In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation.

The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs' underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction's reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

Toxicokinetics and bioaccumulation of silver sulfide nanoparticles in benthic invertebrates in an indoor stream mesocosm.

Mesocosms allow the simulation of environmentally relevant conditions and can be used to establish more realistic scenarios of organism exposure to nanoparticles. An indoor mesocosm experiment simulating an aquatic stream ecosystem was conducted to assess the toxicokinetics and bioaccumulation of silver sulfide nanoparticles (Ag2S NPs) and AgNO3 in the freshwater invertebrates Girardia tigrina, Physa acuta and Chironomus riparius, and determine if previous single-species tests can predict bioaccumulation in the mesocosm. Water was daily spiked at 10 µg Ag L-1. Ag concentrations in water and sediment reached values of 13.4 µg Ag L-1 and 0.30 µg Ag g-1 in the Ag2S NP exposure, and 12.8 µg Ag L-1 and 0.20 µg Ag g-1 in the AgNO3. Silver was bioaccumulated by the species from both treatments, but with approximately 1.5, 3 and 11 times higher body Ag concentrations in AgNO3 compared to Ag2S NP exposures in snails, chironomids and planarians, respectively. In the Ag2S NP exposures, the observed uptake was probably of the particulate form. This demonstrates that this more environmentally relevant Ag nanoform may be bioavailable for uptake by benthic organisms. Interspecies interactions likely occurred, namely predation (planarians fed on chironomids and snails), which somehow influenced Ag uptake/bioaccumulation, possibly by altering organisms´ foraging behaviour. Higher Ag uptake rate constants were determined for AgNO3 (0.64, 80.4 and 1.12 Lwater g-1organism day-1) than for Ag2S NPs (0.05, 2.65 and 0.32 Lwater g-1organism day-1) for planarians, snails and chironomids, respectively. Biomagnification under environmentally realistic exposure seemed to be low, although it was likely to occur in the food chain P. acuta to G. tigrina exposed to AgNO3. Single-species tests generally could not reliably predict Ag bioaccumulation in the more complex mesocosm scenario. This study provides methodologies/data to better understand exposure, toxicokinetics and bioaccumulation of Ag in complex systems, reinforcing the need to use mesocosm studies to improve the risk assessment of environmental contaminants, specifically NPs, in aquatic environments.

Toxicokinetics of silver and silver sulfide nanoparticles in Chironomus riparius under different exposure routes.

Engineered nanoparticles released into surface water may accumulate in sediments, potentially threatening benthic organisms. This study determined the toxicokinetics in Chironomus riparius of Ag from pristine silver nanoparticles (Ag NPs), a simulating aged Ag NP form (Ag2S NPs), and AgNO3 as an ionic control. Chironomid larvae were exposed to these Ag forms through water, sediment, or food. The potential transfer of Ag from larvae to adult midges was also evaluated. Results revealed higher Ag uptake by C. riparius upon exposure to Ag2S NPs, while larvae exposed to pristine Ag NPs and AgNO3 generally presented similar uptake kinetics. Uptake patterns of the different Ag forms were generally similar in the tests with water or sediment exposures, suggesting that uptake from water was the most important route of Ag uptake in both experiments. For the sediment bioaccumulation test, uptake was likely a combination of water uptake and sediment particles ingestion. Ag uptake via food exposure was only significant for Ag2S NPs. Ag transfer to the terrestrial compartment was low. In our environmentally relevant exposure scenario, chironomid larvae accumulated relatively high Ag concentrations and elimination was extremely low in some cases. These results suggest that bioaccumulation of Ag in its nanoparticulate and/or ionic form may occur in the environment, raising concerns regarding chronic exposure and trophic transfer. This is the first study determining the toxicokinetics of NPs in Chironomus, providing important information for understanding chironomid exposure to NPs and their potential interactions in the environment.

Metal transfer to sediments, invertebrates and fish following waterborne exposure to silver nitrate or silver sulfide nanoparticles in an indoor stream mesocosm.

The fate of engineered nanomaterials in ecosystems is unclear. An aquatic stream mesocosm explored the fate and bioaccumulation of silver sulfide nanoparticles (Ag2S NPs) compared to silver nitrate (AgNO3). The aims were to determine the total Ag in water, sediment and biota, and to evaluate the bioavailable fractions of silver in the sediment using a serial extraction method. The total Ag in the water column from a nominal daily dose of 10 µg L-1 of Ag for the AgNO3 or Ag2S NP treatments reached a plateau of around 13 and 12 µg L-1, respectively, by the end of the study. Similarly, the sediment of both Ag-treatments reached ~380 µg Ag kg-1, and with most of it being acid-extractable/labile. The biota accumulated 4-59 µg Ag g-1 dw, depending on the type of Ag-treatment and organism. The oligochaete worm, Lumbriculus variegatus, accumulated Ag from the Ag2S exposure over time, which was similar to the AgNO3 treatment by the end of the experiment. The planarian, Girardia tigrina, and the chironomid larva, Chironomus riparius, showed much higher Ag concentrations than the oligochaete worms; and with a clearer time-dependent statistically significant Ag accumulation relative to the untreated controls. For the pulmonate snail, Physa acuta, bioaccumulation of Ag from AgNO3 and Ag2S NP exposures was observed, but was lower from the nano treatment. The AgNO3 exposure caused appreciable Ag accumulation in the water flea, Daphnia magna, but accumulation was higher in the Ag2S NP treatment (reaching 59 µg g-1 dw). In the rainbow trout, Oncorhynchus mykiss, AgNO3, but not Ag2S NPs, caused total Ag concentrations to increase in the tissues. Overall, the study showed transfer of total Ag from the water column to the sediment, and Ag bioaccumulation in the biota, with Ag from Ag2S NP exposure generally being less bioavailable than that from AgNO3.

European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials.

Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide examples of its use in various H2020-funded nanosafety projects.

Effects of sulfidation of silver nanoparticles on the Ag uptake kinetics in Brassica rapa plants.

Land application of sewage sludge containing increasing levels of silver nanoparticles (AgNPs) raises concerns about the risk for plant exposure. This study compared the uptake kinetics and distribution of Ag in Brassica rapa seedlings grown in Lufa 2.2 natural soil spiked with 20 nm Ag2S NPs, with those from 3 to 8 nm AgNPs, 50 nm AgNPs and AgNO3 exposures (10 mg Ag/kg dry soil). A two-compartment model was used to describe the uptake kinetics of Ag in plants, distinguishing two stages: stage I with increasing Ag uptake followed by stage II with decreasing Ag uptake. The concentration of Ag in roots from Ag2S NPs was about 14 and 10 times lower than for the other AgNPs and AgNO3 exposures, respectively, at the end of stage I, with root translocation rate constants being higher for Ag2S NPs. In stage II, Ag uptake occurred only for the 50 nm AgNPs. The distribution of Ag in B. rapa exposed to pristine, ionic and sulfidized AgNPs differed at the end of exposure. This study shows that Ag uptake and distribution in plants depends on the Ag form in soil, highlighting the importance of studying the environmentally relevant chemical species in NPs risk assessment.

Toxicokinetics of silver nanoparticles in the mealworm Tenebrio molitor exposed via soil or food.

Silver nanoparticles (AgNPs) may reach the soil compartment via sewage sludge or nanoagrochemical applications. Understanding how NPs interact with biological systems is crucial for an accurate hazard assessment. Therefore, this study aimed at determining the Ag toxicokinetics in the mealworm Tenebrio molitor, exposed via Lufa 2.2 soil or via food to different Ag forms (uncoated 50 nm AgNPs, paraffin coated 3-8 nm and PVP-stabilised 60 nm, Ag2S NPs 20 nm, and ionic Ag). Mealworms were exposed for 21 days followed by a 21-day elimination phase (clean soil/food). A one-compartment kinetics model with inert fraction (simulating a storage compartment, where detoxified forms are located) was used to describe Ag accumulation. Fully understanding the uptake route in mealworms is difficult. For that reason several approaches were used, showing that food, soil and pore water all are valid uptake routes, but with different importance. Silver taken up from soil pore water or from soil showed to be related to Ag dissolution in soil pore water. In general, the uptake and elimination rate constants were similar for 3-8 nm and 60 nm AgNPs and for AgNO3, but significantly different for the uncoated 50 nm AgNPs. Upon food exposure, uptake rate constants were similar for 50 nm AgNPs and AgNO3, while those for 60 nm and 3-8 nm AgNPs and for Ag2S NPs also grouped together. NP exposure in soil appeared more difficult to characterize, with different patterns obtained for the different NPs. But it was evident that upon soil or food exposure, particle characteristics highly affected Ag bioavailability and bioaccumulation. Although Ag2S NPs were taken up, their elimination was faster than for other Ag forms, showing the lowest inert fraction. The significantly different elimination rate constants suggest that the mechanism of elimination may not be the same for different AgNPs either.

Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.

. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.

Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials.

Toxicogenomics (TGx) approaches are increasingly applied to gain insight into the possible toxicity mechanisms of engineered nanomaterials (ENMs). Omics data can be valuable to elucidate the mechanism of action of chemicals and to develop predictive models in toxicology. While vast amounts of transcriptomics data from ENM exposures have already been accumulated, a unified, easily accessible and reusable collection of transcriptomics data for ENMs is currently lacking. In an attempt to improve the FAIRness of already existing transcriptomics data for ENMs, we curated a collection of homogenized transcriptomics data from human, mouse and rat ENM exposures in vitro and in vivo including the physicochemical characteristics of the ENMs used in each study.

Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform.

The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (rion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/nO) and the absolute electronegativity (χabs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (NInChI). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data.

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base.

In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the Daphnia magna model and assessing acute toxicity determined as immobilisation. Cadmium (Cd2+) and bovine serum albumin (BSA) were selected as co-pollutant and protein model system, respectively. Albumin corona formation on GO dramatically increased its colloidal stability (ca. 60%) and Cd2+ adsorption capacity (ca. 4.5 times) in reconstituted water (Daphnia medium). The acute toxicity values (48 h-EC50) observed were 0.18 mg L-1 for Cd2+-only and 0.29 and 0.61 mg L-1 following co-exposure of Cd2+ with GO and BSA@GO materials, respectively, at a fixed non-toxic concentration of 1.0 mg L-1. After coronation of GO with BSA, a reduction in cadmium toxicity of 110 % and 238% was achieved when compared to bare GO and Cd2+-only, respectively. Integration of datasets associated with graphene-based materials, heavy metals and mixture toxicity is essential to enable re-use of the data and facilitate nanoinformatics approaches for design of safer nanomaterials for water quality monitoring and remediation technologies. Hence, all data from this work were annotated and integrated into the NanoCommons Knowledge Base, connecting the experimental data to nanoinformatics platforms under the FAIR data principles and making them interoperable with similar datasets.

NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.

Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of commercial applications; this, however, may come at a cost, if an association to long-term health and environmental risks is discovered or even just perceived. Many nanomaterials (NMs) have not yet had their potential adverse biological effects fully assessed, due to costs and time constraints associated with the experimental assessment, frequently involving animals. Here, the available NM libraries are analyzed for their suitability for integration with novel nanoinformatics approaches and for the development of NM specific Integrated Approaches to Testing and Assessment (IATA) for human and environmental risk assessment, all within the NanoSolveIT cloud-platform. These established and well-characterized NM libraries (e.g. NanoMILE, NanoSolutions, NANoREG, NanoFASE, caLIBRAte, NanoTEST and the Nanomaterial Registry (>2000 NMs)) contain physicochemical characterization data as well as data for several relevant biological endpoints, assessed in part using harmonized Organisation for Economic Co-operation and Development (OECD) methods and test guidelines. Integration of such extensive NM information sources with the latest nanoinformatics methods will allow NanoSolveIT to model the relationships between NM structure (morphology), properties and their adverse effects and to predict the effects of other NMs for which less data is available. The project specifically addresses the needs of regulatory agencies and industry to effectively and rapidly evaluate the exposure, NM hazard and risk from nanomaterials and nano-enabled products, enabling implementation of computational 'safe-by-design' approaches to facilitate NM commercialization.

Zeta-Potential Read-Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform.

Zeta potential is one of the most critical properties of nanomaterials (NMs) which provides an estimation of the surface charge, and therefore electrostatic stability in medium and, in practical terms, influences the NM's tendency to form agglomerates and to interact with cellular membranes. This paper describes a robust and accurate read-across model to predict NM zeta potential utilizing as the input data a set of image descriptors derived from transmission electron microscopy (TEM) images of the NMs. The image descriptors are calculated using NanoXtract (http://enaloscloud.novamechanics.com/EnalosWebApps/NanoXtract/), a unique online tool that generates 18 image descriptors from the TEM images, which can then be explored by modeling to identify those most predictive of NM behavior and biological effects. NM TEM images are used to develop a model for prediction of zeta potential based on grouping of the NMs according to their nearest neighbors. The model provides interesting insights regarding the most important similarity features between NMs-in addition to core composition the main elongation emerged, which links to key drivers of NM toxicity such as aspect ratio. Both the NanoXtract image analysis tool and the validated model for zeta potential (http://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.

Synthesis and characterization of Zr- and Hf-doped nano-TiO2 as internal standards for analytical quantification of nanomaterials in complex matrices.

The reliable quantification of nanomaterials (NMs) in complex matrices such as food, cosmetics and biological and environmental compartments can be challenging due to interactions with matrix components and analytical equipment (vials and tubing). The resulting losses along the analytical process (sampling, extraction, clean-up, separation and detection) hamper the quantification of the target NMs in these matrices as well as the compatibility of results and meaningful interpretations in safety assessments. These issues can be overcome by the addition of known amounts of internal/recovery standards to the sample prior to analysis. These standards need to replicate the behaviour of target analytes in the analytical process, which is mainly defined by the surface properties. Moreover, they need to carry a tag that can be quantified independently of the target analyte. As inductively coupled plasma mass spectrometry is used for the identification and quantification of NMs, doping with isotopes, target analytes or with chemically related rare elements is a promising approach. We present the synthesis of a library of TiO2 NMs doped with hafnium (Hf) and zirconium (Zr) (both low in environmental abundance). Zirconia NMs doped with Hf were also synthesized to complement the library. NMs were synthesized with morphological and size properties similar to commercially available TiO2. Characterization included: transmission electron microscopy coupled with energy-dispersive X-ray spectroscopy, X-ray diffraction spectroscopy, Brunauer-Emmett-Teller total specific surface area analysis, cryofixation scanning electron microscopy, inductively coupled plasma optical emission spectroscopy and UV-visible spectrometry. The Ti : Hf and Ti : Zr ratios were verified and calculated using Rietveld refinement. The labelled NMs can serve as internal standards to track the extraction efficiency from complex matrices, and increase method robustness and traceability of characterization/quantification.

Microscopy-based high-throughput assays enable multi-parametric analysis to assess adverse effects of nanomaterials in various cell lines.

Manufactured nanomaterials (MNMs) selected from a library of over 120 different MNMs with varied compositions, sizes, and surface coatings were tested by four different laboratories for toxicity by high-throughput/-content (HT/C) techniques. The selected particles comprise 14 MNMs composed of CeO2, Ag, TiO2, ZnO and SiO2 with different coatings and surface characteristics at varying concentrations. The MNMs were tested in different mammalian cell lines at concentrations between 0.5 and 250 µg/mL to link physical-chemical properties to multiple adverse effects. The cell lines are derived from relevant organs such as liver, lung, colon and the immune system. Endpoints such as viable cell count, cell membrane permeability, apoptotic cell death, mitochondrial membrane potential, lysosomal acidification and steatosis have been studied. Soluble MNMs, Ag and ZnO, were toxic in all cell types. TiO2 and SiO2 MNMs also triggered toxicity in some, but not all, cell types and the cell type-specific effects were influenced by the specific coating and surface modification. CeO2 MNMs were nearly ineffective in our test systems. Differentiated liver cells appear to be most sensitive to MNMs, Whereas most of the investigated MNMs showed no acute toxicity, it became clear that some show adverse effects dependent on the assay and cell line. Hence, it is advised that future nanosafety studies utilise a multi-parametric approach such as HT/C screening to avoid missing signs of toxicity. Furthermore, some of the cell type-specific effects should be followed up in more detail and might also provide an incentive to address potential adverse effects in vivo in the relevant organ.

Corneal confocal microscopy findings in sporadic cases of pre-descemet corneal dystrophy.

PURPOSE: To present corneal confocal microscopy (CCM) findings in a series of patients with pre-Descemet corneal dystrophy (PDCD). METHODS: A 28-year-old man, a 50-year-old man, a 30-year-old woman, and a 31-year-old man were clinically diagnosed with PDCD on slit lamp microscopic evaluation. All patients were evaluated by means of CCM. The parents of the patients were clinically evaluated. Two of the patients underwent photorefractive keratectomy. RESULTS: In all the patients, CCM revealed highly reflective stromal particles and pleomorphic structures that included particles in the deep stroma, immediately anterior to the Descemet membrane extending up to 60 µm from endothelium. No evidence of PDCD was observed clinically in the parents of the patients. Postoperative course of photorefractive keratectomy was uneventful for both of the patients. CONCLUSIONS: With the use of CCM, a specific pattern of findings seemed to be related with PDCD in this series of sporadic cases.