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.

Research on theoretical prediction methods for the mixture toxicity of engineered nanoparticles (ENPs) faces significant challenges. The application of in silico methods based on machine learning is emerging as an effective strategy to address the toxicity prediction of chemical mixtures. Herein, we combined toxicity data generated in our lab with experimental data reported in the literature to predict the combined toxicity of seven metallic ENPs for Escherichia coli at different mixing ratios (22 binary combinations). We thereafter applied two machine learning (ML) techniques, support vector machine (SVM) and neural network (NN), and compared the differences in the ability to predict the combined toxicity by means of the ML-based methods and two component-based mixture models: independent action and concentration addition. Among 72 developed quantitative structure-activity relationship (QSAR) models by the ML methods, two SVM-QSAR models and two NN-QSAR models showed good performance. Moreover, an NN-based QSAR model combined with two molecular descriptors, namely enthalpy of formation of a gaseous cation and metal oxide standard molar enthalpy of formation, showed the best predictive power for the internal dataset (R²_test = 0.911, adjusted R²_test = 0.733, RMSE_test = 0.091, and MAE_test = 0.067) and for the combination of internal and external datasets (R²_test = 0.908, adjusted R²_test = 0.871, RMSE_test = 0.255, and MAE_test = 0.181). In addition, the developed QSAR models performed better than the component-based models. The estimation of the applicability domain of the selected QSAR models showed that all the binary mixtures in training and test sets were in the applicability domain. This study approach could provide a methodological and theoretical basis for the ecological risk assessment of mixtures of ENPs.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease-19 (COVID-19) pandemic spread across the world and remains difficult to control. Environmental pollution and habitat conditions do facilitate SARS-CoV-2 transmission as well as increase the risk of exposure to SARS-CoV-2. The coexistence of microplastics (MPs) with SARS-CoV-2 affects the viral behavior in the indoor and outdoor environment, and it is essential to study the interactions between MPs and SARS-CoV-2 because they both are ubiquitously present in our environment. To determine the mechanisms underlying the impact of MPs on SARS-CoV-2, we used molecular dynamic simulations to investigate the molecular interactions between five MPs and a SARS-CoV-2 RNA fragment at temperatures ranging from 223 to 310 K in vacuum and in water. We furthermore compared the interactions of MPs and SARS-CoV-2 RNA fragment to the performance of SARS-CoV-1 and Hepatitis B virus (HBV) RNA fragments in interacting with the MPs. The interaction affinity between the MPs and the SARS-CoV-2 RNA fragment was found to be greater than the affinity between the MPs and the SARS-CoV-1 or HBV RNA fragments, independent of the environmental media, temperature, and type of MPs. The mechanisms of the interaction between the MPs and the SARS-CoV-2 RNA fragment involved electrostatic and hydrophobic processes, and the interaction affinity was associated with the inherent structural parameters (i.e., molecular volume, polar surface area, and molecular topological index) of the MPs monomers. Although the evidence on the infectious potential of SARS-CoV-2 RNA is not fully understood, humans are exposed to MPs via their lungs, and the strong interaction with the gene materials of SARS-CoV-2 likely affects the exposure of humans to SARS-CoV-2.