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Dr. Ann Richard obtained her Ph.D. from UNC-Chapel Hill in Physical Chemistry in 1983. She was a Principal Research Investigator in the Environmental Carcinogenesis Division, National Health and Environmental Effects Research Laboratory of the US EPA in RTP from 1987-2005. She is currently on the scientific staff of the newly formed National Center for Computational Toxicology within ORD. Her research activities have involved the application of computational chemistry methods to developing structure-activity relationships (SAR) in environmental toxicology, and the review and critique of structure-based predictive toxicology methods. She has published over 50 articles, and has been on the editorial boards of Mutation Research and Chemical Research in Toxicology. She has developed the DSSTox Database Network and public website to improve the accessibility and standardization of public toxicity data. This effort is providing a foundation for improved chemoinformatics capabilities, SAR model development, and broader integration of legacy toxicity data with newer high-throughput screening and toxicogenomics data.
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Toxico-Cheminformatics in Support of Predictive Toxicology
Ann Richard (1)*, Maritja Wolf (2), ClarLynda Williams-Devane (3), Richard Judson (1)
(1) National Center for Computational Toxicology, US EPA, RTP, NC;
(2) Lockheed Martin, Contractor to the US EPA, RTP, NC;
(3) Bioinformatics Graduate Program, NC State University, Raleigh, NC 27599.
Efforts to improve public access to chemical toxicity information resources, coupled with new high-throughput screening (HTS) data and efforts to systematize legacy toxicity studies, have the potential to significantly improve predictive capabilities in toxicology. Important developments include: 1) large and growing public resources that link chemical structures to biological activity and toxicity data in searchable format, and that offer more nuanced and varied representations of activity; 2) standardized relational data models that capture relevant details of chemical treatment and effects of published in vivo experiments; and 3) the generation of large amounts of new data from public efforts that are employing HTS technologies to probe a wide range of bioactivity and cellular processes across large swaths of chemical space. Chemical structure effectively links data across diverse study domains (e.g., ‘omics’, HTS, traditional toxicity studies), toxicity domains (carcinogenicity, developmental toxicity, neurotoxicity, immunotoxicity, etc) and database sources (EPA, FDA, NCI, PubChem, GEO, ArrayExpress, etc.). The DSSTox database network is evolving to more effectively support these capabilities. In addition, public initiatives (such as ToxML) are developing systematized data models of toxicity study areas and introducing standardized templates, controlled vocabularies, hierarchical organization, and powerful relational searching capability across newly captured data. Cheminformatics and data models, in turn, are providing the underpinning for the large public HTS efforts of the NIH Molecular Libraries Initiative, as well as new toxicity-targeted HTS programs within the EPA and the NIEHS National Toxicology Program. These initiatives are turning the structure-activity paradigm on its head, using chemicals to probe biological space and generating “biological profiles” of chemicals that, along with chemical structure considerations, offer the promise of providing richer, and more relevant and predictive associations to in vivo responses. This work was reviewed by EPA and approved for publication, but does not necessarily reflect EPA policy.
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