Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial‐based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)‐based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure–activity relationships at nanoscale (nano‐QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

In an in vitro nanotoxicity system, cell−nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of nanomaterials and their chemical composition or "corona" have been widely studied in context with nanotoxicology. However, rare reports are available, which delineate the details of the cell shape index (CSI) and nuclear area factors (NAFs) as a descriptor of the type of nanomaterials. In this paper, we propose a machine-learning-based graph modeling and correlation-establishing approach using tight junction protein ZO-1-mediated alteration in the cell/nuclei phenotype to quantify and propose it as indices of cell−NP interactions. We believe that the phenotypic variation (CSI and NAF) in the epithelial cell is governed by the physicochemical descriptors (e.g., shape, size, zeta potential, concentration, diffusion coefficients, polydispersity, and so on) of the different classes of nanomaterials, which critically determines the intracellular uptake or cell membrane interactions when exposed to the epithelial cells at sub-lethal concentrations. The intrinsic and extrinsic physicochemical properties of the representative nanomaterials (NMs) were measured using optical (dynamic light scattering, NP tracking analysis) methods to create a set of nanodescriptors contributing to cell−NM interactions via phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine.

Daniel Rosenkranz received his B.Sc. degree in 2011 and his M.Sc. degree in 2014 from the University of Oldenburg. He later worked as a junior researcher at the University of Oldenburg till 2015, funded by the foundation of the metal industry in the northwest Germany. Since 2016, he is working as a Ph.D. scholar at the German Federal Institute of Risk Assessment and the German Federal Institute for Material Research and Testing. His research interests include nanomaterial characterization, fundamentals in analytics, matrix matched measurement strategies, low volume injection systems, protein-nanomaterial interactions.

The knowledge about a potential in vivo uptake and subsequent toxicological effects of aluminum (Al), especially in the nanoparticulate form, is still limited. This paper focuses on a three day oral gavage study with three different Al species in Sprague Dawley rats. The Al amount was investigated in major organs in order to determine the oral bioavailability and distribution. Al-containing nanoparticles (NMs composed of Al 0 and aluminum oxide (Al 2 o 3)) were administered at three different concentrations and soluble aluminum chloride (AlCl 3 •6H 2 O) was used as a reference control at one concentration. A microwave assisted acid digestion approach followed by inductively coupled plasma mass spectrometry (ICP-MS) analysis was developed to analyse the Al burden of individual organs. Special attention was paid on how the sample matrix affected the calibration procedure. After 3 days exposure, AlCl 3 •6H 2 o treated animals showed high Al levels in liver and intestine, while upon treatment with Al 0 NMs significant amounts of Al were detected only in the latter. In contrast, following Al 2 o 3 NMs treatment, Al was detected in all investigated organs with particular high concentrations in the spleen. A rapid absorption and systemic distribution of all three Al forms tested were found after 3-day oral exposure. The identified differences between Al 0 and Al 2 o 3 NMs point out that both, particle shape and surface composition could be key factors for Al biodistribution and accumulation. Aluminum is one of the most abundant elements on earth and is widely used in many different consumer product applications due to its unique characteristics. As an ubiquitous element, Al occurs in natural sources, e.g. food and drinking water, as well as in food additives, packaging and kitchenware 1,2. Besides industrial, also agricultural, medical and consumer product uses are known for synthetic alumina, mixed Al silicate and Al oxide NMs. Especially composite materials containing Al show favourable properties when being used as packaging materials for the protection of food against humidity and oxidation and are more and more common 3-6. Moreover, various Al salts are used in food as additives, e.g. as stabilizers, pH regulators and anti-caking agents 7,8. As a consequence of modern life style Al-containing materials and substances are of high abundance in the human environment. In our study we could show de novo synthesis of Al-containing nanomaterials from ions after induction of a pH shift 9. Based on this and because of the detection and quantification of Al in biological media is difficult, one should assume at least partial particle concentration in the body after oral uptake of Al. This raises questions about the potential hazard of particulate Al species. Adverse effects of Al have been repeatedly discussed in the past. Neurodegenerative diseases such as Alzheimer's 10,11 as well as certain bone diseases 12-14 and dialysis dementia 15,16 have been attributed to Al exposure. Although inhalation and dermal uptake of Al may ...

With the advent of Nanotechnology, the use of nanomaterials in consumer products is increasing on a daily basis, due to which a deep understanding and proper investigation regarding their safety and risk assessment should be a major priority. To date, there is no investigation regarding the microrheological properties of nanomaterials (NMs) in biological media. In our study, we utilized in silico models to select the suitable NMs based on their physicochemical properties such as solubility and lipophilicity. Then, we established a new method based on dynamic light scattering (DLS) microrheology to get the mean square displacement (MSD) and viscoelastic property of two model NMs that are dendrimers and cerium dioxide nanoparticles in Dulbecco’s Modified Eagle Medium (DMEM) complete media at three different concentrations for both NMs. Subsequently, we established the cytotoxicological profiling using water-soluble tetrazolium salt-1 (WST-1) and a reactive oxygen species (ROS) assay. To take one step forward, we further looked into the tight junction properties of the cells using immunostaining with Zonula occluden-1 (ZO-1) antibodies and found that the tight junction function or transepithelial resistance (TEER) was affected in response to the microrheology and cytotoxicity. The quantitative polymerase chain reaction (q-PCR) results in the gene expression of ZO-1 after the 24 h treatment with NPs further validates the findings of immunostaining results. This new method that we established will be a reference point for other NM studies which are used in our day-to-day consumer products.

Computational Nanotoxicology Machine learning tools are making great strides in advancing computational nanotoxicology via in‐silico modeling and ab‐initio simulations to understand the nano‐bio interactions from environmental and health safety perspectives. In article number http://doi.wiley.com/10.1002/aisy.202000084, Ajay Vikram Singh and co‐workers describe the potential, reality, challenges, and future advances that artifi cial intelligence (AI) and machine learning (ML) present in advanced material design and toxicity predictions.

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Nano-carrier systems such as liposomes have promising biomedical applications. Nevertheless, characterization of these complex samples is a challenging analytical task. In this study a coupled hydrodynamic chromatography-single particle-inductively coupled plasma mass spectrometry (HDC-spICP-MS) approach was validated based on the technical specification (TS) 19590:2017 of the international organization for standardization (ISO). The TS has been adapted to the hyphenated setup. The quality criteria (QC), e.g., linearity of the calibration, transport efficiency, were investigated. Furthermore, a cross calibration of the particle size was performed with values from dynamic light scattering (DLS) and transmission electron microscopy (TEM). Due to an additional Y-piece, an online-calibration routine was implemented. This approach allows the calibration of the ICP-MS during the dead time of the chromatography run, to reduce the required time and enhance the robustness of the results. The optimized method was tested with different gold nanoparticle (Au-NP) mixtures to investigate the characterization properties of HDC separations for samples with increasing complexity. Additionally, the technique was successfully applied to simultaneously determine both the hydrodynamic radius and the Au-NP content in liposomes. With the established hyphenated setup, it was possible to distinguish between different subpopulations with various NP loads and different hydrodynamic diameters inside the liposome carriers.