The Toxicity Reference Database (ToxRefDB) contains approximately 30 years and $2 billion worth of animal studies. ToxRefDB allows scientists and the interested public to search and download thousands of animal toxicity testing results for hundreds of chemicals that were previously found only in paper documents. Currently, there are 474 chemicals in ToxRefDB, primarily the data rich pesticide active ingredients, but the number will continue to expand.

TOXNET is a group of databases hosted by the National Library of Medicine containing factual information related to the toxicity and other hazards of chemicals. They are structured around chemical records.

 There are four basic groupings of TOXNET databases. Within each of these
 groupings are one or more databases. The first grouping is Toxicology Data
 including factual information on toxicity and other hazards of chemicals. The
 databases included in Toxicology Databases are the following four - Hazardous
 Substances Data Bank (HSDB) providing broad scope in human and animal toxicity,
 safety and handling, environmental fate, and more. Scientifically
 peer-reviewed; the Integrated Risk Information System (IRIS) providing Data
 from the Environmental Protection Agency (EPA) in support of human health risk
 assessment. It, focuses on identifying hazards and assessing the connection
 between dose and response; Chemical Carcinogenesis Research Information System
 (CCRIS) providing carcinogenicity, mutagenicity, tumor promotion, and tumor
 inhibition data provided by the National Cancer Institute (NCI) and GENE-TOX
 providing peer-reviewed mutagenicity test data from the EPA.

 The second grouping is Toxicology Literature. These TOXNET databases contain
 bibliographic information with citations to the scientific literature. You can
 use this information to locate the article in a journal. Many provide
 hyperlinks to Medical Subject Headings (MESH) and other keywords. The
 databases included in Toxicology Literature are the following three - TOXLINE,
 providing an extensive array of references to literature on biochemical,
 pharmacological, physiological, and toxicological effects of drugs and other
 chemicals; Environmental Mutagen Information Center (EMIC) providing current
 and older literature on agents tested for genotoxic activity and Developmental
 and Reproductive Toxicology (DART) and Environmental Teratology Information
 Center (ETIC) providing current and older literature on developmental and
 reproductive toxicology.

 The third grouping is the Toxic Release Information which includes just The
 Toxics Release Inventory (TRI) database created by the Environmental Protection
 Agency and contains data on the estimated quantities of chemicals released to
 the environment or transferred off-site for waste treatment. TRI also holds
 information related to source reduction and recycling. Data for the most recent
 and two prior reporting years is currently available. This particular TRI
 database includes the years 1995 to 1999.

 The fourth grouping is Chemical Information. Chemicals are identified in a
 number of ways, including by name and structural diagram. Using a dictionary or
 a thesaurus can help you find information for a particular substance. SIS
 maintains several chemical online resources to help you determine the identity
 of a substance and point you to files or resources of interest. There are three
 databases within this grouping. The first is ChemIDplus providing Numerous
 chemical synonyms, structures, regulatory list information, and links to other
 databases containing information about the chemicals; HSDB Structures providing
 2D and 3D structural information on the HSDB chemicals and NCI-3D providing 2D
 and 3Dstructural information on compounds tested for anti-tumor activity
 compiled by the National Cancer Institute.

 The TOXNET Basic Search screens are intuitive and straightforward. Most screens
 (except for the TRI database) allow you to place all query information in a
 single input box. For example, you may be looking for a chemical name, a
 particular author, a CAS Registry Number, or any type of concept term. You may
 enter any combination of these into the single input box. In some databases,
 you may also limit your search by author, title or dates.

Aggregated Computational Toxicology Online Resource (AcTOR) is EPA's online aggregator of all the public sources of chemical toxicity data. ACToR aggregates data from over 1,000 public sources on over 500,000 chemicals and is searchable by chemical name, other identifiers and by chemical structure. It can be used to query a specific chemical and find all publicly available hazard, exposure and risk assessment data. It also provides access to EPA's ToxCast, ToxRefDB, DSSTox, Dashboard and DSSTox data.

Description: The most updated EPA DSSTox data files are available https://doi.org/10.23645/epacomptox.5588566 . EPA’s Distributed Structure-Searchable Toxicity (DSSTox) database contains curated chemical substances mapped to data including chemical identifiers (i.e., chemical synonyms, systematic names, CAS Registry Numbers and others) and, where appropriate, chemical structure representations. The goal for DSSTox is to accurately represent chemical substances, their structures and identifiers, as well as relevant chemical lists which are important to the environmental research and regulatory community.Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

ToxCast is used as a cost-effective approach for efficiently prioritizing the toxicity testing of thousands of chemicals. It uses data from state-of-the-art high throughput screening (HTS) bioassay and builds computational models to forecast potential chemical toxicity in humans. ToxRefDB stores data related to ToxCast.

Abstract Sifter Excel data from the CompTox Chemistry Dashboard. The Chemistry Dashboard is part of a suite of dashboards developed by EPA to help evaluate the safety of chemicals. It provides access to a variety of information on over 700,000 chemicals currently in use. Within the Chemistry Dashboard, users can access chemical structures, experimental and predicted physicochemical and toxicity data, and additional links to relevant websites and applications. It maps curated physicochemical property data associated with chemical substances to their corresponding chemical structures. These data are compiled from sources including the EPA�s computational toxicology research databases, and public domain databases such as the National Center for Biotechnology Information�s PubChem database.

In food safety, hazard identification and hazard characterisation aim to determine safe levels of exposure for substances “reference values” to protect human health, animal health or the environment. Such reference values are most often derived for the relevant species by applying an uncertainty factor on the “reference point determined from the pivotal toxicological study.

Since its creation in 2002, EFSA scientific panels and staff have produced risk assessments for more than 4,400 substances in over 1,650 scientific opinions, statements and conclusions through the work of its scientists.

OpenFoodTox is a structured database summarising the outcome of hazard characterisation for human health and – depending on the relevant legislation and intended uses – animal health and the environment.

For each individual substance, the data model of OpenFoodTox has been designed using OECD Harmonised Template as a basis to collect and structure the data in a harmonised manner. OpenFoodTox reports the substance characterisation, EFSA outputs, reference points, reference values and genotoxicity. OpenFoodTox and can be searched under the following link using a microstrategy tool: https://dwh.efsa.europa.eu/bi/asp/Main.aspx?rwtrep=400.

In order to disseminate OpenFoodTox to a wider community, two sets of data can be downloaded:

1. Five individual spreadsheets extracted from the EFSA microstrategy tool providing for all compounds: a. substance characterisation, b.EFSA outputs, c.reference points, d.reference values and e.genotoxicity.

2. The full database.

OpenFoodTox contributes actively to EFSA’s 2020 Science Strategy and to the aim of widening EFSA’s evidence base and optimising access to its data as a valuable open source database that can be shared with all scientific advisory bodies and stakeholders with an interest in chemical risk assessment. In addition, OpenFoodTox has been submitted to the OECD’s Global Portal to Information on Chemical Substances (eChemPortal) so that individual substances can be searched as part of the national and international databases. Further description and associated references are described in the EFSA journal editorial (Dorne et al., 2017).

Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix–matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.

While exposure of humans to environmental hazards often occurs with complex chemical mixtures, the majority of existing toxicity data are for single compounds. The Globally Harmonized System of chemical classification (GHS) developed by the Organization for Economic Cooperation and Development uses the additivity formula for acute oral toxicity classification of mixtures, which is based on the acute toxicity estimate of individual ingredients. We evaluated the prediction of GHS category classifications for mixtures using toxicological data collected in the Integrated Chemical Environment (ICE) developed by the National Toxicology Program (United States Department of Health and Human Services). The ICE database contains in vivo acute oral toxicity data for ∼10,000 chemicals and for 582 mixtures with one or multiple active ingredients. By using the available experimental data for individual ingredients, we were able to calculate a GHS category for only half of the mixtures. To expand a set of components with acute oral toxicity data, we used the Collaborative Acute Toxicity Modeling Suite (CATMoS) implemented in the Open Structure–Activity/Property Relationship App to make predictions for active ingredients without available experimental data. As a result, we were able to make predictions for 503 mixtures/formulations with 72% accuracy for the GHS classification. For 186 mixtures with two or more active ingredients, the accuracy rate was 76%. The structure-based analysis of the misclassified mixtures did not reveal any specific structural features associated with the mispredictions. Our results demonstrate that CATMoS together with an additivity formula can be used to predict the GHS category for chemical mixtures.

The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable, standardized chemical structure files associated with toxicity data.

ToxValDB v9.6.1 DCAPData associated with manuscript "Estimation of database-calibrated toxicity values for human health assessment using existing toxicology data for over a thousand chemicals"Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact the EPA Center for Computational Toxicology and Exposure's (CCTE's) Great Lakes Toxicology Ecology Division (GLTED) at CCTE@epa.gov "The ECOTOXicology Knowledgebase (ECOTOX) is a source for locating single chemical toxicity data for aquatic life, terrestrial plants and wildlife. ECOTOX was created and is maintained by the U.S.EPA's Center for Computational Toxicology and Exposure's (CCTE's) Great Lakes Toxicology Ecology Division (GLTED). ECOTOX integrates three previously independent databases - AQUIRE, PHYTOTOX, and TERRETOX - into a unique system which includes toxicity data derived predominately from the peer-reviewed literature, for aquatic life, terrestrial plants, and terrestrial wildlife, respectively. You should review the limitations of ECOTOX data retrieval for an understanding of system and minimum data requirements prior to performing searches on this site. You should consult the original scientific paper to ensure an understanding of the context of the data retrieved from ECOTOX." [Quote from a href="https://cfpub.epa.gov/ecotox/help.cfm">https://cfpub.epa.gov/ecotox/help.cfm]

ToxRefDB was developed by the National Center for Computational Toxicology (NCCT) in partnership with EPA's Office of Pesticide Programs (OPP), to store data from in vivo animal toxicity studies. The database:

-Contains pesticide registration toxicity data that used to be stored as hard-copy and scanned documents by OPP. -Currently includes chronic, cancer, sub-chronic, developmental, and reproductive studies on hundreds of chemicals (many are pesticide active ingredients). -Provides data that is accessible and computable. -Provides reference toxicity data for Agency research and retrospective analyses. -Provides toxicity endpoints for development of ToxCast predictive signatures that will be used for primary research applications. -Contains only certain hazard information and does not represent all information needed for a complete risk assessment for pesticides or other chemicals. -Effect designation should not be taken as determination that existing EPA risk assessments and risk management decisions need revisions.

For example, in addition to studies in ToxRefDB, for purposes of registration or tolerance determination, EPA evaluated information on other mammalian toxicity effects, metabolism, aquatic life, wildlife and plant toxicity studies, and use patterns, environmental fate and persistence, and pesticide residue levels.

Background: Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use and the thousands of environmental chemicals lacking toxicity data. EPA's ToxCast program aims to address these concerns by screening and prioritizing chemicals for potential human toxicity using in vitro assays and in silico approaches. Objectives: This project aims to evaluate the use of in vitro assays for understanding the types of molecular and pathway perturbations caused by environmental chemicals and to build initial prioritization models of in vivo toxicity. Methods: We tested 309 mostly pesticide active chemicals in 467 assays across 9 technologies, including high-throughput cell-free assays and cell-based assays in multiple human primary cells and cell lines, plus rat primary hepatocytes. Both individual and composite scores for effects on genes and pathways were analyzed. Results: Chemicals display a broad spectrum of activity at the molecular and pathway levels. Many expected interactions are seen, including endocrine and xenobiotic metabolism enzyme activity. Chemicals range in promiscuity across pathways, from no activity to affecting dozens of pathways. We find a statistically significant inverse association between the number of pathways perturbed by a chemical at low in vitro concentrations and the lowest in vivo dose at which a chemical causes toxicity. We also find associations between a small set in vitro assays and rodent liver lesion formation. Conclusions: This approach promises to provide meaningful data on the thousands of untested environmental chemicals, and to guide targeted testing of environmental contaminants.

The ECOTOXicology Knowledgebase (ECOTOX) has been in development since the early 1980s and is maintained by the U.S. EPA Great Lakes Toxicology and Ecology Division. ECOTOX includes curated data from toxicity tests from aquatic and terrestrial species, with results available on the web-based application: www.epa.gov/ecotox. This paper includes overview summaries of the entirety of the data currently included in ECOTOX (as of September 2020 update), with the source data for these summaries included in this Excel file. This dataset is associated with the following publication: Olker, J., C. Elonen, A. Pilli, A. Anderson, B. Kinzinger, S. Erickson, M. Skopinski, A. Pomplun, C. LaLone, C. Russom, and D. Hoff. The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 41(6): 1520-1539, (2022).

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is still limited, and their potential for addressing a wide range of chemicals is unknown. We prioritized chemical-related parameters for chemical toxicity characterization to inform ML model development based on two criteria: (1) each parameter’s relevance to robustly characterize chemical toxicity described by the uncertainty in characterization results attributable to each parameter and (2) the potential for ML-based approaches to predict parameter values for a wide range of chemicals described by the availability of chemicals with measured parameter data. We prioritized 13 out of 38 parameters for developing ML-based approaches, while flagging another nine with critical data gaps. For all prioritized parameters, we performed a chemical space analysis to assess further the potential for ML-based approaches to predict data for diverse chemicals considering the structural diversity of available measured data, showing that ML-based approaches can potentially predict 8–46% of marketed chemicals based on 1–10% with available measured data. Our results can systematically inform future ML model development efforts to address data gaps in chemical toxicity characterization.

We describe screening level estimates of potential aquatic toxicity posed by 227 chemical analytes that were measured in 25 ambient water samples collected as part of a joint USGS/USEPA drinking water plant study. Measured concentrations were compared to biological effect concentration (EC) estimates, including USEPA aquatic life criteria, effective plasma concentrations of pharmaceuticals, published toxicity data summarized in the USEPA ECOTOX database, and chemical structure-based predictions. Potential dietary exposures were estimated using a generic 3-tiered food web accumulation scenario. This dataset is associated with the following publication: Kostich , M., R. Flick , A. Batt , H. Mash , S. Boone , E. Furlong, D. Kolpin, and S. Glassmeyer. Aquatic concentrations of chemical analytes compared to ecotoxicity estimates. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 579: 1649-1657, (2017).

Bibliographic database providing references to developmental and reproductive toxicology literature on the National Library of Medicine's Toxicology Data Network. It covers teratology and other aspects of developmental and reproductive toxicology. It contains over 200,000 references to literature published since 1965. DART/ETIC is easily accessible and free of charge. Search by subject terms, title words, chemical name, Chemical Abstracts Service Registry Number (RN), and author. Search results can easily be viewed, printed or downloaded. Search results are displayed in relevancy ranked order, but may be sorted by publication date, author or title.

The US Environmental Protection Agency’s (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database, launched publicly in 2004, currently exceeds 875 K substances spanning hundreds of lists of interest to EPA and environmental researchers. From its inception, DSSTox has focused curation efforts on resolving chemical identifier errors and conflicts in the public domain towards the goal of assigning accurate chemical structures to data and lists of importance to the environmental research and regulatory community. In 2014, the legacy, manually curated DSSTox_V1 content was migrated to a MySQL data model, with modern cheminformatics tools supporting both manual and automated curation processes to increase efficiencies. Currently, DSSTox serves as the core foundation of EPA’s CompTox Chemicals Dashboard [https://comptox.epa.gov/dashboard], which provides public access to DSSTox content in support of a broad range of modeling and research activities within EPA and, increasingly, across the field of computational toxicology. This dataset is associated with the following publication: Grulke, C., A. Williams, I. Thillainadarajah, and A. Richard. EPA’s DSSTox database: History of development of a curated chemistry resource supporting computational toxicology research. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 12: 100096, (2019).

Quantitative data on product chemical composition is a necessary parameter for characterizing near-field exposure. This data set comprises reported and predicted information on >75,000 chemicals contained in >15,000 consumer products. The data’s primary intended use is for exposure, risk, and safety assessments. The data set includes specific products with quantitative or qualitative ingredient information, which has been publicly disclosed through material safety data sheets (MSDS) and ingredient lists. A single product category from a refined and harmonized set of categories has been assigned to each product. The data set also contains information on the functional role of chemicals in products, which can inform predictions of the concentrations in which they occur. These data will be useful to exposure and risk assessors evaluating chemical and product safety.

The data set presented here is in the form of a MySQL relational database, which mimics CPDat data available under the ‘Exposure’ tab of the CompTox Chemistry Dashboard (https://comptox.epa.gov/dashboard) as of August 2017.