A human cell panel for evaluating safe application of nano-ZrO2/polymer composite in water remediation.

Nanomaterials, such as ZrO2 nanoparticles (ZrO2 NPs), are very effective in water remediation. However, the safety issues related to nanoparticle release and toxicity to humans remain to be resolved. Here we evaluated the cytotoxicity of ZrO2 NPs and their adducts with pollutants using a human cell panel containing stomach, intestine, liver and kidney cells. We found that different pollutants or ZrO2NP/pollutant adducts targeted cells from different organs, suggesting the necessity of a cell panel to model oral exposures. The cooperation of ZrO2 NPs and pollutants was quite complex, consisting of synergistic, antagonistic, or additive effects. For example, ZrO2 NPs enhanced the cytotoxicity of Pb2+ in GES-1 cells and of Pb2+, Cd2+ in FHC cells, while alleviating the toxicity of Pb2+ and As (III) in HepG2 and Hek293 cells. Our results also indicated that even concentrations of pollutants that meet the national standard, the ZrO2 NPs concentration should be kept below 17 µg/mL to avoid ZrO2 NP/pollutant adduct synergistic toxicity.

Design of Small Nanoparticles Decorated with Amphiphilic Ligands: Self-Preservation Effect and Translocation into a Plasma Membrane.

Design of nanoparticles (NPs) for biomedical applications requires a thorough understanding of cascades of nano-bio interactions at different interfaces. Here, we take into account the cascading effect of NP functionalization on interactions with target cell membranes by determining coatings of biomolecules in biological media. Cell culture experiments show that NPs with more hydrophobic surfaces are heavily ingested by cells in both the A549 and HEK293 cell lines. However, before reaching the target cell, both the identity and amount of recruited biomolecules can be influenced by the pristine NPs' hydrophobicity. Dissipative particle dynamics (DPD) simulations show that hydrophobic NPs acquire coatings of more biomolecules, which may conceal the properties of the as-engineered NPs and impact the targeting specificity. Based on these results, we propose an amphiphilic ligand coating on NPs. DPD simulations reveal the design principle, following which the amphiphilic ligands first curl in solvent to reduce the surface hydrophobicity, thus suppressing the assemblage of biomolecules. Upon attaching to the membrane, the curled ligands extend and rearrange to gain contacts with lipid tails, thus dragging NPs into the membrane for translocation. Three NP-membrane interaction states are identified that are found to depend on the NP size and membrane surface tension. These results can provide useful guidelines to fabricate ligand-coated NPs for practical use in targeted drug delivery, and motivate further studies of nano-bio-interactions with more consideration of cascading effects.

Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling.

The discovery of biocompatible or bioactive nanoparticles for medicinal applications is an expensive and time-consuming process that may be significantly facilitated by incorporating more rational approaches combining both experimental and computational methods. However, it is currently hindered by two limitations: (1) the lack of high-quality comprehensive data for computational modeling and (2) the lack of an effective modeling method for the complex nanomaterial structures. In this study, we tackled both issues by first synthesizing a large library of nanoparticles and obtained comprehensive data on their characterizations and bioactivities. Meanwhile, we virtually simulated each individual nanoparticle in this library by calculating their nanostructural characteristics and built models that correlate their nanostructure diversity to the corresponding biological activities. The resulting models were then used to predict and design nanoparticles with desired bioactivities. The experimental testing results of the designed nanoparticles were consistent with the model predictions. These findings demonstrate that rational design approaches combining high-quality nanoparticle libraries, big experimental data sets, and intelligent computational models can significantly reduce the efforts and costs of nanomaterial discovery.