DTT Data Collections Guided Search

The ICCVAM in vitro basal cytotoxicity assays proposed for setting starting doses for in vivo acute oral toxicity studies.

The dataset provides data on the effects of chemicals on the Estrogen Receptor (ER) from uterotrophic assays.

The dataset provides a list of recommended reference substances for validation of In Vitro Murine Local Lymph Node Assay (LLNA) for skin sensitization.

The dataset provides a list of recommended reference substances for evaluation or validation of In Vitro Androgen Receptor (AR) agonism assays.

The purity data is for the DTT Tox21 Phase 2 chemicals provided to NCATS for the Tox21 Phase 2 program. These data results from the analysis of neat chemicals, prior to the preparation of the DMSO solutions sent to NCATS and should not be equated with NCATS QC Day 0 or QC Day 4 data.

Key for the purity flags in the MRI 1-page reports

The dataset provides DTT conclusions from bioassay level of evidence, and genetic toxicology measures of bacterial mutagenicity, micronucleus, and comet assay; February 2022 version.

In TR544 and subsequent reports, for studies showing multiple chemical-related neoplastic effects that if considered individually would be assigned to different levels of evidence categories. In a study with clear evidence of carcinogenic activity at some tissue sites, other responses that alone might be deemed some evidence are indicated as “were also related” to chemical exposure. In studies with clear or some evidence of carcinogenic activity, other responses that alone might be termed equivocal evidence are indicated as “may have been” related to chemical exposure.  These levels of evidence are now included in this dataset.

Conclusions from Ames studies for each strain, activation condition, etc.

Phase I and II C. elegans and zebrafish toxicity estimates. Developmental Effects of the ToxCast™ Phase I and Phase II Chemicals in Caenorhabditis elegans and Corresponding Responses in Zebrafish, Rats, and Rabbits. Windy A. Boyd , Marjolein V. Smith , Caroll A. Co , Jason R. Pirone , Julie R. Rice , Keith R. Shockley , and Jonathan H. Freedman. Published:01May2016. https://doi.org/10.1289/ehp.1409645.

Toxicogenomic studies consisting of microarrays to evaluate the biological response to a toxicological challenge at the genome level.

Conclusions from Drosophila germ cell mutagenicity studies.

Conclusions from mammalian cell cytogenetics studies for CHO-W-B1 hamsters.

Conclusions from mammalian cell mutagenics studies for each activation condition.

Conclusions from rodent cytogenetics studies for B6C3F1 mice.

Study and trial conclusions from in vivo micronucleus assays for each strain and tissue, etc.

This dataset contains lesions found in studies where each dose group had two or more of the lesion.  Lesions with a statistically significant difference (p<0.05) in incidence between the exposed and control animals are flagged.

The data contained herein were collected in 2020 from a source now known to contain omissions.  We plan to replace with an up to date dataset in 2023.

Conclusions from comet assays for each strain, organ, etc.

Evaluation of androgen assay results using a curated Hershberger database. Kleinstreuer NC, Browne P, Chang X, Judson R, Casey W, Ceger P, Deisenroth C, Baker N, Markey K, Thomas RS. Published: 25 May 2018. https://doi.org/10.1016/j.reprotox.2018.08.017. Performance data from applying the ToxCast/Tox 21 androgen receptor model, based on 11 high throughput assays, to 39 reference chemicals.

Inhalation Threshold Limit Values (TLVs) for 103 chemicals originally listed in Schaper, M. (1993), Development of a database for sensory irritants and its use in establishing occupational exposure limits, Am. Ind. Hyg. Assoc. J. 54: 488-544; doi: 10.1080/15298669391355017, were updated in 2015. Data resulting from this update are included in this data table.

Inhalation Threshold Limit Values (TLVs) for 112 chemicals listed in the 2015, American Conference of Governmental Industrial Hygienists (ACGIH) booklet entitled "TLVs and BEIs Based on the Documentation of the Threshold Limit Values for Chemical Substances and Physical Agents & Biological Exposure Indices".

Evaluation of androgen assay results using a curated Hershberger database. Kleinstreuer NC, Browne P, Chang X, Judson R, Casey W, Ceger P, Deisenroth C, Baker N, Markey K, Thomas RS. Published: 25 May 2018. https://doi.org/10.1016/j.reprotox.2018.08.017. Performance data from applying the ToxCast/Tox 21 androgen receptor model, based on 11 high throughput assays, to 39 reference chemicals.

Evaluation of androgen assay results using a curated Hershberger database. Kleinstreuer NC, Browne P, Chang X, Judson R, Casey W, Ceger P, Deisenroth C, Baker N, Markey K, Thomas RS. Published: 25 May 2018. https://doi.org/10.1016/j.reprotox.2018.08.017. Performance data from applying the ToxCast/Tox 21 androgen receptor model, based on 11 high throughput assays, to 39 reference chemicals.

Annotation of Hallmark Gene Sets using NextBio platform to identify correlations between Hallmark Gene Sets and gene expression studies focusing on liver, heart and kidney tissue in rodents and humans. Additionally, correlations to gene expression studies in other tissues in rodents are described herein.

Study and trial conclusions from micronucleus assays for each cell type and activation condition, etc.

Biomarkers predictive of molecular and toxicological effects are needed to interpret emerging high-throughput transcriptomic data streams. The TGx-DDI biomarker was compared to microarray data in a compendium derived from human cells using the Running Fisher test, a nonparametric correlation test. In addition, the Tox21 p53 assay examined p53 responses at multiple doses at 16 h and in parallel identified doses that were cytotoxic. This dataset contains 19 biosets that were positive in the Tox21 Assay.

Biomarkers predictive of molecular and toxicological effects are needed to interpret emerging high-throughput transcriptomic data streams. The TGx-DDI biomarker was compared to microarray data in a compendium derived from human cells using the Running Fisher test, a nonparametric correlation test. This dataset contains 274 biosets with a significant TGx-DDI biomarker correlation.

Data of animal biological systems affected by chemical exposure with relevance to human health effects.

Summary body weight data with ratio of the treatment group body weight mean to the control group body weight mean.

Data of animal neurological and developmental/congenital outcomes from chemical exposure with relevance to human health effects.

This dataset contains statistically significant lesions from DTT studies as collated in the CEBSR database, including legacy and recently reported studies. Tissue name and morphology terminology have been harmonized for this dataset. New endpoints for each lesion are available: BMD (benchmark dose), LEL (lowest effect level) and NEL (no effect level).

The data contained herein were collected in 2021 from a source now known to contain omissions.  We plan to replace with an up to date dataset in 2023.

This dataset contains clinical pathology summary data from the CEBSR database, including legacy and recently reported studies. The test names and units have been harmonized for this dataset.  Documentation including test name descriptions and study exclusion are available here: https://cebs.niehs.nih.gov/cebs/paper/14893.

Regarding statistical analyses and interpretation of clinical pathology data: statistical analyses are a starting point in the interpretation of clinical pathology data and should not be used as a primary tool to identify test article-related effects.  Statistical significance testing of clinical pathology data should not replace critical scientific interpretation of data (ideally by an experienced veterinary toxicologic clinical pathologist) and consideration of clinical and biological relevance of any observed changes.  Clinical pathology data should be evaluated in context with all other available study data including in-life data (e.g., food consumption, body weight) and histopathology.  For more information about statistical significance testing for clinical pathology in toxicology studies, please refer to the below listed references.  

Aulbach A, Vitsky A, Arndt T, et al. Overview and considerations for the reporting of clinical pathology interpretations in nonclinical toxicology studies. Vet Clin Pathol. 2019;48:389–399. https://doi.org/10.1111/vcp.12772

Hall RL. Practical Considerations in Clinical Pathology Data Interpretation and Description: The Use of Statistics, Reference Intervals, and Severity Descriptors. Toxiol Path. 2017;45:362-365.  https://doi.org/10.1177/0192623316669824

Siska W, Gupta A, Tomlinson L, Tripathi N, von Beust B. Recommendations for clinical pathology data generation, interpretation, and reporting in target animal safety studies for veterinary drug development. Int J Toxicol. 2017;36:293-302.  https://doi.org/10.1177/1091581817711876

This dataset contains control group clinical pathology summary data from the CEBSR database, including legacy and recently reported studies. The test names and units have been harmonized for this dataset.  Documentation including test name descriptions and study exclusion are available here: https://cebs.niehs.nih.gov/cebs/paper/14893.

Regarding statistical analyses and interpretation of clinical pathology data: statistical analyses are a starting point in the interpretation of clinical pathology data and should not be used as a primary tool to identify test article-related effects.  Statistical significance testing of clinical pathology data should not replace critical scientific interpretation of data (ideally by an experienced veterinary toxicologic clinical pathologist) and consideration of clinical and biological relevance of any observed changes.  Clinical pathology data should be evaluated in context with all other available study data including in-life data (e.g., food consumption, body weight) and histopathology.  For more information about statistical significance testing for clinical pathology in toxicology studies, please refer to the below listed references.  

Aulbach A, Vitsky A, Arndt T, et al. Overview and considerations for the reporting of clinical pathology interpretations in nonclinical toxicology studies. Vet Clin Pathol. 2019;48:389–399. https://doi.org/10.1111/vcp.12772

Hall RL. Practical Considerations in Clinical Pathology Data Interpretation and Description: The Use of Statistics, Reference Intervals, and Severity Descriptors. Toxiol Path. 2017;45:362-365.  https://doi.org/10.1177/0192623316669824

Siska W, Gupta A, Tomlinson L, Tripathi N, von Beust B. Recommendations for clinical pathology data generation, interpretation, and reporting in target animal safety studies for veterinary drug development. Int J Toxicol. 2017;36:293-302.  https://doi.org/10.1177/1091581817711876

This dataset contains lesions from control groups in DTT studies as collated in the CEBSR database, including legacy and recently reported studies. Tissue name and morphology terminology have been harmonized for this dataset.

The data contained herein were collected in 2021 from a source now known to contain omissions.  We plan to replace with an up to date dataset in 2023.

This dataset contains individual animal lesions from the Division of Translational Toxicology (DTT) studies including legacy and recently reported data.  Tissue name, locator, morphology, and modifier terminologies have been harmonized for this dataset.  

We have a current DTT dictionary available at https://doi.org/10.22427/NTP-DATA-002-00092-0002-0000-5. The terms used to develop this collection is available here: https://doi.org/10.22427/NTP-DATA-002-00092-0001-0000-4.
 
The data contained herein were collected in 2021 from a source now known to contain omissions.  We plan to replace with an up to date dataset in 2023.

Catalog of studies of Environmental Health (EH) interest with expression data performed by members of DTT, NIEHS and other collaborators.  Work can be filtered using common metadata using the CEBS data collections interface.
 
Other metadata useful to interpret a toxicology or EH study are being collected; this project is expected to evolve.  For the present time, links to external files with metadata and other data for the subjects are provided where they are available from the author.
 
Each study is linked to a public repository or other source to permit the data themselves to be retrieved.  If the study is deposited in the Gene Expression Omnibus (GEO) then the link to the tools provided by GEO to analyze the data is 
also provided.
 
This catalog is from voluntary submissions.  To submit data for consideration please contact CEBS-Support@mail.nih.gov 

We took as a case study the challenge to find work on flame retardants conducted by authors affiliated with the National Toxicology Program

Findings:
24 NTP-authored reports
26 publications in CEBS (many conducted on libraries of compounds, some FR
23 in Pubmed (and not in CEBS)  search: (affiliation = “National Toxicology Program”) and (flame)
7 in both CEBS and PubMed

We then reviewed the abstract to identify the species, cell system, duration of exposure and assays / endpoints measured. 

Result – a data set listing 70 publications, 68 distinct chemicals or chemical mixtures, and 295 chemical/publication combination (i.e. a chemical listed in a publication)

This dataset provides an overview of public studies accessioned into the DTT Collection, including test compounds and standardized study factors like sex, species, strain, route, role, and vehicle. Standard terms are derived from the Data Dictionary.

This dataset provides a mapping of individual subjects that have data occurring in multiple legacy databases. GDB is a legacy database that contains data relating to hematology, organ weights, andrology, and other clinical chemistry recorded observations (Single Point Data). TDMSE is a legacy database that contains data relating to histopathology and clinical pathology findings (Observation Data). When a subject underwent assays from both categories (Single Point Data and Observation Data), then the subjects are represented in both databases, by different unique identifiers. This dataset provides the mapping necessary to identify a single subject across single point and observation data findings.