ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses

•ToxRefDB v2 is a public resource that better supports predictive toxicology.•Data quality was improved using a controlled manual curation process.•Complex, heterogeneous designs (e.g. multigeneration studies) were aggregated.•Increased data interoperability has been facilitated using controlled voc...

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Published inReproductive toxicology (Elmsford, N.Y.) Vol. 89; pp. 145 - 158
Main Authors Watford, Sean, Ly Pham, Ly, Wignall, Jessica, Shin, Robert, Martin, Matthew T., Friedman, Katie Paul
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.10.2019
Subjects
Animals
Computational Biology - methods
Computational Biology - trends
Databases, Factual - trends
Dose-Response Relationship, Drug
Hazardous Substances - chemistry
Hazardous Substances - classification
Hazardous Substances - toxicity
In vivo toxicology
Models, Biological
Predictive toxicology
Software
Toxicology - methods
Toxicology - trends
Toxicology database
United States
United States Environmental Protection Agency
United States
Predictive toxicology
In vivo toxicology
Toxicology database
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Abstract •ToxRefDB v2 is a public resource that better supports predictive toxicology.•Data quality was improved using a controlled manual curation process.•Complex, heterogeneous designs (e.g. multigeneration studies) were aggregated.•Increased data interoperability has been facilitated using controlled vocabulary.•Quantitative data extraction enables dose-response modeling for many datasets. The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
AbstractList The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
The Toxicity Reference Database (ToxRefDB) structures information from over 5,000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling results for nearly 28,000 datasets from 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
•ToxRefDB v2 is a public resource that better supports predictive toxicology.•Data quality was improved using a controlled manual curation process.•Complex, heterogeneous designs (e.g. multigeneration studies) were aggregated.•Increased data interoperability has been facilitated using controlled vocabulary.•Quantitative data extraction enables dose-response modeling for many datasets. The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications from the US Environmental Protection Agency and the National Toxicology Program, into a public resource for training and validation of predictive models. Herein, ToxRefDB version 2.0 (ToxRefDBv2) development is described. Endpoints were annotated (e.g. required, not required) according to guidelines for subacute, subchronic, chronic, developmental, and multigenerational reproductive designs, distinguishing negative responses from untested. Quantitative data were extracted, and dose-response modeling for nearly 28,000 datasets from nearly 400 endpoints using Benchmark Dose (BMD) Modeling Software were generated and stored. Implementation of controlled vocabulary improved data quality; standardization to guideline requirements and cross-referencing with United Medical Language System (UMLS) connects ToxRefDBv2 observations to vocabularies linked to UMLS, including PubMed medical subject headings. ToxRefDBv2 allows for increased connections to other resources and has greatly enhanced quantitative and qualitative utility for predictive toxicology.
Author Friedman, Katie Paul
Ly Pham, Ly
Wignall, Jessica
Watford, Sean
Martin, Matthew T.
Shin, Robert
AuthorAffiliation ORISE Postdoctoral Research Participant
ICF, Burlington, VT
National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
Currently at Drug Safety Research and Development, Global Investigative Toxicology, Pfizer, Groton, CT
ORAU, contractor to U.S. Environmental Protection Agency through the National Student Services Contract
AuthorAffiliation_xml – name: Currently at Drug Safety Research and Development, Global Investigative Toxicology, Pfizer, Groton, CT
– name: ORAU, contractor to U.S. Environmental Protection Agency through the National Student Services Contract
– name: National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency
– name: ORISE Postdoctoral Research Participant
– name: ICF, Burlington, VT
Author_xml – sequence: 1
  givenname: Sean
  surname: Watford
  fullname: Watford, Sean
  organization: ORAU, Contractor to U.S. Environmental Protection Agency through the National Student Services Contract, United States
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  surname: Ly Pham
  fullname: Ly Pham, Ly
  organization: ORAU, Contractor to U.S. Environmental Protection Agency through the National Student Services Contract, United States
– sequence: 3
  givenname: Jessica
  surname: Wignall
  fullname: Wignall, Jessica
  organization: ICF, Burlington, VT, United States
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  surname: Shin
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  organization: ICF, Burlington, VT, United States
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/31340180$$D View this record in MEDLINE/PubMed
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Keywords Predictive toxicology
In vivo toxicology
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Snippet •ToxRefDB v2 is a public resource that better supports predictive toxicology.•Data quality was improved using a controlled manual curation process.•Complex,...
The Toxicity Reference Database (ToxRefDB) structures information from over 5000 in vivo toxicity studies, conducted largely to guidelines or specifications...
The Toxicity Reference Database (ToxRefDB) structures information from over 5,000 in vivo toxicity studies, conducted largely to guidelines or specifications...
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SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 145
SubjectTerms Animals
Computational Biology - methods
Computational Biology - trends
Databases, Factual - trends
Dose-Response Relationship, Drug
Hazardous Substances - chemistry
Hazardous Substances - classification
Hazardous Substances - toxicity
In vivo toxicology
Models, Biological
Predictive toxicology
Software
Toxicology - methods
Toxicology - trends
Toxicology database
United States
United States Environmental Protection Agency
Title ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses
URI https://dx.doi.org/10.1016/j.reprotox.2019.07.012
https://www.ncbi.nlm.nih.gov/pubmed/31340180
https://www.proquest.com/docview/2264226407
https://pubmed.ncbi.nlm.nih.gov/PMC6944327
Volume 89
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