As an emerging pollutant in the life cycle of plastic products, micro/nanoplastics (M/NPs) are increasingly being released into the natural environment. Substantial concerns have been raised regarding the environmental and health impacts of M/NPs. Although diverse M/NPs have been detected in natural environment, most of them display two similar features, i.e.,high surface area and strong binding affinity, which enable extensive interactions between M/NPs and surrounding substances. This results in the formation of coronas, including eco-coronas and bio-coronas, on the plastic surface in different media. In real exposure scenarios, corona formation on M/NPs is inevitable and often displays variable and complex structures. The surface coronas have been found to impact the transportation, uptake, distribution, biotransformation and toxicity of particulates. Different from conventional toxins, packages on M/NPs rather than bare particles are more dangerous. We, therefore, recommend seriously consideration of the role of surface coronas in safety assessments. This review summarizes recent progress on the eco–coronas and bio-coronas of M/NPs, and further discusses the analytical methods to interpret corona structures, highlights the impacts of the corona on toxicity and provides future perspectives.

Background: The wide application of engineered nanoparticles has induced increasing exposure to humans and environment, which led to substantial concerns on their biosafety. Some metal oxides (MOx) have shown severe toxicity in cells and animals, thus safe designs of MOx with reduced hazard potential are desired. Currently, there is a lack of a simple yet effective safe design approach for the toxic MOx. In this study, we determined the key physicochemical properties of MOx that lead to cytotoxicity and explored a safe design approach for toxic MOx by modifying their hazard properties. Results: THP-1 and BEAS-2B cells were exposed to 0–200 μg/mL MOx for 24 h, we found some toxic MOx including CoO, CuO, Ni₂O₃ and Co₃O₄, could induce reactive oxygen species (ROS) generation and cell death due to the toxic ion shedding and/or oxidative stress generation from the active surface of MOx internalized into lysosomes. We thus hypothesized that surface passivation could reduce or eliminate the toxicity of MOx. We experimented with a series of surface coating molecules and discovered that ethylenediamine tetra (methylene phosphonic acid) (EDTMP) could form stable hexadentate coordination with MOx. The coating layer can effectively reduce the surface activity of MOx with 85-99% decrease of oxidative potential, and 65-98% decrease of ion shedding. The EDTMP coated MOx show negligible ROS generation and cell death in THP-1 and BEAS-2B cells. The protective effect of EDTMP coating was further validated in mouse lungs exposed to 2 mg/kg MOx by oropharyngeal aspiration. After 40 h exposure, EDTMP coated MOx show significant decreases of neutrophil counts, lactate dehydrogenase (LDH) release, MCP-1, LIX and IL-6 in bronchoalveolar lavage fluid (BALF), compared to uncoated particles. The haematoxylin and eosin (H&E) staining results of lung tissue also show EDTMP coating could significantly reduce the pulmonary inflammation of MOx. Conclusions: The surface reactivity of MOx including ion shedding and oxidative potential is the dominated physicochemical property that is responsible for the cytotoxicity induced by MOx. EDTMP coating could passivate the surface of MOx, reduce their cytotoxicity and pulmonary hazard effects. This coating would be an effective safe design approach for a broad spectrum of toxic MOx, which will facilitate the safe use of MOx in commercial nanoproducts.

Potential immune responses resulting from exposure to metal oxide nanoparticles (MeONPs) have been the subject of intensive discussion in the last decade. Despite the extensive use of MeONPs in several applications, their toxic effects on immune cells have rarely been predicted in silico because of the complexity of immune responses and the complicated properties of MeONPs. In the present study, machine learning (ML) methods coupled with high-throughput in vitro bioassays were used to develop models for predicting the toxicity of MeONPs in immune cells. An ML model with a high prediction accuracy (97% and 96% in the training and test sets, respectively) was constructed by resolving the class imbalance problem in training and applying an ensembled algorithm. Further, to verify the model, MeONPs outside the scope of the datasets were selected to examine their cytotoxicity experimentally. The model was validated against independent MeONPs, with an accuracy of 91%. ML methods coupled with intracellular imaging revealed that the toxic ions released in the lysosome were an important determinant of toxicity in immune cells. Furthermore, ζ-potential, electronegativity, and size are crucial factors for predicting nanotoxicity. We believe the established modeling framework will provide useful insights for designing and applying safe nanoparticles and facilitating decision-making for environmental and health protection.

Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and need to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment of chemicals, predicting chronic in vivo nanotoxicity remains challenging due to the intricate interactions at multiple interfaces like nano-biofluids and nano-subcellular organelles. Herein, we develop a multimodal feature fusion analysis framework to predict the fibrogenic potential of metal oxide nanoparticles (MeONPs) in female mice. Treating each nano-bio interface as an independent entity, eighty-seven features derived from MeONP-lung interactions are used to develop a machine learning-based predictive framework for lung fibrosis. We identify cell damage and cytokine (IL-1β and TGF-β1) production in macrophages and epithelial cells as key events closely associated with particle size, surface charge, and lysosome interactions. Experimental validations show that the developed in silico model has 85% accuracy. Our findings demonstrate the potential usefulness of this predictive model for risk assessment of nanomaterials and in assisting regulatory decision-making. While the model is developed based on 52 MeONPs, further validation using a larger nanoparticle library is necessary to confirm its broader applicability.