Much of the work carried out by DTT is in support of the National Toxicology Program (NTP), an interagency partnership of the Food and Drug Administration, National Institute for Occupational Safety and Health, and NIEHS.
Public Health Significance
The NIEHS Division of Translational Toxicology (DTT) aims to improve public health through development of data and knowledge that are translatable, predictive, and timely. In its pursuit of that mission, a wealth of important research information is generated, but data are spread across many resource providers, such as traditional information technology (IT) offices and scientific contractors that deliver informatics, data science, and computing capabilities. To advance DTT goals and better inform public health, coordination of such data as well as the scientific cyber infrastructure that supports research at DTT is needed.
The Scientific Cyberinfrastructure (SCI) program objectives include the following:
- Provide oversight, guidance, and prioritization for DTT SCI investments, ensuring that investments meet DTT needs and that resource providers can plan for and meet requests.
- Engage and partner with other SCI providers to increase capability and lower costs, including with NIEHS and National Institutes of Health SCI providers, as well as external providers.
- Supply strategic capabilities that advance the DTT toxicology pipeline, including ensuring data are managed according to FAIR (findability, accessibility, interoperability, and reusability) and TRUST (transparency, responsibility, user focus, sustainability, and technology) principles; providing SCI resources to support predictive toxicology; and promoting automation of evidence-based informatics.
This program will work to strategically plan for cyberinfrastructure capabilities in anticipation of evolving DTT needs and coordinate efforts across providers, recognizing the interdependency of IT, scientific computing, data science, and informatics.
DTT programs and studies generate an increasingly diverse and large amount of toxicology data related to multiple in vivo and in vitro biological systems. Maximizing the value of that research information is important to ensure the investment in producing such data is realized.
Research data often have value beyond immediate study goals. For example, combining data across DTT studies can help researchers recognize commonalities among chemical and biological mechanisms. Such integration also can enhance tools that guide chemical and assay selection decisions, reduce redundancy in testing, and fill important knowledge gaps.
Finally, DTT uses biomedical and toxicological information from external sources such as the U.S. Environmental Protection Agency, and integration with DTT data is important to support the increasing use of informatics and predictive research approaches.
|Key Area||Description||Example Project|
|Core||Provides common capabilities for data- and informatics-related needs||Training programs in R and Python|
|Evidence informatics||Provides capabilities and methods to gather scientific knowledge from various sources and to automate extraction of information from unstructured sources||Dextr, data extraction software, that incorporates natural language processing methods in literature reviews|
|Tox informatics||Provides capabilities and methods to assist with analysis of chemical and toxicology data||Integrated Chemical Environment computational toxicology workflows for chemical characterization and high-throughput screening data analysis; DNT Diver visualization tool for neurotoxicology assessments|
|Data management||Efforts to ensure DTT research data meet FAIR and TRUST principles||Chemical Effects in Biological Systems data collections|
|Knowledge management||Efforts to provide standardized language for DTT data and to develop knowledge organization systems to support research||DTT data dictionary of standardized terms|