A meta-analysis of carbon nanotube pulmonary toxicity studies--how physical dimensions and impurities affect the toxicity of carbon nanotubes

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.

Evaluating the adverse effects and mechanisms of nanomaterial exposure on longevity of C. elegans: A literature meta-analysis and bioinformatics analysis of multi-transcriptome data

Exposure to large-size particulate air pollution (PM2.5 or PM10) has been reported to increase risks of aging-related diseases and human death, indicating the potential pro-aging effects of airborne nanomaterials with ultra-fine particle size (which have been widely applied in various fields). However, this hypothesis remains inconclusive. Here, a meta-analysis of 99 published literatures collected from electronic databases (PubMed, EMBASE and Cochrane Library; from inception to June 2023) was performed to confirm the effects of nanomaterial exposure on aging-related indicators and molecular mechanisms in model animal C. elegans. The pooled analysis by Stata software showed that compared with the control, nanomaterial exposure significantly shortened the mean lifespan [standardized mean difference (SMD) = -2.30], reduced the survival rate (SMD = -4.57) and increased the death risk (hazard ratio = 1.36) accompanied by upregulation of ced-3, ced-4 and cep-1, while downregulation of ctl-2, ape-1, aak-2 and pmk-1. Furthermore, multi-transcriptome data associated with nanomaterial exposure were retrieved from Gene Expression Omnibus (GSE32521, GSE41486, GSE24847, GSE59470, GSE70509, GSE14932, GSE93187, GSE114881, and GSE122728) and bioinformatics analyses showed that pseudogene prg-2, mRNAs of abu, car-1, gipc-1, gsp-3, kat-1, pod-2, acdh-8, hsp-60 and egrh-2 were downregulated, while R04A9.7 was upregulated after exposure to at least two types of nanomaterials. Resveratrol (abu, hsp-60, pod-2, egrh-2, acdh-8, gsp-3, car-1, kat-1, gipc-1), naringenin (kat-1, egrh-2), coumestrol (egrh-2) or swainsonine/niacin/ferulic acid (R04A9.7) exerted therapeutic effects by reversing the expression levels of target genes. In conclusion, our study demonstrates the necessity to use phytomedicines that target hub genes to delay aging for populations with nanomaterial exposure.

Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials

Although engineered nanomaterials (ENMs) have tremendous potential to generate technological benefits in numerous sectors, uncertainty on the risks of ENMs for human health and the environment may impede the advancement of novel materials. Traditionally, the risks of ENMs can be evaluated by experimental methods such as environmental field monitoring and animal-based toxicity testing. However, it is time-consuming, expensive, and impractical to evaluate the risk of the increasingly large number of ENMs with the experimental methods. On the contrary, with the advancement of artificial intelligence and machine learning, in silico methods have recently received more attention in the risk assessment of ENMs. This review discusses the key progress of computational nanotoxicology models for assessing the risks of ENMs, including material flow analysis models, multimedia environmental models, physiologically based toxicokinetics models, quantitative nanostructure-activity relationships, and meta-analysis. Several challenges are identified and a perspective is provided regarding how the challenges can be addressed.

Therapeutic applications of carbon nanomaterials in renal cancer

Carbon nanomaterials (CNMs), including carbon nanotubes (CNTs), graphene, and nanodiamonds (NDs), have shown great promise in detecting and treating numerous cancers, including kidney cancer. CNMs can increase the sensitivity of diagnostic techniques for better kidney cancer identification and surveillance. They enable targeted medicine delivery specifically to tumour locations, with little effect on healthy tissue. Because of their unique chemical and physical characteristics, they can avoid the body's defence mechanisms, making it easier to accumulate where tumours exist. Consequently, CNMs provide more effective drug delivery to kidney cancer cells. It also helps in improving the efficacy of treatment. This review explores the potential of several CNMs in improving therapeutic strategies for kidney cancer. We briefly covered the physicochemical properties and therapeutic applications of CNMs. Additionally, we discussed how structural modifications in CNMs enhance their precision in treating renal cancer. A thorough overview of CNM-based gene, peptide, and drug delivery strategies for the treatment of renal cancer is presented in this review. It covers information on other CNM-based therapeutic approaches, such as hyperthermia, photodynamic therapy, and photoacoustic therapy. Also, the interactions of CNMs with the tumour microenvironment (TME) are explored, including modulation of the immune response, regulation of tumour hypoxia, interactions between CNMs and TME cells, effects of TME pH on CNMs, and more. Finally, potential side effects of CNMs, such as toxicity, bio corona formation, enzymatic degradation, and biocompatibility, are also discussed.

A data reusability assessment in the nanosafety domain based on the NSDRA framework followed by an exploratory quantitative structure activity relationships (QSAR) modeling targeting cellular viability

INTRODUCTION

The current effort towards the digital transformation across multiple scientific domains requires data that is Findable, Accessible, Interoperable and Reusable (FAIR). In addition to the FAIR data, what is required for the application of computational tools, such as Quantitative Structure Activity Relationships (QSARs), is a sufficient data volume and the ability to merge sources into homogeneous digital assets. In the nanosafety domain there is a lack of FAIR available metadata.

METHODOLOGY

To address this challenge, we utilized 34 datasets from the nanosafety domain by exploiting the NanoSafety Data Reusability Assessment (NSDRA) framework, which allowed the annotation and assessment of dataset's reusability. From the framework's application results, eight datasets targeting the same endpoint (i.e. numerical cellular viability) were selected, processed and merged to test several hypothesis including universal versus nanogroup-specific QSAR models (metal oxide and nanotubes), and regression versus classification Machine Learning (ML) algorithms.

RESULTS

Universal regression and classification QSARs reached an 0.86 R2 and 0.92 accuracy, respectively, for the test set. Nanogroup-specific regression models reached 0.88 R2 for nanotubes test set followed by metal oxide (0.78). Nanogroup-specific classification models reached 0.99 accuracy for nanotubes test set, followed by metal oxide (0.91). Feature importance revealed different patterns depending on the dataset with common influential features including core size, exposure conditions and toxicological assay. Even in the case where the available experimental knowledge was merged, the models still failed to correctly predict the outputs of an unseen dataset, revealing the cumbersome conundrum of scientific reproducibility in realistic applications of QSAR for nanosafety. To harness the full potential of computational tools and ensure their long-term applications, embracing FAIR data practices is imperative in driving the development of responsible QSAR models.

CONCLUSIONS

This study reveals that the digitalization of nanosafety knowledge in a reproducible manner has a long way towards its successful pragmatic implementation. The workflow carried out in the study shows a promising approach to increase the FAIRness across all the elements of computational studies, from dataset's annotation, selection, merging to FAIR modeling reporting. This has significant implications for future research as it provides an example of how to utilize and report different tools available in the nanosafety knowledge system, while increasing the transparency of the results. One of the main benefits of this workflow is that it promotes data sharing and reuse, which is essential for advancing scientific knowledge by making data and metadata FAIR compliant. In addition, the increased transparency and reproducibility of the results can enhance the trustworthiness of the computational findings.

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.

Meta-Analysis of Cytotoxicity Studies Using Machine Learning Models on Physical Properties of Plant Extract-Derived Silver Nanoparticles

Silver nanoparticles (Ag-NPs) demonstrate unique properties and their use is exponentially increasing in various applications. The potential impact of Ag-NPs on human health is debatable in terms of toxicity. The present study deals with MTT(3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl-tetrazolium-bromide) assay on Ag-NPs. We measured the cell activity resulting from molecules' mitochondrial cleavage through a spectrophotometer. The machine learning models Decision Tree (DT) and Random Forest (RF) were utilized to comprehend the relationship between the physical parameters of NPs and their cytotoxicity. The input features used for the machine learning were reducing agent, types of cell lines, exposure time, particle size, hydrodynamic diameter, zeta potential, wavelength, concentration, and cell viability. These parameters were extracted from the literature, segregated, and developed into a dataset in terms of cell viability and concentration of NPs. DT helped in classifying the parameters by applying threshold conditions. The same conditions were applied to RF to extort the predictions. K-means clustering was used on the dataset for comparison. The performance of the models was evaluated through regression metrics, viz. root mean square error (RMSE) and R². The obtained high value of R² and low value of RMSE denote an accurate prediction that could best fit the dataset. DT performed better than RF in predicting the toxicity parameter. We suggest using algorithms for optimizing and designing the synthesis of Ag-NPs in extended applications such as drug delivery and cancer treatments.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Predicting the cytotoxicity of nanomaterials through explainable, extreme gradient boosting

Nanoparticles (NPs) are a wide class of materials currently used in several industrial and biomedical applications. Due to their small size (1-100 nm), NPs can easily enter the human body, inducing tissue damage. NP toxicity depends on physical and chemical NP properties (e.g., size, charge and surface area) in ways and magnitudes that are still unknown. We assess the average as well as the individual importance of NP atomic descriptors, along with chemical properties and experimental conditions, in determining cytotoxicity endpoints for several nanomaterials. We employ a multicenter cytotoxicity nanomaterial database (12 different materials with first and second dimensions ranging between 2.70 and 81.2 nm and between 4.10 and 4048 nm, respectively). We develop a regressor model based on extreme gradient boosting with hyperparameter optimization. We employ Shapley additive explanations to obtain good cytotoxicity prediction performance. Model performances are quantified as statistically significant Spearman correlations between the true and predicted values, ranging from 0.5 to 0.7. Our results show that i) size in situ and surface areas larger than 200 nm and 50 m2/g, respectively, ii) primary particles smaller than 20 nm; iii) irregular (i.e., not spherical) shapes and iv) positive Z-potentials contribute the most to the prediction of NP cytotoxicity, especially if lactate dehydrogenase (LDH) assays are employed for short experimental times. These results were moderately stable across toxicity endpoints, although some degree of variability emerged across dose quantification methods, confirming the complexity of nano-bio interactions and the need for large, systematic experimental characterization to reach a safer-by-design approach.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

Meta-analysis of in-vitro cytotoxicity evaluation studies of zinc oxide nanoparticles: Paving way for safer innovations

Nano-based products have shown their daunting presence in several sectors. Among them, Zinc Oxide (ZnO) nanoparticles wangled the reputation of providing "next-generation solutions" and are being utilized in plethora of products. Their widespread application has led to increased exposure of these particles, raising concerns regarding toxicological repercussions to the human health and environment. The diversity, complexity, and heterogeneity in the available literature, along with correlation of befitting attributes, makes it challenging to develop one systematic framework to predict this toxicity. The present study aims at developing predictive modelling framework to tap the prospective features responsible for causing cytotoxicity in-vitro on exposure to ZnO nanoparticles. Rigorous approach was used to mine the information from complete body of evidence published to date. The attributes, features and experimental conditions were systematically extracted to unmask the effect of varied features. 1240 data points from 76 publications were obtained, containing 14 qualitative and quantitative attributes, including physiochemical properties of nanoparticles, cell culture and experimental parameters to perform meta-analysis. For the first time, the efforts were made to investigate the degree of significance of attributes accountable for causing cytotoxicity on exposure to ZnO nanoparticles. We show that in-vitro cytotoxicity is closely related with dose concentration of nanoparticles, followed by exposure time, disease state of the cell line and size of these nanoparticles among other attributes.

An EPA database on the effects of engineered nanomaterials-NaKnowBase

The US EPA Office of Research and Development (ORD) has conducted a research program assessing potential risks of emerging materials and technologies, including engineered nanomaterials (ENM). As a component of that program, a nanomaterial knowledge base, termed “NaKnowBase”, was developed containing the results of published ORD research relevant to the potential environmental and biological actions of ENM. The experimental data address issues such as ENM release into the environment; fate, transport and transformations in environmental media; exposure to ecological species or humans; and the potential for effects on those species. The database captures information on the physicochemical properties of ENM tested, assays performed and their parameters, and the results obtained. NaKnowBase (NKB) is a relational SQL database, and may be queried either with SQL code or through a user-friendly web interface. Filtered results may be output in spreadsheet format for subsequent user-defined analyses. Potential uses of the data might include input to quantitative structure-activity relationships (QSAR), meta-analyses, or other investigative approaches.

Measurement(s)	engineered nanomaterial effects
Technology Type(s)	digital curation
Factor Type(s)	physicochemical property
Sample Characteristic - Environment	nanomaterial

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.17060120

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

Utilizing literature-based rodent toxicology data to derive potency estimates for quantitative risk assessment

Evaluating the potential occupational health risk of engineered nanomaterials is an ongoing need. The objective of this meta-analysis, which consisted of 36 studies containing 86 materials, was to assess the availability of published in vivo rodent pulmonary toxicity data for a variety of nanoscale and microscale materials and to derive potency estimates via benchmark dose modeling. Additionally, the potency estimates based on particle mass lung dose associated with acute pulmonary inflammation were used to group materials based on toxicity. The commonalities among the physicochemical properties of the materials in each group were also explored. This exploration found that a material's potency tended to be associated primarily with the material class based on chemical composition and form (e.g. carbon nanotubes, TiO₂, ZnO) rather than with particular physicochemical properties. Limitations in the data available precluded a more extensive analysis of these associations. Issues such as data reporting and appropriate experimental design for use in quantitative risk assessment are the main reasons publications were excluded from these analyses and are discussed.

Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles

The development of machine learning provides solutions for predicting the complicated immune responses and pharmacokinetics of nanoparticles (NPs) in vivo. However, highly heterogeneous data in NP studies remain challenging because of the low interpretability of machine learning. Here, we propose a tree-based random forest feature importance and feature interaction network analysis framework (TBRFA) and accurately predict the pulmonary immune responses and lung burden of NPs, with the correlation coefficient of all training sets >0.9 and half of the test sets >0.75. This framework overcomes the feature importance bias brought by small datasets through a multiway importance analysis. TBRFA also builds feature interaction networks, boosts model interpretability, and reveals hidden interactional factors (e.g., various NP properties and exposure conditions). TBRFA provides guidance for the design and application of ideal NPs and discovers the feature interaction networks that contribute to complex systems with small-size data in various fields.

Use of size-dependent electron configuration fingerprint to develop general prediction models for nanomaterials

Due to the lack of nano descriptors that can appropriately represent the wide chemical space of engineered nanomaterials (ENMs), applicability domain of nano-quantitative structure-activity relationship models are limited to certain types of ENMs, such as metal oxides, metals, carbon-based nanomaterials, or quantum dots. In this study, a size-dependent electron configuration fingerprint (SDEC FP) was introduced to estimate the quantity of electrons based on the core, doping, and coating materials of ENMs in different sizes. SDEC FP was used in prediction model development and nanostructure similarity analysis on datasets including metal and carbon-based nanomaterials with and without surface modifications. Cytotoxicity and zeta potential prediction models developed with SDEC FP achieved good prediction accuracies on test set. Nanostructure similarity analysis was performed through principal component analysis which showed that structural similarity between ENMs measured by SDEC FP was highly correlated with their properties.

Physicochemical characterization and genotoxicity of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities

BACKGROUND

Carbon nanotubes and nanofibers (CNT/F) have known toxicity but simultaneous comparative studies of the broad material class, especially those with a larger diameter, with computational analyses linking toxicity to their fundamental material characteristics was lacking. It was unclear if all CNT/F confer similar toxicity, in particular, genotoxicity. Nine CNT/F (MW #1-7 and CNF #1-2), commonly found in exposure assessment studies of U.S. facilities, were evaluated with reported diameters ranging from 6 to 150 nm. All materials were extensively characterized to include distributions of physical dimensions and prevalence of bundled agglomerates. Human bronchial epithelial cells were exposed to the nine CNT/F (0-24 μg/ml) to determine cell viability, inflammation, cellular oxidative stress, micronuclei formation, and DNA double-strand breakage. Computational modeling was used to understand various permutations of physicochemical characteristics and toxicity outcomes.

RESULTS

Analyses of the CNT/F physicochemical characteristics illustrate that using detailed distributions of physical dimensions provided a more consistent grouping of CNT/F compared to using particle dimension means alone. In fact, analysis of binning of nominal tube physical dimensions alone produced a similar grouping as all characterization parameters together. All materials induced epithelial cell toxicity and micronuclei formation within the dose range tested. Cellular oxidative stress, DNA double strand breaks, and micronuclei formation consistently clustered together and with larger physical CNT/F dimensions and agglomerate characteristics but were distinct from inflammatory protein changes. Larger nominal tube diameters, greater lengths, and bundled agglomerate characteristics were associated with greater severity of effect. The portion of tubes with greater nominal length and larger diameters within a sample was not the majority in number, meaning a smaller percentage of tubes with these characteristics was sufficient to increase toxicity. Many of the traditional physicochemical characteristics including surface area, density, impurities, and dustiness did not cluster with the toxicity outcomes.

CONCLUSION

Distributions of physical dimensions provided more consistent grouping of CNT/F with respect to toxicity outcomes compared to means only. All CNT/F induced some level of genotoxicity in human epithelial cells. The severity of toxicity was dependent on the sample containing a proportion of tubes with greater nominal lengths and diameters.

Nanomaterial Additives for Fabrication of Stimuli-Responsive Skeletal Muscle Tissue Engineering Constructs

Volumetric muscle loss necessitates novel tissue engineering strategies for skeletal muscle repair, which have traditionally involved cells and extracellular matrix-mimicking scaffolds and have thus far been unable to successfully restore physiologically relevant function. However, the incorporation of various nanomaterial additives with unique physicochemical properties into scaffolds has recently been explored as a means of fabricating constructs that are responsive to electrical, magnetic, and photothermal stimulation. Herein, several classes of nanomaterials that are used to mediate external stimulation to tissue engineered skeletal muscle are reviewed and the impact of these stimuli-responsive biomaterials on cell growth and differentiation and in vivo muscle repair is discussed. The degradation kinetics and biocompatibilities of these nanomaterial additives are also briefly examined and their potential for incorporation into clinically translatable skeletal muscle tissue engineering strategies is considered. Overall, these nanomaterial additives have proven efficacious and incorporation in tissue engineering scaffolds has resulted in enhanced functional skeletal muscle regeneration.

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.

Biomass-derived nanocarbon materials for biological applications: challenges and prospects

Biomass-derived nanocarbons (BNCs) have attracted significant research interests due to their promising economic and environmental benefits. Following their extensive uses in physical and chemical research domains, BNCs are now growing in biological applications. However, their practical biological applications are still in their infancy, requiring critical evaluations and strategic directions, which are provided in this review. The carbonization of biomass sources and major types of BNCs are introduced, encompassing carbon nanodots, nanofibres, nanotubes, and graphenes. Next, essential biological uses of BNCs, antibacterial/antibiofilm materials (nanofibres and nanodots) and bioimaging agents (predominantly nanodots), are summarized. Furthermore, the future potential of BNCs, for designing wound dressing/healing materials, water and air disinfection platforms, and microbial electrochemical systems, is discussed. We reach the conclusion that a crucial challenge is the structural control of BNCs. Furthermore, a key knowledge gap for realizing practical biological applications is the lack of systematic comparisons of BNCs with nanocarbons of synthetic origin in the current literature. Although we did not attempt to perform an exhaustive literature survey, the evaluation of the existing results indicates that BNCs are promising as easily accessible materials for various biomedically and environmentally relevant applications.

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.

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.

Nanotoxicology data for in silico tools: a literature review

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

QSAR modeling of the toxicity classification of superparamagnetic iron oxide nanoparticles (SPIONs) in stem-cell monitoring applications: an integrated study from data curation to model development

The use of in silico approaches for the prediction of biomedical properties of nano-biomaterials (NBMs) can play a significant role in guiding and reducing wetlab experiments. Computational methods, such as data mining and machine learning techniques, can increase the efficiency and reduce the time and cost required for hazard and risk assesment and for designing new safer NBMs. A major obstacle in developing accurate and well-validated in silico models such as Nano Quantitative Structure-Activity Relationships (Nano-QSARs) is that although the volume of data published in the literature is increasing, the data are fragmented in many different publications and are not sufficiently curated for modelling purposes. Moreover, NBMs exhibit high complexity and heterogeneity in their structures, making data collection and curation and QSAR model development more challenging compared to traditional small molecules. The aim of this study was to construct and fully validate a Nano-QSAR model for the prediction of toxicological properties of superparamagnetic iron oxide nanoparticles (SPIONs), focusing on their application as Magnetic Resonance Imaging (MRI) contrast agents for non-invasive stem cell labelling and tracking. To achieve this goal, we first performed an extensive search through the literature for collecting and curating relevant data and we developed a dataset containing both physicochemical and toxicological properties of SPIONs. The data were analysed next, using Automated machine learning (Auto-ML) approaches for optimising the development and validation of nanotoxicity classification QSAR models of SPIONs. Further analysis of relative attribute importances revealed that physicochemical properties such as the size and the magnetic core are the dominant attributes correlated to the toxicity of SPIONs. Our results suggest that as more systematic information from NBM experimental tests becomes available, computational tools could play an important role in supporting the safety-by-design (SbD) concept in regenerative medicine and disease therapeutics.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Applications of Carbon Nanotubes in Bone Tissue Regeneration and Engineering: Superiority, Concerns, Current Advancements, and Prospects

With advances in bone tissue regeneration and engineering technology, various biomaterials as artificial bone substitutes have been widely developed and innovated for the treatment of bone defects or diseases. However, there are no available natural and synthetic biomaterials replicating the natural bone structure and properties under physiological conditions. The characteristic properties of carbon nanotubes (CNTs) make them an ideal candidate for developing innovative biomimetic materials in the bone biomedical field. Indeed, CNT-based materials and their composites possess the promising potential to revolutionize the design and integration of bone scaffolds or implants, as well as drug therapeutic systems. This review summarizes the unique physicochemical and biomedical properties of CNTs as structural biomaterials and reinforcing agents for bone repair as well as provides coverage of recent concerns and advancements in CNT-based materials and composites for bone tissue regeneration and engineering. Moreover, this review discusses the research progress in the design and development of novel CNT-based delivery systems in the field of bone tissue engineering.

Mitsui-7, heat-treated, and nitrogen-doped multi-walled carbon nanotubes elicit genotoxicity in human lung epithelial cells

Background

The unique physicochemical properties of multi-walled carbon nanotubes (MWCNT) have led to many industrial applications. Due to their low density and small size, MWCNT are easily aerosolized in the workplace making respiratory exposures likely in workers. The International Agency for Research on Cancer designated the pristine Mitsui-7 MWCNT (MWCNT-7) as a Group 2B carcinogen, but there was insufficient data to classify all other MWCNT. Previously, MWCNT exposed to high temperature (MWCNT-HT) or synthesized with nitrogen (MWCNT-ND) have been found to elicit attenuated toxicity; however, their genotoxic and carcinogenic potential are not known. Our aim was to measure the genotoxicity of MWCNT-7 compared to these two physicochemically-altered MWCNTs in human lung epithelial cells (BEAS-2B & SAEC).

Results

Dose-dependent partitioning of individual nanotubes in the cell nuclei was observed for each MWCNT material and was greatest for MWCNT-7. Exposure to each MWCNT led to significantly increased mitotic aberrations with multi- and monopolar spindle morphologies and fragmented centrosomes. Quantitative analysis of the spindle pole demonstrated significantly increased centrosome fragmentation from 0.024–2.4 μg/mL of each MWCNT. Significant aneuploidy was measured in a dose-response from each MWCNT-7, HT, and ND; the highest dose of 24 μg/mL produced 67, 61, and 55%, respectively. Chromosome analysis demonstrated significantly increased centromere fragmentation and translocations from each MWCNT at each dose. Following 24 h of exposure to MWCNT-7, ND and/or HT in BEAS-2B a significant arrest in the G1/S phase in the cell cycle occurred, whereas the MWCNT-ND also induced a G2 arrest. Primary SAEC exposed for 24 h to each MWCNT elicited a significantly greater arrest in the G1 and G2 phases. However, SAEC arrested in the G1/S phase after 72 h of exposure. Lastly, a significant increase in clonal growth was observed one month after exposure to 0.024 μg/mL MWCNT-HT & ND.

Conclusions

Although MWCNT-HT & ND cause a lower incidence of genotoxicity, all three MWCNTs cause the same type of mitotic and chromosomal disruptions. Chromosomal fragmentation and translocations have not been observed with other nanomaterials. Because in vitro genotoxicity is correlated with in vivo genotoxic response, these studies in primary human lung cells may predict the genotoxic potency in exposed human populations.

Electronic supplementary material

The online version of this article (10.1186/s12989-019-0318-0) contains supplementary material, which is available to authorized users.

The Advances in Biomedical Applications of Carbon Nanotubes

Unique chemical, physical, and biological features of carbon nanotubes make them an ideal candidate for myriad applications in industry and biomedicine. Carbon nanotubes have excellent electrical and thermal conductivity, high biocompatibility, flexibility, resistance to corrosion, nano-size, and a high surface area, which can be tailored and functionalized on demand. This review discusses the progress and main fields of bio-medical applications of carbon nanotubes based on recently-published reports. It encompasses the synthesis of carbon nanotubes and their application for bio-sensing, cancer treatment, hyperthermia induction, antibacterial therapy, and tissue engineering. Other areas of carbon nanotube applications were out of the scope of this review. Special attention has been paid to the problem of the toxicity of carbon nanotubes.

Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature

Developing predictive modeling frameworks of potential cytotoxicity of engineered nanoparticles is critical for environmental and health risk analysis. The complexity and the heterogeneity of available data on potential risks of nanoparticles, in addition to interdependency of relevant influential attributes, makes it challenging to develop a generalization of nanoparticle toxicity behavior. Lack of systematic approaches to investigate these risks further adds uncertainties and variability to the body of literature and limits generalizability of existing studies. Here, we developed a rigorous approach for assembling published evidence on cytotoxicity of several organic and inorganic nanoparticles and unraveled hidden relationships that were not targeted in the original publications. We used a machine learning approach that employs decision trees together with feature selection algorithms ( e.g., Gain ratio) to analyze a set of published nanoparticle cytotoxicity sample data (2896 samples). The specific studies were selected because they specified nanoparticle-, cell-, and screening method-related attributes. The resultant decision-tree classifiers are sufficiently simple, accurate, and with high prediction power and should be widely applicable to a spectrum of nanoparticle cytotoxicity settings. Among several influential attributes, we show that the cytotoxicity of nanoparticles is primarily predicted from the nanoparticle material chemistry, followed by nanoparticle concentration and size, cell type, and cytotoxicity screening indicator. Overall, our study indicates that following rigorous and transparent methodological experimental approaches, in parallel to continuous addition to this data set developed using our approach, will offer higher predictive power and accuracy and uncover hidden relationships. Results obtained in this study help focus future studies to develop nanoparticles that are safe by design.

Screening Priority Factors Determining and Predicting the Reproductive Toxicity of Various Nanoparticles

Due to the numerous factors (e.g., nanoparticle [NP] properties and experimental conditions) influencing nanotoxicity, it is difficult to identify the priority factors dominating nanotoxicity. Herein, by integrating data from the literature and a random forest model, the priority factors determining reproductive toxicity were successfully screened from highly heterogeneous data. Among 10 factors from more than 18 different NPs, the NP type and the exposure pathway were found to dominantly determine NP accumulation. The reproductive toxicity of various NPs primarily depended on the NP type and the toxicity indicators. Nanoparticles containing major elements (e.g., Zn and Fe) tended to accumulate in rats but induced lower toxicity than NPs containing noble elements. Compared with other exposure pathways, i.p. injection posed significantly higher risks for NP accumulation. By combining similarity network analysis and hierarchical clustering, the sources of highly heterogeneous data were identified, the factor-toxicity dependencies were extracted and visualized, and the prediction of nanotoxicity was then achieved based on the screened priority factors. The present work provides insights for the design of animal experiments and the illustration and prediction of nanotoxicity.

Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells

Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R² for internal validation data sets: 0.60-0.80; R²_pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 μm; surface area, 11.3-380.0 m²/g; and dose, 0-200 ppm.

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

A quantitative framework to group nanoscale and microscale particles by hazard potency to derive occupational exposure limits: Proof of concept evaluation

The large and rapidly growing number of engineered nanomaterials (ENMs) presents a challenge to assessing the potential occupational health risks. An initial database of 25 rodent studies including 1929 animals across various experimental designs and material types was constructed to identify materials that are similar with respect to their potency in eliciting neutrophilic pulmonary inflammation, a response relevant to workers. Doses were normalized across rodent species, strain, and sex as the estimated deposited particle mass dose per gram of lung. Doses associated with specific measures of pulmonary inflammation were estimated by modeling the continuous dose-response relationships using benchmark dose modeling. Hierarchical clustering was used to identify similar materials. The 18 nanoscale and microscale particles were classified into four potency groups, which varied by factors of approximately two to 100. Benchmark particles microscale TiO2 and crystalline silica were in the lowest and highest potency groups, respectively. Random forest methods were used to identify the important physicochemical predictors of pulmonary toxicity, and group assignments were correctly predicted for five of six new ENMs. Proof-of-concept was demonstrated for this framework. More comprehensive data are needed for further development and validation for use in deriving categorical occupational exposure limits.

Toward the suppression of cellular toxicity from single-walled carbon nanotubes

In the multidisciplinary fields of nanobiology and nanomedicine, single-walled carbon nanotubes (SWCNTs) have shown great promise due to their unique morphological, physical and chemical properties. However, understanding and suppressing their cellular toxicity is a mandatory step before promoting their biomedical applications. In light of the flourishing recent literature, we provide here an extensive review on SWCNT cellular toxicity and an attempt to identify the key parameters to be considered in order to obtain SWCNT samples with minimal or no cellular toxicity.

Evaluating the mechanistic evidence and key data gaps in assessing the potential carcinogenicity of carbon nanotubes and nanofibers in humans

In an evaluation of carbon nanotubes (CNTs) for the IARC Monograph 111, the Mechanisms Subgroup was tasked with assessing the strength of evidence on the potential carcinogenicity of CNTs in humans. The mechanistic evidence was considered to be not strong enough to alter the evaluations based on the animal data. In this paper, we provide an extended, in-depth examination of the in vivo and in vitro experimental studies according to current hypotheses on the carcinogenicity of inhaled particles and fibers. We cite additional studies of CNTs that were not available at the time of the IARC meeting in October 2014, and extend our evaluation to include carbon nanofibers (CNFs). Finally, we identify key data gaps and suggest research needs to reduce uncertainty. The focus of this review is on the cancer risk to workers exposed to airborne CNT or CNF during the production and use of these materials. The findings of this review, in general, affirm those of the original evaluation on the inadequate or limited evidence of carcinogenicity for most types of CNTs and CNFs at this time, and possible carcinogenicity of one type of CNT (MWCNT-7). The key evidence gaps to be filled by research include: investigation of possible associations between in vitro and early-stage in vivo events that may be predictive of lung cancer or mesothelioma, and systematic analysis of dose-response relationships across materials, including evaluation of the influence of physico-chemical properties and experimental factors on the observation of nonmalignant and malignant endpoints.

Carbon Nanostructures in Bone Tissue Engineering

BACKGROUND

Recent advances in developing biocompatible materials for treating bone loss or defects have dramatically changed clinicians' reconstructive armory. Current clinically available reconstructive options have certain advantages, but also several drawbacks that prevent them from gaining universal acceptance. A wide range of synthetic and natural biomaterials is being used to develop tissue-engineered bone. Many of these materials are currently in the clinical trial stage.

METHODS

A selective literature review was performed for carbon nanostructure composites in bone tissue engineering.

RESULTS

Incorporation of carbon nanostructures significantly improves the mechanical properties of various biomaterials to mimic that of natural bone. Recently, carbon-modified biomaterials for bone tissue engineering have been extensively investigated to potentially revolutionize biomaterials for bone regeneration.

CONCLUSION

This review summarizes the chemical and biophysical properties of carbon nanostructures and discusses their functionality in bone tissue regeneration.

Approaches to Develop Alternative Testing Strategies to Inform Human Health Risk Assessment of Nanomaterials

The development of alternative testing strategies (ATS) for hazard assessment of new and emerging materials is high on the agenda of scientists, funders, and regulators. The relatively large number of nanomaterials on the market and under development means that an increasing emphasis will be placed on the use of reliable, predictive ATS when assessing their safety. We have provided recommendations as to how ATS development for assessment of nanomaterial hazard may be accelerated. Predefined search terms were used to identify the quantity and distribution of peer-reviewed publications for nanomaterial hazard assessment following inhalation, ingestion, or dermal absorption. A summary of knowledge gaps relating to nanomaterial hazard is provided to identify future research priorities and areas in which a rich data set might exist to allow ATS identification. Consultation with stakeholders (e.g., academia, industry, regulators) was critical to ensure that current expert opinion was reflected. The gap analysis revealed an abundance of studies that assessed the local and systemic impacts of inhaled particles, and so ATS are available for immediate use. Development of ATS for assessment of the dermal toxicity of chemicals is already relatively advanced, and these models should be applied to nanomaterials as relatively few studies have assessed the dermal toxicity of nanomaterials to date. Limited studies have investigated the local and systemic impacts of ingested nanomaterials. If the recommendations for research prioritization proposed are adopted, it is envisioned that a comprehensive battery of ATS can be developed to support the risk assessment process for nanomaterials. Some alternative models are available for immediate implementation, while others require more developmental work to become widely adopted. Case studies are included that can be used to inform the selection of alternative models and end points when assessing the pathogenicity of fibers and mode of action of nanomaterial toxicity.

Meta-analysis of cellular toxicity for cadmium-containing quantum dots

Understanding the relationships between the physicochemical properties of engineered nanomaterials and their toxicity is critical for environmental and health risk analysis. However, this task is confounded by material diversity, heterogeneity of published data and limited sampling within individual studies. Here, we present an approach for analysing and extracting pertinent knowledge from published studies focusing on the cellular toxicity of cadmium-containing semiconductor quantum dots. From 307 publications, we obtain 1,741 cell viability-related data samples, each with 24 qualitative and quantitative attributes describing the material properties and experimental conditions. Using random forest regression models to analyse the data, we show that toxicity is closely correlated with quantum dot surface properties (including shell, ligand and surface modifications), diameter, assay type and exposure time. Our approach of integrating quantitative and categorical data provides a roadmap for interrogating the wide-ranging toxicity data in the literature and suggests that meta-analysis can help develop methods for predicting the toxicity of engineered nanomaterials.

Nanoparticles and direct immunosuppression

Targeting the immune system with nanomaterials is an intensely active area of research. Specifically, the capability to induce immunosuppression is a promising complement for drug delivery and regenerative medicine therapies. Many novel strategies for immunosuppression rely on nanoparticles as delivery vehicles for small-molecule immunosuppressive compounds. As a consequence, efforts in understanding the mechanisms in which nanoparticles directly interact with the immune system have been overshadowed. The immunological activity of nanoparticles is dependent on the physiochemical properties of the nanoparticles and its subsequent cellular internalization. As the underlying factors for these reactions are elucidated, more nanoparticles may be engineered and evaluated for inducing immunosuppression and complementing immunosuppressive drugs. This review will briefly summarize the state-of-the-art and developments in understanding how nanoparticles induce immunosuppressive responses, compare the inherent properties of nanomaterials which induce these immunological reactions, and comment on the potential for using nanomaterials to modulate and control the immune system.

Size effects of single-walled carbon nanotubes on in vivo and in vitro pulmonary toxicity

To elucidate the effect of size on the pulmonary toxicity of single-wall carbon nanotubes (SWCNTs), we prepared two types of dispersed SWCNTs, namely relatively thin bundles with short linear shapes (CNT-1) and thick bundles with long linear shapes (CNT-2), and conducted rat intratracheal instillation tests and in vitro cell-based assays using NR8383 rat alveolar macrophages. Total protein levels, MIP-1α expression, cell counts in BALF, and histopathological examinations revealed that CNT-1 caused pulmonary inflammation and slower recovery and that CNT-2 elicited acute lung inflammation shortly after their instillation. Comprehensive gene expression analysis confirmed that CNT-1-induced genes were strongly associated with inflammatory responses, cell proliferation, and immune system processes at 7 or 30 d post-instillation. Numerous genes were significantly upregulated or downregulated by CNT-2 at 1 d post-instillation. In vitro assays demonstrated that CNT-1 and CNT-2 SWCNTs were phagocytized by NR8383 cells. CNT-2 treatment induced cell growth inhibition, reactive oxygen species production, MIP-1α expression, and several genes involved in response to stimulus, whereas CNT-1 treatment did not exert a significant impact in these regards. These results suggest that SWCNTs formed as relatively thin bundles with short linear shapes elicited delayed pulmonary inflammation with slower recovery. In contrast, SWCNTs with a relatively thick bundle and long linear shapes sensitively induced cellular responses in alveolar macrophages and elicited acute lung inflammation shortly after inhalation. We conclude that the pulmonary toxicity of SWCNTs is closely associated with the size of the bundles. These physical parameters are useful for risk assessment and management of SWCNTs.

Sparking connections: toward better linkages between research and human health policy-an example with multiwalled carbon nanotubes

Risk assessment and subsequent risk management of environmental contaminants can benefit from early collaboration among researchers, risk assessors, and risk managers. The benefits of collaboration in research planning are particularly evident in light of (1) increasing calls to expand upon the risk assessment paradigm to include a greater focus on problem formulation and consideration of potential tradeoffs between risk management options, and (2) decreasing research budgets. Strategically connecting research planning to future decision making may be most critical in areas of emerging science for which data are often insufficient to clearly direct targeted research to support future risk assessment and management efforts. This article illustrates an application of the comprehensive environmental assessment approach to inform research planning for future risk assessment and management of one emerging material, multiwalled carbon nanotubes (MWCNTs). High-priority research areas identified for MWCNTs in flame-retardant coatings applied to upholstery textiles included the following: release across the product life cycle; environmental transport, transformation and fate in air, wastewater and sediment; exposure in human occupational and consumer groups; kinetics in the human body; impacts on human health and aquatic populations; and impacts on economic, social, and environmental resources. This article focuses on specific research questions related to human health and how these may connect to future risk assessments and risk management efforts. Such connections will support more effective collaborations across the scientific community and may inform the prioritization of research funding opportunities for emerging materials like MWCNTs.