Cytotoxicity assessment of HDPE microplastic on Tetrahymena thermophila (Protozoa, Ciliate): Assuring quality and FAIR data.

Microplastics is recognized as an emerging pollutant and adapting and harmonizing existing test methods is essential to advancing research. The aim of our work was to provide a case study on how to ensure quality and FAIR data in the assessment of microplastic hazards with the unicellular organism Tetrahymena thermophila (Protozoa, Ciliata). We selected high density polyethylene (HDPE) microplastics as a model material.

Governance of advanced materials: Shaping a safe and sustainable future.

The past few decades of managing the uncertain risks associated with nanomaterials have provided valuable insights (knowledge gaps, tools, methods, etc.) that are equally important to promote safe and sustainable development and use of advanced materials. Based on these insights, the current paper proposes several actions to optimize the risk and sustainability governance of advanced materials.

A template wizard for the cocreation of machine-readable data-reporting to harmonize the evaluation of (nano)materials.

Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles.

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.

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.

Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating.

ELIXIR and Toxicology: a community in development.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine.

Current status and future challenges of genotoxicity OECD Test Guidelines for nanomaterials: a workshop report.

Genotoxicity testing for nanomaterials remains challenging as standard testing approaches require some adaptation, and further development of nano-specific OECD Test Guidelines (TGs) and Guidance Documents (GDs) are needed. However, the field of genotoxicology continues to progress and new approach methodologies (NAMs) are being developed that could provide relevant information on the range of mechanisms of genotoxic action that may be imparted by nanomaterials. There is a recognition of the need for implementation of new and/or adapted OECD TGs, new OECD GDs, and utilization of NAMs within a genotoxicity testing framework for nanomaterials.

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

Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier.

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).

Possibilities to group nanomaterials across different substances - A case study on organic pigments.

Grouping concepts to reduce the testing of NFs have been developed for regulatory purposes for different forms of the same substance. Here we explore possibilities to group nanomaterials across different substances for non-regulatory applications, using the example of 16 organic pigments from six chemical classes. Organic pigments are particles consisting of low-molar-mass organic molecules, and rank by tonnage among the most important substances manufactured in nanoform (NF).

Assessing the similarity of nanoforms based on the biodegradation of organic surface treatment chemicals.

A substance may have one or more nanoforms, defined for regulatory purposes under EU chemicals legislation REACH based on differences in physicochemical properties such as size, shape, specific surface area and surface chemistry including coatings. To reduce the burden of testing each unique nanoform for the environmental risk assessment of nanomaterials, grouping approaches allow simultaneous assessment of multiple nanoforms. Nanoforms with initially different intrinsic properties, could still be considered similar if their environmental fate and effects can be demonstrated to be similar.

Bayesian based similarity assessment of nanomaterials to inform grouping.

Nanoforms can be manufactured in plenty of variants by differing their physicochemical properties and toxicokinetic behaviour which can affect their hazard potential. To avoid testing of each single nanomaterial and nanoform variation and subsequently save resources, grouping and read-across strategies are used to estimate groups of substances, based on carefully selected evidence, that could potentially have similar human health and environmental hazard impact. A novel computational similarity method is presented aiming to compare dose-response curves and identify sets of similar nanoforms.

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

The risk of each nanoform (NF) of the same substance cannot be assumed to be the same, as they may vary in their physicochemical characteristics, exposure and hazard. However, neither can we justify a need for more animal testing and resources to test every NF individually. To reduce the need to test all NFs, (regulatory) information requirements may be fulfilled by grouping approaches.

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.

FAIRification of nanosafety data to improve applicability of (Q)SAR approaches: A case study on 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.

Ambit-SLN: an Open Source Software Library for Processing of Chemical Objects via SLN Linear Notation.

SLN (SYBYL Line Notation) is the most comprehensive and rich linear notation for representation of chemical objects of various kinds facilitating a wide range of cheminformatics algorithms. Though, it is not the most popular linear notation nowadays, SLN has capabilities for supporting the most challenging tasks of the present day cheminformatics research. We present Ambit-SLN, a new software library for cheminformatics processing of chemical objects via linear notation SLN.

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.

Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database.

The field of nanoinformatics is rapidly developing and provides data driven solutions in the area of nanomaterials (NM) safety. Safe by Design approaches are encouraged and promoted through regulatory initiatives and multiple scientific projects. Experimental data is at the core of nanoinformatics processing workflows for risk assessment.