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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. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials., (© 2024. Springer Nature Limited.)

The rapid development of engineered nanomaterials (ENMs) causes humans to become increasingly exposed to them. Therefore, a better understanding of the health impact of ENMs is highly demanded. Considering the 3Rs (Replacement, Reduction, and Refinement) principle, in vitro and computational methods are excellent alternatives for testing on animals. Among computational methods, nano-quantitative structure-activity relationship (nano-QSAR), which links the physicochemical and structural properties of EMNs with biological activities, is one of the leading method. The nature of toxicological experiments has evolved over the last decades; currently, one experiment can provide thousands of measurements of the organism's functioning at the molecular level. At the same time, the capacity of the in vitro systems to mimic the human organism is also improving significantly. Hence, the authors would like to discuss whether the nano-QSAR approach follows modern toxicological studies and takes full advantage of the opportunities offered by modern toxicological platforms. Challenges and possibilities for improving data integration are underlined narratively, including the need for a consensus built between the in vitro and the QSAR domains., (© 2023 Wiley-VCH GmbH.)

Assessing research activity is an important step for planning future initiatives oriented toward filling the remaining gaps in a field. Therefore, the objective of the current study was to review recently published research on pulmonary toxicity caused by nanomaterials. However, here, instead of reviewing possible toxic effects and discussing their mode of action, the goal was to establish trends considering for example examined so far nanomaterials or used testing strategies. A total of 2316 related articles retrieved from the three most cited databases (PubMed Scopus, Web of Science), selected based on the title and abstract requirements, were used as the source of the review. Based on the bibliometric analysis, the nano-meter metal oxides, and carbon-based nanotubes were identified as the most frequently studied nanomaterials, while quantum dots, which might induce possible harmful effects, were not considered so far. The majority of testing of pulmonary safety is based on in vitro studies with observed growth of the contribution of novel testing strategies, such as 3D lung model, air-liquid interface system, or omic analysis.

Nanoinformatics demands accurate predictive models to assess the potential hazards of nanomaterials (NMs). However, limited data availability and the diverse nature of NMs physicochemical properties and their interaction with biological media, hinder the development of robust nano-Quantitative Structure-Activity Relationship (QSAR) models. This article proposes an approach that combines artificially data generation techniques and topological projections to address the challenges of insufficient dataset sizes and their limited representativeness of the chemical space. By leveraging the rich information embedded in the topological features, this methodology enhances the representation of the chemical space, enabling a more an exploration of the structure-activity relationships. We demonstrate the efficacy of our approach through extensive experiments, employing various machine learning regression algorithms to validate the methodology. Finally, we compare two different resampling approaches based on different modeling scenarios. The results showcase a significant improved predictive performance of QSAR models demonstrating a promising strategy to overcome the limitations of small datasets in the field of nanoinformatics. The proposed approach offers noteworthy potential for advancing nanoinformatics research within the nanosafety domain by enabling the development of more accurate predictive models for assessing the potential hazards associated with NMs.

To control stability in a biological medium, several factors affecting the zeta potential (ζ) of nanoparticles (NPs) must be considered, including complex interactions between the nanostructure and the composition of the protein corona (PC). Effective in silico methods (based on machine learning and quantitative structure-property relationship (QSPR) models) could help predict and characterize the relationship between the physicochemical properties of NP and the formation of PC and biological outcomes in the medium at an early stage of the experiment. However, the models currently developed are limited to simple descriptors that do not represent the complex interactions between the core, the coating, and their PC fingerprints. To be useful, the models developed should be described as a function of both the structural properties determined by the core and coating of the NPs and the biological medium determined by the formation of the protein corona. We have developed a set of complex descriptors that describe the quantitative relationship between the value of the zeta potential (ζ), core, the coating of NPs, and their PC fingerprints (the so-called nano-QSPR model). The nano-QSPR model was developed based on a genetic algorithm using a partial least-squares regression method (GA-PLS), which is characterized by high external predictive power ( Q 2 EXT = 0.89). The GA-PLS model was developed using descriptors that describe (i) the core structure (determined by 7 different types of polymer-based NMs in the range of 20 different sizes), (ii) the coating structure with 7 different functional groups, and (iii) 80 different types of protein compositions adsorbed on the surface of the NPs. The presented study answers the question of how complex interactions between the corona and NP determine the zeta potential (ζ) of NP in a given medium. Moreover, our current study is a proof-of-concept that the zeta potential of NPs modeled on the original structure depends not only on the NPs themselves but also on the structure and properties determined by the NP core and coating, as well as the biological medium determined by the formation of the protein corona. On the basis of these results, our studies will be useful in determining the stability and mechanism of cell uptake, toxicity, and ability to predict the zeta potential of compounds not yet tested.

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., (© 2022. Springer Nature Limited.)

Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO 2 -based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO 2 -based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.

It is acknowledged that the physicochemical properties of nanomaterials (NMs) have an impact on their toxicity and, eventually, their pathogenicity. These properties may include the NMs' surface chemical composition, size, shape, surface charge, surface area, and surface coating with ligands (which can carry different functional groups as well as proteins). Nanotopography, defined as the specific surface features at the nanoscopic scale, is not widely acknowledged as an important physicochemical property. It is known that the size and shape of NMs determine their nanotopography which, in turn, determines their surface area and their active sites. Nanotopography may also influence the extent of dissolution of NMs and their ability to adsorb atoms and molecules such as proteins. Consequently, the surface atoms (due to their nanotopography) can influence the orientation of proteins as well as their denaturation. However, although it is of great importance, the role of surface topography (nanotopography) in nanotoxicity is not much considered. Many of the issues that relate to nanotopography have much in common with the fundamental principles underlying classic catalysis. Although these were developed over many decades, there have been recent important and remarkable improvements in the development and study of catalysts. These have been brought about by new techniques that have allowed for study at the nanoscopic scale. Furthermore, the issue of quantum confinement by nanosized particles is now seen as an important issue in studying nanoparticles (NPs). In catalysis, the manipulation of a surface to create active surface sites that enhance interactions with external molecules and atoms has much in common with the interaction of NP surfaces with proteins, viruses, and bacteria with the same active surface sites of NMs. By reviewing the role that surface nanotopography plays in defining many of the NMs' surface properties, it reveals the need for its consideration as an important physicochemical property in descriptive and predictive toxicology. Through the manipulation of surface topography, and by using principles developed in catalysis, it may also be possible to make safe-by-design NMs with a reduction of the surface properties which contribute to their toxicity.

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge. The inconsistency of data measurements leads to inaccuracies in predictive modeling. In this article, we report a new curated dataset of zeta potentials measured for 208 silica- and metal oxide nanoparticles in different media. We discuss the data curation framework for zeta potentials designed to assess the quality and usefulness of the literature data for further computational modeling. We also provide an analysis of specific trends for the datapoints harvested from different literature sources. In addition to that, we present for the first time a structure-property relationship model for nanoparticles (nano-SPR) that predicts values of zeta potential values measured in different environmental conditions (i.e., biological media and pH)., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Manufactured nanomaterials (NMs) are increasingly used in a wide range of industrial applications leading to a constant increase in the market size of nano-enabled products. The increased production and use of NMs are raising concerns among different stakeholder groups with regard to their effects on human and environmental health. Currently, nanosafety hazard assessment is still widely performed using in vivo (animal) models, however the development of robust and reg­ulatory relevant strategies is required to prioritize and/or reduce animal testing. An adverse outcome pathway (AOP) is a structured representation of biological events that start from a molecular initiating event (MIE) leading to an adverse outcome (AO) through a series of key events (KEs). The AOP framework offers great advancement to risk assessment and regulatory safety assessments. While AOPs for chemicals have been more frequently reported, the AOP collection for NMs is limited. By using existing AOPs, we aimed to generate simple and testable strategies to predict if a given NM has the potential to induce a MIE leading to an AO through a series of KEs. Firstly, we identified potential MIEs or initial KEs reported for NMs in the literature. Then, we searched the identified MIE or initial KEs as keywords in the AOP-Wiki to find associated AOPs. Finally, using two case studies, we demonstrate how in vitro strategies can be used to test the identified MIE/KEs.

Nanotechnologies have reached maturity and market penetration that require nano-specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)-by-Design (SbD) concepts. This paper provides an overview of the state-of-the-art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re-thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science-based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally., (© 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property 'lowest unoccupied molecular orbital') need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low- one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before.

A first European Conference on Computational Nanotoxicology, CompNanoTox, was held in November 2015 in Benahavís, Spain with the objectives to disseminate and integrate results from the European modeling and database projects (NanoPUZZLES, ModENPTox, PreNanoTox, MembraneNanoPart, MODERN, eNanoMapper and EU COST TD1204 MODENA) as well as to create synergies within the European NanoSafety Cluster. This conference was supported by the COST Action TD1204 MODENA on developing computational methods for toxicological risk assessment of engineered nanoparticles and provided a unique opportunity for cross fertilization among complementary disciplines. The efforts to develop and validate computational models crucially depend on high quality experimental data and relevant assays which will be the basis to identify relevant descriptors. The ambitious overarching goal of this conference was to promote predictive nanotoxicology, which can only be achieved by a close collaboration between the computational scientists (e.g. database experts, modeling experts for structure, (eco) toxicological effects, performance and interaction of nanomaterials) and experimentalists from different areas (in particular toxicologists, biologists, chemists and material scientists, among others). The main outcome and new perspectives of this conference are summarized here.

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure-activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called 'nano-QSAR' models. 'External' leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R²) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends., (2016 FRAME.)

Creating suitable chemical categories and developing read-across methods, supported by quantum mechanical calculations, can be an effective solution to solving key problems related to current scarcity of data on the toxicity of various nanoparticles. This study has demonstrated that by applying a nano-read-across, the cytotoxicity of nano-sized metal oxides could be estimated with a similar level of accuracy as provided by quantitative structure-activity relationship for nanomaterials (nano-QSAR model(s)). The method presented is a suitable computational tool for the preliminary hazard assessment of nanomaterials. It also could be used for the identification of nanomaterials that may pose potential negative impact to human health and the environment. Such approaches are especially necessary when there is paucity of relevant and reliable data points to develop and validate nano-QSAR models.

Regulation for nanomaterials is urgently needed, and the drive to adopt an intelligent testing strategy is evident. Such a strategy will not only provide economic benefits but will also reduce moral and ethical concerns arising from animal testing. For regulatory purposes, such an approach is promoted by REACH, particularly the use of quantitative structure-activity relationships [(Q)SAR] as a tool for the categorisation of compounds according to their physicochemical and toxicological properties. In addition to compounds, (Q)SAR has also been applied to nanomaterials in the form of nano(Q)SAR. Although (Q)SAR in chemicals is well established, nano(Q)SAR is still in early stages of development and its successful uptake is far from reality. This article aims to identify some of the pitfalls and challenges associated with nano-(Q)SARs in relation to the categorisation of nanomaterials. Our findings show clear gaps in the research framework that must be addressed if we are to have reliable predictions from such models. Three major barriers were identified: the need to improve quality of experimental data in which the models are developed from, the need to have practical guidelines for the development of the nano(Q)SAR models and the need to standardise and harmonise activities for the purpose of regulation. Of these three, the first, i.e. the need to improve data quality requires immediate attention, as it underpins activities associated with the latter two. It should be noted that the usefulness of data in the context of nano-(Q)SAR modelling is not only about the quantity of data but also about the quality, consistency and accessibility of those data.

The most significant achievements and challenges relating to an application of quantitative structure-activity relationship (QSAR) approach in the risk assessment of nanometer-sized materials are highlighted. Recent advances are discussed in the context of "classical" QSAR methodology. The possible ways for the structural characterization of compounds existing at the nanoscale (at least one dimension of 100 nm or less) are briefly reviewed. The applicability of the existing toxicological data for developing QSAR models is evaluated. Finally, the existing models are presented. The need to develop new interpretative descriptors for the nanosystems is also highlighted. It is suggested that, due to high variability in the molecular structures and different mechanisms of toxicity, individual classes of nanoparticles should be modeled separately.