Using a one-step thermal reduction and non-covalent chemical functionalization process, PEGylated reduced nanographene oxide (rGOn-PEG) was produced from nanographene oxide (GOn) and characterized in terms of particle size, dispersion stability, chemistry, and photothermal properties, in view of its use for photothermal therapy (PTT) of non-melanoma skin cancer. GOn infrared spectrum presented more intense bands assigned to oxygen containing functional groups than observed for rGOn-PEG. GOn C/O ratio decreased more than 50% comparing with rGOn-PEG and nitrogen was present in the latter (N at % = 20.6) due to introduction of PEG-NH2. Thermogravimetric analysis allowed estimating the amount of PEG in rGOn-PEG to be of about 56.1%. Simultaneous reduction and PEGylation increased the lateral dimensions from 287 ± 139 nm to 521 ± 397 nm, as observed by transmission electron microscopy and dynamic light scattering. rGOn-PEG exhibited ≈13-fold higher absorbance in the near-infrared radiation (NIR) region, as compared to unmodified GOn. Low power (150 mW cm−2) NIR irradiation using LEDs resulted in rGOn-PEG heating up to 47 °C, which is within the mild PTT temperature range. PEGylation strongly enhanced the dispersibility of rGOn in physiological media (phosphate buffered saline, fetal bovine serum, and cell culture medium) and also improved the biocompatibility of rGOn-PEG, in comparison to GOn (25–250 μg mL−1). After a single NIR LED irradiation treatment of 30 min, a decrease of ≈38% in A-431 cells viability was observed for rGOn-PEG (250 μg mL−1). Together, our results demonstrate the potential of irradiating rGOn-PEG using lower energy, cheaper, smaller, and safer LEDs, as alternative to high power lasers, for NIR mild hyperthermia therapy of cancer, namely non-melanoma skin cancer.

Nanostructured carriers have been widely used in pharmaceutical formulations for dermatological treatment. They offer targeted drug delivery, sustained release, improved biostability, and low toxicity, usually presenting advantages over conventional formulations. Due to its large surface area, small size and photothermal properties, graphene oxide (GO) has the potential to be used for such applications. Nanographene oxide (GOn) presented average sizes of 197.6 ± 11.8 nm, and a surface charge of −39.4 ± 1.8 mV, being stable in water for over 6 months. 55.5% of the mass of GOn dispersion (at a concentration of 1000 µg mL−1) permeated the skin after 6 h of exposure. GOn dispersions have been shown to absorb near-infrared radiation, reaching temperatures up to 45.7 °C, within mild the photothermal therapy temperature range. Furthermore, GOn in amounts superior to those which could permeate the skin were shown not to affect human skin fibroblasts (HFF-1) morphology or viability, after 24 h of incubation. Due to its large size, no skin permeation was observed for graphite particles in aqueous dispersions stabilized with Pluronic P-123 (Gt–P-123). Altogether, for the first time, Gon’s potential as a topic administration agent and for delivery of photothermal therapy has been demonstrated.

Allergic contact dermatitis is the most frequent manifestation of immunotoxicity in humans with a prevalence rate of 15% to 20% over general population. Skin sensitization is a complex end point that was for a long time being evaluated using animal testing. Great efforts have been made to completely substitute the use of animals and replace them by integrating data from in vitro and in chemico assays with in sil ico calculated parameters. However, it remains undefined how to make the best use of the cumulative data in such a way that information gain is maximized and accomplished with the fewest number of tests possible. In this work, 3 skin sensitization prediction models were considered: one to discriminate sensitizers from non-sensitizers, considering a 2-level scale; one according to the GHS, considering a 3-level scale; and the other to categorize potency in a 6-level scale, according to available human data. We used a data set of known human skin allergens for which i n vitro, in chemico, and in silico descriptors where available to build classifiers based on soft and hard multivariate modeling. Model building, optimization, and refinement resulted in 100% accuracy in distinguishing between sensitizers and non-sensitizers. The same model was able to perform the characterization, in 3 and 6 levels, respectively, with 98.8 and 97.5% accuracy. Combining data from in vitro and in chemico tests with in silico descriptors is relatively simple to implement and some predictors are fitting the adverse outcome pathway for skin sensitization.