Graphene materials have entered a phase of maturity in their development that is characterized by their explorative utilization in various types of applications and fields from electronics to biomedicine. Herein, we describe the recent advances made with graphene-related materials in the biomedical field and the challenges facing these exciting new tools both in terms of biological activity and toxicological profiling in vitro and in vivo. Graphene materials today have mainly been explored as components of biosensors and for construction of matrices in tissue engineering. Their antimicrobial activity and their capacity to act as drug delivery platforms have also been reported, however, not as coherently. This report will attempt to offer some perspective as to which areas of biomedical applications can expect graphene-related materials to constitute a tool offering improved functionality and previously unavailable options.

Prompted by the excitement from the description of single layer graphene, increased attention for potential applications in the biomedical field has been recently placed on graphene oxide (GO). Determination of the opportunities and limitations that GO offers in biomedicine are particularly prone to inaccuracies due to wide variability in the preparation methodologies of GO material in different laboratories, that results in significant variation in the purity of the material and the yield of the oxidation reactions, primarily the Hummers method used. Herein, the fabrication of highly pure, colloidally stable, and evenly dispersed GO in physiologically-relevant aqueous buffers in comparison to conventional GO is investigated. The purified GO material is thoroughly characterized by a battery of techniques, and is shown to consist of single layer GO sheets of lateral dimensions below 500 nm. The cytotoxic impact of the GO in vitro and its inflammation profile in vivo is investigated. The purified GO prepared and characterized here does not induce significant cytotoxic responses in vitro, or inflammation and granuloma formation in vivo following intraperitoneal injection. This is one of the initial steps towards determination of the safety risks associated with GO material that may be interacting with living tissue.

Food matrix effects impact the bioavailability and toxicity of pharmaceuticals, nutraceuticals, pesticides, and engineered nanomaterials (ENMs). However, there are currently no standardized food models to test the impact of food matrix effects using in vitro gastrointestinal models. The purpose of this study was to establish a standardized food model (SFM) for evaluating the toxicity and fate of ingested ENMs and then to assess its efficacy by examining the impact of food matrix effects on the toxicity of TiO2 nanoparticles. The formulation of the SFM was based on the average composition of the US diet: 3.4% protein (sodium caseinate); 4.6% sugar (sucrose); 5.2% digestible carbohydrates (modified corn starch); 0.7% dietary fiber (pectin); 3.4% fat (corn oil); and, 0.5% minerals (sodium chloride). The SFM consisted of an oil-in-water emulsion suitable for use in both wet and dried forms. The dried form was produced by spray drying the emulsion to improve its handling and extend its shelf-life. The particle size (D32 = 135 nm), surface charge (−37.8 mV), viscosity, color (L*, a,* b* = 82.1, −2.5, 1.3), and microstructure of the wet SFM were characterized. The hydration properties, flowability (repose angle ≈ 27.9°; slide angle ≈ 28.2°), and moisture sorption isotherms of the dry SFM were comparable to commercial food powders. The potential gastrointestinal fate of the SFM was determined using a simulated gastrointestinal tract, including mouth, stomach, and small intestine steps. Conversion of the SFM into a powdered form did not impact its gastrointestinal fate. A nanotoxicology case study with TiO2 nanoparticles exposed to a tri-culture epithelial cell model showed that food matrix effects reduced ENM cytotoxicity more than 5-fold. The SFM developed in the current study could facilitate studies of the impact of food matrix effects on the gastrointestinal fate and toxicity of various types of food NPs.

Developing innovative delivery strategies remains an ongoing task to improve both efficacy and safety of drug-based therapy. Nanomedicine is now a promising field of investigation, rising high expectancies for treating various diseases such as malignancies. Putting drugs into liposome is an old story that started in the late 1960s. Because of the near-total biocompatibility of their lipidic bilayer, liposomes are less concerned with the safety issue related to the possible long-term accumulation in the body of most nanoobjects currently developed in nanomedicine. Additionally, novel techniques and recent efforts to achieve better stability (e.g., through sheddable coating), combined with a higher selectivity towards target cells (e.g., by anchoring monoclonal antibodies or incorporating phage fusion protein), make new liposomal drugs an attractive and challenging opportunity to improve clinical outcome in a variety of disease. This review covers the physicochemistry of liposomes and the recent technical improvements in the preparation of liposome-encapsulated drugs in regard to the scientific and medical stakes.

Effective in silico methods to predict protein corona compositions on engineered nanomaterials (ENMs) could help elucidate the biological outcomes of ENMs in biosystems without the need for conducting lengthy experiments for corona characterization. However, the physicochemical properties of ENMs, used as the descriptors in current modeling methods, are insufficient to represent the complex interactions between ENMs and proteins. Herein, we utilized the fluorescence change (FC) from fluorescamine labeling on a protein, with or without the presence of the ENM, as a novel descriptor of the ENM to build machine learning models for corona formation. FCs were significantly correlated with the abundance of the corresponding proteins in the corona on diverse classes of ENMs, including metal and metal oxides, nanocellulose, and 2D ENMs. Prediction models established by the random forest algorithm using FCs as the ENM descriptors showed better performance than the conventional descriptors, such as ENM size and surface charge, in the prediction of corona formation. Moreover, they were able to predict protein corona formation on ENMs with very heterogeneous properties. We believe this novel descriptor can improve in silico studies of corona formation, leading to a better understanding on the protein adsorption behaviors of diverse ENMs in different biological matrices. Such information is essential for gaining a comprehensive view of how ENMs interact with biological systems in ENM safety and sustainability assessments.