Grouping of nanomaterials to read-across hazard endpoints: a review

Application of read-across methods as a framework for the estimation of emissions from chemical processes

The read-across method is a popular data gap filling technique with developed application for multiple purposes, including regulatory. Within the US Environmental Protection Agency's (US EPA) New Chemicals Program under Toxic Substances Control Act (TSCA), read-across has been widely used, as well as within technical guidance published by the Organization for Economic Co-operation and Development, the European Chemicals Agency, and the European Center for Ecotoxicology and Toxicology of Chemicals for filling chemical toxicity data gaps. Under the TSCA New Chemicals Review Program, US EPA is tasked with reviewing proposed new chemical applications prior to commencing commercial manufacturing within or importing into the United States. The primary goal of this review is to identify any unreasonable human health and environmental risks, arising from environmental releases/emissions during manufacturing and the resulting exposure from these environmental releases. The authors propose the application of read-across techniques for the development and use of a framework for estimating the emissions arising during the chemical manufacturing process. This methodology is to utilize available emissions data from a structurally similar analogue chemical or a group of structurally similar chemicals in a chemical family taking into consideration their physicochemical properties under specified chemical process unit operations and conditions. This framework is also designed to apply existing knowledge of read-across principles previously utilized in toxicity estimation for an analogue or category of chemicals and introduced and extended with a concurrent case study.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Occupational Exposure to Incidental Nanomaterials in Metal Additive Manufacturing: An Innovative Approach for Risk Management

The benefits of metal 3D printing seem unquestionable. However, this additive manufacturing technology brings concerns to occupational safety and health professionals, since recent studies show the existence of airborne nanomaterials in these workplaces. This article explores different approaches to manage the risk of exposure to these incidental nanomaterials, on a case study conducted in a Portuguese organization using Selective Laser Melting (SLM) technology. A monitoring campaign was performed using a condensation particle counter, a canning mobility particle sizer and air sampling for later scanning electron microscopy and energy dispersive X-ray analysis, proving the emission of nano-scale particles and providing insights on number particle concentration, size, shape and chemical composition of airborne matter. Additionally, Control Banding Nanotool v2.0 and Stoffenmanager Nano v1.0 were applied in this case study as qualitative tools, although designed for engineered nanomaterials. This article highlights the limitations of using these quantitative and qualitative approaches when studying metal 3D Printing workstations. As a result, this article proposes the IN Nanotool, a risk management method for incidental nanomaterials designed to overcome the limitations of other existing approaches and to allow non-experts to manage this risk and act preventively to guarantee the safety and health conditions of exposed workers.

A Nanomedicine Structure-Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

Despite their clinical success in drug delivery applications, the potential of theranostic nanomedicines is hampered by mechanistic uncertainty and a lack of science-informed regulatory guidance. Both the therapeutic efficacy and the toxicity of nanoformulations are tightly controlled by the complex interplay of the nanoparticle's physicochemical properties and the individual patient/tumor biology; however, it can be difficult to correlate such information with observed outcomes. Additionally, as nanomedicine research attempts to gradually move away from large-scale animal testing, the need for computer-assisted solutions for evaluation will increase. Such models will depend on a clear understanding of structure-activity relationships. This review provides a comprehensive overview of the field of cancer nanomedicine and provides a knowledge framework and foundational interaction maps that can facilitate future research, assessments, and regulation. By forming three complementary maps profiling nanobio interactions and pathways at different levels of biological complexity, a clear picture of a nanoparticle's journey through the body and the therapeutic and adverse consequences of each potential interaction are presented.

Regulatory safety evaluation of nanomedical products: key issues to refine

Nanotechnologies enable great opportunities for the development and use of innovative (nano)medicines. As is common for scientific and technical developments, recognized safety evaluation methods for regulatory purposes are lagging behind. The specific properties responsible for the desired functioning also hamper the safety evaluation of such products. Pharmacokinetics determination of the active pharmaceutical ingredient as well as the nanomaterial component is crucial. Due to their particulate nature, nanomedicines, similar to all nanomaterials, are primarily removed from the circulation by phagocytizing cells that are part of the immune system. Therefore, the immune system can be potentially a specific target for adverse effects of nanomedicines, and thus needs special attention during the safety evaluation. This DDTR special issue on the results of the REFINE project on a regulatory science framework for nanomedical products presents a highly valuable body of knowledge needed to address regulatory challenges and gaps in currently available testing methods for the safety evaluation of nanomedicines.

Research trends in biomedical applications of two-dimensional nanomaterials over the last decade - A bibliometric analysis

Two-dimensional (2D) nanomaterials with versatile properties have been widely applied in the field of biomedicine. Despite various studies having reviewed the development of biomedical 2D nanomaterials, there is a lack of a study that objectively summarizes and analyzes the research trend of this important field. Here, we employ a series of bibliometric methods to identify the development of the 2D nanomaterial-related biomedical field during the past 10 years from a holistic point of view. First, the annual publication/citation growth, country/institute/author distribution, referenced sources, and research hotspots are identified. Thereafter, based on the objectively identified research hotspots, the contributions of 2D nanomaterials to the various biomedical subfields, including those of biosensing, imaging/therapy, antibacterial treatment, and tissue engineering are carefully explored, by considering the intrinsic properties of the nanomaterials. Finally, prospects and challenges have been discussed to shed light on the future development and clinical translation of 2D nanomaterials. This review provides a novel perspective to identify and further promote the development of 2D nanomaterials in biomedical research.

In vitro and in silico study of mixtures cytotoxicity of metal oxide nanoparticles to Escherichia coli: a mechanistic approach

Metal oxide nanoparticles (MONPs) are commonly found in the aquatic and terrestrial systems as chemical mixtures. Assessment of cytotoxicity associated with single and combination of MONPs can truly identify the concerned environmental risk. Thus, using Escherichia coli as a test model, in vitro cytotoxicity of 6 single MONPs, 15 binary and 20 tertiary mixtures with equitoxic ratios was evaluated following standard bioassay protocols. Assessment of oxidative stress suggested that the production of reactive oxygen species (ROS) was negligible, and the release of metal zinc ions played an important role in the toxicity of MONP mixtures. From our experimental data points, seven quantitative structure-activity relationships (QSARs) models were developed to model the cytotoxicity of these MONPs, based on our created periodic table-based descriptors and experimentally analyzed Zeta-potential. Two strategic approaches i.e. pharmacological and mathematical hypotheses were considered to identify the mixture descriptors pool for modeling purposes. The stringent validation criteria suggested that the model (Model M4) developed with mixture descriptors generated by square-root mole contribution outperformed the other six models considering validation criteria. While considering the pharmacological approach, the 'independent action' generated descriptor pool offered the best model (Model M2), which firmly confirmed that each MONP in the mixture acts through 'independent action' to induce cytotoxicity to E. coli instead of fostering an additive, antagonistic or synergistic effect among MONPs. The total metal electronegativity in a specific metal oxide relative to the number of oxygen atoms and metal valence was associated with a positive contribution to cytotoxicity. At the same time, the core count, which gives a measure of molecular bulk and Zeta potential, had a negative contribution to cytotoxicity.

The application of existing genotoxicity methodologies for grouping of nanomaterials: towards an integrated approach to testing and assessment

The incorporation of nanomaterials (NMs) in consumer products has proven to be highly valuable in many sectors. Unfortunately, however, the same nano specific physicochemical properties, which make these material attractive, might also contribute to hazards for people exposed to these materials. The physicochemical properties of NMs will impact their interaction with biological surroundings and influence their fate and their potential adverse effects such as genotoxicity. Due to the large and expanding number of NMs produced, their availability in different nanoforms (NFs) and their utilization in various formats, it is impossible for risk assessment to be conducted on an individual NF basis. Alternative methods, such as grouping are needed for streamlining hazard assessment. The GRACIOUS Framework provides a logical and science evidenced approach to group similar NFs, allowing read-across of hazard information from source NFs (or non-NFs) with adequate hazard data to target NFs that lack such data. Here, we propose a simple three-tiered testing strategy to gather evidence to determine whether different NFs are sufficiently similar with respect to their potential to induce genotoxicity, in order to be grouped. The tiered testing strategy includes simple in vitro models as well as a number of alternative more complex multi-cellular in vitro models to allow for a better understanding of secondary NM-induced DNA damage, something that has been more appropriate in vivo until recently.

Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment

The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.

Towards health-based nano reference values (HNRVs) for occupational exposure: Recommendations from an expert panel

Unique physicochemical characteristics of engineered nanomaterials (ENMs) suggest the need for nanomaterial-specific occupational exposure limits (OELs). Setting these limits remains a challenge. Therefore, the aim of this study was to set out a framework to evaluate the feasibility of deriving advisory health-based occupational limit values for groups of ENMs, based on scientific knowledge. We have used an expert panel approach to address three questions: 1) What ENM-categories should be distinguished to derive advisory health-based occupational limit values (or health-based Nano Reference Values, HNRVs) for groups of ENMs? 2) What evidence would be needed to define values for these categories? And 3) How much effort would it take to achieve this? The panel experts distinguished six possible categories of HNRVs: A) WHO-fiber-like high aspect ratio ENMs (HARNs), B) Non-WHO-fiber-like HARNs and other non-spheroidal ENMs, C) readily soluble spheroidal ENMs, D) biopersistent spheroidal ENMs with unknown toxicity, E) biopersistent spheroidal ENMs with substance-specific toxicity and F) biopersistent spheroidal ENMs with relatively low substance-specific toxicity. For category A, the WHO-fiber-like HARNs, agreement was reached on criteria defining this category and the approach of using health-based risk estimates for asbestos to derive the HNRV. For category B, a quite heterogeneous category, more toxicity data are needed to set an HNRV. For category C, readily soluble spheroidal ENMs, using the OEL of their molecular or ionic counterpart would be a good starting point. For the biopersistent ENMs with unknown toxicity, HNRVs cannot be applied as case-by-case testing is required. For the other biopersistent ENMs in category E and F, we make several recommendations that can facilitate the derivation of these HNRVs. The proposed categories and recommendations as outlined by this expert panel can serve as a reference point for derivation of HNRVs when health-based OELs for ENMs are not yet available.

Nextcast: a software suite to analyse and model toxicogenomics data

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Toxicogenomics is emerging as a valid approach to characterise the mechanism of action of chemicals.

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Structured pipelines for toxicogenomics increase standardisation and regulatory acceptance.

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We developed the Nextcast software suite for robust analysis and modelling of toxicogenomic data.

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Nextcast offers customisable modular pipelines to tackle multiple biological questions.

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

Nanoparticle Food Applications and Their Toxicity: Current Trends and Needs in Risk Assessment Strategies

ABSTRACT

Nanotechnology has developed into one of the most groundbreaking scientific fields in the last few decades because it exploits the enhanced reactivity of materials at the atomic scale. The current classification of nanoparticles (NPs) used in foods is outlined in relation to the production and physicochemical characteristics. This review aims to concisely present the most popular and widely used inorganic and organic NPs in food industries. Considering that the toxicity of NPs is often associated with chemical reactivity, a series of in vitro toxicity studies are also summarized, integrating information on the type of NP studies and reported specifications, type of cells used, exposure conditions, and assessed end points. The important role of the digestive system in the absorption and distribution of nanoformulated foods within the body and how this affects the resultant cytotoxicity. Examples of how NPs and their accumulation within different organs are presented in relation to the consumption of specific foods. Finally, the role of developing human health risk assessments to characterize both the potential impact of the hazard and the likelihood or level of human exposure is outlined. Uncertainties exist around risk and exposure assessments of NPs due to limited information on several aspects, including toxicity, behavior, and bioaccumulation. Overall, this review presents current trends and needs for future assessments in toxicity evaluation to ensure the safe application of NPs in the food industry.

HIGHLIGHTS

Bayesian based similarity assessment of nanomaterials to inform grouping

Nanoforms can be manufactured in plenty of variants by differing their physicochemical properties and toxicokinetic behaviour which can affect their hazard potential. To avoid testing of each single nanomaterial and nanoform variation and subsequently save resources, grouping and read-across strategies are used to estimate groups of substances, based on carefully selected evidence, that could potentially have similar human health and environmental hazard impact. A novel computational similarity method is presented aiming to compare dose-response curves and identify sets of similar nanoforms. The suggested method estimates the statistical model that best fits the data by leveraging pairwise Bayes Factor analysis to compare pairs of curves and evaluate whether each of the nanoforms is sufficiently similar to all other nanoforms. Pairwise comparisons to benchmark materials are used to define threshold similarity values and set the criteria for identifying groups of nanoforms with comparatively similar toxicity. Applications to use case data are shown to demonstrate that the method can support grouping hypotheses linked to a certain hazard endpoint and route of exposure.

Nanomaterials, a New Challenge in the Workplace

Nanomaterials are a nanotechnological product of increasing importance given the possibilities they offer to improve quality of life and support sustainable development. Safe management of nanomaterials is needed to ensure that this emerging technology has the highest levels of acceptance among different interest groups, including workers. This chapter reviews the current state that presents the different stages of risk management applied to nanomaterials, including standardisation, regulation, risk assessment and risk control. Particularly, the chapter contextualizes the development of nanotechnologies at European level and analyses the scientific evidence available on the risks derived from nanomaterials use. Furthermore, it highlights the required conditions to encourage the responsible development of nanomaterials, as well as reflects on the lack of consensus in terms of approaches and frameworks that could facilitate standardisation adoption, regulatory enforcement and industry intervention concerning nanomaterials.

How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches. For such grouping to be acceptable, it is important to demonstrate similarities in physicochemical properties, toxicokinetic behaviour, and (eco)toxicological behaviour. The GRACIOUS Framework supports the grouping of NFs, by identifying suitable grouping hypotheses that describe the key similarities between different NFs. The Framework then supports the user to gather the evidence required to test these hypotheses and to subsequently assess the similarity of the NFs within the proposed group. The evidence needed to support a hypothesis is gathered by an Integrated Approach to Testing and Assessment (IATA), designed as decision trees constructed of decision nodes. Each decision node asks the questions and provides the methods needed to obtain the most relevant information. This White paper outlines existing and novel methods to assess similarity of the data generated for each decision node, either via a pairwise analysis conducted property-by-property, or by assessing multiple decision nodes simultaneously via a multidimensional analysis. For the pairwise comparison conducted property-by-property we included in this White paper: The x-fold, Bayesian and Arsinh-OWA distance algorithms performed comparably in the scoring of similarity between NF pairs. The Euclidean distance was also useful, but only with proper data transformation. The x-fold method does not standardize data, and thus produces skewed histograms, but has the advantage that it can be implemented without programming knowhow. A range of multidimensional evaluations, using for example dendrogram clustering approaches, were also investigated. Multidimensional distance metrics were demonstrated to be difficult to use in a regulatory context, but from a scientific perspective were found to offer unexpected insights into the overall similarity of very different materials. In conclusion, for regulatory purposes, a property-by-property evaluation of the data matrix is recommended to substantiate grouping, while the multidimensional approaches are considered to be tools of discovery rather than regulatory methods.

A critical review of the environmental impacts of manufactured nano-objects on earthworm species

The presence of manufactured nano-objects (MNOs) in various consumer or their (future large-scale) use as nanoagrochemical have increased with the rapid development of nanotechnology and therefore, concerns associated with its possible ecotoxicological effects are also arising. MNOs are releasing along the product life cycle, consequently accumulating in soils and other environmental matrices, and potentially leading to adverse effects on soil biota and their associated processes. Earthworms, of the group of Oligochaetes, are an ecologically significant group of organisms and play an important role in soil remediation, as well as acting as a potential vector for trophic transfer of MNOs through the food chain. This review presents a comprehensive and critical overview of toxic effects of MNOs on earthworms in soil system. We reviewed pathways of MNOs in agriculture soil environment with its expected production, release, and bioaccumulation. Furthermore, we thoroughly examined scientific literature from last ten years and critically evaluated the potential ecotoxicity of 16 different metal oxide or carbon-based MNO types. Various adverse effects on the different earthworm life stages have been reported, including reduction in growth rate, changes in biochemical and molecular markers, reproduction and survival rate. Importantly, this literature review reveals the scarcity of long-term toxicological data needed to actually characterize MNOs risks, as well as an understanding of mechanisms causing toxicity to earthworm species. This review sheds light on this knowledge gap as investigating bio-nano interplay in soil environment improves our major understanding for safer applications of MNOs in the agriculture environment.

An Integrated Approach to Testing and Assessment to Support Grouping and Read-Across of Nanomaterials After Inhalation Exposure

Introduction: Here, we describe the generation of hypotheses for grouping nanoforms (NFs) after inhalation exposure and the tailored Integrated Approaches to Testing and Assessment (IATA) with which each specific hypothesis can be tested. This is part of a state-of-the-art framework to support the hypothesis-driven grouping and read-across of NFs, as developed by the EU-funded Horizon 2020 project GRACIOUS.

Development of Grouping Hypotheses and IATA: Respirable NFs, depending on their physicochemical properties, may dissolve either in lung lining fluid or in acidic lysosomal fluid after uptake by cells. Alternatively, NFs may also persist in particulate form. Dissolution in the lung is, therefore, a decisive factor for the toxicokinetics of NFs. This has led to the development of four hypotheses, broadly grouping NFs as instantaneous, quickly, gradually, and very slowly dissolving NFs. For instantaneously dissolving NFs, hazard information can be derived by read-across from the ions. For quickly dissolving particles, as accumulation of particles is not expected, ion toxicity will drive the toxic profile. However, the particle aspect influences the location of the ion release. For gradually dissolving and very slowly dissolving NFs, particle-driven toxicity is of concern. These NFs may be grouped by their reactivity and inflammation potency. The hypotheses are substantiated by a tailored IATA, which describes the minimum information and laboratory assessments of NFs under investigation required to justify grouping.

Conclusion: The GRACIOUS hypotheses and tailored IATA for respiratory toxicity of inhaled NFs can be used to support decision making regarding Safe(r)-by-Design product development or adoption of precautionary measures to mitigate potential risks. It can also be used to support read-across of adverse effects such as pulmonary inflammation and subsequent downstream effects such as lung fibrosis and lung tumor formation after long-term exposure.

Characteristics and risk assessment of occupational exposure to ultrafine particles generated from cooking in the Chinese restaurant

Ultrafine particles have been increasingly linked to adverse health effects in restaurant workers. This study aimed to clarify the exposure characteristics and risks of ultrafine particles during the cooking process, and to provide a reasonable standard for protecting the workers in the Chinese restaurant. The temporal variations in particle concentrations (number concentration (NC), mass concentration (MC), surface area concentration (SAC), and personal NC), and size distributions by number were measured by real-time system. The hazard, exposure, and risk levels of ultrafine particles were analyzed using the control banding tools. The NC, MC, and SAC increased during the cooking period and decreased gradually to background levels post-operation. The concentration ratios of MC, total NC, SAC, and personal NC ranged from 3.82 to 9.35. The ultrafine particles were mainly gathered at 10.4 and 100 nm during cooking. The exposure, hazard and risk levels of the ultrafine particles were high. These findings indicated that the workers during cooking were at high risk due to exposure to high levels of ultrafine particles associated with working activity and with a bimodal size distribution. The existing control strategies, including engineering control, management control, and personal protection equipment need to be improved to reduce the risk.

Extrinsically Conductive Nanomaterials for Cardiac Tissue Engineering Applications

Myocardial infarction (MI) is the consequence of coronary artery thrombosis resulting in ischemia and necrosis of the myocardium. As a result, billions of contractile cardiomyocytes are lost with poor innate regeneration capability. This degenerated tissue is replaced by collagen-rich fibrotic scar tissue as the usual body response to quickly repair the injury. The non-conductive nature of this tissue results in arrhythmias and asynchronous beating leading to total heart failure in the long run due to ventricular remodelling. Traditional pharmacological and assistive device approaches have failed to meet the utmost need for tissue regeneration to repair MI injuries. Engineered heart tissues (EHTs) seem promising alternatives, but their non-conductive nature could not resolve problems such as arrhythmias and asynchronous beating for long term in-vivo applications. The ability of nanotechnology to mimic the nano-bioarchitecture of the extracellular matrix and the potential of cardiac tissue engineering to engineer heart-like tissues makes it a unique combination to develop conductive constructs. Biomaterials blended with conductive nanomaterials could yield conductive constructs (referred to as extrinsically conductive). These cell-laden conductive constructs can alleviate cardiac functions when implanted in-vivo. A succinct review of the most promising applications of nanomaterials in cardiac tissue engineering to repair MI injuries is presented with a focus on extrinsically conductive nanomaterials.

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

Machine learning methods for multi-walled carbon nanotubes (MWCNT) genotoxicity prediction

Multi-walled carbon nanotubes (MWCNTs) are made of multiple single-walled carbon nanotubes (SWCNTs) which are nested inside one another forming concentric cylinders. These nanomaterials are widely used in industrial and biomedical applications, due to their unique physicochemical characteristics. However, previous studies have shown that exposure to MWCNTs may lead to toxicity and some of the physicochemical properties of MWCNTs can influence their toxicological profiles. In silico modelling can be applied as a faster and less costly alternative to experimental (in vivo and in vitro) testing for the hazard characterization of MWCNTs. This study aims at developing a fully validated predictive nanoinformatics model based on statistical and machine learning approaches for the accurate prediction of genotoxicity of different types of MWCNTs. Towards this goal, a number of different computational workflows were designed, combining unsupervised (Principal Component Analysis, PCA) and supervised classification techniques (Support Vectors Machine, "SVM", Random Forest, "RF", Logistic Regression, "LR" and Naïve Bayes, "NB") and Bayesian optimization. The Recursive Feature Elimination (RFE) method was applied for selecting the most important variables. An RF model using only three features was selected as the most efficient for predicting the genotoxicity of MWCNTs, exhibiting 80% accuracy on external validation and high classification probabilities. The most informative features selected by the model were "Length", "Zeta average" and "Purity".

In silico nanosafety assessment tools and their ecosystem-level integration prospect

Engineered nanomaterials (ENMs) have tremendous potential in many fields, but their applications and commercialization are difficult to widely implement due to their safety concerns. Recently, in silico nanosafety assessment has become an important and necessary tool to realize the safer-by-design strategy of ENMs and at the same time to reduce animal tests and exposure experiments. Here, in silico nanosafety assessment tools are classified into three categories according to their methodologies and objectives, including (i) data-driven prediction for acute toxicity, (ii) fate modeling for environmental pollution, and (iii) nano-biological interaction modeling for long-term biological effects. Released ENMs may cross environmental boundaries and undergo a variety of transformations in biological and environmental media. Therefore, the potential impacts of ENMs must be assessed from a multimedia perspective and with integrated approaches considering environmental and biological effects. Ecosystems with biodiversity and an abiotic environment may be used as an excellent integration platform to assess the community- and ecosystem-level nanosafety. In this review, the advances and challenges of in silico nanosafety assessment tools are carefully discussed. Furthermore, their integration at the ecosystem level may provide more comprehensive and reliable nanosafety assessment by establishing a site-specific interactive system among ENMs, abiotic environment, and biological communities.

Antibacterial and Antiviral Functional Materials: Chemistry and Biological Activity toward Tackling COVID-19-like Pandemics

The ongoing worldwide pandemic due to COVID-19 has created awareness toward ensuring best practices to avoid the spread of microorganisms. In this regard, the research on creating a surface which destroys or inhibits the adherence of microbial/viral entities has gained renewed interest. Although many research reports are available on the antibacterial materials or coatings, there is a relatively small amount of data available on the use of antiviral materials. However, with more research geared toward this area, new information is being added to the literature every day. The combination of antibacterial and antiviral chemical entities represents a potentially path-breaking intervention to mitigate the spread of disease-causing agents. In this review, we have surveyed antibacterial and antiviral materials of various classes such as small-molecule organics, synthetic and biodegradable polymers, silver, TiO2, and copper-derived chemicals. The surface protection mechanisms of the materials against the pathogen colonies are discussed in detail, which highlights the key differences that could determine the parameters that would govern the future development of advanced antibacterial and antiviral materials and surfaces.

Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies

The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er₂O₃, Gd₂O₃, CeO₂, Co₂O₃, Mn₂O₃, Co₃O₄, Fe₃O₄/WO₃ (in descending order). The computed EC₅₀ values from experimental data suggested that Er₂O₃ and Gd₂O₃ were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1^st and 2^nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.

Use of size-dependent electron configuration fingerprint to develop general prediction models for nanomaterials

Due to the lack of nano descriptors that can appropriately represent the wide chemical space of engineered nanomaterials (ENMs), applicability domain of nano-quantitative structure-activity relationship models are limited to certain types of ENMs, such as metal oxides, metals, carbon-based nanomaterials, or quantum dots. In this study, a size-dependent electron configuration fingerprint (SDEC FP) was introduced to estimate the quantity of electrons based on the core, doping, and coating materials of ENMs in different sizes. SDEC FP was used in prediction model development and nanostructure similarity analysis on datasets including metal and carbon-based nanomaterials with and without surface modifications. Cytotoxicity and zeta potential prediction models developed with SDEC FP achieved good prediction accuracies on test set. Nanostructure similarity analysis was performed through principal component analysis which showed that structural similarity between ENMs measured by SDEC FP was highly correlated with their properties.

Understanding Nanomaterial Biotransformation: An Unmet Challenge to Achieving Predictive Nanotoxicology

More than a decade has passed since the first concepts of predictive nanotoxicology were formulated. During this time, many advancements have been achieved in multiple disciplines, including the success stories of the fiber paradigm and the oxidative stress paradigm. However, important knowledge gaps are slowing down the development of predictive nanotoxicology and require a mutidisciplinary effort to be overcome. Among these gaps, understanding, reproducing, and modeling of nanomaterial biotransformation in biological environments is a central challenge, both in vitro and in silico. This dynamic and complex process is still a challenge for today's bioanalytics. This work explores and discusses selected approaches of the multidisciplinary efforts taken in the last decade and the challenges that remain unmet, in particular concerning nanomaterial biotransformation. It highlights some future advancements that, together, can help to understand such complex processes and accelerate the development of predictive nanotoxicology.

Nanotoxicology data for in silico tools: a literature review

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Potential Role of Soluble Metal Impurities in the Acute Lung Inflammogenicity of Multi-Walled Carbon Nanotubes

Multi-walled carbon nanotubes (MWCNTs) have variable metal impurities, but little is known about the impact of soluble metal impurities on the toxicity of MWCNTs. Here, we evaluated the role of soluble metal impurities to the acute inflammogenic potential of MWCNTs, using five types of high purity MWCNTs (>95%). MWCNTs and their soluble fractions collected at 24 h after incubation in phosphate-buffered saline showed diverse metal impurities with variable concentrations. The fiber-free soluble fractions produced variable levels of reactive oxygen species (ROS), and the iron level was the key determinant for ROS production. The acute inflammation at 24 h after intratracheal instillation of MWCNTs to rats at 0.19, 0.63, and 1.91 mg MWCNT/kg body weight (bw) or fiber-free supernatants from MWCNT suspensions at 1.91 and 7.64 mg MWCNT/kg bw showed that the number of granulocytes, a marker for acute inflammation, was significantly increased with a good dose-dependency. The correlation study showed that neither the levels of iron nor the ROS generation potential of the soluble fractions showed any correlations with the inflammogenic potential. However, the total concentration of transition metals in the soluble fractions showed a good correlation with the acute lung inflammogenic potential. These results implied that metal impurities, especially transitional metals, can contribute to the acute inflammogenic potential of MWCNTs, although the major parameter for the toxicity of MWCNTs is size and shape.

WS2 Nanosheets at Noncytotoxic Concentrations Enhance the Cytotoxicity of Organic Pollutants by Disturbing the Plasma Membrane and Efflux Pumps

Emerging transition-metal dichalcogenide (TMDC) nanosheets, such as WS₂ nanosheets, have shown tremendous potential for use in many fields such as intelligent manufacturing and environmental protection. However, considerable knowledge gaps still exist regarding the impact of TMDCs on environmental risks, especially risks involving organic pollutants. Here, a synergistic toxicity between WS₂ nanosheets and organic pollutants (triclosan or tris(1,3-dichloro-2-propyl) phosphate) was found using the median-effect and combination index equations. In particular, the effect of synergy had a higher magnitude at low cytotoxicity levels and a noncytotoxic concentration of WS₂ nanosheets clearly enhanced the cytotoxicity and intracellular accumulation of organic pollutants. On the one hand, WS₂ nanosheets damaged the plasma membrane and cytoskeleton, resulting in increased membrane permeability and organic pollutant uptake. On the other hand, as shown by fluorescence substrate accumulation experiments and molecular dynamics simulations, WS₂ nanosheets affected the secondary structure of the efflux pumps and competitively bound with efflux pumps, blocking xenobiotic removal. This work emphasized that TMDCs, especially at the noncytotoxic level, in combination with organic pollutants caused damage that cannot be ignored, providing insight into comprehensive safety assessment and the specific toxicological mechanisms of TMDCs that accompany organic pollutant exposure.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Safer-by-design flame-sprayed silicon dioxide nanoparticles: the role of silanol content on ROS generation, surface activity and cytotoxicity

BACKGROUND

Amorphous silica nanoparticles (SiO2 NPs) have been regarded as relatively benign nanomaterials, however, this widely held opinion has been questioned in recent years by several reports on in vitro and in vivo toxicity. Surface chemistry, more specifically the surface silanol content, has been identified as an important toxicity modulator for SiO2 NPs. Here, quantitative relationships between the silanol content on SiO2 NPs, free radical generation and toxicity have been identified, with the purpose of synthesizing safer-by-design fumed silica nanoparticles.

RESULTS

Consistent and statistically significant trends were seen between the total silanol content, cell membrane damage, and cell viability, but not with intracellular reactive oxygen species (ROS), in the macrophages RAW264.7. SiO2 NPs with lower total silanol content exhibited larger adverse cellular effects. The SAEC epithelial cell line did not show any sign of toxicity by any of the nanoparticles. Free radical generation and surface reactivity of these nanoparticles were also influenced by the temperature of combustion and total silanol content.

CONCLUSION

Surface silanol content plays an important role in cellular toxicity and surface reactivity, although it might not be the sole factor influencing fumed silica NP toxicity. It was demonstrated that synthesis conditions for SiO2 NPs influence the type and quantity of free radicals, oxidative stress, nanoparticle interaction with the biological milieu they come in contact with, and determine the specific mechanisms of toxicity. We demonstrate here that it is possible to produce much less toxic fumed silicas by modulating the synthesis conditions.

An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization

BACKGROUND

Nanomaterials (NMs) can be fine-tuned in their properties resulting in a high number of variants, each requiring a thorough safety assessment. Grouping and categorization approaches that would reduce the amount of testing are in principle existing for NMs but are still mostly conceptual. One drawback is the limited mechanistic understanding of NM toxicity. Thus, we conducted a multi-omics in vitro study in RLE-6TN rat alveolar epithelial cells involving 12 NMs covering different materials and including a systematic variation of particle size, surface charge and hydrophobicity for SiO₂ NMs. Cellular responses were analyzed by global proteomics, targeted metabolomics and SH2 profiling. Results were integrated using Weighted Gene Correlation Network Analysis (WGCNA).

RESULTS

Cluster analyses involving all data sets separated Graphene Oxide, TiO2_NM105, SiO2_40 and Phthalocyanine Blue from the other NMs as their cellular responses showed a high degree of similarities, although apical in vivo results may differ. SiO2_7 behaved differently but still induced significant changes. In contrast, the remaining NMs were more similar to untreated controls. WGCNA revealed correlations of specific physico-chemical properties such as agglomerate size and redox potential to cellular responses. A key driver analysis could identify biomolecules being highly correlated to the observed effects, which might be representative biomarker candidates. Key drivers in our study were mainly related to oxidative stress responses and apoptosis.

CONCLUSIONS

Our multi-omics approach involving proteomics, metabolomics and SH2 profiling proved useful to obtain insights into NMs Mode of Actions. Integrating results allowed for a more robust NM categorization. Moreover, key physico-chemical properties strongly correlating with NM toxicity were identified. Finally, we suggest several key drivers of toxicity that bear the potential to improve future testing and assessment approaches.

Toxicological assessment of nanoparticle interactions with the pulmonary system

Nanoparticle(NP)-based materials have breakthrough applications in many fields of life, such as in engineering, communications and textiles industries; food and bioenvironmental applications; medicines and cosmetics, etc. Biomedical applications of NPs are very active areas of research with successful translation to pharmaceutical and clinical uses overcoming both pharmaceutical and clinical challenges. Although the attractiveness and enhanced applications of these NPs stem from their exceptional properties at the nanoscale size, i.e. 1-1000 nm, they exhibit completely different physicochemical profiles and, subsequently, toxicological profiles from their parent bulk materials. Hence, the clinical evaluation and toxicological assessment of NPs interactions within biological systems are continuously evolving to ensure their safety at the nanoscale. The pulmonary system is one of the primary routes of exposure to airborne NPs either intentionally, via aerosolized nanomedicines targeting pulmonary pathologies such as cancer or asthma, or unintentionally, via natural NPs and anthropogenic (man-made) NPs. This review presents the state-of-the-art, contemporary challenges, and knowledge gaps in the toxicological assessment of NPs interactions with the pulmonary system. It highlights the main mechanisms of NP toxicity, factors influencing their toxicity, the different toxicological assessment methods and their drawbacks, and the recent NP regulatory guidelines based on literature collected from the research pool of NPs interactions with lung cell lines, in vivo inhalation studies, and clinical trials.

Material-specific properties applied to an environmental risk assessment of engineered nanomaterials - implications on grouping and read-across concepts

Engineered nanomaterials (ENMs) are intentionally designed in different nano-forms of the same parent material in order to meet application requirements. Different grouping and read-across concepts are proposed to streamline risk assessments by pooling nano-forms in one category. Environmental grouping concepts still are in their infancy and mainly focus on grouping by hazard categories. Complete risk assessments require data on environmental release and exposure not only for ENMs but also for their nano-forms. The key requirement is to identify and to distinguish the production volumes of the ENMs regarding nano-form-specific applications. The aim of our work was to evaluate whether such a grouping is possible with the available data and which influence it has on the environmental risk assessment of ENMs. A functionality-driven approach was applied to match the material-specific property (i.e. crystal form/morphology) with the functions employed in the applications. We demonstrate that for nano-TiO₂, carbon nanotubes (CNTs), and nano-Al₂O₃ the total production volume can be allocated to specific nano-forms based on their functionalities. The differentiated assessments result in a variation of the predicted environmental concentrations for anatase vs. rutile nano-TiO₂, single-wall vs. multi-wall CNTs and α- vs. γ-nano-Al₂O₃ by a factor of 2 to 13. Additionally, the nano-form-specific predicted no-effect concentrations for these ENMs were derived. The risk quotients for all nano-forms indicated no immediate risk in freshwaters. Our results suggest that grouping and read-across concepts should include both a nano-form release potential for estimating the environmental exposure and separately consider the nano-forms in environmental risk assessments.

Grouping of nanomaterials to read-across hazard endpoints: from data collection to assessment of the grouping hypothesis by application of chemoinformatic techniques

BACKGROUND

An increasing number of manufactured nanomaterials (NMs) are being used in industrial products and need to be registered under the REACH legislation. The hazard characterisation of all these forms is not only technically challenging but resource and time demanding. The use of non-testing strategies like read-across is deemed essential to assure the assessment of all NMs in due time and at lower cost. The fact that read-across is based on the structural similarity of substances represents an additional difficulty for NMs as in general their structure is not unequivocally defined. In such a scenario, the identification of physicochemical properties affecting the hazard potential of NMs is crucial to define a grouping hypothesis and predict the toxicological hazards of similar NMs. In order to promote the read-across of NMs, ECHA has recently published "Recommendations for nanomaterials applicable to the guidance on QSARs and Grouping", but no practical examples were provided in the document. Due to the lack of publicly available data and the inherent difficulties of reading-across NMs, only a few examples of read-across of NMs can be found in the literature. This manuscript presents the first case study of the practical process of grouping and read-across of NMs following the workflow proposed by ECHA.

METHODS

The workflow proposed by ECHA was used and slightly modified to present the read-across case study. The Read-Across Assessment Framework (RAAF) was used to evaluate the uncertainties of a read-across within NMs. Chemoinformatic techniques were used to support the grouping hypothesis and identify key physicochemical properties.

RESULTS

A dataset of 6 nanoforms of TiO₂ with more than 100 physicochemical properties each was collected. In vitro comet assay result was selected as the endpoint to read-across due to data availability. A correlation between the presence of coating or large amounts of impurities and negative comet assay results was observed.

CONCLUSION

The workflow proposed by ECHA to read-across NMs was applied successfully. Chemoinformatic techniques were shown to provide key evidence for the assessment of the grouping hypothesis and the definition of similar NMs. The RAAF was found to be applicable to NMs.