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

Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations

Liposomes, nanoscale spherical structures composed of amphiphilic lipids, hold great promise for various pharmaceutical applications, especially as nanocarriers in targeted drug delivery, due to their biocompatibility, biodegradability, and low immunogenicity. Understanding the factors influencing their physicochemical properties is crucial for designing and optimizing liposomes. In this study, we have presented the kernel-weighted local polynomial regression (KwLPR) nano-quantitative structure-property relationships (nano-QSPR) model to predict the zeta potential (ZP) based on the structure of 12 liposome formulations, including 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 3ß-[N-(N',N'-dimethylaminoethane)-carbamoyl]cholesterol (DC-Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and L-α-phosphatidylcholine (EPC). The developed model is well-fitted (R 2 = 0.96, RMSE C = 5.76), flexible (Q CVloo 2 = 0.83, RMSE CVloo = 10.77), and reliable (Q Ext 2 = 0.89 RMSE Ext = 5.17). Furthermore, we have established the formula for computing molecular nanodescriptors for liposomes, based on constituent lipids' molar fractions. Through the correlation matrix and principal component analysis (PCA), we have identified two key structural features affecting liposomes' zeta potential: hydrophilic-lipophilic balance (HLB) and enthalpy of formation. Lower HLB values, indicating a more lipophilic nature, are associated with a higher zeta potential, and thus stability. Higher enthalpy of formation reflects reduced zeta potential and decreased stability of liposomes. We have demonstrated that the nano-QSPR approach allows for a better understanding of how the composition and molecular structure of liposomes affect their zeta potential, filling a gap in ZP nano-QSPR modeling methodologies for nanomaterials (NMs). The proposed proof-of-concept study is the first step in developing a comprehensive and computationally based system for predicting the physicochemical properties of liposomes as one of the most important drug nano-vehicles.

Bioinformatics and machine learning to support nanomaterial grouping

Nanomaterials (NMs) offer plenty of novel functionalities. Moreover, their physicochemical properties can be fine-tuned to meet the needs of specific applications, leading to virtually unlimited numbers of NM variants. Hence, efficient hazard and risk assessment strategies building on New Approach Methodologies (NAMs) become indispensable. Indeed, the design, the development and implementation of NAMs has been a major topic in a substantial number of research projects. One of the promising strategies that can help to deal with the high number of NMs variants is grouping and read-across. Based on demonstrated structural and physicochemical similarity, NMs can be grouped and assessed together. Within an established NM group, read-across may be performed to fill in data gaps for data-poor variants using existing data for NMs within the group. Establishing a group requires a sound justification, usually based on a grouping hypothesis that links specific physicochemical properties to well-defined hazard endpoints. However, for NMs these interrelationships are only beginning to be understood. The aim of this review is to demonstrate the power of bioinformatics with a specific focus on Machine Learning (ML) approaches to unravel the NM Modes-of-Action (MoA) and identify the properties that are relevant to specific hazards, in support of grouping strategies. This review emphasizes the following messages: 1) ML supports identification of the most relevant properties contributing to specific hazards; 2) ML supports analysis of large omics datasets and identification of MoA patterns in support of hypothesis formulation in grouping approaches; 3) omics approaches are useful for shifting away from consideration of single endpoints towards a more mechanistic understanding across multiple endpoints gained from one experiment; and 4) approaches from other fields of Artificial Intelligence (AI) like Natural Language Processing or image analysis may support automated extraction and interlinkage of information related to NM toxicity. Here, existing ML models for predicting NM toxicity and for analyzing omics data in support of NM grouping are reviewed. Various challenges related to building robust models in the field of nanotoxicology exist and are also discussed.

A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.

Next generation risk assessment of biocides (PHMG-p and CMIT/MIT)-induced pulmonary fibrosis using adverse outcome pathway-based transcriptome analysis

Next-generation risk assessment (NGRA) has emerged as a promising alternative to non-animal studies owing to the increasing demand for the risk assessment of inhaled toxicants. In this study, NGRA was used to assess the inhalation risks of two biocides commonly used as humidifier disinfectants: polyhexamethylene guanidine phosphate (PHMG-p) and chloromethylisothiazolinone/methylisothiazolinone (CMIT/MIT). Human bronchial epithelial cell transcriptomic data were processed based on adverse outcome pathways and used to establish transcriptome-based points of departure (tPODs) for each biocide. tPOD values were 0.00500-0.0510 μg/cm2 and 0.0342-0.0544 μg/cm2 for PHMG-p and CMIT/MIT, respectively. tPODs may provide predictive power comparable to that of traditional animal-based PODs (aPODs). The tPOD-based NGRA determined that both PHMG-p and CMIT/MIT present a high inhalation risk. Moreover, the identified PHMG-p posed a higher risk than CMIT/MIT, and children were identified as more susceptible population compared to adults. This finding is consistent with observations from actual exposure events. Our findings suggest that NGRA with transcriptomics offers a reliable approach for risk assessment of specific humidifier disinfectant biocides, while acknowledging the limitations of current models and in vitro systems, particularly regarding uncertainties in pharmacokinetics (PK) and pharmacodynamics (PD).

Toward Nano-Specific In Silico NAMs: How to Adjust Nano-QSAR to the Recent Advancements of Nanotoxicology?

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

Pulmonary Surfactant Homeostasis Dysfunction Mediates Multiwalled Carbon Nanotubes Induced Lung Fibrosis via Elevating Surface Tension

Multiwalled carbon nanotubes (MWCNTs) have been widely used in many disciplines and raised great concerns about their negative health impacts, especially environmental and occupational exposure. MWCNTs have been reported to induce fibrotic responses; however, the underlying mechanisms remain largely veiled. Here, we reported that MWCNTs inhalation induced lung fibrosis together with decreased lung compliance, increased elastance in the mice model, and elevated surface tension in vitro. Specifically, MWCNTs increased surface tension by impairing the function of the pulmonary surfactant. Mechanistically, MWCNTs induced lamellar body (LB) dysfunction through autophagy dysfunction, which then leads to surface tension elevated by pulmonary surfactant dysfunction in the context of lung fibrosis. This is a study to investigate the molecular mechanism of MWCNTs-induced lung fibrosis and focus on surface tension. A direct mechanistic link among impaired LBs, surface tension, and fibrosis has been established. This finding elucidates the detailed molecular mechanisms of lung fibrosis induced by MWCNTs. It also highlights that pulmonary surfactants are expected to be potential therapeutic targets for the prevention and treatment of lung fibrosis induced by MWCNTs.

Pathway-dependent toxic interaction between polystyrene microbeads and methylmercury on the brackish water flea Diaphanosoma celebensis: Based on mercury bioaccumulation, cytotoxicity, and transcriptomic analysis

Given their worldwide distribution and toxicity to aquatic organisms, methylmercury (MeHg) and microplastics (MP) are major pollutants in marine ecosystems. Although they commonly co-exist in the ocean, information on their toxicological interactions is limited. Therefore, to understand the toxicological interactions between MeHg and MP (6-μm polystyrene), we investigated the bioaccumulation of MeHg, its cytotoxicity, and transcriptomic modulation in the brackish water flea Diaphanosoma celebensis following single and combined exposure to MeHg and MP. After single exposure to MeHg for 48-h, D. celebensis showed high Hg accumulation (34.83 ± 0.40 μg/g dw biota) and cytotoxicity, which was reduced upon co-exposure to MP. After transcriptomic analysis, 2, 253, and 159 differentially expressed genes were detected in the groups exposed to MP, MeHg, and MeHg+MP, respectively. Genes related to metabolic pathways and the immune system were significantly affected after MeHg exposure, but the effect of MeHg on these pathways was alleviated by MP co-exposure. However, MeHg and MP exhibited synergistic effects on the expression of gene related to DNA replication. These findings suggest that MP can reduce the toxicity of MeHg but that their toxicological interactions differ depending on the molecular pathway.

Identification of a Gene Signature Predicting (Nano)Particle-Induced Adverse Lung Outcome in Rats

Nanoparticles are extensively used in industrial products or as food additives. However, despite their contribution to improving our quality of life, concerns have been raised regarding their potential impact on occupational and public health. To speed up research assessing nanoparticle-related hazards, this study was undertaken to identify early markers of harmful effects on the lungs. Female Sprague Dawley rats were either exposed to crystalline silica DQ-12 with instillation, or to titanium dioxide P25, carbon black Printex-90, or multi-walled carbon nanotube Mitsui-7 with nose-only inhalation. Tissues were collected at three post-exposure time points to assess short- and long-term effects. All particles induced lung inflammation. Histopathological and biochemical analyses revealed phospholipid accumulation, lipoproteinosis, and interstitial thickening with collagen deposition after exposure to DQ-12. Exposure to the highest dose of Printex-90 and Mitsui-7, but not P25, induced some phospholipid accumulation. Comparable histopathological changes were observed following exposure to P25, Printex-90, and Mitsui-7. Comparison of overall gene expression profiles identified 15 potential early markers of adverse lung outcomes induced by spherical particles. With Mitsui-7, a distinct gene expression signature was observed, suggesting that carbon nanotubes trigger different toxicity mechanisms to spherical particles.

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.

Modeling study for predicting altered cellular activity induced by nanomaterials based on Dlk1-Dio3 gene expression and structural relationships

Nanomaterials have been widely applied and developed due to its unique physicochemical characteristics, such as their small size. The environmental and biological effects caused by nanomaterials have raised concerns. In particular, some nanometal oxides have obvious biological toxicity and pose a major safety problem. The prediction model established by combining the expression levels of key genes with quantitative structure-activity relationship (QSAR) studies can predict the biotoxicity of nanomaterials by relying on both structural information and gene regulation information. This model can fill the gap of missing mechanisms in QSAR studies. In this study, we exposed A549 cells and BEAS-2B cells to 21 nanometal oxides for 24 h. Cell viability was assessed by measuring absorbance values using the CCK8 assay, and the expression levels of the Dlk1-Dio3 gene cluster were measured. By using the theoretical basis of the nano-QSAR model and the improved principles of the SMILES-based descriptors to combine specific gene expression and structural factors, new models were constructed using Monte Carlo partial least squares (MC-PLS) for the biotoxicity of the nanometal oxides on two different lung cells. The overall quality of the nano-QSAR models constructed by combining specific gene expression and structural parameters for A549 and BEAS-2B cells was better than that of the models constructed based on structural parameters only. The coefficient of determination (R²) of the A549 cell model increased from 0.9044 to 0.9969, and the Root Mean Square Error (RMSE) decreased from 0.1922 to 0.0348. The R² of the BEAS-2B cell model increased from 0.9355 to 0.9705, and the RMSE decreased from 0.1206 to 0.0874. The model validation proved the proposed models have a good prediction, generalization ability and model stability. This study offers a new research perspective for the toxicity assessment of nanometal oxides, contributing to a more systematic safety evaluation of nanomaterials.

Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health

In analogy to the periodic system that groups elements by their similarity in structure and chemical properties, the hazard of chemicals can be assessed in groups having similar structures and similar toxicological properties. Here we review case studies of chemical grouping strategies that supported the assessment of hazard, exposure, and risk to human health. By the EU-REACH and the US-TSCA New Chemicals Program, structural similarity is commonly used as the basis for grouping, but that criterion is not always adequate and sufficient. Based on the lessons learned, we derive ten principles for grouping, including: transparency of the purpose, criteria, and boundaries of the group; adequacy of methods used to justify the group; and inclusion or exclusion of substances in the group by toxicological properties. These principles apply to initial grouping to prioritize further actions as well as to definitive grouping to generate data for risk assessment. Both can expedite effective risk management.

Application of Inorganic Nanomaterials in Cultural Heritage Conservation, Risk of Toxicity, and Preventive Measures

Nanotechnology has allowed for significant progress in architectural, artistic, archaeological, or museum heritage conservation for repairing and preventing damages produced by deterioration agents (weathering, contaminants, or biological actions). This review analyzes the current treatments using nanomaterials, including consolidants, biocides, hydrophobic protectives, mechanical resistance improvers, flame-retardants, and multifunctional nanocomposites. Unfortunately, nanomaterials can affect human and animal health, altering the environment. Right now, it is a priority to stop to analyze its advantages and disadvantages. Therefore, the aims are to raise awareness about the nanotoxicity risks during handling and the subsequent environmental exposure to all those directly or indirectly involved in conservation processes. It reports the human-body interaction mechanisms and provides guidelines for preventing or controlling its toxicity, mentioning the current toxicity research of main compounds and emphasizing the need to provide more information about morphological, structural, and specific features that ultimately contribute to understanding their toxicity. It provides information about the current documents of international organizations (European Commission, NIOSH, OECD, Countries Normative) about worker protection, isolation, laboratory ventilation control, and debris management. Furthermore, it reports the qualitative risk assessment methods, management strategies, dose control, and focus/receptor relationship, besides the latest trends of using nanomaterials in masks and gas emissions control devices, discussing their risk of toxicity.

PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping

Toxicological evaluation of substances in regulation still often relies on animal experiments. Understanding the substances' mode-of-action is crucial to develop alternative test strategies. Omics methods are promising tools to achieve this goal. Until now, most attention was focused on transcriptomics, while proteomics is not yet routinely applied in toxicology despite the large number of datasets available in public repositories. Exploiting the full potential of these datasets is hampered by differences in measurement procedures and follow-up data processing. Here we present the tool PROTEOMAS, which allows meta-analysis of proteomic data from public origin. The workflow was designed for analyzing proteomic studies in a harmonized way and to ensure transparency in the analysis of proteomic data for regulatory purposes. It agrees with the Omics Reporting Framework guidelines of the OECD with the intention to integrate proteomics to other omic methods in regulatory toxicology. The overarching aim is to contribute to the development of AOPs and to understand the mode of action of substances. To demonstrate the robustness and reliability of our workflow we compared our results to those of the original studies. As a case study, we performed a meta-analysis of 25 proteomic datasets to investigate the toxicological effects of nanomaterials at the lung level. PROTEOMAS is an important contribution to the development of alternative test strategies enabling robust meta-analysis of proteomic data. This workflow commits to the FAIR principles (Findable, Accessible, Interoperable and Reusable) of computational protocols.

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.

Predicting nanomaterials pulmonary toxicity in animals by cell culture models: Achievements and perspectives

Animal experiments are highly relevant models for the assessment of toxicological effects of engineered nanomaterials (ENMs), due to lack of biomonitoring and epidemiological studies. However, the expanding number of ENMs with different physico-chemical properties strains this approach, as there are ethical concerns and economical challenges with the use of animals in toxicology. There is an urgent need for cell culture models that predict the level of toxicological responses in vivo, consequently reducing or replacing the use of animals in nanotoxicology. However, there is still a limited number of studies on in vitro-in vivo correlation of toxicological responses following ENMs exposure. In this review, we collected studies that have compared in vitro and in vivo toxic effects caused by ENMs. We discuss the influence of cell culture models and exposure systems on the predictability of in vitro models to equivalent toxic effects in animal lungs after pulmonary exposure to ENMs. In addition, we discuss approaches to qualitatively or quantitatively compare the effects in vitro and in vivo. The magnitude of toxicological responses in cells that are exposed in submerged condition is not systematically different from the response in cells exposed in air-liquid interface systems, and there appears to be similar ENMs hazard ranking between the two exposure systems. Overall, we show that simple in vitro models with cells exposed to ENMs in submerged condition can be used to predict toxic effects in vivo, and identify future strategies to improve the associations between in vitro and in vivo ENMs-induced pulmonary toxicity. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials.

How Nanoparticles Open the Paracellular Route of Biological Barriers: Mechanisms, Applications, and Prospects

Biological barriers are essential physiological protective systems and obstacles to drug delivery. Nanoparticles (NPs) can access the paracellular route of biological barriers, either causing adverse health impacts on humans or producing therapeutic opportunities. This Review introduces the structural and functional influences of NPs on the key components that govern the paracellular route, mainly tight junctions, adherens junctions, and cytoskeletons. Furthermore, we evaluate their interaction mechanisms and address the influencing factors that determine the ability of NPs to open the paracellular route, which provides a better knowledge of how NPs can open the paracellular route in a safer and more controllable way. Finally, we summarize limitations in the research models and methodologies of the existing research in the field and provide future research direction. This Review demonstrates the in-depth causes for the reversible opening or destruction of the integrity of barriers generated by NPs; more importantly, it contributes insights into the design of NP-based medications to boost paracellular drug delivery efficiency.

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.

Grouping MWCNTs based on their similar potential to cause pulmonary hazard after inhalation: a case-study

BACKGROUND

The EU-project GRACIOUS developed an Integrated Approach to Testing and Assessment (IATA) to support grouping high aspect ratio nanomaterials (HARNs) presenting a similar inhalation hazard. Application of grouping reduces the need to assess toxicity on a case-by-case basis and supports read-across of hazard data from substances that have the data required for risk assessment (source) to those that lack such data (target). The HARN IATA, based on the fibre paradigm for pathogenic fibres, facilitates structured data gathering to propose groups of similar HARN and to support read-across by prompting users to address relevant questions regarding HARN morphology, biopersistence and inflammatory potential. The IATA is structured in tiers, allowing grouping decisions to be made using simple in vitro or in silico methods in Tier1 progressing to in vivo approaches at the highest Tier3. Here we present a case-study testing the applicability of GRACIOUS IATA to form an evidence-based group of multiwalled carbon nanotubes (MWCNT) posing a similar predicted fibre-hazard, to support read-across and reduce the burden of toxicity testing.

RESULTS

The case-study uses data on 15 different MWCNT, obtained from the published literature. By following the IATA, a group of 2 MWCNT was identified (NRCWE006 and NM-401) based on a high degree of similarity. A pairwise similarity assessment was subsequently conducted between the grouped MWCNT to evaluate the potential to conduct read-across and fill data gaps required for regulatory hazard assessment. The similarity assessment, based on expert judgement of Tier 1 assay results, predicts both MWCNT are likely to cause a similar acute in vivo hazard. This result supports the possibility for read-across of sub-chronic and chronic hazard endpoint data for lung fibrosis and carcinogenicity between the 2 grouped MWCNT. The implications of accepting the similarity assessment based on expert judgement of the MWCNT group are considered to stimulate future discussion on the level of similarity between group members considered sufficient to allow regulatory acceptance of a read-across argument.

CONCLUSION

This proof-of-concept case-study demonstrates how a grouping hypothesis and IATA may be used to support a nuanced and evidence-based grouping of 'similar' MWCNT and the subsequent interpolation of data between group members to streamline the hazard assessment process.

In vitro to in vivo extrapolation to support the development of the next generation risk assessment (NGRA) strategy for nanomaterials

There is growing interest in developing novel strategies to support assessment of human health risks due to chemicals. Regulatory and decision-making agencies have recommended that non-animal-based alternatives should be applied whenever possible instead of experimentation on living animals. These alternative methods are beneficial because they are ethical, inexpensive, and rapid. Herein, we review recent activities aimed at developing in vitro to in vivo extrapolation (IVIVE) models as a part of the Next Generation Risk Assessment (NGRA) of nanomaterials. In this context, we show the adverse outcome pathway (AOP)-based methodology for the identification of mechanistically relevant events serving as biomarkers for the targeted selection of in vitro assays. Considered events need to be biologically plausible, regulatory relevant, and crucial for the examination of occurrence of adverse outcomes. The promising advantages of using high-throughout-based omics data are highlighted. Furthermore, the application of 3D in vitro models and nano genome atlases to study nanoparticle toxicity is briefly summarized. Additionally, the challenges related to the extrapolation of in vitro doses into in vivo-relevant responses are presented. We also discuss the limitations of models applied thus far to study the fate of chemicals in the human body, which exist due to the lack of available knowledge regarding transformations of nanomaterials occurring in biological systems.

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.

The Road to Achieving the European Commission's Chemicals Strategy for Nanomaterial Sustainability-A PATROLS Perspective on New Approach Methodologies

The European Green Deal outlines ambitions to build a more sustainable, climate neutral, and circular economy by 2050. To achieve this, the European Commission has published the Chemicals Strategy for Sustainability: Towards a Toxic-Free Environment, which provides targets for innovation to better protect human and environmental health, including challenges posed by hazardous chemicals and animal testing. The European project PATROLS (Physiologically Anchored Tools for Realistic nanOmateriaL hazard aSsessment) has addressed multiple aspects of the Chemicals Strategy for Sustainability by establishing a battery of new approach methodologies, including physiologically anchored human and environmental hazard assessment tools to evaluate the safety of engineered nanomaterials. PATROLS has delivered and improved innovative tools to support regulatory decision-making processes. These tools also support the need for reducing regulated vertebrate animal testing; when used at an early stage of the innovation pipeline, the PATROLS tools facilitate the safe and sustainable development of new nano-enabled products before they reach the market.

Pro-inflammatory response and genotoxicity caused by clay and graphene nanomaterials in A549 and THP-1 cells

Nanoclays and graphene oxide nanomaterials represent a class of materials sharing similar shapes constituted of high aspect ratio platelets. The increased production of these materials for various industrial applications increases the risk of occupational exposure, consequently with elevated risk of adverse reactions and development of pulmonary diseases, including lung cancer. In this study, pro-inflammatory responses and genotoxicity were assessed in alveolar epithelial cells (A549) and activated THP-1 macrophages (THP-1a) after exposure to three nanoclays; a pristine (Bentonite) and two surface modified (benzalkonium chloride-coated Nanofil9, and dialkyldimethyl-ammonium-coated NanofilSE3000); graphene oxide (GO) and reduced graphene oxide (r-GO) nanomaterials. The pro-inflammatory response in terms of IL-8 expression was strongest in cells exposed to Bentonite, whereas surface modification resulted in decreased toxicity in both cell lines when exposed to Nanofil9 and NanofilSE3000. GO and r-GO induced a pro-inflammatory response in A549 cells, while no effect was detected with the two nanomaterials on THP-1a cells. The pro-inflammatory response was strongly correlated with in vivo inflammation in mice after intra-tracheal instillation when doses were normalized against surface area. Genotoxicity was assessed as DNA strand breaks, using the alkaline comet assay. In A549 cells, an increase in DNA strand breaks was detected only in cells exposed to Bentonite, whereas Bentonite, NanofilSE3000 and GO caused an increased level of genotoxicity in THP-1a cells. Genotoxicity in THP-1a cells was concordant with the DNA damage in bronchoalveolar lavage fluid cells following 1 and 3 days after intra-tracheal instillation in mice. In conclusion, this study shows that surface modification of pristine nanoclays reduces the inflammatory and genotoxic response in A549 and THP-1a cells, and these in vitro models show comparable toxicity to what seen in previous mouse studies with the same materials.

Safe-by-design strategies for lowering the genotoxicity and pulmonary inflammation of multiwalled carbon nanotubes: Reduction of length and the introduction of COOH groups

Potentially, the toxicity of multiwalled carbon nanotubes (MWCNTs) can be reduced in a safe-by-design strategy. We investigated if genotoxicity and pulmonary inflammation of MWCNTs from the same batch were lowered by a) reducing length and b) introducing COOH-groups into the structure. Mice were administered: 1) long and pristine MWCNT (CNT-long) (3.9 μm); 2) short and pristine CNT (CNT-short) (1 μm); 3) CNT modified with high ratio COOH-groups (CNT-COOH-high); 4) CNT modified with low ratio COOH-groups (CNT-COOH-low). MWCNTs were dosed by intratracheal instillation at 18 or 54 μg/mouse (∼0.9 and 2.7 mg/kg bw). Neutrophils numbers were highest after CNT-long exposure, and both shortening the MWCNT and addition of COOH-groups lowered pulmonary inflammation (day 1 and 28). Likewise, CNT-long induced genotoxicity, which was absent with CNT-short and after introduction of COOH groups. In conclusion, genotoxicity and pulmonary inflammation of MWCNTs were lowered, but not eliminated, by shortening the fibres or introducing COOH-groups.

In vitro-in vivo correlations of pulmonary inflammogenicity and genotoxicity of MWCNT

Background

Multi-walled carbon nanotubes (MWCNT) have received attention due to extraordinary properties, resulting in concerns for occupational health and safety. Costs and ethical concerns of animal testing drive a need for in vitro models with predictive power in respiratory toxicity. The aim of this study was to assess pro-inflammatory response (Interleukin-8 expression, IL-8) and genotoxicity (DNA strand breaks) caused by MWCNT with different physicochemical properties in different pulmonary cell models and correlate these to previously published in vivo data. Seven MWCNT were selected; two long/thick (NRCWE-006/Mitsui-7 and NM-401), two short/thin (NM-400 and NM-403), a pristine (NRCWE-040) and two surface modified; hydroxylated (NRCWE-041) and carboxylated (NRCWE-042). Carbon black Printex90 (CB) was included as benchmark material. Human alveolar epithelial cells (A549) and monocyte-derived macrophages (THP-1a) were exposed to nanomaterials (NM) in submerged conditions, and two materials (NM-400 and NM-401) in co-cultures of A549/THP-1a and lung fibroblasts (WI-38) in an air-liquid interface (ALI) system. Effective doses were quantified by thermo-gravimetric-mass spectrometry analysis (TGA-MS). To compare genotoxicity in vitro and in vivo, we developed a scoring system based on a categorization of effects into standard deviation (SD) units (< 1, 1, 2, 3 or 4 standard deviation increases) for the increasing genotoxicity.

Results

Effective doses were shown to be 25 to 53%, and 21 to 57% of the doses administered to A549 and THP-1a, respectively. In submerged conditions (A549 and THP-1a cells), all NM induced dose-dependent IL-8 expression. NM-401 and NRCWE-006 caused the strongest pro-inflammatory response. In the ALI-exposed co-culture, only NM-401 caused increased IL-8 expression, and no DNA strand breaks were observed. Strong correlations were found between in vitro and in vivo inflammation when doses were normalized by surface area (also proxy for diameter and length). Significantly increased DNA damage was found for all MWCNT in THP-1a cells, and for short MWCNT in A549 cells. A concordance in genotoxicity of 83% was obtained between THP-1a cells and broncho-alveolar lavaged (BAL) cells.

Conclusion

This study shows correlations of pro-inflammatory potential in A549 and THP-1a cells with neutrophil influx in mice, and concordance in genotoxic response between THP-1a cells and BAL cells, for seven MWCNT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-021-00413-2.

Non-Animal Strategies for Toxicity Assessment of Nanoscale Materials: Role of Adverse Outcome Pathways in the Selection of Endpoints

Faster, cheaper, sensitive, and mechanisms-based animal alternatives are needed to address the safety assessment needs of the growing number of nanomaterials (NM) and their sophisticated property variants. Specifically, strategies that help identify and prioritize alternative schemes involving individual test models, toxicity endpoints, and assays for the assessment of adverse outcomes, as well as strategies that enable validation and refinement of these schemes for the regulatory acceptance are needed. In this review, two strategies 1) the current nanotoxicology literature review and 2) the adverse outcome pathways (AOPs) framework, a systematic process that allows the assembly of available mechanistic information concerning a toxicological response in a simple modular format, are presented. The review highlights 1) the most frequently assessed and reported ad hoc in vivo and in vitro toxicity measurements in the literature, 2) various AOPs of relevance to inhalation toxicity of NM that are presently under development, and 3) their applicability in identifying key events of toxicity for targeted in vitro assay development. Finally, using an existing AOP for lung fibrosis, the specific combinations of cell types, exposure and test systems, and assays that are experimentally supported and thus, can be used for assessing NM-induced lung fibrosis, are proposed.

Bringing together scientific disciplines for collaborative undertakings: a vision for advancing the adverse outcome pathway framework

BACKGROUND

Decades of research to understand the impacts of various types of environmental occupational and medical stressors on human health have produced a vast amount of data across many scientific disciplines. Organizing these data in a meaningful way to support risk assessment has been a significant challenge. To address this and other challenges in modernizing chemical health risk assessment, the Organisation for Economic Cooperation and Development (OECD) formalized the adverse outcome pathway (AOP) framework, an approach to consolidate knowledge into measurable key events (KEs) at various levels of biological organisation causally linked to disease based on the weight of scientific evidence (http://oe.cd/aops). Currently, AOPs have been considered predominantly in chemical safety but are relevant to radiation. In this context, the Nuclear Energy Agency's (NEA's) High-Level Group on Low Dose Research (HLG-LDR) is working to improve research co-ordination, including radiological research with chemical research, identify synergies between the fields and to avoid duplication of efforts and resource investments. To this end, a virtual workshop was held on 7 and 8 October 2020 with experts from the OECD AOP Programme together with the radiation and chemical research/regulation communities. The workshop was a coordinated effort of Health Canada, the Electric Power Research Institute (EPRI), and the Nuclear Energy Agency (NEA). The AOP approach was discussed including key issues to fully embrace its value and catalyze implementation in areas of radiation risk assessment.

CONCLUSIONS

A joint chemical and radiological expert group was proposed as a means to encourage cooperation between risk assessors and an initial vision was discussed on a path forward. A global survey was suggested as a way to identify priority health outcomes of regulatory interest for AOP development. Multidisciplinary teams are needed to address the challenge of producing the appropriate data for risk assessments. Data management and machine learning tools were highlighted as a way to progress from weight of evidence to computational causal inference.