Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug-drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

Study of the mechanism underlying the role of PINK1/Parkin in the formic acid-induced autophagy of PC12 cells

This study aimed to explore PINK1/Parkin's role in methanol metabolite formic acid-induced autophagy in PC12 cells and provide a theoretical basis for elucidating methanol-induced neurotoxicity. After treatment with different formic acid concentrations, we observed the morphology and mitochondria of PC12 cells. We used an ultra-micro enzyme kit to detect the mitochondrial Na⁺ -K⁺ -ATPase and Ca²⁺ -Mg²⁺ -ATPase activities; a JC-1 kit to detect changes in the mitochondrial membrane potential (MMP); MDC staining to detect the autophagy levels; and western blotting to measure the expression levels of the mitochondrial marker protein COX IV and the autophagy-related proteins Beclin1, P62 and LC3II/LC3I, and the mitochondrial and cytoplasmic levels of PINK1, Parkin and P-Parkin. Compared with the control group, the mitochondrial diameters, the mitochondrial Na⁺ -K⁺ -ATP and Ca²⁺ -Mg²⁺ -ATPase activities, the MMP, and the COX IV expression levels decreased significantly (P < 0.05). The fluorescence signal intensity (indicating autophagy); relative Beclin1 and LC3II/LC3I protein expression levels; and relative mitochondrial PINK1, Parkin and P-Parkin levels increased significantly, and the relative P62 protein expression levels and relative cytoplasmic PINK1, Parkin and P-Parkin levels decreased significantly (P < 0.05) compared with the control group. Thus, formic acid alters mitochondrial morphology, causes mitochondrial dysfunction, affects the PINK/Parkin pathway and, thus, activates the process of mitochondrial autophagy.

Perspectives of nanomaterials in microbial remediation of heavy metals and their environmental consequences: A review

Nanomaterials (NMs) have diverse applications in various sectors, such as decontaminating heavy metals from drinking water, wastewater, and soil. Their degradation efficiency can be enhanced through the application of microbes. As microbial strain releases enzymes, which leads to the degradation of HMs. Therefore, nanotechnology and microbial-assisted remediation-based methods help us develop a remediation process with practical utility, speed, and less environmental toxicity. This review focuses on the success achieved for the bioremediation of heavy metals by nanoparticles and microbial strains and in their integrated approach. Still, the use of NMs and heavy metals (HMs) can negatively affect the health of living organisms. This review describes various aspects of the bioremediation of heavy materials using microbial nanotechnology. Their safe and specific use supported by bio-based technology paves the way for their better remediation. We discuss the utility of nanomaterials for removing heavy metals from wastewater, toxicity studies and issues to the environment with their practical implications. Nanomaterial assisted heavy metal degradation coupled with microbial technology and disposal issues are described along with detection methods. Environmental impact of nanomaterials is also discussed based on the recent work conducted by the researchers. Therefore, this review opens new avenues for future research with an impact on the environment and toxicity issues. Also, applying new biotechnological tools will help us develop better heavy metal degradation routes.

Integration of in silico methods to determine endocrine-disrupting tobacco pollutants binding potency with steroidogenic genes: comprehensive QSAR modeling and ensemble docking strategies

A myriad of tobacco-associated chemicals may have possibilities to developmental/reproductive axis and endocrine-disruption impacts. Mostly they breach the biotransformation of cholesterol in mitochondria by interfering with steroidogenic pathway genes, prompting to adverse effects in steroid biosynthesis; however, studies are scanty. The quantitative structure-activity relationship (QSAR) modeling and comparative docking strategies were used to understand structural features of dataset compounds that influence developmental/reproductive toxicity and estrogen and androgen receptor-binding abilities, and to predict binding levels of toxicants with steroidogenic acute regulatory protein (StAR) and cholesterol side-chain cleavage enzyme (CYP11A1) active sites. Developed QSAR models presented good robustness and predictive ability that were determined from the applicability domain and, clustering and classification of chemicals by performing self-organizing maps. Accordingly, the exorbitant amount of polycyclic aromatic hydrocarbons (PAHs) and a limited number of other chemicals including N-nitrosamines and nicotine was represented as potential developmental/reproductive toxicants as well as estrogen and androgen receptor binders. From the docking analysis, hydrogen bonding, nonpolar, atomic π-stacking, and π-cation interactions were found between PAHs (bay and fjord structural pockets) and functional hotspot residues of StAR and CYP11A1, which strengthened the subtle structural changes at domains. These govern barrier effects to cholesterol binding and/or locking cholesterol to complicate its ejection from the Ω1 loop of StAR, and further mitigates steroid biosynthesis through cholesterol by CYP11A1; therefore, they are presumably considered as block-cluster mechanisms. These outcomes are significant to be hopeful to estimate developmental/reproductive toxicity and endocrine-disruption activities of other environmental pollutants, and could be useful for further assessment to discover binding mechanisms of PAHs with other steroidogenesis pathway genes.

Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines

Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

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

Nano-QSAR models can be effectively used for prediction of the biological activity of nanomaterials that have not been experimentally tested before. However, their use is associated with the need to have appropriate knowledge and skills in chemoinformatics. Thus, they are mainly aimed at specialists in the field. This significantly limits the potential group of recipients of the developed solutions. In this perspective, the purpose of the presented research was to develop an easily accessible and user-friendly web-based application that could enable the prediction of TiO₂-based multicomponent nanomaterials cytotoxicity toward Chinese Hamster Ovary (CHO-K1) cells. The graphical user interface is clear and intuitive and the only information required from the user is the type and concentration of the metals which will be modifying TiO₂-based nanomaterial. Thanks to this, the application will be easy to use not only by cheminformatics but also by specialists in the field of nanotechnology or toxicology, who will be able to quickly predict cytotoxicity of desired nanoclusters. We have performed case studies to demonstrate the features and utilities of developed application. The NanoMixHamster application is freely available at https://nanomixhamster.cloud.nanosolveit.eu/.

Comparison of Enhanced Photocatalytic Degradation Efficiency and Toxicity Evaluations of CeO2 Nanoparticles Synthesized Through Double-Modulation

We demonstrated that Fe/Cr doped and pH-modified CeO₂ nanoparticles (NPs) exhibit enhanced photocatalytic performance as compared to bare CeO₂ NPs, using photocatalytic degradation. To assess the toxicity level of these double-modified CeO₂ NPs on the human skin, they were introduced into HaCaT cells. The results of our conventional cellular toxicity assays (neutral red uptake and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide for assays) indicated that Cr@CeO_x NPs prompt severe negative effects on the viability of human cells. Moreover, the results obtained by scanning transmission X-ray microscopy and bio-transmission electron microscope analysis showed that most of the NPs were localized outside the nucleus of the cells. Thus, serious genetic toxicity was unlikely. Overall, this study highlights the need to prevent the development of Cr@CeO_x NP toxicity. Moreover, further research should aim to improve the photocatalytic properties and activity of these NPs while accounting for their stability issues.

Biomedical Applications of TiO2 Nanostructures: Recent Advances

Titanium dioxide (TiO2) nanostructures are one of the most plentiful compounds that have emerged in various fields of technology such as medicine, energy and biosensing. Various TiO2 nanostructures (nanotubes [NTs] and nanowires) have been employed in photoelectrochemical (PEC) biosensing applications, greatly enhancing the detection of targets. TiO2 nanostructures, used as reinforced material or coatings for the bare surface of titanium implants, are excellent additive materials to compensate titanium implants deficiencies-like poor surface interaction with surrounding tissues-by providing nanoporous surfaces and hierarchical structures. These nanostructures can also be loaded by diversified drugs-like osteoporosis drugs, anticancer and antibiotics-and used as local drug delivery systems. Furthermore, TiO2 nanostructures and their derivatives are new emerging antimicrobial agents to overcome human pathogenic microorganisms. However, like all other nanomaterials, toxicity and biocompatibility of TiO2 nanostructures must be considered. This review highlights recent advances, along with the properties and numerous applications of TiO2-based nanostructure compounds in nano biosensing, medical implants, drug delivery and antibacterial fields. Moreover, in the present study, some recent advances accomplished on the pharmaceutical applications of TiO2 nanostructures, as well as its toxicity and biocompatibility, are presented.

A Near-infrared Persistent Luminescence Imaging Technique for Tracking Nanoparticles in Zebrafish (Danio rerio)

The development of nanotechnology has drawn increased attention to the risks of nanoparticles (NPs). In this work, the near-infrared persistent luminescence imaging technique was used to track the biodistribution of NPs in vivo in zebrafish. Zebrafish were used as a vertebrate animal model to show NPs distribution and the effects of exposure. ZnGa₂O₄:Cr (ZGOC) was chosen as the probe in this work. In continuous exposure experiments, the results showed more particles accumulated in the intestines than in the gills in both groups. In both the gills and abdomen, the NPs contents were greater in the ZGOC-NH₂-treated groups than in the ZGOC groups, and the NPs caused damage to the gills and intestines. Removal exposure experiments indicated that ZGOC and ZGOC-NH₂ could be excreted from the body. The metabolism, excretion of NPs, the quantification and monitoring of NPs behavior in biological systems should be examined in further studies.

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

The increasing use of nanoparticles (NPs) in a wide range of consumer and industrial applications has necessitated significant effort to address the challenge of characterizing and quantifying the underlying nanostructure - biological response relationships to ensure that these novel materials can be exploited responsibly and safely. Such efforts demand reliable experimental data not only in terms of the biological dose-response, but also regarding the physicochemical properties of the NPs and their interaction with the biological environment. The latter has not been extensively studied, as a large surface to bind biological macromolecules is a unique feature of NPs that is not relevant for chemicals or pharmaceuticals, and thus only limited data have been reported in the literature quantifying the protein corona formed when NPs interact with a biological medium and linking this with NP cellular association/uptake. In this work we report the development of a predictive model for the assessment of the biological response (cellular association, which can include both internalized NPs and those attached to the cell surface) of surface-modified gold NPs, based on their physicochemical properties and protein corona fingerprints, utilizing a dataset of 105 unique NPs. Cellular association was chosen as the end-point for the original experimental study due to its relevance to inflammatory responses, biodistribution, and toxicity in vivo. The validated predictive model is freely available online through the Enalos Cloud Platform ( http://enalos.insilicotox.com/NanoProteinCorona/ ) to be used as part of a regulatory or NP safe-by-design decision support system. This online tool will allow the virtual screening of NPs, based on a list of the significant NP descriptors, identifying those NPs that would warrant further toxicity testing on the basis of predicted NP cellular association.

Advanced tools for the safety assessment of nanomaterials

Engineered nanomaterials (ENMs) have tremendous potential to produce beneficial technological impact in numerous sectors in society. Safety assessment is, of course, of paramount importance. However, the myriad variations of ENM properties makes the identification of specific features driving toxicity challenging. At the same time, reducing animal tests by introducing alternative and/or predictive in vitro and in silico methods has become a priority. It is important to embrace these new advances in the safety assessment of ENMs. Indeed, remarkable progress has been made in recent years with respect to mechanism-based hazard assessment of ENMs, including systems biology approaches as well as high-throughput screening platforms, and new tools are also emerging in risk assessment and risk management for humans and the environment across the whole life-cycle of nano-enabled products. Here, we highlight some of the key advances in the hazard and risk assessment of ENMs.

How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach

Application of predictive modeling approaches can solve the problem of missing data. Numerous studies have investigated the effects of missing values on qualitative or quantitative modeling, but only a few studies have discussed it for the case of applications in nanotechnology-related data. The present study is aimed at the development of a multi-nano-read-across modeling technique that helps in predicting the toxicity of different species such as bacteria, algae, protozoa, and mammalian cell lines. Herein, the experimental toxicity of 184 metal and silica oxide (30 unique chemical types) nanoparticles from 15 datasets is analyzed. A hybrid quantitative multi-nano-read-across approach that combines interspecies correlation analysis and self-organizing map analysis is developed. In the first step, hidden patterns of toxicity among nanoparticles are identified using a combination of methods. Subsequently, the developed model based on categorization of the toxicity of the metal oxide nanoparticle outcomes is evaluated via the combination of supervised and unsupervised machine learning techniques to determine the underlying factors responsible for the toxicity.

Evaluation of the Toxicity Effects of Silk Fibroin on Isolated Fibroblast and Huvec Cells

Emerging line research showed that silk nanoparticles (NPs) have toxicity on the fibroblast and Huvec cells without any toxicity recognized mechanisms. Recently, it suggested peripheral arterial disease confounds almost eight million Americans. Also, due to the main effect of fibroblast in a production of extracellular matrix (ECM), adhesive molecules, glycoproteins and various cytokines, it decided to define the toxicity mechanistic of silk NPs in fibroblast and Huvec cells based on oxidative stress markers. Therefore, it investigated whether silk NPs is able to induce any abnormality in the fibroblast and Huvec cells based on reliable and documented oxidative stress methods. Our results indicated that silk NPs (0.5, 1 and 2 mg/mL) induces cellular and mitochondrial dysfunction including an increase in ROS production, lipid peroxidation, mitochondria membrane potential (MMP) collapse, and oxidation of thiol groups which caused to cytochrome c release. Besides, lysosomal integrity damage and decreased in ATP/ADP ratio proposed disruptive effect of silk NPs on the mitochondrial respiratory chain and cell death signaling induction.

Nanomaterial libraries and model organisms for rapid high-content analysis of nanosafety

Safety analysis of engineered nanomaterials (ENMs) presents a formidable challenge regarding environmental health and safety, due to their complicated and diverse physicochemical properties. Although large amounts of data have been published regarding the potential hazards of these materials, we still lack a comprehensive strategy for their safety assessment, which generates a huge workload in decision-making. Thus, an integrated approach is urgently required by government, industry, academia and all others who deal with the safe implementation of nanomaterials on their way to the marketplace. The rapid emergence and sheer number of new nanomaterials with novel properties demands rapid and high-content screening (HCS), which could be performed on multiple materials to assess their safety and generate large data sets for integrated decision-making. With this approach, we have to consider reducing and replacing the commonly used rodent models, which are expensive, time-consuming, and not amenable to high-throughput screening and analysis. In this review, we present a ‘Library Integration Approach’ for high-content safety analysis relevant to the ENMs. We propose the integration of compositional and property-based ENM libraries for HCS of cells and biologically relevant organisms to be screened for mechanistic biomarkers that can be used to generate data for HCS and decision analysis. This systematic approach integrates the use of material and biological libraries, automated HCS and high-content data analysis to provide predictions about the environmental impact of large numbers of ENMs in various categories. This integrated approach also allows the safer design of ENMs, which is relevant to the implementation of nanotechnology solutions in the pharmaceutical industry.

NanoEHS beyond Toxicity - Focusing on Biocorona

The first phase of environmental health and safety of nanomaterials (nanoEHS) studies has been mainly focused on evidence-based investigations that probe the impact of nanoparticles, nanomaterials and nano-enabled products on biological and ecological systems. The integration of multiple disciplines, including colloidal science, nanomaterial science, chemistry, toxicology/immunology and environmental science, is necessary to understand the implications of nanotechnology for both human health and the environment. While strides have been made in connecting the physicochemical properties of nanomaterials with their hazard potential in tiered models, fundamental understanding of nano-biomolecular interactions and their implications for nanoEHS is largely absent from the literature. Research on nano-biomolecular interactions within the context of natural systems not only provides important clues for deciphering nanotoxicity and nanoparticle-induced pathology, but also presents vast new opportunities for screening beneficial material properties and designing greener products from bottom up. This review highlights new opportunities concerning nano-biomolecular interactions beyond the scope of toxicity.

What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps

Over the past decade, computational nanotoxicology, in particular Quantitative Structure-Activity Relationship models (Nano-QSAR) that help in assessing the biological effects of nanomaterials, have received much attention. In effect, a solid basis for uncovering the relationships between the structure and property/activity of nanoparticles has been created. Nonetheless, six years after the first pioneering computational studies focusing on the investigation of nanotoxicity were commenced, these computational methods still suffer from many limitations. These are mainly related to the paucity of widely available, systematically varied, libraries of experimental data necessary for the development and validation of such models. This results in the still-low acceptance of these methods as valuable research tools for nanosafety and raises the query as to whether these methods could gain wide acceptance of regulatory bodies as alternatives for traditional in vitro methods. This study aimed to give an answer to the following question: How to remedy the paucity of experimental nanotoxicity data and thereby, overcome key roadblock that hinders the development of approaches for data-driven modeling of nanoparticle properties and toxicities? Here, a simple and transparent read-across algorithm for a pre-screening hazard assessment of nanomaterials that provides reasonably accurate results by making the best use of existing limited set of observations will be introduced.

Physico-chemical properties based differential toxicity of graphene oxide/reduced graphene oxide in human lung cells mediated through oxidative stress

Goraphene derivatives (GD) are currently being evaluated for technological and biomedical applications owing to their unique physico-chemical properties over other carbon allotrope such as carbon nanotubes (CNTs). But, the possible association of their properties with underlying in vitro effects have not fully examined. Here, we assessed the comparative interaction of three GD - graphene oxide (GO), thermally reduced GO (TRGO) and chemically reduced GO (CRGO), which significantly differ in their lateral size and functional groups density, with phenotypically different human lung cells; bronchial epithelial cells (BEAS-2B) and alveolar epithelial cells (A549). The cellular studies demonstrate that GD significantly ineternalize and induce oxidative stress mediated cytotoxicity in both cells. The toxicity intensity was in line with the reduced lateral size and increased functional groups revealed more toxicity potential of TRGO and GO respectively. Further, A549 cells showed more susceptibility than BEAS-2B which reflected cell type dependent differential cellular response. Molecular studies revealed that GD induced differential cell death mechanism which was efficiently prevented by their respective inhibitors. This is prior study to the best of our knowledge involving TRGO for its safety evaluation which provided invaluable information and new opportunities for GD based biomedical applications.

Zero-valent Fe confined mesoporous silica nanocarriers (Fe(0) @ MCM-41) for targeting experimental orthotopic glioma in rats

Mesoporous silica nanoparticles (MSNs) impregnated with zero-valent Fe (Fe(0) @ MCM-41) represent an attractive nanocarrier system for drug delivery into tumor cells. The major goal of this work was to assess whether MSNs can penetrate the blood-brain barrier in a glioblastoma rat model. Synthesized MSNs nanomaterials were characterized by energy dispersive X-ray spectroscopy, measurements of X-ray diffraction, scanning electron microscopy and Mössbauer spectroscopy. For the detection of the MSNs by MR and for biodistribution studies MSNs were labeled with zero-valent Fe. Subsequent magnetometry and nonlinear-longitudinal-response-M2 (NLR-M2) measurements confirmed the MR negative contrast enhancement properties of the nanoparticles. After incubation of different tumor (C6 glioma, U87 glioma, K562 erythroleukemia, HeLa cervix carcinoma) and normal cells such as fibroblasts and peripheral blood mononuclear cells (PBMCs) MSNs rapidly get internalized into the cytosol. Intracellular residing MSNs result in an enhanced cytotoxicity as Fe(0) @ MCM-41 promote the reactive oxygen species production. MRI and histological studies indicated an accumulation of intravenously injected Fe(0) @ MCM-41 MSNs in orthotopic C6 glioma model. Biodistribution studies with measurements of second harmonic of magnetization demonstrated an increased and dose-dependent retention of MSNs in tumor tissues. Taken together, this study demonstrates that MSNs can enter the blood-brain barrier and accumulate in tumorous tissues.

Semiconductor Electronic Label-Free Assay for Predictive Toxicology

While animal experimentations have spearheaded numerous breakthroughs in biomedicine, they also have spawned many logistical concerns in providing toxicity screening for copious new materials. Their prioritization is premised on performing cellular-level screening in vitro. Among the screening assays, secretomic assay with high sensitivity, analytical throughput, and simplicity is of prime importance. Here, we build on the over 3-decade-long progress on transistor biosensing and develop the holistic assay platform and procedure called semiconductor electronic label-free assay (SELFA). We demonstrate that SELFA, which incorporates an amplifying nanowire field-effect transistor biosensor, is able to offer superior sensitivity, similar selectivity, and shorter turnaround time compared to standard enzyme-linked immunosorbent assay (ELISA). We deploy SELFA secretomics to predict the inflammatory potential of eleven engineered nanomaterials in vitro, and validate the results with confocal microscopy in vitro and confirmatory animal experiment in vivo. This work provides a foundation for high-sensitivity label-free assay utility in predictive toxicology.

A critical review of in vitro dosimetry for engineered nanomaterials

A major obstacle in the development of accurate cellular models for investigating nanobio interactions in vitro is determination of physiologically relevant measures of dose. Comparison of biological responses to nanoparticle exposure typically relies on administered dose metrics such as mass concentration of suspended particles, rather than the effective dose of particles that actually comes in contact with the cells over the time of exposure. Adoption of recently developed dosimetric methodologies will facilitate determination of effective dose delivered to cells in vitro, thereby improving the accuracy and reliability of in vitro screening data, validation of in vitro with in vivo data, and comparison across multiple datasets for the large variety of nanomaterials currently in the market.

Evaluation of Toxicity Ranking for Metal Oxide Nanoparticles via an in Vitro Dosimetry Model

It has been argued that in vitro toxicity testing of engineered nanoparticles (NPs) should consider delivered dose (i.e., NP mass settled per suspension volume) rather than relying exclusively on administered dose (initial NP mass concentration). Delivered dose calculations require quantification of NP sedimentation in tissue cell culture media, taking into consideration fundamental suspension properties. In this article, we calculate delivered dose using a first-principles "particles in a box" sedimentation model, which accounts for the particle size distribution, fractal dimension, and permeability of agglomerated NPs. The sedimentation model was evaluated against external and our own experimental sedimentation data for metal oxide NPs. We then utilized the model to construct delivered dose-response analysis for a library of metal oxide NPs (previously used for hazard ranking and prediction making) in different cell culture media. Hierarchical hazard ranking of the seven (out of 24) toxic metal oxide NPs in our library, using EC50 calculated on the basis of delivered dose, did not measurably differ from our ranking based on administered dose. In contrast, simplified sedimentation calculations based on the assumption of impermeable NP agglomerates of a single average size significantly underestimated the settled NPs' mass, resulting in misinterpretation of toxicity ranking. It is acknowledged that in vitro dose-response outcomes are likely to be shaped by complex toxicodynamics, which include NP/cellular association, triggering of dynamic cell response pathways involved in NP uptake, and multiple physicochemical parameters that influence NP sedimentation and internalization.

Rosette Nanotubes Alter IgE-Mediated Degranulation in the Rat Basophilic Leukemia (RBL)-2H3 Cell Line

In this study, the effects of rosette nanotube (RNT) exposure on immune cell viability and function were investigated in vitro using the rat basophilic leukemia (RBL)-2H3 cell line. RBL-2H3 viability was decreased in a dose- and time-dependent manner after lysine-functionalized RNT (K-RNT) exposure. In addition, K-RNTs had a significant effect on RBL-2H3 degranulation. When K-RNT exposure was concurrent with IgE sensitization, 50 and 100 mg l(-1) K-RNTs elicited a heightened degranulatory response compared with IgE alone. Exposure to 50 and 100 mg l(-1) K-RNTs also caused degranulation in RBL-2H3 cells not sensitized with IgE (0 ng ml(-1) IgE). Furthermore, in cells preexposed to K-RNTs for 2 h and subsequently washed, sensitized, and stimulated with IgE, a potentiated degranulatory response was observed. Using confocal laser scanning microscopy and a fluorescein isothiocyanate (FITC)-functionalized RNT construct (termed FITC(1)/TBL(19)-RNT), we demonstrated a strong and direct affiliation between RNTs and RBL-2H3 cell membranes. We also demonstrated cellular internalization of RNTs after 2 h of exposure. Together, these data demonstrate that RNTs may affiliate with the cellular membrane of RBL-2H3 cells and can be internalized. These interactions can affect viability and alter the ability of these cells to elicit IgE-FcεR mediated degranulation.

Comparative hazard analysis and toxicological modeling of diverse nanomaterials using the embryonic zebrafish (EZ) metric of toxicity

The integration of rapid assays, large datasets, informatics, and modeling can overcome current barriers in understanding nanomaterial structure-toxicity relationships by providing a weight-of-the-evidence mechanism to generate hazard rankings for nanomaterials. Here, we present the use of a rapid, low-cost assay to perform screening-level toxicity evaluations of nanomaterials in vivo. Calculated EZ Metric scores, a combined measure of morbidity and mortality in developing embryonic zebrafish, were established at realistic exposure levels and used to develop a hazard ranking of diverse nanomaterial toxicity. Hazard ranking and clustering analysis of 68 diverse nanomaterials revealed distinct patterns of toxicity related to both the core composition and outermost surface chemistry of nanomaterials. The resulting clusters guided the development of a surface chemistry-based model of gold nanoparticle toxicity. Our findings suggest that risk assessments based on the size and core composition of nanomaterials alone may be wholly inappropriate, especially when considering complex engineered nanomaterials. Research should continue to focus on methodologies for determining nanomaterial hazard based on multiple sub-lethal responses following realistic, low-dose exposures, thus increasing the availability of quantitative measures of nanomaterial hazard to support the development of nanoparticle structure-activity relationships.

Analytical approaches to support current understanding of exposure, uptake and distributions of engineered nanoparticles by aquatic and terrestrial organisms

Initiatives to support the sustainable development of the nanotechnology sector have led to rapid growth in research on the environmental fate, hazards and risk of engineered nanoparticles (ENP). As the field has matured over the last 10 years, a detailed picture of the best methods to track potential forms of exposure, their uptake routes and best methods to identify and track internal fate and distributions following assimilation into organisms has begun to emerge. Here we summarise the current state of the field, focussing particularly on metal and metal oxide ENPs. Studies to date have shown that ENPs undergo a range of physical and chemical transformations in the environment to the extent that exposures to pristine well dispersed materials will occur only rarely in nature. Methods to track assimilation and internal distributions must, therefore, be capable of detecting these modified forms. The uptake mechanisms involved in ENP assimilation may include a range of trans-cellular trafficking and distribution pathways, which can be followed by passage to intracellular compartments. To trace toxicokinetics and distributions, analytical and imaging approaches are available to determine rates, states and forms. When used hierarchically, these tools can map ENP distributions to specific target organs, cell types and organelles, such as endosomes, caveolae and lysosomes and assess speciation states. The first decade of ENP ecotoxicology research, thus, points to an emerging paradigm where exposure is to transformed materials transported into tissues and cells via passive and active pathways within which they can be assimilated and therein identified using a tiered analytical and imaging approach.

The biomechanisms of metal and metal-oxide nanoparticles' interactions with cells

Humans are increasingly exposed to nanoparticles (NPs) in medicine and in industrial settings, where significant concentrations of NPs are common. However, NP interactions with and effects on biomolecules and organisms have only recently been addressed. Within we review the literature regarding proposed modes of action for metal and metal-oxide NPs, two of the most prevalent types manufactured. Iron-oxide NPs, for instance, are used as tracers for magnetic resonance imaging of oncological tumors and as vehicles for therapeutic drug delivery. Factors and theories that determine the physicochemical and biokinetic behaviors of NPs are discussed, along with the observed toxicological effects of NPs on cells. Key thermodynamic and kinetic models that explain the sources of energy transfer from NPs to biological targets are summarized, in addition to quantitative structural activity relationship (QSAR) modeling efforts. Future challenges for nanotoxicological research are discussed. We conclude that NP studies based on cell culture are often inconsistent and underestimate the toxicity of NPs. Thus, the effect of NPs needs to be examined in whole animal systems.

Association rule mining of cellular responses induced by metal and metal oxide nanoparticles

Relationships among fourteen different biological responses (including ten signaling pathway activities and four cytotoxicity effects) of murine macrophage (RAW264.7) and bronchial epithelial (BEAS-2B) cells exposed to six metal and metal oxide nanoparticles (NPs) were analyzed using both statistical and data mining approaches. Both the pathway activities and cytotoxicity effects were assessed using high-throughput screening (HTS) over an exposure period of up to 24 h and concentration range of 0.39-200 mg L(-1). HTS data were processed by outlier removal, normalization, and hit-identification (for significantly regulated cellular responses) to arrive at reliable multiparametric bioactivity profiles for the NPs. Association rule mining was then applied to the bioactivity profiles followed by a pruning process to remove redundant rules. The non-redundant association rules indicated that "significant regulation" of one or more cellular responses implies regulation of other (associated) cellular response types. Pairwise correlation analysis (via Pearson's χ(2) test) and self-organizing map clustering of the different cellular response types indicated consistency with the identified non-redundant association rules. Furthermore, in order to explore the potential use of association rules as a tool for data-driven hypothesis generation, specific pathway activity experiments were carried out for ZnO NPs. The experimental results confirmed the association rule identified for the p53 pathway and mitochondrial superoxide levels (via MitoSox reagent) and further revealed that blocking of the transcriptional activity of p53 lowered the MitoSox signal. The present approach of using association rule mining for data-driven hypothesis generation has important implications for streamlining multi-parameter HTS assays, improving the understanding of NP toxicity mechanisms, and selection of endpoints for the development of nanomaterial structure-activity relationships.

Multimedia environmental distribution of engineered nanomaterials

A compartmental multimedia model was developed to enable evaluation of the dynamic environmental multimedia mass distribution and concentrations of engineered nanomaterials (ENMs). The approach considers the environment as a collection of compartments, linked via fundamental environmental intermedia transport processes. Model simulations for various environmental scenarios indicated that ENM accumulation in the sediment increased significantly with increased ENMs attachment to suspended solids in water. Atmospheric dry and wet depositions can be important pathways for ENMs input to the terrestrial environment in the absence of direct and distributed ENM release to soil. Increased ENM concentration in water due to atmospheric deposition (wet and dry) is expected as direct ENM release to water diminishes. However, for soluble ENMs dissolution can be the dominant pathway for suspended ENM removal from water even compared to advection. Mass accumulation in the multimedia environment for the evaluated ENMs (metal, metal oxides, carbon nanotubes (CNT), nanoclays) was mostly in the soil and sediment. The present modeling approach, as illustrated via different test cases, is suited for "what if" first tier analyses to assess the multimedia mass distribution of ENMs and associated potential exposure concentrations.

Toxicity mechanisms in Escherichia coli vary for silver nanoparticles and differ from ionic silver

Silver nanoparticles (Ag NPs) are commonly added to various consumer products and materials to impair bacterial growth. Recent studies suggested that the primary mechanism of antibacterial action of silver nanoparticles is release of silver ion (Ag(+)) and that particle-specific activity of silver nanoparticles is negligible. Here, we used a genome-wide library of Escherichia coli consisting of ∼4000 single gene deletion mutants to elucidate which physiological pathways are involved in how E. coli responds to different Ag NPs. The nanoparticles studied herein varied in both size and surface charge. AgNO3 was used as a control for soluble silver ions. Within a series of differently sized citrate-coated Ag NPs, smaller size resulted in higher Ag ion dissolution and toxicity. Nanoparticles functionalized with cationic, branched polyethylene imine (BPEI) exhibited equal toxicity with AgNO3. When we used a genome-wide approach to investigate the pathways involved in the response of E. coli to different toxicants, we found that only one of the particles (Ag-cit10) exhibited a pattern of response that was statistically similar to that of silver ion. By contrast, the pathways involved in E. coli response to Ag-BPEI particles were more similar to those observed for another cationic nanoparticle that did not contain Ag. Overall, we found that the pathways involved in bacterial responses to Ag nanoparticles are highly dependent on physicochemical properties of the nanoparticles, particularly the surface characteristics. These results have important implications for the regulation and testing of silver nanoparticles.

Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles

A QNAR model, available online through Enalos InSilicoNano platform, has been developed and validated for the risk assessment of nanoparticles (NPs).

Intracellular signal modulation by nanomaterials

A thorough understanding of the interactions of nanomaterials with biological systems and the resulting activation of signal transduction pathways is essential for the development of safe and consumer friendly nanotechnology. Here we present an overview of signaling pathways induced by nanomaterial exposures and describe the possible correlation of their physicochemical characteristics with biological outcomes. In addition to the hierarchical oxidative stress model and a review of the intrinsic and cell-mediated mechanisms of reactive oxygen species (ROS) generating capacities of nanomaterials, we also discuss other oxidative stress dependent and independent cellular signaling pathways. Induction of the inflammasome, calcium signaling, and endoplasmic reticulum stress are reviewed. Furthermore, the uptake mechanisms can be of crucial importance for the cytotoxicity of nanomaterials and membrane-dependent signaling pathways have also been shown to be responsible for cellular effects of nanomaterials. Epigenetic regulation by nanomaterials, effects of nanoparticle-protein interactions on cell signaling pathways, and the induction of various cell death modalities by nanomaterials are described. We describe the common trigger mechanisms shared by various nanomaterials to induce cell death pathways and describe the interplay of different modalities in orchestrating the final outcome after nanomaterial exposures. A better understanding of signal modulations induced by nanomaterials is not only essential for the synthesis and design of safer nanomaterials but will also help to discover potential nanomedical applications of these materials. Several biomedical applications based on the different signaling pathways induced by nanomaterials are already proposed and will certainly gain a great deal of attraction in the near future.

Implementation of alternative test strategies for the safety assessment of engineered nanomaterials

Nanotechnology introduces a new field that requires novel approaches and methods for hazard and risk assessment. For an appropriate scientific platform for safety assessment, nanoscale properties and functions of engineered nanomaterials (ENMs), including how the physicochemical properties of the materials relate to mechanisms of injury at the nano-bio interface, must be considered. Moreover, this rapidly advancing new field requires novel test strategies that allow multiple toxicants to be screened in robust, mechanism-based assays in which the bulk of the investigation can be carried out at the cellular and biomolecular level whilst maintaining limited animal use and is based on the contribution of toxicological pathways to the pathophysiology of disease. First, a predictive toxicological approach for the safety assessment of ENMs will be discussed against the background of a '21st-century vision' for using alternative test strategies (ATSs) to perform toxicological assessment of large numbers of untested chemicals, thereby reducing a backlog that could otherwise become a problem for nanotechnology. An ATS is defined here as an alternative to animal experiments or refinement/reduction alternative to traditional animal testing. Secondly, the approach of selecting pathways of toxicity to screen for the pulmonary hazard potential of carbon nanotubes and metal oxides will be discussed, as well as how to use these pathways to perform high-content or high-throughput testing and how the data can be used for hazard ranking, risk assessment, regulatory decision-making and 'safer-by-design' strategies. Finally, the utility and disadvantages of this predictive toxicological approach to ENM safety assessment, and how it can assist the 21st-century vision, will be addressed.

Evidencing the mask effect of graphene oxide: a comparative study on primary human and murine phagocytic cells

Graphene oxide (GO) is attracting an ever-growing interest in different fields and applications. Not much is known about the possible impact of GO sheet lateral dimensions on their effects in vitro, especially on human primary cells. In an attempt to address this issue, we present a study to evaluate, how highly soluble 2-dimensional GO constituted of large or small flakes affects human monocyte derived macrophages (hMDM). For this purpose, the lateral size of GO was tuned using sonication and three samples were obtained. The non sonicated one presented large flakes (~1.32 μm) while sonication for 2 and 26 hours generated small (~0.27 μm) and very small (~0.13 μm) sheets of GO, respectively. Cell studies were then conducted to evaluate the cytotoxicity, the oxidative stress induction, the activation potential and the pro-inflammatory effects of these different types of GO at increasing concentrations. In comparison, the same experiments were run on murine intraperitoneal macrophages (mIPM). The interaction between GO and cells was further examined by TEM and Raman spectroscopy. Our data revealed that the GO sheet size had a significant impact on different cellular parameters (i.e. cellular viability, ROS generation, and cellular activation). Indeed, the more the lateral dimensions of GO were reduced, the higher were the cellular internalization and the effects on cellular functionality. Our data also revealed a particular interaction of GO flakes with the cellular membrane. In fact, a GO mask due to the parallel arrangement of the graphene sheets on the cellular surface was observed. Considering the mask effect, we have hypothesized that this particular contact between GO sheets and the cell membrane could either promote their internalization or isolate cells from their environment, thus possibly accounting for the following impact on cellular parameters.

Development of structure-activity relationship for metal oxide nanoparticles

Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L(-1) and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions.

Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries

The development of classification nano-structure-activity Relationships (nano-SARs) of nanoparticle (NP) bioactivity is presented with the aim of demonstrating the integration of multiparametric toxicity/bioactivity assays to arrive at statistically meaningful class definitions (i.e., bioactivity/inactivity endpoints), as well as the implications of nano-SAR applicability domains and decision boundaries. Nano-SARs are constructed based on a dataset of 44 iron oxide core nanoparticles (NPs), used in molecular imaging and nano-sensing, containing bioactivity profiles for four cell types and four different assays. Class definitions are developed on the basis of 'hit' (i.e., significant bioactivity) identification analysis and self-organizing map based consensus clustering; these class definitions enable construction of nano-SARs of a high classification accuracy (>78%) with different NP descriptor combinations that include primary size, spin-lattice and spin-spin relaxivities, and zeta potentials. Analysis of the nano-SAR performance for different class definitions suggests that H4 (i.e., class with at least four hits) is a reasonable endpoint (from a 'regulatory' viewpoint) for keeping the level of false negatives (i.e., incorrect labeling of bioactive NPs as inactive) low. The establishment of a quantitative nano-SAR applicability domain is demonstrated, making use of a probability density with the H4 class definition and naive Bayesian classifier (NBC) model (with spin-lattice relaxivity and zeta potential as descriptors). Decision boundaries are determined for the above H4/NBC nano-SAR for different acceptance levels of false negative to false positive predictions, illustrating a practical approach that may assist in regulatory decision making with a consideration of reducing the likelihood of identifying bioactive NPs as being inactive.

Implementation of a multidisciplinary approach to solve complex nano EHS problems by the UC Center for the Environmental Implications of Nanotechnology

UC CEIN was established with funding from the US National Science Foundation and the US Environmental Protection Agency in 2008 with the mission to study the impact of nanotechnology on the environment, including the identification of hazard and exposure scenarios that take into consideration the unique physicochemical properties of engineered nanomaterials (ENMs). Since its inception, the Center has made great progress in assembling a multidisciplinary team to develop the scientific underpinnings, research, knowledge acquisition, education and outreach that is required for assessing the safe implementation of nanotechnology in the environment. In this essay, the development of the infrastructure, protocols, and decision-making tools that are required to effectively integrate complementary scientific disciplines allowing knowledge gathering in a complex study area that goes beyond the traditional safety and risk assessment protocols of the 20th century is outlined. UC CEIN's streamlined approach, premised on predictive hazard and exposure assessment methods, high-throughput discovery platforms and environmental decision-making tools that consider a wide range of nano/bio interfaces in terrestrial and aquatic ecosystems, demonstrates the implementation of a 21st-century approach to the safe implementation of nanotechnology in the environment.

Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening

The production of engineered nanomaterials (ENMs) is a scientific breakthrough in material design and the development of new consumer products. While the successful implementation of nanotechnology is important for the growth of the global economy, we also need to consider the possible environmental health and safety (EHS) impact as a result of the novel physicochemical properties that could generate hazardous biological outcomes. In order to assess ENM hazard, reliable and reproducible screening approaches are needed to test the basic materials as well as nanoenabled products. A platform is required to investigate the potentially endless number of biophysicochemical interactions at the nano/bio interface, in response to which we have developed a predictive toxicological approach. We define a predictive toxicological approach as the use of mechanisms-based high-throughput screening in vitro to make predictions about the physicochemical properties of ENMs that may lead to the generation of pathology or disease outcomes in vivo. The in vivo results are used to validate and improve the in vitro high-throughput screening (HTS) and to establish structure-activity relationships (SARs) that allow hazard ranking and modeling by an appropriate combination of in vitro and in vivo testing. This notion is in agreement with the landmark 2007 report from the US National Academy of Sciences, "Toxicity Testing in the 21st Century: A Vision and a Strategy" (http://www.nap.edu/catalog.php?record_id=11970), which advocates increased efficiency of toxicity testing by transitioning from qualitative, descriptive animal testing to quantitative, mechanistic, and pathway-based toxicity testing in human cells or cell lines using high-throughput approaches. Accordingly, we have implemented HTS approaches to screen compositional and combinatorial ENM libraries to develop hazard ranking and structure-activity relationships that can be used for predicting in vivo injury outcomes. This predictive approach allows the bulk of the screening analysis and high-volume data generation to be carried out in vitro, following which limited, but critical, validation studies are carried out in animals or whole organisms. Risk reduction in the exposed human or environmental populations can then focus on limiting or avoiding exposures that trigger these toxicological responses as well as implementing safer design of potentially hazardous ENMs. In this Account, we review the tools required for establishing predictive toxicology paradigms to assess inhalation and environmental toxicological scenarios through the use of compositional and combinatorial ENM libraries, mechanism-based HTS assays, hazard ranking, and development of nano-SARs. We will discuss the major injury paradigms that have emerged based on specific ENM properties, as well as describing the safer design of ZnO nanoparticles based on characterization of dissolution chemistry as a major predictor of toxicity.

In silico analysis of nanomaterials hazard and risk

Because a variety of human-related activities, engineer-ed nanoparticles (ENMs) may be released to various environmental media and may cross environmental boundaries, and thus will be found in most media. Therefore, the potential environmental impacts of ENMs must be assessed from a multimedia perspective and with an integrated risk management approach that considers rapid developments and increasing use of new nanomaterials. Accordingly, this Account presents a rational process for the integration of in silico ENM toxicity and fate and transport analyses for environmental impact assessment. This approach requires knowledge of ENM toxicity and environmental exposure concentrations. Considering the large number of current different types of ENMs and that those numbers are likely to increase, there is an urgent need to accelerate the evaluation of their toxicity and the assessment of their potential distribution in the environment. Developments in high throughput screening (HTS) are now enabling the rapid generation of large data sets for ENM toxicity assessment. However, these analyses require the establishment of reliable toxicity metrics, especially when HTS includes data from multiple assays, cell lines, or organisms. Establishing toxicity metrics with HTS data requires advanced data processing techniques in order to clearly identify significant biological effects associated with exposure to ENMs. HTS data can form the basis for developing and validating in silico toxicity models (e.g., quantitative structure-activity relationships) and for generating data-driven hypotheses to aid in establishing and/or validating possible toxicity mechanisms. To correlate the toxicity of ENMs with their physicochemical properties, researchers will need to develop quantitative structure-activity relationships for nanomaterials (i.e., nano-SARs). However, as nano-SARs are applied in regulatory applications, researchers must consider their applicability and the acceptance level of false positive relative to false negative predictions and the reliability of toxicity data. To establish the environmental impact of ENMs identified as toxic, researchers will need to estimate the potential level of environmental exposure concentration of ENMs in the various media such as air, water, soil, and vegetation. When environmental monitoring data are not available, models of ENMs fate and transport (at various levels of complexity) serve as alternative approaches for estimating exposure concentrations. Risk management decisions regarding the manufacturing, use, and environmental regulations of ENMs would clearly benefit from both the assessment of potential ENMs exposure concentrations and suitable toxicity metrics. The decision process should consider the totality of available information: quantitative and qualitative data and the analysis of nanomaterials toxicity, and fate and transport behavior in the environment. Effective decision-making to address the potential impacts of nanomaterials will require considerations of the relevant environmental, ecological, technological, economic, and sociopolitical factors affecting the complete lifecycle of nanomaterials, while accounting for data and modeling uncertainties. Accordingly, researchers will need to establish standardized data management and analysis tools through nanoinformatics as a basis for the development of rational decision tools.

Magnetic targeting of nanoparticles across the intact blood-brain barrier

Delivery of therapeutic or diagnostic agents across an intact blood-brain barrier (BBB) remains a major challenge. Here we demonstrate in a mouse model that magnetic nanoparticles (MNPs) can cross the normal BBB when subjected to an external magnetic field. Following a systemic administration, an applied external magnetic field mediates the ability of MNPs to permeate the BBB and accumulate in a perivascular zone of the brain parenchyma. Direct tracking and localization inside endothelial cells and in the perivascular extracellular matrix in vivo was established using fluorescent MNPs. These MNPs were inert and associated with low toxicity, using a non-invasive reporter for astrogliosis, biochemical and histological studies. Atomic force microscopy demonstrated that MNPs were internalized by endothelial cells, suggesting that trans-cellular trafficking may be a mechanism for the MNP crossing of the BBB observed. The silica-coated magnetic nanocapsules (SiMNCs) allow on-demand drug release via remote radio frequency (RF) magnetic field. Together, these results establish an effective strategy for regulating the biodistribution of MNPs in the brain through the application of an external magnetic field.

Genome-wide assessment in Escherichia coli reveals time-dependent nanotoxicity paradigms

The use of engineered nanomaterials (eNM) in consumer and industrial products is increasing exponentially. Our ability to rapidly assess their potential effects on human and environmental health is limited by our understanding of nanomediated toxicity. High-throughput screening (HTS) enables the investigation of nanomediated toxicity on a genome-wide level, thus uncovering their novel mechanisms and paradigms. Herein, we investigate the toxicity of zinc-containing nanomaterials (Zn-eNMs) using a time-resolved HTS methodology in an arrayed Escherichia coli genome-wide knockout (KO) library. The library was screened against nanoscale zerovalent zinc (nZn), nanoscale zinc oxide (nZnO), and zinc chloride (ZnCl(2)) salt as reference. Through sequential screening over 24 h, our method identified 173 sensitive clones from diverse biological pathways, which fell into two general groups: early and late responders. The overlap between these groups was small. Our results suggest that bacterial toxicity mechanisms change from pathways related to general metabolic function, transport, signaling, and metal ion homeostasis to membrane synthesis pathways over time. While all zinc sources shared pathways relating to membrane damage and metal ion homeostasis, Zn-eNMs and ZnCl(2) displayed differences in their sensitivity profiles. For example, ZnCl(2) and nZnO elicited unique responses in pathways related to two-component signaling and monosaccharide biosynthesis, respectively. Single isolated measurements, such as MIC or IC(50), are inadequate, and time-resolved approaches utilizing genome-wide assays are therefore needed to capture this crucial dimension and illuminate the dynamic interplay at the nano-bio interface.

Analyzing high dimensional toxicogenomic data using consensus clustering

Rapid development of high-throughput toxicogenomics technologies has created new approaches to screen environmental samples for mechanistic toxicity assessment. However, challenges remain in the analysis, especially clustering of the resulting high-dimensional data. Because of the lack of commonly accepted validation methods, it is difficult to compare clustering results between studies or to identify the key experimental or data features that impact the clustering results. We applied consensus clustering (CC), an approach that clusters the input data repeatedly through iterative resampling, and identifies frequently occurring high-confidence clusters. We used CC to analyze a set of high dimensional transcriptomics data with temporal resolution, which were generated using our E. coli whole-cell array system for a diverse variety of toxicants at different dose concentrations. The CC analysis allowed us to evaluate the clustering results' robustness and sensitivity against a number of conditions that represent the common variations in high-throughput experiments, including noisy data, subsets of treatments, subsets of reporter genes, and subsets of time points. We demonstrated the value of utilizing rich time-series data and underscored the importance of careful selection of sampling times for a given experimental system. The results also indicated that temporal data compression using our proposed Transcriptional Effect Level Index (TELI) concept followed by CC largely conserved the cluster resolution. We also found that for our cellular stress response ensemble-based high-throughput transcriptomics assay platform, the size and composition of the reporter gene set are critical factors that affect the resulting coherency of clusters. Taken together, these results demonstrated that more robust consensus clustering such as CC may be valuable in analyzing high-dimensional toxicogenomic data sets.

Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation

We demonstrate for 24 metal oxide (MOx) nanoparticles that it is possible to use conduction band energy levels to delineate their toxicological potential at cellular and whole animal levels. Among the materials, the overlap of conduction band energy (E(c)) levels with the cellular redox potential (-4.12 to -4.84 eV) was strongly correlated to the ability of Co(3)O(4), Cr(2)O(3), Ni(2)O(3), Mn(2)O(3), and CoO nanoparticles to induce oxygen radicals, oxidative stress, and inflammation. This outcome is premised on permissible electron transfers from the biological redox couples that maintain the cellular redox equilibrium to the conduction band of the semiconductor particles. Both single-parameter cytotoxic as well as multi-parameter oxidative stress assays in cells showed excellent correlation to the generation of acute neutrophilic inflammation and cytokine responses in the lungs of C57 BL/6 mice. Co(3)O(4), Ni(2)O(3), Mn(2)O(3), and CoO nanoparticles could also oxidize cytochrome c as a representative redox couple involved in redox homeostasis. While CuO and ZnO generated oxidative stress and acute pulmonary inflammation that is not predicted by E(c) levels, the adverse biological effects of these materials could be explained by their solubility, as demonstrated by ICP-MS analysis. These results demonstrate that it is possible to predict the toxicity of a large series of MOx nanoparticles in the lung premised on semiconductor properties and an integrated in vitro/in vivo hazard ranking model premised on oxidative stress. This establishes a robust platform for modeling of MOx structure-activity relationships based on band gap energy levels and particle dissolution. This predictive toxicological paradigm is also of considerable importance for regulatory decision-making about this important class of engineered nanomaterials.

Automated phenotype recognition for zebrafish embryo based in vivo high throughput toxicity screening of engineered nano-materials

A phenotype recognition model was developed for high throughput screening (HTS) of engineered Nano-Materials (eNMs) toxicity using zebrafish embryo developmental response classified, from automatically captured images and without manual manipulation of zebrafish positioning, by three basic phenotypes (i.e., hatched, unhatched, and dead). The recognition model was built with a set of vectorial descriptors providing image color and texture information. The best performing model was attained with three image descriptors (color histogram, representative color, and color layout) identified as most suitable from an initial pool of six descriptors. This model had an average recognition accuracy of 97.40±0.95% in a 10-fold cross-validation and 93.75% in a stress test of low quality zebrafish images. The present work has shown that a phenotyping model can be developed with accurate recognition ability suitable for zebrafish-based HTS assays. Although the present methodology was successfully demonstrated for only three basic zebrafish embryonic phenotypes, it can be readily adapted to incorporate more subtle phenotypes.

Interactions of engineered nanomaterials in physiological media and implications for in vitro dosimetry

In vitro toxicity assays are efficient and inexpensive tools for screening the increasing number of engineered nanomaterials (ENMs) entering the consumer market. However, the data produced by in vitro studies often vary substantially among different studies and from in vivo data. In part, these discrepancies may be attributable to lack of standardisation in dispersion protocols and inadequate characterisation of particle-media interactions which may affect the particle kinetics and the dose delivered to cells. In this study, a novel approach for preparation of monodisperse, stabilised liquid suspensions is presented and coupled with a numerical model which estimates delivered dose values. Empirically derived material- and media-specific functions are presented for each media-ENM system that can be used to convert administered doses to delivered doses. The interactions of ENMs with a variety of physiologic media were investigated and the importance of this approach was demonstrated by in vitro cytotoxicity assays using THP-1 macrophages.