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Public Databases Supporting Computational Toxicology
Citation:
JUDSON, R. Public Databases Supporting Computational Toxicology. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH - PART B: CRITICAL REVIEWS. Taylor & Francis, Inc., Philadelphia, PA, 13(2):218-31, (2010).
Impact/Purpose:
This article described the rationale for and design of databases being developed for use in computational toxicology research. A number of databases and knowledgebases are compiling high-quality data on environmental chemicals from in vitro and in vivo experiments in formats that allow for cross-chemical modeling. Key issues that these efforts have had to contend with are extraction of quantitative data into tabular, computable formats, and the mapping of data from multiple sources onto standard terms, using predefined vocabularies or ontologies. Each of the database development teams has had to make design choices that involve compromises between completeness and comprehensiveness, and curated (e.g., CTD) versus unfiltered content (PubChem). For instance, the CEBS database contains detailed information down to the individual animal level, including whole genome microarray data, but is limited to 132 chemicals. At the other end, the ACToR database contains information on >500,000 chemicals, but much of that is highly summarized, and no curation, beyond what is provided by the original sources of the data, is provided. CTD is completely hand curated and has much broader chemical coverage than CEBS, but contains only summary information on genes and diseases.
Description:
A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, researchers need quantitative in vitro and ideally in vivo data for large numbers of chemicals for common sets of assays and endpoints. A number of groups are compiling such data sets into publicly available web-based databases. This article (1) reviews some of the underlying challenges to the development of the databases, (2) describes key technologies used (relational databases, ontologies, and knowledgebases), and (3) summarizes several major database efforts that are widely used in the computational toxicology field.
