ToxBioBridge: an ontological approach for toxicological assays
Anna Maria Masci1, Jennifer Fostel1, Stephanie Holmgren1, Charles Schmitt1.
1 Office of Data Science, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, North Carolina, USA
Toxicological in vitro assays are central to toxicology research. Through the performance of these assays, it is possible to reveal a toxic effect such as immunotoxicity, genotoxicity, reproductive and developmental toxicity, and carcinogenicity for a given species, organ, and dose. In vitro measurements and a variety of new technologies are used to develop in vitro signatures and computational models predictive of in vivo response.
Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. The type of assay, inputs, devices used, and protocols all impact the reliability, interpretation and reusability of the data generated.
Although several databases collect toxicological assay information (EPA Dashboard, Tox21, and Bioplanet) most of the information associated with an assay is in the form of text. As a result, to understand what the assay is about requires a human being to read through the text and extract the information. Currently, the information is not structured in a way that would enable a computer to use it. Standardization1is one of the components of enabling FAIR assay data. Describing the assays using an ontological approach provides a mechanism to capture data provenance as well as facilitate the ability to integrate search and compare results from different studies.
Ontology is a formal representation of a body of knowledge within a given domain. Ontologies usually consist of a set of classes (or terms or concepts) with relations that operate between them. Ontologies are used to provide the underlying semantic structure for knowledge graphs to ensure shared meaning and understanding of the data both by humans and machine.
Ontology of Biomedical Investigation (OBI) is an OBO Foundry ontology that covers a domain of biomedical assays. Although OBI covers many assay types, it does not include the toxicological assays routinely used to test toxic compounds.
We created an initial prototype application ontology called ToxBioBridge (Toxicology Biology Bridge). In doing so we have taken advantage of the ontological representation to describe assays by expanding on a well-known OBI format to capture the toxicological assays. An innovative part of our approach is linking the ontological assay description with the associated gene ontology biological process, molecular function, and additional OBO Foundry ontologies such as UBERON, HPO etc.). A better understanding of mechanisms will improve some areas of toxicology, such as • Understanding chemical transport into sensitive biological compartments (e.g., brain, fetus) • • Modeling to support decision-making in cases of data gaps • • • Optimizing in vitro alternative methods. •••• Understanding how Adverse Outcome Pathways (AOPs) can reduce uncertainty in identifying hazards and assessing risk.
1 To address this lack of standardized language, NIEHS has launched the Environmental Health Language Collaborative (EHLC) https://www.niehs.nih.gov/research/programs/ehlc/index.cfm.