Quality of physicochemical data on nanomaterials: an assessment of data completeness and variability

Nanoscale reference and test materials for the validation of characterization methods for engineered nanomaterials - current state, limitations, and needs

The rational design of engineered nanomaterials (NMs) with improved functionality and their increasing industrial application requires reliable, validated, and ultimately standardized characterization methods for their application-relevant, physicochemical key properties such as size, size distribution, shape, or surface chemistry. This calls for nanoscale (certified) reference materials (CRMs; RMs) and well-characterized reference test materials (RTMs) termed also quality control (QC) samples, assessed, e.g., in interlaboratory comparisons, for the validation and standardization of commonly used characterization methods. Thereby, increasing concerns regarding potential risks of NMs are also addressed and the road for safe and sustainable-by-design concepts for the development of new functional NMs and their use as nanomedicines is paved. With this respect, we will provide an overview of relevant international standardization and regulatory activities, definitions, and recommendations on characterization methods and review currently available organic or inorganic nanoscale CRMs, RMs, and RTMs, including their characterization or certification. In addition, we will highlight typical applications to streamline the regulatory approval process and improve manufacturability including the special challenges imposed by the colloidal nature and sometimes limited stability of NMs. Subsequently, we will critically assess the limitations of currently available nanoscale RMs and RTMs and address the gaps to be filled in the future such as the availability of NMs that come with reference data on properties other than commonly addressed particle size, such as surface chemistry or particle number concentration, or more closely resemble commercially available formulations or address application-relevant matrices.

Emerging trends and future challenges of advanced 2D nanomaterials for combating bacterial resistance

The number of multi-drug-resistant bacteria has increased over the last few decades, which has caused a detrimental impact on public health worldwide. In resolving antibiotic resistance development among different bacterial communities, new antimicrobial agents and nanoparticle-based strategies need to be designed foreseeing the slow discovery of new functioning antibiotics. Advanced research studies have revealed the significant disinfection potential of two-dimensional nanomaterials (2D NMs) to be severed as effective antibacterial agents due to their unique physicochemical properties. This review covers the current research progress of 2D NMs-based antibacterial strategies based on an inclusive explanation of 2D NMs' impact as antibacterial agents, including a detailed introduction to each possible well-known antibacterial mechanism. The impact of the physicochemical properties of 2D NMs on their antibacterial activities has been deliberated while explaining the toxic effects of 2D NMs and discussing their biomedical significance, dysbiosis, and cellular nanotoxicity. Adding to the challenges, we also discussed the major issues regarding the current quality and availability of nanotoxicity data. However, smart advancements are required to fabricate biocompatible 2D antibacterial NMs and exploit their potential to combat bacterial resistance clinically.

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

FAIR assessment of nanosafety data reusability with community standards

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets' reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.

From papers to RDF-based integration of physicochemical data and adverse outcome pathways for nanomaterials

Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge. SCIENTIFIC CONTRIBUTION: This study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize this knowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety.

SciRAPnano: a pragmatic and harmonized approach for quality evaluation of in vitro toxicity data to support risk assessment of nanomaterials

Large amounts of nanotoxicity data from alternative non-animal (in vitro) test methods have been generated, but there is a lack of harmonized quality evaluation approaches for these types of data. Tools for scientifically sound and structured evaluation of the reliability and relevance of in vitro toxicity data to effectively inform regulatory hazard assessment of nanomaterials (NMs), are needed. Here, we present the development of a pragmatic approach to facilitate such evaluation. The tool was developed based on the Science in Risk Assessment and Policy (SciRAP) tool currently applicable to quality evaluation of chemical toxicity studies. The approach taken to develop the tool, referred to as SciRAPnano, included refinement of the original SciRAP in vitro tool through implementation of identified NM-relevant criteria, and further refined based on a set of case studies involving evaluation of 11 studies investigating in vitro toxicity of nano-sized titanium dioxide. Parameters considered cover key physicochemical properties as well as assay-specific aspects that impact NM toxicity, including NM interference with test methods and NM transformation. The final SciRAPnano tool contains 38 criteria for reporting quality, 19 criteria for methodological quality, and 4 guidance items to evaluate relevance. The approach covers essential parameters for pragmatic and harmonized evaluation of NM in vitro toxicity studies and allows for structured use of in vitro data in regulatory hazard assessment of NMs, including transparency on data quality.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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.

Every nano-step counts: a critical reflection on do's and don'ts in researching nanomedicines for retinal gene therapy

INTRODUCTION

Retinal disease affects millions of people worldwide, generating a massive social and economic burden. Current clinical trials for retinal diseases are dominated by gene augmentation therapies delivered with recombinant viruses as key players. As an alternative, nanoparticles hold great promise for the delivery of nucleic acid therapeutics as well. Nevertheless, despite numerous attempts, 'nano' is in practice not as successful as aspired and major breakthroughs in retinal gene therapy applying nanomaterials are yet to be seen.

AREAS COVERED

In this review, we summarize the advantages of nanomaterials and give an overview of nanoparticles designed for retinal nucleic acid delivery up to now. We furthermore critically reflect on the predominant issues that currently limit nano to progress to the clinic, where faulty study design and the absence of representative models play key roles.

EXPERT OPINION

Since the current approach of in vitro - in vivo experimentation is highly inefficient and creates misinformation, we advocate for a more prominent role for ex vivo testing early on in nanoparticle research. In addition, we elaborate on several concepts, including systematic studies and open science, which could aid in pushing the field of nanomedicine beyond the preclinical stage.

A systematic quality evaluation and review of nanomaterial genotoxicity studies: a regulatory perspective

The number of publications in the field of nanogenotoxicology and the amount of genotoxicity data on nanomaterials (NMs) in several databases generated by European Union (EU) funded projects have increased during the last decade. In parallel, large research efforts have contributed to both our understanding of key physico-chemical (PC) parameters regarding NM characterization as well as the limitations of toxicological assays originally designed for soluble chemicals. Hence, it is becoming increasingly clear that not all of these data are reliable or relevant from the regulatory perspective. The aim of this systematic review is to investigate the extent of studies on genotoxicity of NMs that can be considered reliable and relevant by current standards and bring focus to what is needed for a study to be useful from the regulatory point of view. Due to the vast number of studies available, we chose to limit our search to two large groups, which have raised substantial interest in recent years: nanofibers (including nanotubes) and metal-containing nanoparticles. Focusing on peer-reviewed publications, we evaluated the completeness of PC characterization of the tested NMs, documentation of the model system, study design, and results according to the quality assessment approach developed in the EU FP-7 GUIDEnano project. Further, building on recently published recommendations for best practices in nanogenotoxicology research, we created a set of criteria that address assay-specific reliability and relevance for risk assessment purposes. Articles were then reviewed, the qualifying publications discussed, and the most common shortcomings in NM genotoxicity studies highlighted. Moreover, several EU projects under the FP7 and H2020 framework set the aim to collectively feed the information they produced into the eNanoMapper database. As a result, and over the years, the eNanoMapper database has been extended with data of various quality depending on the existing knowledge at the time of entry. These activities are highly relevant since negative results are often not published. Here, we have reviewed the NanoInformaTIX instance under the eNanoMapper database, which hosts data from nine EU initiatives. We evaluated the data quality and the feasibility of use of the data from a regulatory perspective for each experimental entry.

Environmental Risk Assessment of Silver Nanoparticles in Aquatic Ecosystems Using Fuzzy Logic

The rapid development of nanotechnology has stimulated the use of silver nanoparticles (AgNPs) in various fields that leads to their presence in different ecosystem compartments, in particular aquatic ecosystems. Several studies have shown that a variety of living organisms are affected by AgNPs. Therefore, a methodology to assess the risk of AgNPs for aquatic ecosystems was developed. The methodology is based on fuzzy logic, a proven method for dealing with variables with an associated uncertainty, as is the case with many variables related to AgNPs. After a careful literature search, a selection of relevant variables was carried out and the fuzzy model was designed. From inputs such as AgNPs’ size, shape, and coating, it is possible to determine their level of toxicity which, together with their level of concentration, are sufficient to create a risk assessment. Two case studies to assess this methodology are presented, one involving continuous effluent from a wastewater treatment plant and the second involving an accidental spill. The results showed that the accidental spills have a higher risk than WWTP release, with the combination of Plates–BPEI being the most toxic one. This approach can be adapted to different situations and types of nanoparticles, making it highly useful for both stakeholders and decision makers.

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.

A methodology for the automatic evaluation of data quality and completeness of nanomaterials for risk assessment purposes

This manuscript proposes a methodology to assess the completeness and quality of physicochemical and hazard datasets for risk assessment purposes. The approach is also specifically applicable to similarity assessment as a basis for grouping of (nanoforms of) chemical substances as well as for classification of the substances according to the Classification, Labeling and Packaging regulation. The unique goal of this approach is to assess data quality in such a way that all the steps are automatized, thus reducing reliance on expert judgment. The analysis starts from available (meta)data as provided in the data entry templates developed by the NanoSafety community and used for import into the eNanoMapper database. The methodology is implemented in the templates as a traffic light system-the providers of the data can see in real time the completeness scores calculated by the system for their datasets in green, yellow, or red. This is an interactive feedback feature that is intended to provide an incentive for anyone inserting data into the database to deliver more complete and higher quality datasets. The users of the data can also see this information both in the data entry templates and on the database interface, which enables them to select better datasets for their assessments. The proposed methodology has been partially implemented in the eNanoMapper database and in a Weight of Evidence approach for the regulatory classification of nanomaterials. It was fully implemented in a publicly available online R tool.

Meta-analysis of Bioaccumulation Data for Nondissolvable Engineered Nanomaterials in Freshwater Aquatic Organisms

Understanding the bioaccumulation of engineered nanomaterials (ENMs) is essential for making regulatory decisions on potential environmental risks. Research in the field of ENM bioaccumulation has increased in recent years, but the compilation and statistical analysis of the available experimental data have not been updated. We therefore performed a meta-analysis of the existing literature on the bioaccumulation of eight types of nondissolvable ENMs (titanium dioxide [TiO₂ ], aluminum oxide [Al₂ O₃ ], gold [Au], fullerene [C₆₀ ], carbon nanotubes, iron oxide [FeO_x ], graphene, and polystyrene) in nonmammalian freshwater aquatic organisms across three trophic levels including phytoplankton, zooplankton, and fish. Three typical endpoints were used to assess the bioaccumulation potential: the bioconcentration factor (BCF), the bioaccumulation factor (BAF), and the biomagnification factor (BMF). Our results suggest that zooplankton has greater mean logarithmic BCF and BAF values than phytoplankton (3.31 vs. 1.42) and fish (2.04). The ENMs are biomagnified in zooplankton, with a mean BMF of 17.4, whereas trophic transfer from primary consumers (zooplankton) to secondary consumers (fish) was not observed (mean BMF of 0.13). No clear dependency was identified between the physicochemical characteristics of ENMs (e.g., primary particle size, zeta potential, or shape) and bioaccumulation, except for coated versus uncoated particles accumulated in phytoplankton. Carbonaceous ENMs were found to be more bioaccumulated than the other ENMs we considered, except for TiO₂ . A meta-analysis of bioaccumulation data can (1) deepen the understanding of bioconcentration, bioaccumulation, and biomagnification of ENMs, (2) be used to support grouping strategies as a basis for a safer-by-design approach for ENMs, (3) be integrated into comprehensive hazard and risk assessments, (4) promote the standardization of testing guidelines, and (5) enhance future kinetic bioaccumulation modeling. Environ Toxicol Chem 2022;41:1202-1214. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Safe- and sustainable-by-design: The case of Smart Nanomaterials. A perspective based on a European workshop

The European Commission's Green Deal is a major policy initiative aiming to achieve a climate-neutral, zero-pollution, sustainable, circular and inclusive economy, driving both the New Industrial Strategy for Europe and the Chemicals Strategy for Sustainability. Innovative materials can help to reach these policy goals, but they need to be safe and sustainable themselves. Thus, one aim is to shift the development of chemicals to Safe- and Sustainable-by-Design, and define a new systems approach and criteria for sustainability to achieve this. An online workshop was organised in September 2020 by the Joint Research Centre and the Directorate-General Research and Innovation of the European Commission, with participants from academia, non-governmental organisations, industry and regulatory bodies. The aims were to introduce the concept of Safe- and Sustainable-by-Design, to identify industrial and regulatory challenges in achieving safer and more sustainable Smart Nanomaterials as an example of innovative materials, and to deliver recommendations for directions and actions necessary to meet these challenges. The following needs were identified: (i) an agreed terminology, (ii) a common understanding of the principles of Safe- and Sustainable-by-Design, iii) criteria, assessment tools and incentives to achieve a transition from Safe-by-Design to Safe- and Sustainable-by-Design, and (iv) preparedness of regulators and legislation for innovative chemicals/nanomaterials. This paper presents the authors' view on the state of the art as well as the needs for future activities, based on discussions at the workshop and further considerations. The case of Smart Nanomaterials is used to illustrate the Safe- and Sustainable-by-Design concept and challenges for its implementation. Most of the considerations can be extended to other advanced materials and to chemicals and products in general.

Collection of Controlled Nanosafety Data-The CoCoN-Database, a Tool to Assess Nanomaterial Hazard

Hazard assessment is the first step in nanomaterial risk assessment. The overall number of studies on the biological effects of nanomaterials or innovative materials is steadily increasing and is above 40,000. Several databases have been established to make the amount of data manageable, but these are often highly specialized or can be used only by experts. This paper describes a new database which uses an already existing data collection of about 35,000 publications. The collection from the first phase between the years 2000 and 2013 contains about 11,000 articles and this number has been reduced by specific selection criteria. The resulting publications have been evaluated for their quality regarding the toxicological content and the experimental data have been extracted. In addition to material properties, the most important value to be extracted is the no-observed-adverse-effect-level (NOAEL) for in vivo and the no-observed-effect-concentration (NOEC) for in vitro studies. The correlation of the NOAEL/NOEC values with the nanomaterial properties and the investigated endpoints has been tested in projects such as the OECD-AOP project, where the available data for inflammatory responses have been analysed. In addition, special attention was paid to titanium dioxide particles and this example is used to show with searches for in vitro and in vivo experiments on possible lung toxicity what a typical result of a database query can look like. In this review, an emerging database is described that contains valuable information for nanomaterial hazard estimation and should aid in the progress of nanosafety research.

Digital research data: from analysis of existing standards to a scientific foundation for a modular metadata schema in nanosafety

BACKGROUND

Assessing the safety of engineered nanomaterials (ENMs) is an interdisciplinary and complex process producing huge amounts of information and data. To make such data and metadata reusable for researchers, manufacturers, and regulatory authorities, there is an urgent need to record and provide this information in a structured, harmonized, and digitized way.

RESULTS

This study aimed to identify appropriate description standards and quality criteria for the special use in nanosafety. There are many existing standards and guidelines designed for collecting data and metadata, ranging from regulatory guidelines to specific databases. Most of them are incomplete or not specifically designed for ENM research. However, by merging the content of several existing standards and guidelines, a basic catalogue of descriptive information and quality criteria was generated. In an iterative process, our interdisciplinary team identified deficits and added missing information into a comprehensive schema. Subsequently, this overview was externally evaluated by a panel of experts during a workshop. This whole process resulted in a minimum information table (MIT), specifying necessary minimum information to be provided along with experimental results on effects of ENMs in the biological context in a flexible and modular manner. The MIT is divided into six modules: general information, material information, biological model information, exposure information, endpoint read out information and analysis and statistics. These modules are further partitioned into module subdivisions serving to include more detailed information. A comparison with existing ontologies, which also aim to electronically collect data and metadata on nanosafety studies, showed that the newly developed MIT exhibits a higher level of detail compared to those existing schemas, making it more usable to prevent gaps in the communication of information.

CONCLUSION

Implementing the requirements of the MIT into e.g., electronic lab notebooks (ELNs) would make the collection of all necessary data and metadata a daily routine and thereby would improve the reproducibility and reusability of experiments. Furthermore, this approach is particularly beneficial regarding the rapidly expanding developments and applications of novel non-animal alternative testing methods.

Refinement of the selection of physicochemical properties for grouping and read-across of nanoforms

Before placing a new nanoform (NF) on the market, its potential adverse effects must be evaluated. This may e.g. be done via hazard and risk assessment. Grouping and read-across of NFs is a possible strategy to reduce resource consumption, maximising the use of existing data for assessment of NFs. The GRACIOUS project provides a framework in which possible grouping and read-across for NFs is mainly based on an evaluation of their similarity. The impact of NFs on human health and the environment depends strongly on the concentration of the NF and its physicochemical properties, such as chemical composition, size distribution, shape, etc. Hence, knowledge of the most relevant physicochemical properties is essential information for comparing similarity. The presented work aims to refine existing proposals for sets of descriptors (descriptor array) that are needed to describe distinct NFs of a material to identify the most relevant ones for grouping and read-across. The selection criteria for refining this descriptor array are explained and demonstrated. Relevant protocols and methods are proposed for each physicochemical property. The required and achievable measurement accuracies of the refined descriptor array are reviewed, as this information is necessary for similarity assessment of NFs based on individual physicochemical properties.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

FAIRification of nanosafety data to improve applicability of (Q)SAR approaches: A case study on in vitro Comet assay genotoxicity data

(Quantitative) structure-activity relationship ([Q]SAR) methodologies are widely applied to predict the (eco)toxicological effects of chemicals, and their use is envisaged in different regulatory frameworks for filling data gaps of untested substances. However, their application to the risk assessment of nanomaterials is still limited, also due to the scarcity of large and curated experimental datasets. Despite a great amount of nanosafety data having been produced over the last decade in international collaborative initiatives, their interpretation, integration and reuse has been hampered by several obstacles, such as poorly described (meta)data, non-standard terminology, lack of harmonized reporting formats and criteria. Recently, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles have been established to guide the scientific community in good data management and stewardship. The EU H2020 Gov4Nano project, together with other international projects and initiatives, is addressing the challenge of improving nanosafety data FAIRness, for maximizing their availability, understanding, exchange and ultimately their reuse. These efforts are largely supported by the creation of a common Nanosafety Data Interface, which connects a row of project-specific databases applying the eNanoMapper data model. A wide variety of experimental data relating to characterization and effects of nanomaterials are stored in the database; however, the methods, protocols and parameters driving their generation are not fully mature. This article reports the progress of an ongoing case study in the Gov4nano project on the reuse of in vitro Comet genotoxicity data, focusing on the issues and challenges encountered in their FAIRification through the eNanoMapper data model. The case study is part of an iterative process in which the FAIRification of data supports the understanding of the phenomena underlying their generation and, ultimately, improves their reusability.

A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging Regulation criteria

In the context of the European Union (EU) Horizon 2020 GRACIOUS project (Grouping, Read-Across, Characterisation and classification framework for regulatory risk assessment of manufactured nanomaterials and Safer design of nano-enabled products), we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. In the WoE, results from heterogeneous studies are weighted according to data quality and completeness criteria, integrated, and then evaluated by expert judgment to obtain a hazard classification, resulting in a coherent and justifiable methodology. Moreover, the probabilistic nature of the proposed approach enables highlighting the uncertainty in the analysis. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an online R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in REACH registration dossiers and in recent literature.

Safe-by-Design part II: A strategy for balancing safety and functionality in the different stages of the innovation process

Manufactured nanomaterials have the potential to impact an exceedingly wide number of industries and markets ranging from energy storage, electronic and optical devices, light-weight construction to innovative medical approaches for diagnostics and therapy. In order to foster the development of safer nanomaterial-containing products, two main aspects are of major interest: their functional performance as well as their safety towards human health and the environment. In this paper a first proposal for a strategy is presented to link the functionality of nanomaterials with safety aspects. This strategy first combines information on the functionality and safety early during the innovation process and onwards, and then identifies Safe-by-Design (SbD) actions that allow for optimisation of both aspects throughout the innovation process. The strategy encompasses suggestions for the type of information needed to balance functionality and safety to support decision making in the innovation process. The applicability of the strategy is illustrated using a literature-based case study on carbon nanotube-based transparent conductive films. This is a first attempt to identify information that can be used for balancing functionality and safety in a structured way during innovation processes.

Analyzing the surface of functional nanomaterials-how to quantify the total and derivatizable number of functional groups and ligands

Functional nanomaterials (NM) of different size, shape, chemical composition, and surface chemistry are of increasing relevance for many key technologies of the twenty-first century. This includes polymer and silica or silica-coated nanoparticles (NP) with covalently bound surface groups, semiconductor quantum dots (QD), metal and metal oxide NP, and lanthanide-based NP with coordinatively or electrostatically bound ligands, as well as surface-coated nanostructures like micellar encapsulated NP. The surface chemistry can significantly affect the physicochemical properties of NM, their charge, their processability and performance, as well as their impact on human health and the environment. Thus, analytical methods for the characterization of NM surface chemistry regarding chemical identification, quantification, and accessibility of functional groups (FG) and surface ligands bearing such FG are of increasing importance for quality control of NM synthesis up to nanosafety. Here, we provide an overview of analytical methods for FG analysis and quantification with special emphasis on bioanalytically relevant FG broadly utilized for the covalent attachment of biomolecules like proteins, peptides, and oligonucleotides and address method- and material-related challenges and limitations. Analytical techniques reviewed include electrochemical titration methods, optical assays, nuclear magnetic resonance and vibrational spectroscopy, as well as X-ray based and thermal analysis methods, covering the last 5-10 years. Criteria for method classification and evaluation include the need for a signal-generating label, provision of either the total or derivatizable number of FG, need for expensive instrumentation, and suitability for process and production control during NM synthesis and functionalization.

Predicting and investigating cytotoxicity of nanoparticles by translucent machine learning

Safety concerns of engineered nanoparticles (ENPs) hamper their applications and commercialization in many potential fields. Machine learning has been proved as a great tool to understand the complex ENP-organism-environment relationship. However, good-performance machine learning models usually exist as black boxes, which may be difficult to build trust and whose ways of expressing knowledge rarely directly map to forms familiar to scientists. Here, we present an approach for uncovering causal structure in nanotoxicity datasets by mutual-validated and model-agnostic interpretation methods. Model predictions can be explained from feature importance, feature effects, and feature interactions. The utility of this approach is demonstrated through two case studies, the cytotoxicity of cadmium-containing quantum dots and metal oxide nanoparticles. Further, these case studies indicate the efficacy and impacts at two scales: (i) model interpretation, where the most relevant features for correlating cytotoxicity are identified and their influence on model predictions and interactions with other features are then explained, and (ii) model validation, where the difference among interpretation results of different methods (or the difference between interpretation results and well-known toxicity mechanisms) may reflect some inherent problems in the used dataset (or the developed models). Our approach of integrating machine learning models and interpretation methods provides a roadmap for predicting the toxicity of ENPs in a translucent way.

Understanding the impact of more realistic low-dose, prolonged engineered nanomaterial exposure on genotoxicity using 3D models of the human liver

BACKGROUND

With the continued integration of engineered nanomaterials (ENMs) into everyday applications, it is important to understand their potential for inducing adverse human health effects. However, standard in vitro hazard characterisation approaches suffer limitations for evaluating ENM and so it is imperative to determine these potential hazards under more physiologically relevant and realistic exposure scenarios in target organ systems, to minimise the necessity for in vivo testing. The aim of this study was to determine if acute (24 h) and prolonged (120 h) exposures to five ENMs (TiO2, ZnO, Ag, BaSO4 and CeO2) would have a significantly different toxicological outcome (cytotoxicity, (pro-)inflammatory and genotoxic response) upon 3D human HepG2 liver spheroids. In addition, this study evaluated whether a more realistic, prolonged fractionated and repeated ENM dosing regime induces a significantly different toxicity outcome in liver spheroids as compared to a single, bolus prolonged exposure.

RESULTS

Whilst it was found that the five ENMs did not impede liver functionality (e.g. albumin and urea production), induce cytotoxicity or an IL-8 (pro-)inflammatory response, all were found to cause significant genotoxicity following acute exposure. Most statistically significant genotoxic responses were not dose-dependent, with the exception of TiO2. Interestingly, the DNA damage effects observed following acute exposures, were not mirrored in the prolonged exposures, where only 0.2-5.0 µg/mL of ZnO ENMs were found to elicit significant (p ≤ 0.05) genotoxicity. When fractionated, repeated exposure regimes were performed with the test ENMs, no significant (p ≥ 0.05) difference was observed when compared to the single, bolus exposure regime. There was < 5.0% cytotoxicity observed across all exposures, and the mean difference in IL-8 cytokine release and genotoxicity between exposure regimes was 3.425 pg/mL and 0.181%, respectively.

CONCLUSION

In conclusion, whilst there was no difference between a single, bolus or fractionated, repeated ENM prolonged exposure regimes upon the toxicological output of 3D HepG2 liver spheroids, there was a difference between acute and prolonged exposures. This study highlights the importance of evaluating more realistic ENM exposures, thereby providing a future in vitro approach to better support ENM hazard assessment in a routine and easily accessible manner.

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials

Nanomaterials have drawn increasing attention due to their tunable and enhanced physicochemical and biological performance compared to their conventional bulk materials. Owing to the rapid expansion of the nano-industry, large amounts of data regarding the synthesis, physicochemical properties, and bioactivities of nanomaterials have been generated. These data are a great asset to the scientific community. However, the data are on diverse aspects of nanomaterials and in different sources and formats. To help utilize these data, various databases on specific information of nanomaterials such as physicochemical characterization, biomedicine, and nano-safety have been developed and made available online. Understanding the structure, function, and available data in these databases is needed for scientists to select appropriate databases and retrieve specific information for research on nanomaterials. However, to our knowledge, there is no study to systematically compare these databases to facilitate their utilization in the field of nanomaterials. Therefore, we reviewed and compared eight widely used databases of nanomaterials, aiming to provide the nanoscience community with valuable information about the specific content and function of these databases. We also discuss the pros and cons of these databases, thus enabling more efficient and convenient utilization.

Towards FAIR nanosafety data

Nanotechnology is a key enabling technology with billions of euros in global investment from public funding, which include large collaborative projects that have investigated environmental and health safety aspects of nanomaterials, but the reuse of accumulated data is clearly lagging behind. Here we summarize challenges and provide recommendations for the efficient reuse of nanosafety data, in line with the recently established FAIR (findable, accessible, interoperable and reusable) guiding principles. We describe the FAIR-aligned Nanosafety Data Interface, with an aggregated findability, accessibility and interoperability across physicochemical, bio-nano interaction, human toxicity, omics, ecotoxicological and exposure data. Overall, we illustrate a much-needed path towards standards for the optimized use of existing data, which avoids duplication of efforts, and provides a multitude of options to promote safe and sustainable nanotechnology.

Regulatory landscape of nanotechnology and nanoplastics from a global perspective

Nanotechnology and more particularly nanotechnology-based products and materials have provided a huge potential for novel solutions to many of the current challenges society is facing. However, nanotechnology is also an area of product innovation that is sometimes developing faster than regulatory frameworks. This is due to the high complexity of some nanomaterials, the lack of a globally harmonised regulatory definition and the different scopes of regulation at a global level. Research organisations and regulatory bodies have spent many efforts in the last two decades to cope with these challenges. Although there has been a significant advancement related to analytical approaches for labelling purposes as well as to the development of suitable test guidelines for nanomaterials and their safety assessment, there is a still a need for greater global collaboration and consensus in the regulatory field. Furthermore, with growing societal concerns on plastic litter and tiny debris produced by degradation of littered plastic objects, the impact of micro- and nanoplastics on humans and the environment is an emerging issue. Despite increasing research and initial regulatory discussions on micro- and nanoplastics, there are still knowledge gaps and thus an urgent need for action. As nanoplastics can be classified as a specific type of incidental nanomaterials, current and future scientific investigations should take into account the existing profound knowledge on nanotechnology/nanomaterials when discussing issues around nanoplastics. This review was conceived at the 2019 Global Summit on Regulatory Sciences that took place in Stresa, Italy, on 24-26 September 2019 (GSRS 2019) and which was co-organised by the Global Coalition for Regulatory Science Research (GCRSR) and the European Commission's (EC) Joint Research Centre (JRC). The GCRSR consists of regulatory bodies from various countries around the globe including EU bodies. The 2019 Global Summit provided an excellent platform to exchange the latest information on activities carried out by regulatory bodies with a focus on the application of nanotechnology in the agriculture/food sector, on nanoplastics and on nanomedicines, including taking stock and promoting further collaboration. Recently, the topic of micro- and nanoplastics has become a new focus of the GCRSR. Besides discussing the challenges and needs, some future directions on how new tools and methodologies can improve the regulatory science were elaborated by summarising a significant portion of discussions during the summit. It has been revealed that there are still some uncertainties and knowledge gaps with regard to physicochemical properties, environmental behaviour and toxicological effects, especially as testing described in the dossiers is often done early in the product development process, and the material in the final product may behave differently. The harmonisation of methodologies for quantification and risk assessment of nanomaterials and micro/nanoplastics, the documentation of regulatory science studies and the need for sharing databases were highlighted as important aspects to look at.

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.

Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish

The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present work, we have employed advanced Machine Learning techniques, including stacked model ensembles, to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, tested at different dosage concentrations on embryonic zebrafish. Using both computed and experimental descriptors, we have identified a set of properties most relevant for the assessment of nanomaterial toxicity and successfully correlated these properties with the associated biological responses observed in zebrafish. Our findings suggest that for the group of metal and metal oxide nanomaterials, the core chemical composition, concentration and properties dependent upon nanomaterial surface and medium composition (such as zeta potential and agglomerate size) are significant factors influencing toxicity, albeit the ranking of different variables is sensitive to the exact analysis method and data modeled. Our generalized nano-QSAR ensemble models provide a promising framework for anticipating the toxicity potential of new nanomaterials and may contribute to the transition out of the animal testing paradigm. However, future experimental studies are required to generate comparable, similarly high quality data, using consistent protocols, for well characterized nanomaterials, as per the dataset modeled herein. This would enable the predictive power of our promising ensemble modeling approaches to be robustly assessed on large, diverse and truly external datasets.

Reliable Surface Analysis Data of Nanomaterials in Support of Risk Assessment Based on Minimum Information Requirements

The minimum information requirements needed to guarantee high-quality surface analysis data of nanomaterials are described with the aim to provide reliable and traceable information about size, shape, elemental composition and surface chemistry for risk assessment approaches. The widespread surface analysis methods electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) were considered. The complete analysis sequence from sample preparation, over measurements, to data analysis and data format for reporting and archiving is outlined. All selected methods are used in surface analysis since many years so that many aspects of the analysis (including (meta)data formats) are already standardized. As a practical analysis use case, two coated TiO2 reference nanoparticulate samples, which are available on the Joint Research Centre (JRC) repository, were selected. The added value of the complementary analysis is highlighted based on the minimum information requirements, which are well-defined for the analysis methods selected. The present paper is supposed to serve primarily as a source of understanding of the high standardization level already available for the high-quality data in surface analysis of nanomaterials as reliable input for the nanosafety community.

A European nano-registry as a reliable database for quantitative risk assessment of nanomaterials? A comparison of national approaches

Despite the fact that nanomaterials have been in use for decades and chemicals legislation is largely harmonised within the EU, quantitative and safety-relevant information on nanomaterials is still scarce. In particular, information about production volumes, their unique physicochemical properties (size, specific surface area, etc.) and nanomaterial exposure, which may lead to adverse effects on human health and the environment, is still lacking. While the latest amendments of the REACH Annexes have led to certain improvements, a harmonised EU-wide nano-registry would provide additional quantitative data for risk assessment but is not foreseeable for the near future. Since the European Commission, the European Parliament and some member states take contrasting approaches to the regulation of nanomaterials, France, Belgium, Denmark, Sweden and Norway (as a country of the European Economic Area), launched national mandatory reporting systems to collect quantitative information, thus fostering early risk assessment of nanomaterials. In this study, we compare national registries - based on a literature review and expert interviews - and show differences between the regulations under the respective national laws and REACH regulation. These include, for instance, thresholds for notification and level of detail on the specification of the nanomaterial, mixture and/or product, the definition of exceptions for the requirement to register and the timing of registration. As this heterogenous regulatory framework hinders comparability and potentially creates trade barriers, we argue that a harmonised EU-wide nano-registry would substantially improve the current situation by promoting the safe and sustainable handling of nanomaterials, increasing transparency and trust, and consequently nurturing innovation. Such an EU-wide nano-registry should both cover nanomaterials as substances or mixtures, such as in REACH registration, and the semi-/finished products they will be used in, since the exposure, and thus the hazardous potential of released nanomaterials during their life cycle, depends largely on the scope of application.

A Semi-Automated Workflow for FAIR Maturity Indicators in the Life Sciences

Data sharing and reuse are crucial to enhance scientific progress and maximize return of investments in science. Although attitudes are increasingly favorable, data reuse remains difficult due to lack of infrastructures, standards, and policies. The FAIR (findable, accessible, interoperable, reusable) principles aim to provide recommendations to increase data reuse. Because of the broad interpretation of the FAIR principles, maturity indicators are necessary to determine the FAIRness of a dataset. In this work, we propose a reproducible computational workflow to assess data FAIRness in the life sciences. Our implementation follows principles and guidelines recommended by the maturity indicator authoring group and integrates concepts from the literature. In addition, we propose a FAIR balloon plot to summarize and compare dataset FAIRness. We evaluated the feasibility of our method on three real use cases where researchers looked for six datasets to answer their scientific questions. We retrieved information from repositories (ArrayExpress, Gene Expression Omnibus, eNanoMapper, caNanoLab, NanoCommons and ChEMBL), a registry of repositories, and a searchable resource (Google Dataset Search) via application program interfaces (API) wherever possible. With our analysis, we found that the six datasets met the majority of the criteria defined by the maturity indicators, and we showed areas where improvements can easily be reached. We suggest that use of standard schema for metadata and the presence of specific attributes in registries of repositories could increase FAIRness of datasets.