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.

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 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.

This is an RDFied version of the dataset published in Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018). The original dataset publication DOI: https://doi.org/10.1038/s41598-018-24483-z. The Original publication authors: Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun

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.

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

Additional file 2. Minimum information table.

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)

Global Nanoparticle Toxicology Market is segmented by Application (Pharmaceuticals_Cosmetics_Industrial Chemicals_Environmental Risk Assessment_Nanomaterials Research), Type (In Vitro Toxicology_In Vivo Toxicology_Environmental Toxicology_Occupational Exposure Toxicology_Computational Toxicology), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

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 CSV file contains a comprehensively curated dataset comprising physicochemical descriptors and biological assay data for engineered metal oxide nanoparticles. This dataset was specifically developed to support machine learning model training for toxicity prediction and represents the result of an extensive multi-stage data extraction and curation pipeline. Data sources included peer-reviewed publications and reputable open-access repositories such as the NanoPharos database. Details on the data sourcing process, prompt engineering strategies for large language model (LLM)-based extraction, and validation protocols are provided in the Supplementary Information section.The dataset consists of 20 key features, which are grouped into four categories: physicochemical properties, biological responses, experimental exposure conditions, and fundamental core properties derived from periodic trends. These features were selected based on a comprehensive review of toxicological mechanisms associated with metal oxide nanoparticles and their interactions with biological systems.1. Physicochemical Descriptors:These features represent the primary characteristics of each nanoparticle and play a critical role in determining toxicity. They include:Hydrodynamic diameter (nm): Represents the nanoparticle size in suspension, accounting for solvation and agglomeration effects.Zeta potential (mV): A measure of surface charge, influencing nanoparticle stability and interaction with biological membranes.Surface area (m²/g): Affects the reactivity and potential for cellular interaction.Aggregation state: Describes whether nanoparticles are dispersed, loosely clustered, or highly aggregated.Dissolution rate (mg/L): Important for metal oxides that release toxic ions (e.g., Ag⁺, Zn²⁺).Metal ion release (mg/L): Quantifies ionic dissolution contributing to oxidative stress.Surface chemistry/coating: Encoded categorically, includes PEGylation, citrate, or uncoated states.Impurity content (%): Where reported, captures secondary contaminants from synthesis.2. Biological Response Variables:These outcomes were derived from in vitro cytotoxicity assays and serve as toxicity labels or indicators:Cell viability (%): A central endpoint indicating survival rate post-exposure.Reactive Oxygen Species (ROS) generation: Expressed as fold-increase compared to control.Lactate Dehydrogenase (LDH) leakage, apoptosis (%), necrosis (%): Markers of membrane damage and programmed cell death.IC50 (µg/mL): The concentration at which 50% of cells are inhibited, used as a toxicity threshold.These biological metrics were used to define a binary toxicity label: entries were classified as toxic (1) or non-toxic (0) based on thresholds from standardized guidelines (e.g., ISO 10993-5:2009) and literature consensus. Criteria included IC50 ≤ 100 µg/mL, cell viability ≤ 70%, ROS > 2x control, and zeta potential outside the range of -30 to +30 mV. Entries with high apoptosis/necrosis rates (≥20% increase over control) were also flagged as toxic.3. Experimental Conditions:To capture contextual variation in assay conditions, the dataset includes:Exposure dose (µg/mL): Spanning 0–1000 µg/mL across studies.Exposure time (hours): Captures both acute and extended exposure regimes (12 and 24 hrs).Cell type/model: Consolidated into five broader categories (e.g., human cancer cells, normal human cells, murine cells, etc.).4. Core Material Properties:Four elemental-level descriptors were added to enrich prediction with periodic trends:Atomic weight, group number, period number, and electronegativity difference of elements in the nanoparticle core.These provide mechanistic insights related to ion release potential, surface reactivity, and redox behavior.Data Cleaning and Normalization:To ensure model reliability and generalizability, extensive preprocessing was undertaken:Outlier management: Features with wide value ranges, such as hydrodynamic size or ROS scores, were log-transformed to reduce skewness.Missing values: Numerical fields with missing entries were imputed using the median value of the respective column. Categorical inconsistencies were resolved using consistent label encoding.Correlation reduction: Features with high interdependence (e.g., apoptosis vs. necrosis) were carefully screened using Pearson correlation analysis. The final dataset yielded an average feature Pearson correlation of 0.19, indicating low multicollinearity.Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.Applications and Model Compatibility:The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:Gradient Boosting Machines (GBM),Support Vector Machines (SVM-RBF),Random Forests, andPrincipal Component Analysis (PCA) for feature reduction.Training-validation experiments demonstrated accuracies up to 83% in toxicity classification, affirming the predictive potential of the curated descriptors. The dataset also enables parameter space mapping, allowing the generation of 2D/3D response surfaces showing toxicity trends across varying core sizes and dosages.This curated dataset addresses several limitations of existing toxicological datasets by enhancing feature diversity, standardization, and data quality control. It is publicly available via the Supplementary Information section and aims to serve as a benchmark resource for researchers developing predictive nanotoxicology models.

n.a. – not available.

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.

This repository provides three curated nanotoxicity datasets — HaHa-Auto, HaHa-Manual, and Ha IIIB — comprising structured physicochemical and toxicological information on metal oxide nanoparticles. The datasets are intended for use in predictive modeling, risk assessment, and data-driven research in nanotoxicology.

HaHa-Auto: A semi-automatically generated dataset using large language models (LLMs) with a prompt-engineered data extraction pipeline. Contains 2696 entries with 15 descriptors, filtered for quality based on a physicochemical score.
HaHa-Manual: A manually curated counterpart to HaHa-Auto based on the same source articles. Offers a higher number of entries (3440) for benchmarking automated extraction performance.
Ha IIIB: A high-quality, smaller dataset (666 entries) curated from previously published training data, used to evaluate model applicability and robustness with limited but highly reliable data.

Each dataset includes detailed metadata such as:
1. Core size, hydrodynamic size, surface charge, surface area
2. Quantum-mechanical descriptors (e.g., band gap, enthalpy of formation)
3. Biological assay conditions (e.g., cell type, exposure time, dosage)
4. Toxicity endpoints (e.g., cell viability)

These datasets are suitable for use in automated machine learning (AutoML) applications, (Q)SAR model development, or validation of nanotoxicity prediction pipelines. All data entries were filtered using a scoring system to ensure quality and consistency, and are provided in CSV format with clear headers and descriptors.

Iron oxide nanoparticles (IONPs) are the first generation of nanomaterials approved by the Food and Drug Administration for use as imaging agents and for the treatment of iron deficiency in chronic kidney disease. However, several IONPs-based imaging agents have been withdrawn because of toxic effects and the poor understanding of the underlying mechanisms. This study aimed to evaluate IONPs toxicity and to elucidate the underlying mechanism after intravenous administration in rats. Seven-week-old rats were intravenously administered IONPs at doses of 0, 10, 30, and 90 mg/kg body weight for 14 consecutive days. Toxicity and molecular perturbations were evaluated using traditional toxicological assessment methods and proteomics approaches, respectively. The administration of 90 mg/kg IONPs induced mild toxic effects, including abnormal clinical signs, lower body weight gain, changes in serum biochemical and hematological parameters, and increased organ coefficients in the spleen, liver, heart, and kidneys. Toxicokinetics, tissue distribution, histopathological, and transmission electron microscopy analyses revealed that the spleen was the primary organ for IONPs elimination from the systemic circulation and that the macrophage lysosomes were the main organelles of IONPs accumulation after intravenous administration. We identified 197 upregulated and 75 downregulated proteins in the spleen following IONPs administration by proteomics. Mechanically, the AKT/mTOR/TFEB signaling pathway facilitated autophagy and lysosomal activation in splenic macrophages. This is the first study to elucidate the mechanism of IONPs toxicity by combining proteomics with traditional methods for toxicity assessment.

The unique behavior of engineered nanomaterials (ENM) in aqueous media and dynamic changes in particle settling, agglomeration and dissolution rates is a challenge to the consistency, reliability and interpretation of standard aquatic hazard bioassay results. While the toxicological endpoints (e.g., survival, growth, reproduction, etc.) in ecotoxicity bioassays are largely applicable to ENMs, the standard methods as written for dissolved substances are confounded by the dynamic settling, agglomeration and dissolution of particulate ENMs during the bioassay. A testing framework was designed to serve as a starting point to identify approaches for the consistent conduct of aquatic hazard tests that account for the behavior of ENMs in test media and suitable data collection to support representative exposure metrology. The framework was demonstrated by conducting three case studies testing ENMs with functionally distinct characteristics and behaviors. Pretests with a temporal sampling of particle concentration, agglomeration and dissolution were conducted on each ENM in test media. Results indicated that a silver nanoparticle (AgNP) powder was not dispersible, a nano-TiO2 powder was dispersible but unstable, and a polyvinylpyrrolidinone-coated AgNP was relatively stable in test media. Based on these functional results, Ceriodaphnia dubia bioassays were conducted to compare different exposure summary methods (nominal, arithmetic average, geometric average, time-weighted average) for calculating and expressing toxicity endpoints. Results indicated that while arithmetic means were effective for expressing the toxicity of more stable materials, time-weighted averaged concentrations were appropriate for the unstable nano-TiO2.

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.

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.

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.