A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints

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.

An In Vitro Dosimetry Tool for the Numerical Transport Modeling of Engineered Nanomaterials Powered by the Enalos RiskGONE Cloud Platform

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.

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.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment

The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.

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.

Safe(r) by design guidelines for the nanotechnology industry

Expectations for safer and sustainable chemicals and products are growing to comply with the United Nations and European strategies for sustainability. The application of Safe(r) by Design (SbD) in nanotechnology implies an iterative process where functionality, human health and safety, environmental and economic impact and cost are assessed and balanced as early as possible in the innovation process and updated at each step. The EU H2020 NanoReg2 project was the first European project to implement SbD in six companies handling and/or manufacturing nanomaterials (NMs) and nano-enabled products (NEP). The results from this experience have been used to develop these guidelines on the practical application of SbD. The SbD approach foresees the identification, estimation, and reduction of human and environmental risks as early as possible in the development of a NM or NEP, and it is based on three pillars: (i) safer NMs and NEP; (ii) safer use and end of life and (iii) safer industrial production. The presented guidelines include a set of information and tools that will help deciding at each step of the innovation process whether to continue, apply SbD measures or carry out further tests to reduce uncertainty. It does not intend to be a prescriptive protocol where all suggested steps have to be followed to achieve a SbD NM/NEP or process. Rather, the guidelines are designed to identify risks at an early state and information to be considered to identify those risks. Each company adapts the approach to its specific needs and circumstances as company decisions influence the way forward.

Using the Isalos platform to develop a (Q)SAR model that predicts metal oxide toxicity utilizing facet-based electronic, image analysis-based, and periodic table derived properties as descriptors

Engineered nanoparticles (NPs) are being studied for their potential to harm humans and the environment. Biological activity, toxicity, physicochemical properties, fate, and transport of NPs must all be evaluated and/or predicted. In this work, we explored the influence of metal oxide nanoparticle facets on their toxicity towards bronchial epithelial (BEAS-2B), Murine myeloid (RAW 264.7), and E. coli cell lines. To estimate the toxicity of metal oxide nanoparticles grown to a low facet index, a quantitative structure–activity relationship ((Q)SAR) approach was used. The novel model employs theoretical (density functional theory calculations) and experimental studies (transmission electron microscopy images from which several particle descriptors are extracted and toxicity data extracted from the literature) to investigate the properties of faceted metal oxides, which are then utilized to construct a toxicity model. The classification mode of the k-nearest neighbour algorithm (EnaloskNN, Enalos Chem/Nanoinformatics) was used to create the presented model for metal oxide cytotoxicity. Four descriptors were identified as significant: core size, chemical potential, enthalpy of formation, and electronegativity count of metal oxides. The relationship between these descriptors and metal oxide facets is discussed to provide insights into the relative toxicities of the nanoparticle. The model and the underpinning dataset are freely available on the NanoSolveIT project cloud platform and the NanoPharos database, respectively.

Nanotoxicology and Nanomedicine: The Yin and Yang of Nano-Bio Interactions for the New Decade

Nanotoxicology and nanomedicine are two sub-disciplines of nanotechnology focusing on the phenomena, mechanisms, and engineering at the nano-bio interface. For the better part of the past three decades, these two disciplines have been largely developing independently of each other. Yet recent breakthroughs in microbiome research and the current COVID-19 pandemic demonstrate that holistic approaches are crucial for solving grand challenges in global health. Here we show the Yin and Yang relationship between the two fields by highlighting their shared goals of making safer nanomaterials, improved cellular and organism models, as well as advanced methodologies. We focus on the transferable knowledge between the two fields as nanotoxicological research is moving from pristine to functional nanomaterials, while inorganic nanomaterials - the main subjects of nanotoxicology - have become an emerging source for the development of nanomedicines. We call for a close partnership between the two fields in the new decade, to harness the full potential of nanotechnology for benefiting human health and environmental safety.

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

Blueprint for a self-sustained European Centre for service provision in safe and sustainable innovation for nanotechnology

The coming years are expected to bring rapid changes in the nanotechnology regulatory landscape, with the establishment of a new framework for nano-risk governance, in silico approaches for characterisation and risk assessment of nanomaterials, and novel procedures for the early identification and management of nanomaterial risks. In this context, Safe(r)-by-Design (SbD) emerges as a powerful preventive approach to support the development of safe and sustainable (SSbD) nanotechnology-based products and processes throughout the life cycle. This paper summarises the work undertaken to develop a blueprint for the deployment and operation of a permanent European Centre of collaborating laboratories and research organisations supporting safe innovation in nanotechnologies. The proposed entity, referred to as "the Centre", will establish a 'one-stop shop' for nanosafety-related services and a central contact point for addressing stakeholder questions about nanosafety. Its operation will rely on significant business, legal and market knowledge, as well as other tools developed and acquired through the EU-funded EC4SafeNano project and subsequent ongoing activities. The proposed blueprint adopts a demand-driven service update scheme to allow the necessary vigilance and flexibility to identify opportunities and adjust its activities and services in the rapidly evolving regulatory and nano risk governance landscape. The proposed Centre will play a major role as a conduit to transfer scientific knowledge between the research and commercial laboratories or consultants able to provide high quality nanosafety services, and the end-users of such services (e.g., industry, SMEs, consultancy firms, and regulatory authorities). The Centre will harmonise service provision, and bring novel risk assessment and management approaches, e.g. in silico methodologies, closer to practice, notably through SbD/SSbD, and decisively support safe and sustainable innovation of industrial production in the nanotechnology industry according to the European Chemicals Strategy for Sustainability.

Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform

The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (r_ion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/n_O) and the absolute electronegativity (χ_abs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).

Atomistic Perspective on Biomolecular Adsorption on Functionalized Carbon Nanomaterials under Ambient Conditions

The use of carbon-based nanomaterials is tremendously increasing in various areas of technological, bioengineering, and biomedical applications. The functionality of carbon-based nanomaterials can be further broadened via chemical functionalization of carbon nanomaterial surfaces. On the other hand, concern is rising on possible adverse effects when nanomaterials are taken up by biological organisms. In order to contribute into understanding of interactions of carbon-based nanomaterials with biological matter, we have investigated adsorption of small biomolecules on nanomaterials using enhanced sampling molecular dynamics. The biomolecules included amino acid side chain analogues, fragments of lipids, and sugar monomers. The adsorption behavior on unstructured amorphous carbon, pristine graphene and its derivatives (such as few-layer graphene, graphene oxide, and reduced graphene oxide) as well as pristine carbon nanotubes, and those functionalized with OH-, COOH-, COO-, NH2-, and NH3+ groups was investigated with respect to surface concentration. An adsorption profile, that is, the free energy as a function of distance from the nanomaterial surfaces, was determined for each molecule and surface using the Metadynamics approach. The results were analyzed in terms of chemical specificity, surface charge, and surface concentration. It was shown that although morphology of the nanomaterial has a limited effect on the adsorption properties, functionalization of the surface by various molecular groups can drastically change the adsorption behavior that can be used in the design of nanosurfaces with highly selective adsorption properties and safe for human health and environment.

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.

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time-consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long-term reproductive toxicity assays over multiple generations.

A joint optimization QSAR model of fathead minnow acute toxicity based on a radial basis function neural network and its consensus modeling

Acute toxicity of the fathead minnow (Pimephales promelas) is an important indicator to evaluate the hazards and risks of compounds in aquatic environments. The aim of our study is to explore the predictive power of the quantitative structure-activity relationship (QSAR) model based on a radial basis function (RBF) neural network with the joint optimization method to study the acute toxicity mechanism, and to develop a potential acute toxicity prediction model, for fathead minnow. To ensure the symmetry and fairness of the data splitting and to generate multiple chemically diverse training and validation sets, we used a self-organizing mapping (SOM) neural network to split the modeling dataset (containing 955 compounds) characterized by PaDEL-descriptors. After preliminary selection of descriptors via the mean decrease impurity method, a hybrid quantum particle swarm optimization (HQPSO) algorithm was used to jointly optimize the parameters of RBF and select the key descriptors. We established 20 RBF-based QSAR models, and the statistical results showed that the 10-fold cross-validation results (R cv10 2) and the adjusted coefficients of determination (R adj 2) were all great than 0.7 and 0.8, respectively. The Q ext 2 of these models was between 0.6480 and 0.7317, and the R ext 2 was between 0.6563 and 0.7318. Combined with the frequency and importance of the descriptors used in RBF-based models, and the correlation between the descriptors and acute toxicity, we concluded that the water distribution coefficient, molar refractivity, and first ionization potential are important factors affecting the acute toxicity of fathead minnow. A consensus QSAR model with RBF-based models was established; this model showed good performance with R 2 = 0.9118, R cv10 2 = 0.7632, and Q ext 2 = 0.7430. A frequency weighted and distance (FWD)-based application domain (AD) definition method was proposed, and the outliers were analyzed carefully. Compared with previous studies the method proposed in this paper has obvious advantages and its robustness and external predictive power are also better than Xgboost-based model. It is an effective QSAR modeling method.

Zeta-Potential Read-Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform

Zeta potential is one of the most critical properties of nanomaterials (NMs) which provides an estimation of the surface charge, and therefore electrostatic stability in medium and, in practical terms, influences the NM's tendency to form agglomerates and to interact with cellular membranes. This paper describes a robust and accurate read-across model to predict NM zeta potential utilizing as the input data a set of image descriptors derived from transmission electron microscopy (TEM) images of the NMs. The image descriptors are calculated using NanoXtract (http://enaloscloud.novamechanics.com/EnalosWebApps/NanoXtract/), a unique online tool that generates 18 image descriptors from the TEM images, which can then be explored by modeling to identify those most predictive of NM behavior and biological effects. NM TEM images are used to develop a model for prediction of zeta potential based on grouping of the NMs according to their nearest neighbors. The model provides interesting insights regarding the most important similarity features between NMs-in addition to core composition the main elongation emerged, which links to key drivers of NM toxicity such as aspect ratio. Both the NanoXtract image analysis tool and the validated model for zeta potential (http://enaloscloud.novamechanics.com/EnalosWebApps/ZetaPotential/) are freely available online through the Enalos Nanoinformatics platform.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

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.

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.

QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network

The aim of this study is to establish a promising QSPR model for the Caco-2 permeability prediction.

Nanomaterials in the Environment Acquire an "Eco-Corona" Impacting their Toxicity to Daphnia Magna-a Call for Updating Toxicity Testing Policies

Nanomaterials (NMs) are particles with at least one dimension between 1 and 100 nm and a large surface area to volume ratio, providing them with exceptional qualities that are exploited in a variety of industrial fields. Deposition of NMs into environmental waters during or after use leads to the adsorption of an ecological (eco-) corona, whereby a layer of natural biomolecules coats the NM changing its stability, identity and ultimately toxicity. The eco-corona is not currently incorporated into ecotoxicity tests, although it has been shown to alter the interactions of NMs with organisms such as Daphnia magna (D. magna). Here, the literature on environmental biomolecule interactions with NMs is synthesized and a framework for understanding the eco-corona composition and its role in modulating NMs ecotoxicity is presented, utilizing D. magna as a model. The importance of including biomolecules as part of the current international efforts to update the standard testing protocols for NMs, is highlighted. Facilitating the formation of an eco-corona prior to NMs ecotoxicity testing will ensure that signaling pathways perturbed by the NMs are real rather than being associated with the damage arising from reactive NM surfaces "acquiring" a corona by pulling biomolecules from the organism's surface.

The Correlation Contradictions Index (CCI): Building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES

The interpretation of the mutagenic potential of silver nanoparticles as a mathematical function of (i) dose; (ii) coating; and (iii) type of mutagenicity (TA98 and TA100) gives quantitative models with good statistical quality. So-called quasi-SMILES are used to represent examined objects (silver nanoparticles under different conditions) for building up models. Simplified molecular input-line entry systems (SMILES) is a well-known sequence of symbols for representation of the molecular structure. Quasi-SMILES is a similar sequence of symbols for representation of experimental conditions. The Correlation Contradiction Index (CCI) calculated with data on the calibration set gives possibility to predict quality of correlation of "experimental vs. calculated values of endpoint" for external validation set.

Predicting the Toxicity of Ionic Liquids toward Acetylcholinesterase Enzymes Using Novel QSAR Models

Limited information on the potential toxicity of ionic liquids (ILs) becomes the bottleneck that creates a barrier in their large-scale application. In this work, two quantitative structure-activity relationships (QSAR) models were used to evaluate the toxicity of ILs toward the acetylcholinesterase enzyme using multiple linear regression (MLR) and extreme learning machine (ELM) algorithms. The structures of 57 cations and 21 anions were optimized using quantum chemistry calculations. The electrostatic potential surface area (S_EP) and the screening charge density distribution area (S_σ) descriptors were calculated and used for prediction of IL toxicity. Performance and predictive aptitude between MLR and ELM models were analyzed. Highest squared correlation coefficient (R²), and also lowest average absolute relative deviation (AARD%) and root-mean-square error (RMSE) were observed for training set, test set, and total set for the ELM model. These findings validated the superior performance of ELM over the MLR toxicity prediction model.

A safe-by-design tool for functionalised nanomaterials through the Enalos Nanoinformatics Cloud platform

Multi-walled carbon nanotubes are currently used in numerous industrial applications and products, therefore fast and accurate evaluation of their biological and toxicological effects is of utmost importance. Computational methods and techniques, previously applied in the area of cheminformatics for the prediction of adverse effects of chemicals, can also be applied in the case of nanomaterials (NMs), in an effort to reduce expensive and time consuming experimental procedures. In this context, a validated and predictive nanoinformatics model has been developed for the accurate prediction of the biological and toxicological profile of decorated multi-walled carbon nanotubes. The nanoinformatics workflow was fully validated according to the OECD principles before it was released online via the Enalos Cloud platform. The web-service is a ready-to-use, user-friendly application whose purpose is to facilitate decision making, as part of a safe-by-design framework for novel carbon nanotubes.