How should the completeness and quality of curated nanomaterial data be evaluated?

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.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Knowledge and Instance Mapping: architecture for premeditated interoperability of disparate data for materials

Predicting and elucidating the impacts of materials on human health and the environment is an unending task that has taken on special significance in the context of nanomaterials research over the last two decades. The properties of materials in environmental and physiological media are dynamic, reflecting the complex interactions between materials and these media. This dynamic behavior requires special consideration in the design of databases and data curation that allow for subsequent comparability and interrogation of the data from potentially diverse sources. We present two data processing methods that can be integrated into the experimental process to encourage pre-mediated interoperability of disparate material data: Knowledge Mapping and Instance Mapping. Originally developed as a framework for the NanoInformatics Knowledge Commons (NIKC) database, this architecture and associated methods can be used independently of the NIKC and applied across multiple subfields of nanotechnology and material science.

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.

Precision Nanotoxicology in Drug Development: Current Trends and Challenges in Safety and Toxicity Implications of Customized Multifunctional Nanocarriers for Drug-Delivery Applications

The dire need for the assessment of human and environmental endangerments of nanoparticulate material has motivated the formulation of novel scientific tools and techniques to detect, quantify, and characterize these nanomaterials. Several of these paradigms possess enormous possibilities for applications in many of the realms of nanotoxicology. Furthermore, in a large number of cases, the limited capabilities to assess the environmental and human toxicological outcomes of customized and tailored multifunctional nanoparticles used for drug delivery have hindered their full exploitation in preclinical and clinical settings. With the ever-compounded availability of nanoparticulate materials in commercialized settings, an ever-arising popular debate has been egressing on whether the social, human, and environmental costs associated with the risks of nanomaterials outweigh their profits. Here we briefly review the various health, pharmaceutical, and regulatory aspects of nanotoxicology of engineered multifunctional nanoparticles in vitro and in vivo. Several aspects and issues encountered during the safety and toxicity assessments of these drug-delivery nanocarriers have also been summarized. Furthermore, recent trends implicated in the nanotoxicological evaluations of nanoparticulate matter in vitro and in vivo have also been discussed. Due to the absence of robust and rigid regulatory guidelines, researchers currently frequently encounter a larger number of challenges in the toxicology assessment of nanocarriers, which have also been briefly discussed here. Nanotoxicology has an appreciable and significant part in the clinical translational development as well as commercialization potential of nanocarriers; hence these aspects have also been touched upon. Finally, a brief overview has been provided regarding some of the nanocarrier-based medicines that are currently undergoing clinical trials, and some of those which have recently been commercialized and are available for patients. It is expected that this review will instigate an appreciable interest in the research community working in the arena of pharmaceutical drug development and nanoformulation-based drug delivery.

Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review

Given the rapid development of nanotechnology, it is crucial to understand the effects of nanoparticles on living organisms. However, it is laborious to perform toxicological tests on a case-by-case basis. Quantitative structure-activity relationship (QSAR) is an effective computational technique because it saves time, costs, and animal sacrifice. Therefore, this review presents general procedures for the construction and application of nano-QSAR models of metal-based and metal-oxide nanoparticles (MBNPs and MONPs). We also provide an overview of available databases and common algorithms. The molecular descriptors and their roles in the toxicological interpretation of MBNPs and MONPs are systematically reviewed and the future of nano-QSAR is discussed. Finally, we address the growing demand for novel nano-specific descriptors, new computational strategies to address the data shortage, in situ data for regulatory concerns, a better understanding of the physicochemical properties of NPs with bioactivity, and, most importantly, the design of nano-QSAR for real-life environmental predictions rather than laboratory simulations.

Representing and describing nanomaterials in predictive nanoinformatics

Engineered nanomaterials (ENMs) enable new and enhanced products and devices in which matter can be controlled at a near-atomic scale (in the range of 1 to 100 nm). However, the unique nanoscale properties that make ENMs attractive may result in as yet poorly known risks to human health and the environment. Thus, new ENMs should be designed in line with the idea of safe-and-sustainable-by-design (SSbD). The biological activity of ENMs is closely related to their physicochemical characteristics, changes in these characteristics may therefore cause changes in the ENMs activity. In this sense, a set of physicochemical characteristics (for example, chemical composition, crystal structure, size, shape, surface structure) creates a unique 'representation' of a given ENM. The usability of these characteristics or nanomaterial descriptors (nanodescriptors) in nanoinformatics methods such as quantitative structure-activity/property relationship (QSAR/QSPR) models, provides exciting opportunities to optimize ENMs at the design stage by improving their functionality and minimizing unforeseen health/environmental hazards. A computational screening of possible versions of novel ENMs would return optimal nanostructures and manage ('design out') hazardous features at the earliest possible manufacturing step. Safe adoption of ENMs on a vast scale will depend on the successful integration of the entire bulk of nanodescriptors extracted experimentally with data from theoretical and computational models. This Review discusses directions for developing appropriate nanomaterial representations and related nanodescriptors to enhance the reliability of computational modelling utilized in designing safer and more sustainable ENMs.

(Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies-the case of nanomaterials

The utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance. In this paper, we reflect upon the role that tools might and should play in any system of risk governance. With many tools designed for the risk governance of this emerging technology falling into disuse, this paper provides an overview of extant tools and addresses their potential shortcomings. We also posit the need for a data readiness tool. With the EUs NMP13 family of research consortia about to report to the Commission on ways forward in terms of risk governance of this domain, this is a timely intervention on an important element of any risk governance system.

A Systematic Review on the Hazard Assessment of Amorphous Silica Based on the Literature From 2013 to 2018

Background

Nanomaterials are suspected of causing health problems, as published studies on nanotoxicology indicate. On the other hand, some of these materials, such as nanostructured pyrogenic and precipitated synthetic amorphous silica (SAS) and silica gel, have been used for decades without safety concerns in industrial, commercial, and consumer applications. However, in addition to many in vivo and in vitro studies that have failed to demonstrate the intrinsic toxicity of SAS, articles periodically emerge, in which biological effects of concern have been described. Even though most of these studies do not meet high-quality standards and do not always use equivalent test materials or standardized test systems, the results often trigger substance re-evaluation. To put the results into perspective, an extensive literature study was carried out and an example of amorphous silica will be used to try to unravel the reliability from the unreliable results.

Methods

A systematic search of studies on nanotoxicological effects has been performed covering the years 2013 to 2018. The identified studies have been evaluated for their quality regarding material and method details, and the data have been curated and put into a data collection. This review deals only with investigations on amorphous silica.

Results

Of 18,162 publications 1,217 have been selected with direct reference to experiments with synthetically produced amorphous silica materials. The assessment of these studies based on defined criteria leads to a further reduction to 316 studies, which have been included in this systematic review. Screening for quality with well-defined quantitative criteria following the GUIDE nano concept reveals only 27.3% has acceptable quality. Overall, the in vitro and in vivo data showed low or no toxicity of amorphous silica. The data shown do not support the hypothesis of dependency of biological effects on the primary particle size of the tested materials.

Conclusion

This review demonstrates the relatively low quality of most studies published on nanotoxicological issues in the case of amorphous silica. Moreover, mechanistic studies are often passed off or considered toxicological studies. In general, standardized methods or the Organization for Economic Cooperation and Development (OECD) guidelines are rarely used for toxicological experiments. As a result, the significance of the published data is usually weak and must be reevaluated carefully before using them for regulatory purposes.

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.

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.

Immunotoxicity of nanomaterials in health and disease: Current challenges and emerging approaches for identifying immune modifiers in susceptible populations

Nanosafety assessment has experienced an intense era of research during the past decades driven by a vivid interest of regulators, industry, and society. Toxicological assays based on in vitro cellular models have undergone an evolution from experimentation using nanoparticulate systems on singular epithelial cell models to employing advanced complex models more realistically mimicking the respective body barriers for analyzing their capacity to alter the immune state of exposed individuals. During this phase, a number of lessons were learned. We have thus arrived at a state where the next chapters have to be opened, pursuing the following objectives: (1) to elucidate underlying mechanisms, (2) to address effects on vulnerable groups, (3) to test material mixtures, and (4) to use realistic doses on (5) sophisticated models. Moreover, data reproducibility has become a significant demand. In this context, we studied the emerging concept of adverse outcome pathways (AOPs) from the perspective of immune activation and modulation resulting in pro-inflammatory versus tolerogenic responses. When considering the interaction of nanomaterials with biological systems, protein corona formation represents the relevant molecular initiating event (e.g., by potential alterations of nanomaterial-adsorbed proteins). Using this as an example, we illustrate how integrated experimental-computational workflows combining in vitro assays with in silico models aid in data enrichment and upon comprehensive ontology-annotated (meta)data upload to online repositories assure FAIRness (Findability, Accessibility, Interoperability, Reusability). Such digital twinning may, in future, assist in early-stage decision-making during therapeutic development, and hence, promote safe-by-design innovation in nanomedicine. Moreover, it may, in combination with in silico-based exposure-relevant dose-finding, serve for risk monitoring in particularly loaded areas, for example, workplaces, taking into account pre-existing health conditions. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

Manufacturers of nanomaterial-enabled products need models of endpoints that are relevant to human safety to support the "safe by design" paradigm and avoid late-stage attrition. Increasingly, embryonic zebrafish (Danio Rerio) are recognised as a key human safety relevant in vivo test system. Hence, machine learning models were developed for identifying metal oxide nanomaterials causing lethality to embryonic zebrafish up to 24 hours post-fertilisation, or excess lethality in the period of 24-120 hours post-fertilisation, at concentrations of 250 ppm or less. Models were developed using data from the Nanomaterial Biological-Interactions Knowledgebase for a dataset of 44 diverse, coated and uncoated metal or, in one case, metalloid oxide nanomaterials. Different modelling approaches were evaluated using nested cross-validation on this dataset. Models were initially developed for both lethality endpoints using multiple descriptors representing the composition of the core, shell and surface functional groups, as well as particle characteristics. However, interestingly, the 24 hours post-fertilisation data were found to be harder to predict, which could reflect different exposure routes. Hence, subsequent analysis focused on the prediction of excess lethality at 120 hours-post fertilisation. The use of two data augmentation approaches, applied for the first time in nano-QSAR research, was explored, yet both failed to boost predictive performance. Interestingly, it was found that comparable results to those originally obtained using multiple descriptors could be obtained using a model based upon a single, simple descriptor: the Pauling electronegativity of the metal atom. Since it is widely recognised that a variety of intrinsic and extrinsic nanomaterial characteristics contribute to their toxicological effects, this is a surprising finding. This may partly reflect the need to investigate more sophisticated descriptors in future studies. Future studies are also required to examine how robust these modelling results are on truly external data, which were not used to select the single descriptor model. This will require further laboratory work to generate comparable data to those studied herein.

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.

Engineering of 177Lu-labeled gold encapsulated into dendrimeric nanomaterials for the treatment of lung cancer

As a novel type of theranostic radioactive agents, ¹⁷⁷Lu-labeled nanomaterials conjugated to macromolecules have been described. The study aimed to fabricate PAMAM-G4-(¹⁷⁷Lu-dendrimer)-bombesin-folate in the dendrimeric cavity, assess the radiopharmaceutical ability for specifically targeted radiotherapy and simultaneously detects gastrin-releasing peptide receptors (GRPR) and folate receptors (FRs) overexpressed in lung carcinoma cells, respectively. In an aqueous-basic media, p-SCN-benzyl-DOTA was conjugated to the dendrimer. This dendrimer was formed by activating the carboxylic acid groups of DOTA-folic acid and bombesin with HATU and conjugating them to develop the dendrimer. As part of this process, the conjugate was combined with 1% HAuCl₄, added NaBH₄ and filtered by ultrafiltration. Infrared, UV-Vis, TEM analysis, dynamic light scattering (DLS), and fluorescence spectroscopy were employed to observe the composition of the fabricated sample. Radio-labeled ¹⁷⁷LuCl₃ was used to label the conjugate, which was then evaluated using the radio-HPLC method. Findings demonstrated dendrimeric functionalization with remarkable radiochemical composition purity up to >96%. Because of fluorescence studies, it was determined that the occurrence of AuNMs in the dendrimeric cavities gives beneficial photo-physical characteristics to the radiopharmaceutical for bio-imaging. HEL-299 lung cancer cells exhibited a selective absorption of the drug (%). It might be helpful as nuclear and optical imaging agents for lung cancers that overexpress FRs and GRPR and as a specific target for radiation therapy if combined with folate-bombesin.

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.

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.

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 meta-analysis framework to assess the role of units in describing nanoparticle toxicity

Despite ample research on nanoparticles, their environmental toxicity is still debatable. The lack of consensus is due in part to the challenge of comparing studies because of variability in parameters like test organism, test medium, and duration of experiment. However, the unit used to compare the toxicology of nanoparticles is one variable that experimentalists can control. Traditionally, mass per volume is the most common unit used to make comparisons, but there is growing evidence that alternative units such as surface area per volume or particles per volume may provide a better and more mechanistic measure of toxicity. Herein, we propose and test a meta-analytic framework to study the effect of units on nanotoxicology using data from the NanoE-Tox database, a freely available database containing 1518 toxicology values from 224 published articles of which 42 records met our basic inclusion criteria. These data were augmented with more recent data published over the past five years as archived by the Web of Science citation index. An additional 27 records from 1676 papers met the inclusion criteria and were also included in the analysis. The meta-analysis framework measures the degree of heterogeneity for each of three units (grams/L, particles/L, surface area/L) grouped by the type of test organism, particle chemistry, and manner in which a nanoparticle's size was measured (e.g., nominal particle size reported by the manufacturer vs. measurement of size for particles suspended in the liquid medium used in a subsequent toxicity experiment). The result of the meta-analysis reveals that surface area per volume reduces the heterogeneity in the Ag crustacean subgroup when nanoparticle size was measured in the test medium, and the ZnO crustacean subgroup when nanoparticle size was measured out the test medium and may therefore be a more appropriate estimate of the toxicity of soluble nanoparticles. No subgroups in our analysis showed a reduction in heterogeneity for particles per volume in either soluble or insoluble nanoparticles. The lack of conclusion on insoluble nanoparticles was not due to a limitation of our meta-analysis but rather highlights a critical deficiency in the primary literature. The majority of published studies fail to report common measures of error that are essential for further analysis (i.e. error of the measured nanoparticle size and/or interoperable error of the measured half-maximal concentration of the toxic endpoint). If future nanotoxicity studies report such error, as they should, then the framework of our meta-analysis could be used more broadly to provide a simple, statistically rigorous way to assess the role of units on the toxicity of nanoparticles.

Biomedical nanomaterials: applications, toxicological concerns, and regulatory needs

Advances in cutting-edge technologies such as nano- and biotechnology have created an opportunity for re-engineering existing materials and generating new nano-scale products that can function beyond the limits of conventional ones. While the step change in the properties and functionalities of these new materials opens up new possibilities for a broad range of applications, it also calls for structural modifications to existing safety assessment processes that are primarily focused on bulk material properties. Decades after the need to modify existing risk management practices to include nano-specific behaviors and exposure pathways was recognized, relevant policies for evaluating, and controlling health risks of nano-enabled materials is still lacking. This review provides an overview of current progress in the field of nanobiotechnology rather than intentions and aspirations, summarizes long-recognized but still unresolved issues surrounding materials safety at the nanoscale, and discusses key barriers preventing generation and integration of reliable data in bio/nano-safety domain. Particular attention is given to nanostructured materials that are commonly used in biomedical applications.

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

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

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.

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

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

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

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

QSAR without borders

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

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

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

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

Grouping and read-across has emerged as a reliable approach to generate safety-related data on nanomaterials (NMs). However, its successful implementation relies on the availability of detailed characterisation of NM physicochemical properties, which allows the definition of groups based on read-across similarity. To this end, this study assessed the availability and completeness of existing (meta)data on 11 experimentally determined physicochemical properties and 18 NMs. Data on representative NMs were mainly extracted from existing datasets stored in the eNanoMapper database, now available on the European Observatory on Nanomaterials website, while data on case-study NMs were provided by their industrial manufacturers. The extent of available (meta)data was assessed and data gaps were identified, thereby determining future testing needs. Data completeness was assessed by using the information checklists included in the templates for data logging developed by the EU-funded projects NANoREG and GRACIOUS. A completeness score (CS) between 0 and 1 was calculated for each (meta)data unit, template section, property, technique and NM. The results show a heterogeneous distribution of available (meta)data across materials and properties, with none of the selected NMs fully characterised. The average CS calculated for representative NMs (0.43) was considerably lower than for case-study NMs (0.68). The low CS was largely caused by missing information on sample preparation and standard operating procedures, and was attributed to a lack of harmonised data reporting and entry procedure. This study therefore suggests that a persistent use of well-defined and harmonised reporting schemes for experimental results is a useful tool to increase (meta)data completeness and ensure their integration and reuse.

Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics

Inroads have been made in our understanding of the risks posed to human health and the environment by nanoparticles (NPs) but this area requires continuous research and monitoring. Machine learning techniques have been applied to nanotoxicology with very encouraging results. This study deals with bridging physicochemical properties of NPs, experimental exposure conditions and in vitro characteristics with biological effects of NPs on a molecular cellular level from transcriptomics studies. The bridging is done by developing and implementing Bayesian Networks (BNs) with or without data preprocessing. The BN structures are derived either automatically or methodologically and compared. Early stage nanotoxicity measurements represent a challenge, not least when attempting to predict adverse outcomes and modeling is critical to understanding the biological effects of exposure to NPs. The preprocessed data-driven BN showed improved performance over automatically structured BN and the BN with unprocessed datasets. The prestructured BN captures inter relationships between NP properties, exposure condition and in vitro characteristics and links those with cellular effects based on statistic correlation findings. Information gain analysis showed that exposure dose, NP and cell line variables were the most influential attributes in predicting the biological effects. The BN methodology proposed in this study successfully predicts a number of toxicologically relevant cellular disrupted biological processes such as cell cycle and proliferation pathways, cell adhesion and extracellular matrix responses, DNA damage and repair mechanisms etc., with a success rate >80%. The model validation from independent data shows a robust and promising methodology for incorporating transcriptomics outcomes in a hazard and, by extension, risk assessment modeling framework by predicting affected cellular functions from experimental conditions.

In the Search for Nanospecific Effects of Dissolution of Metallic Nanoparticles at Freshwater-Like Conditions: A Critical Review

Knowledge on relations between particle properties and dissolution/transformation characteristics of metal and metal oxide nanoparticles (NPs) in freshwater is important for risk assessment and product development. This critical review aims to elucidate nanospecific effects on dissolution of metallic NPs in freshwater and similar media. Dissolution rate constants are compiled and analyzed for NPs of silver (Ag), copper (Cu), copper oxide/hydroxide (CuO, Cu(OH)₂), zinc oxide (ZnO), manganese (Mn), and aluminum (Al), showing largely varying (orders of magnitude) constants when modeled using first order kinetics. An effect of small primary sizes (<15 nm) was observed, leading to increased dissolution rate constants and solubility in some cases. However, the often extensive particle agglomeration can result in reduced nanospecific effects on dissolution and also an increased uncertainty related to the surface area, a parameter that largely influence the extent of dissolution. Promising ways to model surface areas of NPs in solution using fractal dimensions and size distributions are discussed in addition to nanospecific aspects related to other processes such as corrosion, adsorption of natural organic matter (NOM), presence of capping agents, and existence of surface defects. The importance of the experimental design on the results of dissolution experiments of metal and metal oxide NPs is moreover highlighted, including the influence of ionic metal solubility and choice of particle dispersion methodology.

Risk Analysis of Cellulose Nanomaterials by Inhalation: Current State of Science

Cellulose nanomaterials (CNs) are emerging advanced materials with many unique properties and growing commercial significance. A life-cycle risk assessment and environmental health and safety roadmap identified potential risks from inhalation of powdered CNs in the workplace as a key gap in our understanding of safety and recommended addressing this data gap to advance the safe and successful commercialization of these materials. Here, we (i) summarize the currently available published literature for its contribution to our current understanding of CN inhalation hazard and (ii) evaluate the quality of the studies for risk assessment purposes using published study evaluation tools for nanomaterials to assess the weight of evidence provided. Our analysis found that the quality of the available studies is generally inadequate for risk assessment purposes but is improving over time. There have been some advances in knowledge about the effects of short-term inhalation exposures of CN. The most recent in vivo studies suggest that short-term exposure to CNs results in transient inflammation, similarly to other poorly soluble, low toxicity dusts such as conventional cellulose, but is markedly different from fibers with known toxicity such as certain types of multiwalled carbon nanotubes or asbestos. However, several data gaps remain, and there is still a lack of understanding of the effects from long-term, low-dose exposures that represent realistic workplace conditions, essential for a quantitative assessment of potential health risk. Therefore, taking precautions when handling dry forms of CNs to avoid dust inhalation exposure is warranted.

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.

Advanced tools for the safety assessment of nanomaterials

Engineered nanomaterials (ENMs) have tremendous potential to produce beneficial technological impact in numerous sectors in society. Safety assessment is, of course, of paramount importance. However, the myriad variations of ENM properties makes the identification of specific features driving toxicity challenging. At the same time, reducing animal tests by introducing alternative and/or predictive in vitro and in silico methods has become a priority. It is important to embrace these new advances in the safety assessment of ENMs. Indeed, remarkable progress has been made in recent years with respect to mechanism-based hazard assessment of ENMs, including systems biology approaches as well as high-throughput screening platforms, and new tools are also emerging in risk assessment and risk management for humans and the environment across the whole life-cycle of nano-enabled products. Here, we highlight some of the key advances in the hazard and risk assessment of ENMs.

Dissolution of Complex Metal Oxides from First-Principles and Thermodynamics: Cation Removal from the (001) Surface of Li(Ni1/3Mn1/3Co1/3)O2

The rapid increase in use of Li-ion batteries in portable electronics has created a pressing need to understand the environmental impact and long-term fate of electonic waste (e-waste) products such as heavy and/or reactive metals. The type of e-waste that we focus on here are the complex metal oxide nanomaterials that compose Li-ion battery cathodes. While in operation the complex metal oxides are in a hermetically sealed container. However, at the end of life, improper disposal can cause structural transformations such as dissolution and metal leaching, resulting in a significant exposure risk to the surrounding environment. The transformations that occur between operational to environmental settings gives rise to a stark knowledge gap between macroscopic design and molecular-level behavior. In this study we use theory and modeling to describe and explain previously published experimental data for cation release from Li(Ni_1/3Mn_1/3Co_1/3)O₂ (NMC) nanoparticles in an aqueous environment ( Chem. Mater. 2016 (28) 1092-1100). To better understand the transformations that may occur when this material is exposed to the environment, we compute the free energy of surface dissolution, Δ G, from the complex metal oxide NMC for a range of surface terminations and pH.

Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources

A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.

A statistical approach to identify, monitor, and manage incomplete curated data sets

Background

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here.

Results

In this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval.

Conclusions

This method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2121-6) contains supplementary material, which is available to authorized users.

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations

Many groups within the broad field of nanoinformatics are already developing data repositories and analytical tools driven by their individual organizational goals. Integrating these data resources across disciplines and with non-nanotechnology resources can support multiple objectives by enabling the reuse of the same information. Integration can also serve as the impetus for novel scientific discoveries by providing the framework to support deeper data analyses. This article discusses current data integration practices in nanoinformatics and in comparable mature fields, and nanotechnology-specific challenges impacting data integration. Based on results from a nanoinformatics-community-wide survey, recommendations for achieving integration of existing operational nanotechnology resources are presented. Nanotechnology-specific data integration challenges, if effectively resolved, can foster the application and validation of nanotechnology within and across disciplines. This paper is one of a series of articles by the Nanomaterial Data Curation Initiative that address data issues such as data curation workflows, data completeness and quality, curator responsibilities, and metadata.

Comparative Analysis of Sequence Clustering Methods for Deduplication of Biological Databases

The massive volumes of data in biological sequence databases provide a remarkable resource for large-scale biological studies. However, the underlying data quality of these resources is a critical concern. A particular challenge is duplication, in which multiple records have similar sequences, creating a high level of redundancy that impacts database storage, curation, and search. Biological database deduplication has two direct applications: for database curation, where detected duplicates are removed to improve curation efficiency, and for database search, where detected duplicate sequences may be flagged but remain available to support analysis.

Clustering methods have been widely applied to biological sequences for database deduplication. Since an exhaustive all-by-all pairwise comparison of sequences cannot scale for a high volume of data, heuristic approaches have been recruited, such as the use of simple similarity thresholds. In this article, we present a comparison between CD-HIT and UCLUST, the two best-known clustering tools for sequence database deduplication. Our contributions include a detailed assessment of the redundancy remaining after deduplication, application of standard clustering evaluation metrics to quantify the cohesion and separation of the clusters generated by each method, and a biological case study that assesses intracluster function annotation consistency to demonstrate the impact of these factors on a practical application of the sequence clustering methods. Our results show that the trade-off between efficiency and accuracy becomes acute when low threshold values are used and when cluster sizes are large. This evaluation leads to practical recommendations for users for more effective uses of the sequence clustering tools for deduplication.