NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO2-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells.

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 TiO2-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 TiO2-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/.

AOP173 key event associated pathway predictor - online application for the prediction of benchmark dose lower bound (BMDLs) of a transcriptomic pathway involved in MWCNTs-induced lung fibrosis.

Nano-QSAR model allows for prediction of the toxicity of materials that have not been experimentally tested before by linking the nano-related structural properties with the biological responses induced by nanomaterials. Prediction of adverse effects caused by substances without having to perform time- and cost-consuming experiments makes QSAR models promising tools for supporting risk assessment. However, very often, newly developed nano-QSAR models are not used in practice due to the complexity of their algorithms, the necessity to have experience in chemoinformatics, and their poor accessibility. In this perspective, the aim of this paper is to encourage developers of the QSAR models to take the effort to prepare user-friendly applications based on predictive models. This would make the developed models accessible to a wider community, and, in effect, promote their further application by regulators and decision-makers. Here, we describe a web-based application that enables to predict the transcriptomic pathway-level response perturbated in the lungs of mice exposed to multiwalled carbon nanotubes. The developed application is freely available at http://aop173-event1.nanoqsar-aop.com/apps/aop_app. It requires only two types of input information related to analyzed nanotubes (their length and diameter) to assess the doses that initiate the inflammation process that may lead to lung fibrosis.

Transcriptomics-Based and AOP-Informed Structure-Activity Relationships to Predict Pulmonary Pathology Induced by Multiwalled Carbon Nanotubes.

This study presents a novel strategy that employs quantitative structure-activity relationship models for nanomaterials (Nano-QSAR) for predicting transcriptomic pathway level response using lung tissue inflammation, an essential key event (KEs) in the existing adverse outcome pathway (AOP) for lung fibrosis, as a model response. Transcriptomic profiles of mouse lungs exposed to ten different multiwalled carbon nanotubes (MWCNTs) are analyzed using statistical and bioinformatics tools. Three pathways "agranulocyte adhesion and diapedesis," "granulocyte adhesion and diapedesis," and "acute phase signaling," that (1) are commonly perturbed across the MWCNTs panel, (2) show dose response (Benchmark dose, BMDs), and (3) are anchored to the KEs identified in the lung fibrosis AOP, are considered in modelling. The three pathways are associated with tissue inflammation. The results show that the aspect ratio (κ) of MWCNTs is directly correlated with the pathway BMDs. The study establishes a methodology for QSAR construction based on canonical pathways and proposes a MWCNTs grouping strategy based on the κ-values of the specific pathway associated genes. Finally, the study shows how the AOP framework can help guide QSAR modelling efforts; conversely, the outcome of the QSAR modelling can aid in refining certain aspects of the AOP in question (here, lung fibrosis).

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

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

Structure-activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept.

A significant number of experimental studies are supported by computational methods such as quantitative structure-activity relationship modeling of nanoparticles (Nano-QSAR). This is especially so in research focused on design and synthesis of new, safer nanomaterials using safe-by-design concepts. However, Nano-QSAR has a number of important limitations. For example, it is not clear which descriptors that describe the nanoparticle physicochemical and structural properties are essential and can be adjusted to alter the target properties. This limitation can be overcome with the use of the Structure-Activity Prediction Network (SAPNet) presented in this paper. There are three main phases of building the SAPNet. First, information about the structural characterization of a nanomaterial, its physical and chemical properties and toxicity is compiled. Then, the most relevant properties (intrinsic/extrinsic) likely to influence the ENM toxicity are identified by developing "meta-models". Finally, these "meta-models" describing the dependencies between the most relevant properties of the ENMs and their adverse biological properties are developed. In this way, the network is built layer by layer from the endpoint (e.g. toxicity or other properties of interest) to descriptors that describe the particle structure. Therefore, SAPNets go beyond the current standards and provide sufficient information on what structural features should be altered to obtain a material with desired properties.

Representation of the Structure-A Key Point of Building QSAR/QSPR Models for Ionic Liquids.

The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model's quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors' selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

How thermal stability of ionic liquids leads to more efficient TiO2-based nanophotocatalysts: Theoretical and experimental studies.

Ionic liquids (ILs) containing distinct nitrogen-bearing organic cations (pyridinium, pyrrolidinium, imidazolium, ammonium, morpholinium) were first used for the preparation of 23 IL-TiO2 types of composites by ionic liquid assisted solvothermal synthesis. These 23 optimal ILs structures (i.e. compounds exhibiting an optimal combination of specific properties, functionality, and safety) for synthesis and experimental validation were selected by computational high-throughput screening from a combinatorically created library containing 836 ILs theoretically designed and characterized candidates. Then, selected IL-TiO2 structures with potential photocatalytic activity were synthesized with the use of solvothermal reaction. Then, the decomposition level, the role of the individual IL cation structure on the morphology, thermal stability, surface and photocatalytic properties of the IL-TiO2 microparticles were determined experimentally. The chemoinformatic analysis of the relationship between the structure of the ionic liquid, its thermal stability under the conditions of synthesis and photocatalytic activity was applied for the first time. The results presented here are the first step in the development of methodology (combined experimental and theoretical) that may simplify the procedure of designing safer and more efficient TiO2-based photocatalyst. The developed computational methodology makes it possible to predict properties of newly synthesized IL-TiO2 materials before synthesis and identifies structural features of ILs that influence the efficiency of IL-TiO2 system. The presented approach reduces the number and cost of necessary experiments, as well as increases the success ratio of efficient TiO2-based photocatalyst design by a selection of optimal IL structures (i.e. ionic liquid characterized by a combination of most promising physicochemical features).

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.

How the configurational changes influence on molecular characteristics. The alkyl 3-azido-2,3-dideoxy-D-hexopyranosides - Theoretical approach.

In this paper we present two currents of our research. The first goal was the geometry optimization of the structures of derivatives of the alkyl 3-azido-2,3-dideoxy-D-hexopyranosides. Then, we examine the influence of the applied quantum methods on the values of molecular features describing these structures. We use two methods for geometry optimization: the semi-empirical PM7 method and DFT B3LYP/6-31++G* method. The results of comparison parameters of descriptors indicate that there are no statistical differences obtained from both methods. Thus, we recommend the PM7 method for geometry optimization of the derivatives of the alkyl 3-azido-2,3-dideoxy-D-hexopyranosides due to its time and computer resources requirements. Another part of the research was the examination, which groups of descriptors are the most suitable for identifying the similarities in the configuration, the substituents pattern and the molecular mass of 232 examined structures. To explore relation between configuration changes of the 3-azidosaccharides and influence of these changes on the molecular characteristic, we use hierarchical cluster analysis (HCA), where WHIM, 3D-MORSE, RDF and GETAWAY descriptors were selected. Depending on the group of descriptors, molecules are divided in various ways. In general, saccharide' structures are divided into groups based on the configuration of substituents (combination of epimers) or length of the O-glycoside chain. Never before, these saccharides derivatives, were investigated by chemometric analysis. The most problematic issue in experimental and theoretical research is the configuration of substituents in pyranoside ring. Due to vast number of configurations, it is possible to obtain massive amount of diverse structures. This problem concerns us and opens opportunity in investigation the effect of configuration on the parameter of molecules.

Chemoinformatic Approach to Assess Toxicity of Ionic Liquids.

Chemoinformatic methods, such as multivariable explorative techniques and quantitative structure-activity relationship (QSAR) modeling, allow for discovering relationships between the activity and the structure of chemical compounds. These techniques can be applied, as preliminary screening methods for designing and/or selecting new compounds with defined activity.Here we describe step by step how to preliminarily screen ionic liquids (a set of 13 ILs) and assess their cytotoxic activity against leukemia cell line IPC-81 as well as ILs' potential to inhibit acetylcholinesterase enzyme using the TRIC method (toxicity ranking index of cations) combined with the QSAR approach.