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.

Carbon nanotubes (CNT) represent a great promise for technological and industrial development but serious concerns on their health effects have also emerged. Rod-shaped CNT are, in fact, able to induce asbestos-like pathogenicity in mice including granuloma formation in abdominal cavity and sub-pleural fibrosis. Exposure to CNT, especially in the occupational context, happens mainly by inhalation. However, little is known about the possible effects of CNT on pulmonary allergic diseases, such as asthma.

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.

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.

Carbon nanotubes (CNT) have been eagerly studied because of their multiple applications in product development and potential risks on health. We investigated the difference of two different CNT and asbestos in inducing proinflammatory reactions in C57BL/6 mice after single pharyngeal aspiration exposure. We used long tangled and long rod-like CNT, as well as crocidolite asbestos at a dose of 10 or 40 µg/mouse. The mice were sacrificed 4 and 16 h or 7, 14, and 28 days after the exposure. To find out the importance of a major inflammatory marker IL-1β in CNT-induced pulmonary inflammation, we used etanercept and anakinra as antagonists as well as Interleukin 1 (IL-1) receptor (IL-1R−/−) mice. The results showed that rod-like CNT, and asbestos in lesser extent, induced strong pulmonary neutrophilia accompanied by the proinflammatory cytokines and chemokines 16 h after the exposure. Seven days after the exposure, neutrophilia had essentially disappeared but strong pulmonary eosinophilia peaked in rod-like CNT and asbestos-exposed groups. After 28 days, pulmonary granulomas, goblet cell hyperplasia, and Charcot-Leyden-like crystals containing acidophilic macrophages were observed especially in rod-like CNT-exposed mice. IL-1R−/− mice and antagonists-treated mice exhibited a significant decrease in neutrophilia and messenger ribonucleic acid (mRNA) levels of proinflammatory cytokines at 16 h. However, rod-like CNT-induced Th2-type inflammation evidenced by the expression of IL-13 and mucus production was unaffected in IL-1R−/− mice at 28 days. This study provides knowledge about the pulmonary effects induced by a single exposure to the CNT and contributes to hazard assessment of carbon nanomaterials on airway exposure.