The increasing nanomedicine usage has raised concerns about their possible impact on human health. Present evaluation strategies for nanomaterials rely on a case-by-case hazard assessment. They take into account material properties, biological interactions, and toxicological responses. Authorities have also emphasized that exposure route and intended use should be considered in the safety assessment of nanotherapeutics. In contrast to an individual assessment of nanomaterial hazards, we propose in the present work a novel and unique evaluation strategy designed to uncover potential adverse effects of such materials. We specifically focus on spherical engineered nanoparticles used as parenterally administered nanomedicines. Standardized assay protocols from the US Nanotechnology Characterization Laboratory as well as the EU Nanomedicine Characterisation Laboratory can be used for experimental data generation. We focus on both cellular uptake and intracellular persistence as main indicators for nanoparticle hazard potentials. Based on existing regulatory specifications defined by authorities such as the European Medicines Agency and the United States Food and Drug Administration, we provide a robust framework for application-oriented classification paired with intuitive decision making. The Hazard Evaluation Strategy (HES) for injectable nanoparticles is a three-tiered concept covering physicochemical characterization, nanoparticle (bio)interactions, and hazard assessment. It is cost-effective and can assist in the design and optimization of nanoparticles intended for therapeutic use. Furthermore, this concept is designed to be adaptable for alternative exposure and application scenarios. To the knowledge of the authors, the HES is unique in its methodology based on exclusion criteria. It is the first hazard evaluation strategy designed for nanotherapeutics.

In the present study, we assessed, if the novel dual phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) inhibitor NVP-BEZ235 radiosensitizes triple negative (TN) MDA-MB-231 and estrogen receptor (ER) positive MCF-7 cells to ionizing radiation under various oxygen conditions, simulating different microenvironments as occurring in the majority of breast cancers (BCs). Irradiation (IR) of BC cells cultivated in hypoxic conditions revealed increased radioresistance compared to normoxic controls. Treatment with NVP-BEZ235 completely circumvented this hypoxia-induced effects and radiosensitized normoxic, reoxygenated, and hypoxic cells to similar extents. Furthermore, NVP-BEZ235 treatment suppressed HIF-1α expression and PI3K/mTOR signaling, induced autophagy, and caused protracted DNA damage repair in both cell lines in all tested oxygen conditions. Moreover, after incubation with NVP-BEZ235, MCF-7 cells revealed depletion of phospho-AKT and considerable signs of apoptosis, which were significantly enhanced by radiation. Our findings clearly demonstrate that NVP-BEZ235 has a clinical relevant potential as a radiosensitizer in BC treatment.

SPIONs) which are clinically used as magnetic resonance imaging contrast agents. [4] In addition, application of an (alternating) external magnetic field for therapeutic purposes offers interesting perspectives in oncology, in that such nanoparticles can be used for a hyperthermia therapy or to target drug loaded particles to tumors. The latter is referred to as magnetic drug targeting. [5] In order to study the in vivo biodistribution of such particles, they have to be labeled by conjugation of fluorescent or radioactive agents. However, these chemical modifications can significantly change the interaction patterns with biological matter since they have a direct impact on particle size and polydispersity, electrokinetic potential (ζ-potential), and possibly the protein corona. [6][7][8][9] It is therefore recommended to carry out, whenever possible, physicochemical characterization, formulation development, pharmacokinetic studies, and hazard and safety evaluations with non-tagged nanoparticles. [10] In recent years, magnetic particle imaging (MPI), magnetic resonance imaging (MRI) or computed tomography [11] have been discussed as label-free alternative to trace engineered metal nanoparticles within a biological system. The latter technology takes advantage of their strong X-ray absorption. [12,13] In medical research, laboratory-based micro-computed tomography (μCT) has been used to monitor the biodistribution of metal-based nanoparticles in vitro or in small experimental animals. [13][14][15][16] A challenge Metal-based nanoparticles are clinically used for diagnostic and therapeutic applications. After parenteral administration, they will distribute throughout different organs. Quantification of their distribution within tissues in the 3D space, however, remains a challenge owing to the small particle diameter. In this study, synchrotron radiation-based hard X-ray tomography (SRμCT) in absorption and phase contrast modes is evaluated for the localization of superparamagnetic iron oxide nanoparticles (SPIONs) in soft tissues based on their electron density and X-ray attenuation. Biodistribution of SPIONs is studied using zebrafish embryos as a vertebrate screening model. This label-free approach gives rise to an isotropic, 3D, direct space visualization of the entire 2.5 mm-long animal with a spatial resolution of around 2 μm. High resolution image stacks are available on a dedicated internet page (http://zebrafish.pharma-te.ch). X-ray tomography is combined with physico-chemical characterization and cellular uptake studies to confirm the safety and effectiveness of protective SPION coatings. It is demonstrated that SRμCT provides unprecedented insights into the zebrafish embryo anatomy and tissue distribution of label-free metal oxide nanoparticles.

Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be directly used or fine-tuned for a new domain. However, in histopathology, the problem domain is tissue-specific and putting together a labelled data set is challenging. On the other hand, whole slide-level annotations, such as biomarker levels, are much easier to obtain. We compare two pretrained models, one histology-specific and one from ImageNet on various computational pathology tasks. We show that a domain-specific model (HistoNet) contains richer information for biomarker classification, localization of biomarker-relevant morphology within a slide, and the prediction of expert-graded features. We use a weakly supervised approach to discriminate slides based on biomarker level and simultaneously predict which regions contribute to that prediction. We employ multitask learning to show that learned representations correlate with morphological features graded by expert pathologists. All of these results are demonstrated in the context of renal toxicity in a mechanistic study of compound toxicity in rat models. Our results emphasize the importance of histology-specific models and their knowledge representations for solving a wide range of computational pathology tasks.

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