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

• Published in: Reproductive 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
• Language: English
• 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
• ISSN: 0890-6238; 1873-1708; 1873-1708
• DOI: 10.1016/j.reprotox.2019.07.012

•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.