HAsO₄, NaAsO₂, Benzylparaben and Benzophenone-3), to Daphnia magna(D. magna), and ii) immobilization of D. magna exposed to one of 98 mixtures containing TiO₂ NPs and one of eleven inorganic/organic compounds (i.e., AgNO₃, Cd(NO₃)₂, Cu(NO₃)₂, CuSO₄, Na₂HAsO₄, NaAsO₂, Benzylparaben Benzophenone-3, Pirimicarb, Pentabromodiphenyl Ether and Triton X-100). The nano-mixture QSAR models were developed with mixture descriptors (D_mix) combing quantum descriptors of mixture components (e.g., TiO₂ NPs and its partners) by using different machine learning techniques (i.e., random forest, neural network, support vector machine, and multiple linear regression). Nano-mixture QSAR models built with the random forest algorithm and proposed mixture descriptors exhibited good performance for predicting logEC₅₀ (Adj.R²_test = 0.955 ± 0.003, RMSE_test = 0.016 ± 0.002, and MAE_test = 0.008 ± 0.001) and immobilization (Adj.R²_test = 0.888 ± 0.011, RMSE_test = 11.327 ± 0.730, and MAE_test = 5.933 ± 0.442). The models developed in this study were implemented in a user-friendly application for assessing the aquatic toxicity of TiO₂ based nano-mixtures.