Knowledge Organization Systems for Systematic Chemical Assessments

Abstract

Background: Although the implementation of systematic review and evidence mapping methods stands to improve the transparency and accuracy of chemical assessments, they also accentuate the challenges that assessors face in ensuring they have located and included all the evidence that is relevant to evaluating the potential health effects an exposure might be causing. This challenge of information retrieval can be characterized in terms of "semantic" and "conceptual" factors that render chemical assessments vulnerable to the streetlight effect.

Objectives: This commentary presents how controlled vocabularies, thesauruses, and ontologies contribute to overcoming the streetlight effect in information retrieval, making up the key components of Knowledge Organization Systems (KOSs) that enable more systematic access to assessment-relevant information than is currently achievable. The concept of Adverse Outcome Pathways is used to illustrate what a general KOS for use in chemical assessment could look like.

Discussion: Ontologies are an underexploited element of effective knowledge organization in the environmental health sciences. Agreeing on and implementing ontologies in chemical assessment is a complex but tractable process with four fundamental steps. Successful implementation of ontologies would not only make currently fragmented information about health risks from chemical exposures vastly more accessible, it could ultimately enable computational methods for chemical assessment that can take advantage of the full richness of data described in natural language in primary studies. https://doi.org/10.1289/EHP6994.

Figure 1.

The relationship between the processes involved in systematically mapping and systematically reviewing evidence. The elements where what we call the “information retrieval challenge” comes into play are highlighted in bold and yellow. Comprehensive evidence maps, if they represent complete inventories of the literature, should ultimately obviate the need for additional literature searches in systematic reviews conducted in response to the findings of a systematic evidence mapping exercise.

Figure 2.

Illustration of how lack of knowledge of relations between concepts relevant to a research topic can result in evidence of potential importance to a given question being overlooked. In this example, awareness that DNA repair is obstructed by oxidative DNA damage allows lung cancer and leukemia to be connected to stressors that cause oxidative DNA damage to be incorporated into a cancer assessment. However, lack of awareness that replication forks regulate DNA repair may result in studies of stressors that stall replication forks by binding to cleavage complexes being excluded from cancer assessments.

Figure 3.

The MeSH CV entry for “polycyclic aromatic hydrocarbons,” 21 July 2020.

Figure 4.

The MeSH thesaurus entries for “polycyclic aromatic hydrocarbons,” 21 July 2020. For brevity, only first-level entries are shown.

Figure 5.

The elements of an Adverse Outcome Pathway, whereby an exposure causes a Molecular Initiating Event, initiating a biological sequence of causally related Key Events that result in a final Adverse Outcome being manifest. Experimental research can target how a challenge might affect a Key Event (Studies A, B, and C) or how one Key Event might cause another Key Event in a Key Event Relationship (Study D). Arranging biological events, exposures, and the evidence around them in these sorts of AOP chains can be very valuable for integrating mechanistic evidence into chemical assessments but requires knowledge organization systems that reflect the complexity and heterogeneity of the relationships and event types.

Figure 6.

Existing biological ontologies can be used to define key events in computable terms and thereby make AOP information more interoperable with other toxicological data sources. The same can be done when describing the assays and biomarkers used to measure the key events. Note: BAO, BioAssay Ontology; CHEAR, Children’s Health Exposure Analysis Resource; CHEBI, Chemical Entities of Biological Interest; CL: Cell Ontology; ECTO, Environment Exposure Ontology; EFO, Experimental Factor Ontology; GO, Gene Ontology; MonDO, Mondo Disease Ontology; MP, Mammalian Phenotype Ontology; PCO, Population and Community Ontology; PRO, Protein Ontology; SNOMED CT, SNOMED Clinical Terms; UBERON, Uber Anatomy Ontology.

Figure 7.

The workflow for matching natural language strings in research reports to a hierarchy of concepts in an ontology. Natural language information is extracted from included studies (e.g., phrases such as “increase in thyroid stimulating hormone”) into an evidence inventory (A). The terms “increase,” “thyroid,” “stimulating,” and “hormone” are cleaned and mapped to ontological classes in preparation for integration with other data sets. The inventory can then be connected to other data models by mapping terminology between CVs (B). Done enough times, a large data inventory begins to accumulate.