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Anna Maria Masci

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)