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Modelling Data (MODA) reporting guidelines have been proposed for common terminology and for recording metadata for physics-based materials modelling and simulations in a CEN Workshop Agreement (CWA 17284:2018). Their purpose is similar to that of the Quantitative Structure-Activity Relationship (QSAR) model report form (QMRF) that aims to increase industry and regulatory confidence in QSAR models, but for a wider range of model types. Recently, the WorldFAIR project’s nanomaterials case study suggested that both QMRF and MODA templates are an important means to enhance compliance of nanoinformatics models, and their underpinning datasets, with the FAIR principles (Findable, Accessible, Interoperable, Reusable). Despite the advances in computational modelling of materials properties and phenomena, regulatory uptake of predictive models has been slow. This is, in part, due to concerns about lack of validation of complex models and lack of documentation of scientific simulations. The models are often complex, output can be hardware- and software-dependent, and there is a lack of shared standards. Despite advocating for standardised and transparent documentation of simulation protocols through its templates, the MODA guidelines are rarely used in practice by modellers because of a lack of tools for automating their creation, sharing, and storage. They also suffer from a paucity of user guidance on their use to document different types of models and systems. Such tools exist for the more well-established QMRF and have aided widespread implementation of QMRFs. To address this gap, a simplified procedure and online tool, Easy-MODA, has been developed to guide users through MODA creation for physics-based and data-based models, and their various combinations. Easy-MODA is available as a web-tool on the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/insight/moda/). The tool streamlines the creation of detailed MODA documentation, even for complex multi-model workflows, and facilitates the registration of MODA workflows and documentation in a database, thereby increasing their Findability and thus Re-usability. This enhances communication, interoperability, and reproducibility in multiscale materials modelling and improves trust in the models through improved documentation. The use of the Easy-MODA tool is exemplified by a case study for nanotoxicity evaluation, involving interlinked models and data transformation, to demonstrate the effectiveness of the tool in integrating complex computational methodologies and its significant role in improving the FAIRness of scientific simulations.

This review explores the state-of-the-art with respect to multicomponent nanomaterials (MCNMs) and high aspect ratio nanomaterials (HARNs), with a focus on their physicochemical characterisation, applications, and hazard, fate, and risk assessment. Utilising the PRISMA approach, this study investigates specific MCNMs including cerium-zirconium mixtures (CexZryO2) and ZnO nanomaterials doped with transition metals and rare earth elements, as well as Titanium Carbide (TiC) nanomaterials contained in Ti-6Al-4V alloy powders. HARNs of interest include graphene, carbon-derived nanotubes (CNTs), and metallic nanowires, specifically Ag-based nanowires. The review reveals a significant shift in research and innovation (R&I) efforts towards these advanced nanomaterials due to their unique properties and functionalities that promise enhanced performance across various applications including photocatalysis, antibacterial and biomedical uses, and advanced manufacturing. Despite the commercial potential of MCNMs and HARNs, the review identifies critical gaps in our understanding of their environmental fate and transformations upon exposure to new environments, and their potential adverse effects on organisms and the environment. The findings underscore the necessity for further research focused on the environmental transformations and toxicological profiles of these nanomaterials to inform Safe and Sustainable by Design (SSbD) strategies. This review contributes to the body of knowledge by cataloguing current research, identifying research gaps, and highlighting future directions for the development of MCNMs and HARNs, facilitating their safe and effective integration into industry.

NanoBioAccumulate is a free-to-use web-based tool hosted on the Enalos DIAGONAL Cloud Platform (https://www.enaloscloud.novamechanics.com/diagonal/pbpk/) that provides users with the capability to model and predict the uptake and bioaccumulation of nanomaterials (NMs) by soil and aquatic invertebrates using two common first-order one-compartment biokinetic models. NanoBioAccumulate offers an approach for comprehensively analyzing the kinetics of different forms of NMs via a nonlinear fitting feature, integrating them with environmental fate models, and considering important physiological processes. NanoBioAccumulate overcomes the constraint of requiring prior knowledge of kinetic rate constants associated with the biokinetic models and eliminates the need for external statistical analysis software as it quantifies the kinetic rate constants and other constants through the application of nonlinear regression, using user-provided experimental data. Furthermore, NanoBioAccumulate incorporates statistical analysis measures like the adjusted R-squared and the bias-corrected Akaike information criterion, allowing for assessment of the goodness-of-fit of the two different biokinetic models, assisting in the identification of the best-performing model for a specific nanoform and its uptake kinetics by a specific invertebrate. The tool also includes model scenarios, retrieved from literature, which involve examining the exposure of soil and aquatic invertebrates to various types of NMs such as TiO2, SiO2, C60, graphene, graphene oxide (GO), Au, Ag and its ionic control AgNO3. These model scenarios aim to enhance understanding of the uptake and elimination rates exhibited by different NM-species. NanoBioAccumulate features advanced integration capabilities, enabled by an extensive Application Programming Interface (API). This functionality promotes efficient data exchange and interoperability with other software and web applications, significantly expanding its utility in research, regulatory risk assessment and environmental surveillance and monitoring contexts. The inclusion of a user-friendly Graphical User Interface (GUI) in NanoBioAccumulate greatly improves the overall user experience by simplifying complex tasks and eliminating the need for programming proficiency, thereby expanding the tool's applicability to a diverse range of users across various fields such as environmental research, monitoring, and regulation.

NanoTube Construct is a web tool for the digital construction of nanotubes based on real and hypothetical single-layer materials including carbon-based materials such as graphene, graphane, graphyne polymorphs, graphidiyene and non-carbon materials such as silicene, germanene, boron nitride, hexagonal bilayer silica, haeckelite silica, molybdene disulfide and tungsten disulfide. Contrary to other available tools, NanoTube Construct has the following features: a) it is not limited to zero thickness materials with specific symmetry, b) it applies energy minimisation to the geometrically constructed Nanotubes to generate realistic ones, c) it derives atomistic descriptors (e.g., the average potential energy per atom, the average coordination number, etc.), d) it provides the primitive unit cell of the constructed Nanotube which corresponds to the selected rolling vector (i.e., the direction in which the starting nanosheet is rolled to form a tube), e) it calculates whether the Nanotube or its corresponding nanosheet is more energetically stable and f) it allows negative chirality indexes. Application of NanoTube Construct for the construction of energy minimised graphane and molybdenum disulfide nanotubes are presented, showcasing the tool's capability. NanoTube Construct is freely accessible through the Enalos Cloud Platform (https://enaloscloud.novamechanics.com/diagonal/nanotube/).

ASCOT (an acronym derived from Ag-Silver, Copper Oxide, Titanium Oxide) is a user-friendly web tool for digital construction of electrically neutral, energy-minimized spherical nanoparticles (NPs) of Ag, CuO, and TiO2 (both Anatase and Rutile forms) in vacuum, integrated into the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/sabydoma/ascot/). ASCOT calculates critical atomistic descriptors such as average potential energy per atom, average coordination number, common neighbour parameter (used for structural classification in simulations of crystalline phases), and hexatic order parameter (which measures how closely the local environment around a particle resembles perfect hexatic symmetry) for both core (over 4 Å from the surface) and shell (within 4 Å of the surface) regions of the NPs. These atomistic descriptors assist in predicting the most stable NP size based on lowest per atom energy and serve as inputs for developing machine learning models to predict the toxicity of these nanomaterials. ASCOT's automated backend requires minimal user input in order to construct the digital NPs: inputs needed are the material type (Ag, CuO, TiO2-Anatase, TiO2-Rutile), target diameter, a Force-Field from a pre-validated list, and the energy minimization parameters, with the tool providing a set of default values for novice users.

Risk governance of nanomaterials and nanotechnologies has been traditionally mainly limited to risk assessment, risk management and life cycle assessment. Recent approaches have experimented with widening the scope and including economic, social, and ethical aspects. This paper reports on tests and stakeholder feedback on fine-tuning the use of ethical impact assessment guidelines (RiskGONE D3.6) and online tools adapting the CEN Workshop Agreement part 2 CWA 17145-2:2017 (E)) to support risk governance of nanomaterials, in the RiskGONE project. The EIA guidelines and tools are intended to be used as one module in a multicriteria decision support framework for risk governance of nanomaterials, but may also be used for a stand-alone ethical impact assessment.

Tobacco smoking has been highlighted as a major health challenge in modern societies. Despite not causing death directly, smoking has been associated with several health issues, such as cardiovascular diseases, respiratory disorders, and several cancer types. Moreover, exposure to nicotine during pregnancy has been associated with adverse neurological disorders in babies. Nicotine Replacement Therapy (NRT) is the most common strategy employed for smoking cessation, but despite its widespread use, NRT presents with low success and adherence rates. This is attributed partially to the rate of nicotine metabolism by cytochrome P450 2A6 (CYP2A6) in each individual. Nicotine addiction is correlated with the high rate of its metabolism, and thus, novel strategies need to be implemented in NRT protocols. Naturally derived products are a cost-efficient and rich source for potential inhibitors, with the main advantages being their abundance and ease of isolation. This systematic review aims to summarize the natural products that have been identified as CYP2A6 inhibitors, validated through in vitro and/or in vivo assays, and could be implemented as nicotine metabolism inhibitors. The scope is to present the different compounds and highlight their possible implementation in NRT strategies. Additionally, this information would provide valuable insight regarding CYP2A6 inhibitors, that can be utilized in drug development via the use of in silico methodologies and machine-learning models to identify new potential lead compounds for optimization and implementation in NRT regimes.

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure–activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform’s graphical user interface (GUI).

(1) Background: Despite the encouraging indications regarding the suitability (biocompatibility) of iron carbide nanoparticles (ICNPs) in various biomedical applications, the published evidence of their biosafety is dispersed and relatively sparse. The present review synthesizes the existing nanotoxicological data from in vitro studies relevant to the diagnosis and treatment of cancer. (2) Methods: A systematic review was performed in electronic databases (PubMed, Scopus, and Wiley Online Library) on December 2023, searching for toxicity assessments of ICNPs of different sizes, coatings, and surface modifications investigated in immortalized human and murine cell lines. The risk of bias in the studies was assessed using the ToxRTool for in vitro studies. (3) Results: Among the selected studies (n = 22), cell viability emerged as the most frequently assessed cellular-level toxicity endpoint. The results of the meta-analysis showed that cell models treated with ICNPs had a reduced cell viability (SMD = −2.531; 95% CI: −2.959 to −2.109) compared to untreated samples. A subgroup analysis was performed due to the high magnitude of heterogeneity (I2 = 77.1%), revealing that ICNP concentration and conjugated ligands are the factors that largely influence toxicity (p < 0.001). (4) Conclusions: A dose-dependent cytotoxicity of ICNP exposure was observed, regardless of the health status of the cell, tested organism, and NP size. Inconsistent reporting of ICNP physicochemical properties was noted, which hinders comparability among the studies. A comprehensive exploration of the available in vivo studies is required in future research to assess the safety of ICNPs’ use in bioimaging and cancer treatment.

In mediaeval Europe, the term “commons” described the way that communities managed land that was held “in common” and provided a clear set of rules for how this “common land” was used and developed by, and for, the community. Similarly, as we move towards an increasingly knowledge-based society where data is the new oil, new approaches to sharing and jointly owning publicly funded research data are needed to maximise its added value. Such common management approaches will extend the data’s useful life and facilitate its reuse for a range of additional purposes, from modelling, to meta-analysis to regulatory risk assessment as examples relevant to nanosafety data. This “commons” approach to nanosafety data and nanoinformatics infrastructure provision, co-development, and maintenance is at the heart of the “NanoCommons” project and underpins its post-funding transition to providing a basis on which other initiatives and projects can build. The present paper summarises part of the NanoCommons infrastructure called the NanoCommons Knowledge Base. It provides interoperability for nanosafety data sources and tools, on both semantic and technical levels. The NanoCommons Knowledge Base connects knowledge and provides both programmatic (via an Application Programming Interface) and a user-friendly graphical interface to enable (and democratise) access to state of the art tools for nanomaterials safety prediction, NMs design for safety and sustainability, and NMs risk assessment, as well. In addition, the standards and interfaces for interoperability, e.g., file templates to contribute data to the NanoCommons, are described, and a snapshot of the range and breadth of nanoinformatics tools and models that have already been integrated are presented Finally, we demonstrate how the NanoCommons Knowledge Base can support users in the FAIRification of their experimental workflows and how the NanoCommons Knowledge Base itself has progressed towards richer compliance with the FAIR principles.

Introduction: Significant progress has been made in terms of best practice in research data management for nanosafety. Some of the underlying approaches to date are, however, overly focussed on the needs of specific research projects or aligned to a single data repository, and this “silo” approach is hampering their general adoption by the broader research community and individual labs.Methods: State-of-the-art data/knowledge collection, curation management FAIrification, and sharing solutions applied in the nanosafety field are reviewed focusing on unique features, which should be generalised and integrated into a functional FAIRification ecosystem that addresses the needs of both data generators and data (re)users.Results: The development of data capture templates has focussed on standardised single-endpoint Test Guidelines, which does not reflect the complexity of real laboratory processes, where multiple assays are interlinked into an overall study, and where non-standardised assays are developed to address novel research questions and probe mechanistic processes to generate the basis for read-across from one nanomaterial to another. By focussing on the needs of data providers and data users, we identify how existing tools and approaches can be re-framed to enable “on-the-fly” (meta) data definition, data capture, curation and FAIRification, that are sufficiently flexible to address the complexity in nanosafety research, yet harmonised enough to facilitate integration of datasets from different sources generated for different research purposes. By mapping the available tools for nanomaterials safety research (including nanomaterials characterisation, nonstandard (mechanistic-focussed) methods, measurement principles and experimental setup, environmental fate and requirements from new research foci such as safe and sustainable by design), a strategy for integration and bridging between silos is presented. The NanoCommons KnowledgeBase has shown how data from different sources can be integrated into a one-stop shop for searching, browsing and accessing data (without copying), and thus how to break the boundaries between data silos.Discussion: The next steps are to generalise the approach by defining a process to build consensus (meta)data standards, develop solutions to make (meta)data more machine actionable (on the fly ontology development) and establish a distributed FAIR data ecosystem maintained by the community beyond specific projects. Since other multidisciplinary domains might also struggle with data silofication, the learnings presented here may be transferrable to facilitate data sharing within other communities and support harmonization of approaches across disciplines to prepare the ground for cross-domain interoperability.

Abstract 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. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide examples of its use in various H2020-funded nanosafety projects. Graphical Abstract

In this in silico study, we conducted an in-depth exploration of the potential of natural products and antihypertensive molecules that could serve as inhibitors targeting the key proteins of the SARS-CoV-2 virus: the main protease (Mpro) and the spike (S) protein. By utilizing Induced Fit Docking (IFD), we assessed the binding affinities of the molecules under study to these crucial viral components. To further comprehend the stability and molecular interactions of the “protein-ligand” complexes that derived from docking studies, we performed molecular dynamics (MD) simulations, shedding light on the molecular basis of potential drug candidates for COVID-19 treatment. Moreover, we employed Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculations on all “protein-ligand” complexes, underscoring the robust binding capabilities of rosmarinic acid, curcumin, and quercetin against Mpro, and salvianolic acid b, rosmarinic acid, and quercetin toward the S protein. Furthermore, in order to expand our search for potent inhibitors, we conducted a structure similarity analysis, using the Enalos Suite, based on the molecules that indicated the most favored results in the in silico studies. The Enalos Suite generated 115 structurally similar compounds to salvianolic acid, rosmarinic acid, and quercetin. These compounds underwent IFD calculations, leading to the identification of two salvianolic acid analogues that exhibited strong binding to all the examined binding sites in both proteins, showcasing their potential as multi-target inhibitors. These findings introduce exciting possibilities for the development of novel therapeutic agents aiming to effectively disrupt the SARS-CoV-2 virus lifecycle.

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

Measurement(s) microarray data • transcriptome • RNA • Toxicogenomics Technology Type(s) digital curation Factor Type(s) exposure to engineered nanomaterials Sample Characteristic - Organism Homo sapiens • Mus musculus • Rattus Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13154381

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.

A freely available “in vitro dosimetry” web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) model for the dispersion of engineered nanoparticles (NPs) in culture medium used for in vitro cellular experiments, in accordance with previously published protocols for cellular dosimetry determination. A series of in vitro experiments for six different NPs, with Ag and Au cores, are performed to demonstrate the convenience of the web application for calculation of exposure concentrations of NPs. Our results show that the exposure concentrations at the cell surface can be more than 30 times higher compared to the nominal or dispersed concentrations, depending on the NPs’ properties and their behavior in the cell culture medium. Therefore, the importance of calculating the exposure concentration at the bottom of the cell culture wells used for in vitro arrays, i.e., the particle concentration at the cell surface, is clearly presented, and the tool introduced here allows users easy access to such calculations. Widespread application of this web tool will increase the reliability of subsequent toxicity data, allowing improved correlation of the real exposure concentration with the observed toxicity, enabling the hazard potentials of different NPs to be compared on a more robust basis.

Tumor necrosis factor (TNF) is a regulator of several chronic inflammatory diseases, such as rheumatoid arthritis. Although anti-TNF biologics have been used in clinic, they render several drawbacks, such as patients’ progressive immunodeficiency and loss of response, high cost, and intravenous administration. In order to find new potential anti-TNF small molecule inhibitors, we employed an in silico approach, aiming to find natural products, analogs of Ampelopsin H, a compound that blocks the formation of TNF active trimer. Two out of nine commercially available compounds tested, Nepalensinol B and Miyabenol A, efficiently reduced TNF-induced cytotoxicity in L929 cells and production of chemokines in mice joints’ synovial fibroblasts, while Nepalensinol B also abolished TNF-TNFR1 binding in non-toxic concentrations. The binding mode of the compounds was further investigated by molecular dynamics and free energy calculation studies, using and advancing the Enalos Asclepios pipeline. Conclusively, we propose that Nepalensinol B, characterized by the lowest free energy of binding and by a higher number of hydrogen bonds with TNF, qualifies as a potential lead compound for TNF inhibitors’ drug development. Finally, the upgraded Enalos Asclepios pipeline can be used for improved identification of new therapeutics against TNF-mediated chronic inflammatory diseases, providing state-of-the-art insight on their binding mode.

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.

Ensuring the safe and responsible use of nanotechnologies and nanoscale materials is imperative to maximize consumer confidence and drive commercialization of nano-enabled products that underpin innovation and advances in every industrial sector [...]

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.

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.

A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⊥ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project’s Integrated Approach to Testing and Assessment (IATA).

A series of nineteen amino acid analogues of amantadine (Amt) and rimantadine (Rim) were synthesized and their antiviral activity was evaluated against influenza virus A (H3N2). Among these analogues, the conjugation of rimantadine with glycine illustrated high antiviral activity combined with low cytotoxicity. Moreover, this compound presented a profoundly high stability after in vitro incubation in human plasma for 24 h. Its thermal stability was established using differential and gravimetric thermal analysis. The crystal structure of glycyl-rimantadine revealed that it crystallizes in the orthorhombic Pbca space group. The structure–activity relationship for this class of compounds was established, with CoMFA (Comparative Molecular Field Analysis) 3D-Quantitative Structure Activity Relationships (3D-QSAR) studies predicting the activities of synthetic molecules. In addition, molecular docking studies were conducted, revealing the structural requirements for the activity of the synthetic molecules.

Autotaxin (ATX) is a secreted glycoprotein, widely present in biological fluids, largely responsible for extracellular lysophosphatidic acid (LPA) production. LPA is a bioactive growth-factor-like lysophospholipid that exerts pleiotropic effects in almost all cell types, exerted through at least six G-protein-coupled receptors (LPAR1-6). Increased ATX expression has been detected in different chronic inflammatory diseases, while genetic or pharmacological studies have established ATX as a promising therapeutic target, exemplified by the ongoing phase III clinical trial for idiopathic pulmonary fibrosis. In this report, we employed an in silico drug discovery workflow, aiming at the identification of structurally novel series of ATX inhibitors that would be amenable to further optimization. Towards this end, a virtual screening protocol was applied involving the search into molecular databases for new small molecules potentially binding to ATX. The crystal structure of ATX in complex with a known inhibitor (HA-155) was used as a molecular model docking reference, yielding a priority list of 30 small molecule ATX inhibitors, validated by a well-established enzymatic assay of ATX activity. The two most potent, novel and structurally different compounds were further structurally optimized by deploying further in silico tools, resulting to the overall identification of six new ATX inhibitors that belong to distinct chemical classes than existing inhibitors, expanding the arsenal of chemical scaffolds and allowing further rational design.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer’s is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer’s small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer’s. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

An in silico drug discovery pipeline for the virtual screening of plant-origin natural products (NPs) was developed to explore new direct inhibitors of TNF and its close relative receptor activator of nuclear factor kappa-B ligand (RANKL), both representing attractive therapeutic targets for many chronic inflammatory conditions. Direct TNF inhibition through identification of potent small molecules is a highly desired goal; however, it is often hampered by severe limitations. Our approach yielded a priority list of 15 NPs as potential direct TNF inhibitors that were subsequently tested in vitro against TNF and RANKL. We thus identified two potent direct inhibitors of TNF function with low micromolar IC50 values and minimal toxicity even at high concentrations. Most importantly, one of them (A11) was proved to be a dual inhibitor of both TNF and RANKL. Extended molecular dynamics simulations with the fully automated EnalosMD suite rationalized the mode of action of the compounds at the molecular level. To our knowledge, these compounds constitute the first NP TNF inhibitors, one of which being the first NP small-molecule dual inhibitor of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

We present an in silico drug discovery pipeline developed and applied for the identification and virtual screening of small-molecule Protein-Protein Interaction (PPI) compounds that act as dual inhibitors of TNF and RANKL through the trimerization interface. The cheminformatics part of the pipeline was developed by combining structure-based with ligand-based modeling using the largest available set of known TNF inhibitors in the literature (2481 small molecules). To facilitate virtual screening, the consensus predictive model was made freely available at: http://enalos.insilicotox.com/TNFPubChem/. We thus generated a priority list of nine small molecules as candidates for direct TNF function inhibition. In vitro evaluation of these compounds led to the selection of two small molecules that act as potent direct inhibitors of TNF function, with IC50 values comparable to those of a previously-described direct inhibitor (SPD304), but with significantly reduced toxicity. These molecules were also identified as RANKL inhibitors and validated in vitro with respect to this second functionality. Direct binding of the two compounds was confirmed both for TNF and RANKL, as well as their ability to inhibit the biologically-active trimer forms. Molecular dynamics calculations were also carried out for the two small molecules in each protein to offer additional insight into the interactions that govern TNF and RANKL complex formation. To our knowledge, these compounds, namely T8 and T23, constitute the second and third published examples of dual small-molecule direct function inhibitors of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

A novel short-step methodology for the synthesis in good yields of functionalized coumarins has been developed starting from an activated precursor, the N-hydroxysuccinimide ester of O-acetylsalicylic acid. The procedure is based on a tandem C-acylation-cyclization process under mild reaction conditions. The structure of 3-methoxycarbonyl-4-hydroxy coumarin has been established by X-ray diffraction analysis and its geometry was compared with optimized parameters by means of DFT calculations.