This dataset is a toxicity dataset consisting of several columns capturing various attributes of nanoparticles (NPs) and their toxicological effects. The dataset contains various features related to nanoparticles (NPs) and their properties, which are likely related to toxicity classification. Here is an overview of the columns in the dataset:

NPs: Type of nanoparticles (e.g., Al2O3). coresize: Core size of the nanoparticles. hydrosize: Hydrodynamic size of the nanoparticles. surfcharge: Surface charge of the nanoparticles. surfarea: Surface area of the nanoparticles. Ec: Electric Charge Expotime: Exposure time. Dosage: amount of material used. e: Energy-related feature. NOxygen: Number of oxygen atoms. class: Class label indicating whether the nanoparticles are toxic or non-toxic.

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 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.

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.

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).

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples. This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1002/smtd.202100223 for the original and latest version of the dataset and data downloads

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

The data set is a matrix of cellular biochemical (metabolites) in HepG2 cells treated with various metal oxide nanomaterials composed of CeO2, SiO2 and CuO.

This dataset is associated with the following publication: Kitchin, K., S. Stirdivant, B. Robinette, B. Castellon, and X. Liang. Metabolomic effects of CeO2, SiO2 and CuO metal oxide nanomaterials on HepG2 cells. Particle and Fibre Toxicology. BioMed Central Ltd, London, UK, 14(50): 1-16, (2017).

The movement towards an animal-free testing approach for risk assessment represents a key paradigm shift in toxicology. Risk assessment of engineered and anthropogenic nanoscale materials (NM) is dependent on reliable hazard characterization, which requires validated test methods and models, and increasingly on mechanistic insights into the mode of action. The properties that make NMs so advantageous for a wide range of commercial and industrial applications also pose a challenge when it comes to safety testing under in vitro and in chemico experimental settings. Their large reactive surface area makes NMs prone to interactions with assay reagents, readout signals, or intermediate steps of many test assays, leading to the potential for biased results and data inconsistencies, collectively referred to as interferences. Therefore, methods and protocols developed and validated for conventional chemicals often require adaptation and checking for reliability in NMs' toxicity assessment. This review presents the collected scientific knowledge on NMs-induced interferences for the most common in vitro toxicity assays and methods related to cytotoxicity, oxidative stress and inflammatory response evaluation. Our analysis of existing scientific literature showed that the challenge of NMs-induced interference was not explicitly addressed in more than 90% of the papers published up to 2014 reporting the safety and toxicity of NMs. In later years, increasing number of studies tackled the interference challenge in toxicity testing of NMs, which initiated exhaustive work on standardization and validation of existing regulatory-relevant in vitro test protocols and guidelines. Due to the specificity of the different NMs and the range of ways they can potentially interfere with in vitro assays, interference and fit-for purpose controls should be included for each NM type and method applied, unless label-free assays are selected. Here, we provide a decision tree to guide researchers on how to design experiments to avoid interferences during in vitro testing by taking appropriate mitigation actions and how to include proper interference controls in their experimental design where complete avoidance is not possible. The application of this decision tree will improve the reliability, comparability and reusability of in vitro toxicity data on engineered NMs or ENMs, increasing the relevance of in silico hazard data for use in risk assessment and in science-based risk governance of NMs. The approach is applicable more broadly also, to advanced materials and to hazard assessment of anthropogenic nanoscale materials such as microplastic and tyre-wear particles. This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to https://doi.org/10.1016/j.nantod.2024.102215 for the original and latest version of the dataset and data downloads

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.

The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona. The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.

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.

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.

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

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.

This dataset comprises dynamic light scattering measurements of silver and zinc oxide nanoparticles over 24 hours of incubation in egg water, obtained as part of acute toxicity tests in zebrafish larvae.Details of this experiment have been published in Nanotoxicology: Brinkmann BW, Koch BEV, Spaink HP, Peijnenburg WJGM, and Vijver MG, 2020. Colonizing microbiota protect zebrafish larvae against silver nanoparticle toxicity. Nanotoxicology, 14(6): 725-739. Doi: 10.1080/17435390.2020.1755469The eleven parameters of the dataset are:- Time. The incubation time in hours.- Compound. One of the two nanoparticles that were tested in the acute toxicity test, including: nAg, silver nanoparticles; and nZnO, zinc oxide nanoparticles.- Concentration. The nominal exposure concentration of the silver and zinc oxide nanoparticles, expressed in mg Ag/L and mg ZnO/L respectively.- d_size. The mean hydrodynamic size of all measurement replicates per sample in nm.- sd_size. The standard deviation of all hydrodynamic size measurement replicates per sample in nm. - PI. The mean polydispersity index of all hydrodynamic size measurement replicates per sample.- sd_PI. The standard deviation of the polydispersity index of all hydrodynamic size measurement replicates per sample.- intercept. The mean Y-intercept of the correlogram of all measurement replicates per sample.- sd_intercept. The standard deviation of the Y-intercept of the correlograms of all measurement replicates per sample.- ZP. The mean zeta potential of all measurement replicates per sample in mV.- sd_ZP. The standard deviation of all zeta potential measurement replicates per sample in mV.This dataset has been saved in a tab-delimited text file format.