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ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses
Citation:
Watford, S., L. Pham, J. Wignall, R. Shin, M. Martin, AND K. Friedman. ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses. REPRODUCTIVE TOXICOLOGY. Elsevier Science Ltd, New York, NY, 89:145-158, (2019). https://doi.org/10.1016/j.reprotox.2019.07.012
Impact/Purpose:
In this work, we describe the development of ToxRefDB v2, including a detailed description of the new content and enhancements to the database. The goal of ToxRefDB v2 is to provide a public database that better supports the needs of predictive toxicology by increasing the qualitative and quantitative information available and by facilitating the interoperability of legacy in vivo hazard information with other tools and databases. Recognizing that predictive toxicology will require iterative efforts to build computational resources like ToxRefDB, work to generate ToxRefDB v2 has been conducted primarily in four main areas: 1) Aggregation of complex and heterogeneous study designs, 2) Controlled vocabulary for accurate data extraction, aggregation, and integration, 3) Quantitative data extraction, 4) Efforts to improve data quality (including quality assurance, QA, and quality control, QC). This work represents a significant advancement in increasing the richness of information available for predictive and retrospective analyses from ToxRefDB.
Description:
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. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the US EPA.
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DOI: ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses