Cu-doped TiO2 nanoparticles improve local antitumor immune activation and optimize dendritic cell vaccine strategies

Nanoparticle-mediated cancer immunotherapy holds great promise, but more efforts are needed to obtain nanoformulations that result in a full scale activation of innate and adaptive immune components that specifically target the tumors. We generated a series of copper-doped TiO₂ nanoparticles in order to tune the kinetics and full extent of Cu²⁺ ion release from the remnant TiO₂ nanocrystals. Fine-tuning nanoparticle properties resulted in a formulation of 33% Cu-doped TiO₂ which enabled short-lived hyperactivation of dendritic cells and hereby promoted immunotherapy. The nanoparticles result in highly efficient activation of dendritic cells ex vivo, which upon transplantation in tumor bearing mice, exceeded the therapeutic outcomes obtained with classically stimulated dendritic cells. Efficacious but simple nanomaterials that can promote dendritic cancer cell vaccination strategies open up new avenues for improved immunotherapy and human health.

Prediction models on biomass and yield of rice affected by metal (oxide) nanoparticles using nano-specific descriptors

The use of in silico tools to investigate the interactions between metal (oxide) nanoparticles (NPs) and plant biological responses is preferred because it allows us to understand molecular mechanisms and improve prediction efficiency by saving time, labor, and cost. In this study, four models (C5.0 decision tree, discriminant function analysis, random forest, and stepwise multiple linear regression analysis) were applied to predict the effect of NPs on rice biomass and yield. Nano-specific descriptors (size-dependent molecular descriptors and image-based descriptors) were introduced to estimate the behavior of NPs in plants to appropriately represent the wide space of NPs. The results showed that size-dependent molecular descriptors (e.g., E-state and connectivity indices) and image-based descriptors (e.g., extension, area, and minimum ferret diameter) were associated with the behavior of NPs in rice. The performance of the constructed models was within acceptable ranges (correlation coefficient ranged from 0.752 to 0.847 for biomass and from 0.803 to 0.905 for yield, while the accuracy ranged from 64% to 77% for biomass and 81% to 89% for yield). The developed model can be used to quickly and efficiently evaluate the impact of NPs under a wide range of experimental conditions and sufficient training data.

Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review

Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.