This dataset contains key characteristics about the data described in the Data Descriptor An EPA database on the effects of engineered nanomaterials-NaKnowBase. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Toxicity data for the impact of nano-silver on anaerobic degradation. This dataset is associated with the following publication: Gitipour, A., S. Thiel, K. Scheckel, and T. Tolaymat. Anaerobic Toxicity of Cationic Silver Nanoparticles. D. Barcelo Culleres, and J. Gan SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 557: 363-368, (2016).

This study collects toxicity data from animal inhalation studies of some nanomaterials and their bulk and ionic counterparts. To allow potential grouping and interpretations, we retrieved the primary physicochemical and exposure data to the extent possible for each of the materials. Reviewed materials are compounds (mainly elements, oxides and salts) of carbon (carbon black, carbon nanotubes, and graphene), silver, cerium, cobalt, copper, iron, nickel, silicium (amorphous silica and quartz), titanium (titanium dioxide), and zinc (chemical symbols: Ag, C, Ce, Co, Cu, Fe, Ni, Si, Ti, TiO2, and Zn). Collected endpoints are: a) pulmonary inflammation, measured as neutrophils in bronchoalveolar lavage (BAL) fluid at 0-24 hours after last exposure; and b) genotoxicity/carcinogenicity. We present the dose descriptors no-observed-adverse-effect concentrations (NOAECs) and lowest-observed-adverse-effect concentrations (LOAECs) for 88 nanomaterial investigations in data-library and graph formats. We also calculate ‘the value where 25% of exposed animals develop tumors’ (T25) for carcinogenicity studies. We describe how the data may be used for hazard assessment of the materials using carbon black as an example. The collected data also enable hazard comparison between different materials. An important observation for poorly soluble particles is that the NOAEC for neutrophil numbers in general lies around 1 to 2 mg/m3. We further discuss why some materials’ dose descriptors deviate from this level, likely reflecting the effects of the ionic form and effects of the fiber-shape. Finally, we discuss that long-term studies, in general, provide the lowest dose descriptors, and dose descriptors are positively correlated with particle size for near-spherical materials.

This dataset houses a research poster, its poster abstract, and its award certificate. The set of documents was first presented at The Virtual 10th International Conference on Nanotoxicology (NanoTox2021) poster presentation, 20th - 22nd April 2021. Poster Title: "New descriptors in toxicology prediction of nanomaterials: Using quasi-ab initio MD simulations for the estimation of aqueous ZnO and TiO2 surface structure parameters.” Our research focuses on understanding the toxicity of nanomaterials, highlighting the need for in-silico methods due to their diverse structures and compositions. We investigate the interactions and surface parameters of ZnO and TiO2 nanoparticles with water using Molecular Dynamics simulations at Density Functional – Tight Binding level methods. By incorporating new structural parameters, we aim to contribute toxicology prediction models and improve safety assessments of nanomaterials. The poster selected and awarded with attendees’ bursary, which is given to 49 attendees over 384 registered attendees, and one of the "Best Student Poster - Highly Commended" prize among 117 poster presentations.

Manufactured nanomaterials with unique properties have been extensively applied in various industrial, agricultural or medical fields. However, some of the properties have been identified to be closely related to nanomaterial toxicity. The “nano-paradox” has aroused concerns over the use and development of nanotechnology, which makes it difficult for regulatory agencies to regulate nanomaterials. The key to fulfilling proper nanomaterial regulation lies in the adequate understanding of the impact of nanomaterial properties on nano-bio interactions. To this end, we start the present work with a brief introduction to nano-bio interactions at different levels. Based on that, how key toxicity-associated properties of manufactured nanomaterials (i.e., size, shape, chemical composition, surface properties, biocorona formation, agglomeration and/or aggregation state, and biodegradability) impact their toxicokinetics, cellular uptake, trafficking and responses, and toxicity mechanisms is deeply explored. Moreover, advanced analytical methods for studying nano-bio interactions are introduced. Furthermore, the current regulatory and legislative frameworks for nanomaterial-containing products in different regions and/or countries are presented. Finally, we propose several challenges facing the nanotoxicology field and their possible solutions to shed light on the safety evaluation of nanomaterials.

This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes".

In detail the following data sources have been included:

• the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);
• supplementary materials of the paper, including:
  • individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);
  • comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);
  • overrepresented network edges in categories of networks (overrepresented_structures.zip)

As part of the research program on the aquatic ecotoxicology of nanomaterials, a study was carried out using the invertebrate Hydra vulgaris in order to determine the influence that the shape of nanoparticles (sphere, cube and prism) have on the toxic potential of silver nanoparticles. The selection of nanoparticles of similar size and identical surface properties (polyvinylpyrrolidone) allowed to highlight the effects related to the geometrical shape. This study also contributes to the expansion of knowledge on the toxic effects of nanoparticles in relation to their geometry—a topic that has very little examination in aquatic ecotoxicology. All data are a part subject of a publication containing method details, full QA/QC, interpretation and conclusions. Citation: Auclair J, Gagné F. Shape-Dependent Toxicity of Silver Nanoparticles on Freshwater Cnidarians. Nanomaterials. 2022; 12(18):3107. doi.org/10.3390/nano12183107 Supplemental Information Supporting programs: Chemicals Management Plan (CMP) The Chemicals Management Plan (CMP) is a Government of Canada initiative aimed at reducing the risks posed by chemicals to Canadians and their environment. A key element of the Chemicals Management Plan is the monitoring and surveillance of levels of harmful chemicals in Canadians and their environment. Monitoring and surveillance are essential to identify and track exposure to hazards in the environment and associated health implications. Monitoring and surveillance programs provide the basis for making sound and effective public health and environmental health policies and interventions, as well as measuring the efficacy of control measures. In support of the Chemicals Management Plan, monitoring and surveillance initiatives were established to support Health Canada and Environment and Climate Change Canada scientists, in collaboration with external partners and researchers, to advance our knowledge. This initiative has allowed the Government of Canada to increase its commitment to a number of existing monitoring initiatives, as well as to support new efforts. For more information on the Chemicals Management Plan, please visit https://www.canada.ca/en/health-canada/services/chemical-substances/chemicals-management-plan.html

Supplementary materials for manuscript: Status quo in data availability and predictive models of nano-mixture toxicity. This table contains the list of 183 curated literature used in this study.

This dataset package contains four curated datasets (MeOX I, MeOX II, MeOX IIIA, and MeOX IIIB) that were originally compiled and published through the S2NANO portal (Safe and Sustainable Nanotechnology, portal.s2nano.org). The datasets compile physicochemical, quantum-mechanical, and in vitro toxicological properties of 26 types of metal oxide nanoparticles extracted from 216 published studies.

MeOX I: Raw compiled dataset with missing values in key physicochemical attributes (core size, hydrodynamic size, surface charge, specific surface area). Mean substitution was applied during modeling.

MeOX II: Dataset with missing physicochemical data filled via manufacturer specifications and estimations, enhancing data completeness and quality.

MeOX IIIA: Subset of MeOX II, selecting the top 50% of samples based on a physicochemical (PChem) data quality score to emphasize high-quality data.

MeOX IIIB: Further refined subset of MeOX II, containing the top 20% highest-scored samples for the highest data quality assurance.

Each dataset is labeled for binary classification (Toxic vs. Nontoxic) based on 50% cell viability thresholds under various experimental conditions. These datasets support nano-SAR model development and facilitate research into structure-activity relationships for engineered nanomaterials.

The increased production and use of engineered nanoparticles (ENPs) have led to their release into the environment. Research has examined their physical, chemical, and biological transformations and the environmental factors influencing these processes. However, understanding these transformations in complex systems, especially around organisms, remains incomplete. Transformations significantly alter ENP properties, affecting their interactions with organisms by enhancing penetration of biological barriers and activation of signaling pathways. These interactions depend on ENP properties like size, shape, surface area, and chemical composition. Recently, studies have focused on the toxicity of transformed ENPs, which generally exhibit lower toxicity than pristine counterparts due to reduced dissolution and limited direct cell contact. Most studies have examined acute toxicity, leaving long-term effects unknown. Future research should explore toxicity mechanisms, environmental influences, and competing transformation processes, potentially using machine learning (ML) and artificial intelligence (AI). This review synthesizes current knowledge on the fate and toxicity of transformed ENPs, assesses ecological risks in aquatic environments, and identifies knowledge gaps to guide future research. Filling data gaps is essential for effective use of AI tools in modeling ENP-environment interactions.

description: Establishing a predictive science is a timely approach for nanotechnology-based enterprises wishing to avoid the problems faced by the chemical industry, where only a few hundred of the ca. 50,000 industrial chemicals have undergone toxicity testing, making it very challenging to control their toxicological impact in the environment. There is also growing recognition in Europe and Asia that a paradigm shift in toxicology is required to deal with anthropogenic activity. The UC-CEIN proposes to conduct predictive toxicological sciencefor engineered nanomaterials (NMs)through the founding of the Center for Environmental Implications of Nanotechnology (CEIN) at UC Los Angeles (UCLA) in partnership with UC Santa Barbara (UCSB), UC Davis (UCD), UC Riverside (UCR), Columbia University (New York), University of Texas (El Paso, TX), Nanyang Technological University (NTU, Singapore), the Molecular Foundry at Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratory (SNL), the University of Bremen (Germany), University College Dublin (UCD, Ireland), and the Universitat Rovira i Virgili (URV, Spain). This Center unites a highly integrated, multidisciplinary, synergistic team with the skill set to solve the complexities of environmental science, eco-toxicity, materials science, nanotechnology, biological mechanisms of injury, and the environmental fate and transport of NMs.The goal of the Center is to develop a broad-based model of predictive toxicologypremised on quantitative structure-a ctivity relationships (QSARs) and NM injury paradigms at the biological level. This predictive scientific modelwill consider: (i) the NMs most likely to come into contact with the environment; (ii) their distribution in the environment; (iii) representative environmental life forms serving as early sentinels to monitor the spread and bio-accumulation of hazardous NMs; (iv) biological screening assays allowing QSARs to be developed based on the bio-physicochemical properties of NMs; (v) High throughput screening (HTS) of a combinatorial NM library; and (vi) a self-learning computational system providing a framework for predictive risk analysis. These research activities will be combined with educational programs informing the public, future generations of scientists, public agencies, and industrial stakeholders of the importance of safe implementation of nanotechnology in the environment. The overall impact will be to reduce uncertainty about the possible consequences of NMs in the environment, while at the same time providing guidelines for their safe design to prevent environmental hazards.; abstract: Establishing a predictive science is a timely approach for nanotechnology-based enterprises wishing to avoid the problems faced by the chemical industry, where only a few hundred of the ca. 50,000 industrial chemicals have undergone toxicity testing, making it very challenging to control their toxicological impact in the environment. There is also growing recognition in Europe and Asia that a paradigm shift in toxicology is required to deal with anthropogenic activity. The UC-CEIN proposes to conduct predictive toxicological sciencefor engineered nanomaterials (NMs)through the founding of the Center for Environmental Implications of Nanotechnology (CEIN) at UC Los Angeles (UCLA) in partnership with UC Santa Barbara (UCSB), UC Davis (UCD), UC Riverside (UCR), Columbia University (New York), University of Texas (El Paso, TX), Nanyang Technological University (NTU, Singapore), the Molecular Foundry at Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratory (SNL), the University of Bremen (Germany), University College Dublin (UCD, Ireland), and the Universitat Rovira i Virgili (URV, Spain). This Center unites a highly integrated, multidisciplinary, synergistic team with the skill set to solve the complexities of environmental science, eco-toxicity, materials science, nanotechnology, biological mechanisms of injury, and the environmental fate and transport of NMs.The goal of the Center is to develop a broad-based model of predictive toxicologypremised on quantitative structure-a ctivity relationships (QSARs) and NM injury paradigms at the biological level. This predictive scientific modelwill consider: (i) the NMs most likely to come into contact with the environment; (ii) their distribution in the environment; (iii) representative environmental life forms serving as early sentinels to monitor the spread and bio-accumulation of hazardous NMs; (iv) biological screening assays allowing QSARs to be developed based on the bio-physicochemical properties of NMs; (v) High throughput screening (HTS) of a combinatorial NM library; and (vi) a self-learning computational system providing a framework for predictive risk analysis. These research activities will be combined with educational programs informing the public, future generations of scientists, public agencies, and industrial stakeholders of the importance of safe implementation of nanotechnology in the environment. The overall impact will be to reduce uncertainty about the possible consequences of NMs in the environment, while at the same time providing guidelines for their safe design to prevent environmental hazards.

Additional file 2. Minimum information table.

This study aims to implement methods for toxicological profiling of engineered nanomaterials using toxicogenomics, proteomics, and metabolomics along with computational analyses. For all three omics layers the human cell lines A549 (lung epithelial cells) and THP1 (monocytes) were separately exposed to the nanomaterials TiO2 NM104, MWNCT NM401, and Ag NM300k. Proteomics and metabolomics samples have been performed as biological triplicates and were taken after 0, 6, 12, 24, and 48 hours. To assess ecotoxic effects C. elegans worms were grown in soil treated with NM300k. Ecotox samples were taken only at 0 and 24 hours. Integrating all three omics layers will enable the identification of (novel) ENM specific modes of action (MoA).

Engineered nanomaterials (ENMs, particles less than 100 nanometers) are being manufactured at increasing levels for a variety of reasons including cosmetics, food packaging and preservation, fertilizers, and medical technology. Thousands of metric tons of ENMs are released to soils, water bodies, air, and landfills each year. These particles have distinct properties owing to their small size and relatively large surface area to volume ratio. These characteristics can result in these materials having higher reactivity and toxicity in biological systems - especially because ENMs are small enough to enter cells. However, studies on environmental and ecological effects of ENMs have shown mixed results. The goal of this project is to address three primary research questions using meta-analysis of existing literature: 1) Which ENMs have been studied and in what context? 2) How do particle identity, size, concentration, and study duration influence toxicity (as estimated by response ratios, lethal concentration (LC50), and effective concentration (EC50)) in different organisms related to the role they play in the environment? 3) What are bioaccumulation, biomagnification, and bioconcentration factors of ENMs as a function of ecosystem role and trophic level in different organisms? We collected data from 191 published scientific papers and extracted 2102 unique observations that are used to address the questions outlined above. We calculated response ratios related to biological responses of biomass, diversity, growth, metabolism, and survival to evaluate how these are influenced by ENM exposure. Values of LC50 (concentration at which 50% of test organisms died) and EC50 (concentration at which 50% of test organisms showed an effect) were collected to estimate concentrations at which toxicity occurs. Bioaccumulation (BAF), bioconcentration (BCF), and biomagnification (BMF) factors were recorded to estimate environmental accumulation and trophic transfer. This work is a result of a graduate-level course focused on environmental problems that was offered by Dr. Walter Dodds in the Division of Biology at Kansas State University during August 2019 - December 2019.

The large amount of existing nanomaterials demands for rapid and reliable ways to test their potential toxicological effect on human health, preferably by means of relevant in vitro tests in order to reduce testing on animals. Combining high throughput workflows with automated high content imaging techniques allows to derive much more information from cell-based assays than with the typical readouts (i.e. one measurement per well) with optical plate-readers. We present here a dataset including up to 14 different read outs including viable cell count, cell membrane permeability, apoptotic cell death, mitochondrial membrane potential and steatosis of the human hepatoma HepaRG cell line treated with a large set of nanomaterials, coatings and supernatants at different concentrations. The database, given its size, can be utilized for the development of in silico hazard assessment and prediction tools or can be combined with toxicity effect on other in vitro test systems.

These files include all data presented in chapter 6 of the dissertation:

"Nanomaterial safety for microbially-colonized hosts: microbiota-mediated physisorption interactions and particle-specific toxicity" by Bregje Brinkmann (2022).

The data presented in chapters 2-5 have previously been published elsewhere:

• Chapter 2: Zenodo (DOI: 10.5281/zenodo.6800734).
• Chapter 3: Mendeley Data (DOI: 10.17632/2d4hcr5cb5.1)
• Chapter 4: Figshare (DOI: 10.6084/m9.figshare.c.4923261)
• Chapter 5: Mendeley Data (DOI: 10.17632/4nfg69v8hy.1)

1. Data presented in Figure 6.1: Survival_CFU_(...)
Tab-delimited file with zebrafish larvae survival, and the number of colony-forming units (CFUs) associated with zebrafish larvae, following exposure to silver nanoparticles (nAg) from 3-5 days-post fertilization (dpf):

• Concentration: Nominal exposure concentration (mg nAg·L^-1).
• Date: The date at which the mortality was scored (Format: DD/MM/YYYY).
• Family: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment.
• Survival: Percentage of larvae that had survived the treatment.
• CFU: Number of colony-forming units that was isolated per larva

The methodology for toxicity tests and the procedures to determine CFU counts, have been published in Nanotoxicology:

Brinkmann BW, Koch BEV, Spaink HP, Peijnenburg WJGM, Vijver MG. 2020. Colonizing microbiota protect zebrafish larvae against silver nanoparticle toxicity. Nanotoxicology. 14: 725-739. DOI: 10.1080/17435390.2020.1755469

2. Data presented in Figure 6.2: ABs_DoseResponses_(...)
Tab-delimited file with zebrafish larvae mortality following a pretreatment of 0, 6 or 72 hours with an antibiotic and antifungal cocktail, and subsequent exposure to nAg from 3-5 dpf:

• Concentration: Nominal exposure concentration (mg nAg·L^-1).
• Mortality: Percentage of larvae that had died.
• Date: The date at which the mortality was scored (Format: DD/MM/YYYY).
• Family: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment.
• ABs: Duration of the antibiotic/ antifungal pretreatment, either 0, 6 or 72 hours.

3. Data presented in Figure 6.3: il1beta_eGFP_(...)
Three folders comprising fluorescence microscopy images (TIFF format) of il1beta:eGFP reporter zebrafish larvae at 5 dpf:

• (...)_replicates1_20200226: images for the first experimental replicate.
• (...)_replicates2_20200304: images for the second experimental replicate.
• (...)_replicates3_20200318: images for the third experimental replicate.

For each of the replicates, the following images were acquired:

• nZnO_GFP: GFP signal for larvae exposed to nZnO.
• Znion_GFP: GFP signal for larvae exposed to zinc ions.
• nZnO_trans: transmitted light images for larvae exposed to nZnO.
• Znion_trans: transmitted light images for larvae exposed to zinc ions.

Additionally, the following images have previously been deposited to Mendeley Data (DOI: 10.17632/4nfg69v8hy.1):

• nAg_GFP: GFP signal for larvae exposed to nAg.
• nAg_trans: transmitted light images for larvae exposed to nAg.
• Agion_GFP: GFP signal for larvae exposed to silver ions.
• Agion_trans: transmitted light images for larvae exposed to silver ions.
• control_GFP: GFP signal for control larvae that had not been exposed to silver ions or nAg
• control_trans: transmitted light images for control larvae that had not been exposed to silver ions or nAg.

All image processing steps have been published in Ecotoxicology and Environmental Safety:

Brinkmann BW, Koch BEV, Peijnenburg WJGM, Vijver MG. 2022. Microbiota-dependent TLR2 signaling reduces silver nanoparticle toxicity to zebrafish larvae. Ecotox Environ Saf. 237: 113522. DOI: 10.1016/j.ecoenv.2022.113522

Abbreviations:

• ABs: antibiotics
• CFU: colony-forming units
• dpf: days post-fertilization
• il1beta: interleukin-1beta
• nAg: silver nanoparticles (NM-300 K)
• nZnO: zinc oxide nanoparticles (NM-110)

This dataset was generated from a laboratory experiment investigating the toxicity of Zinc oxide nanoparticles and non-nanoparticles to the earthworm Eisenia andrei. The experiment followed the OECD protocol 222 OECD guideline for testing of chemicals Earthworm reproduction test (Eisenia fetida/andrei) 2004. Earthworms, Eisenia andrei, were exposed to Zinc oxide particles and nanoparticles, as well as an ionic reference, Zinc chloride, in soil for 28 days after which survival, reproduction and weight change were measured to assess the toxicity of the different zinc compounds.

Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation. Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.

n.a. – not available.

The Nanoparticle Tracking Analyzer (NTA) market is experiencing steady growth, projected to reach a market size of $50 million in 2025, with a Compound Annual Growth Rate (CAGR) of 2.4% from 2025 to 2033. This growth is driven by the increasing demand for advanced characterization techniques in various fields like nanomedicine and materials science. The rising adoption of NTA in drug delivery research, particularly for exosomes and nanoparticles, is a key factor contributing to market expansion. Furthermore, the growing focus on nanoparticle toxicology studies, necessitating precise particle size and concentration analysis, is further fueling market demand. The diverse applications of NTA across various industries, including vaccine production and environmental monitoring, contribute to its sustained growth trajectory. Technological advancements leading to more sophisticated and user-friendly NTA systems, along with increased research funding in nanotechnology, are expected to positively influence market growth in the coming years. The market segmentation reveals that Desktop Devices currently hold a significant share, owing to their cost-effectiveness and ease of use. However, portable devices are expected to gain traction owing to their increasing portability and ability to perform analysis at various locations. The geographic distribution of the NTA market is relatively widespread, with North America and Europe currently dominating. However, the Asia-Pacific region, particularly China and India, is poised for significant growth due to rising research activities and investments in nanotechnology. Competitive landscape analysis reveals a diverse range of established players, including Malvern Instruments, Agilent Technologies, and Beckman Coulter, alongside emerging companies offering innovative NTA solutions. The ongoing technological innovations and increasing research and development activities within the nanotechnology sector are expected to shape the future of the NTA market, driving further expansion and diversification across applications and geographic regions. While some challenges may exist related to cost and specialized expertise requirements, the overall outlook for the NTA market remains positive, fueled by the growing importance of nanotechnology in various sectors.