ArticleJournal of biomedical semantics2016
An accurate and precise representation of drug ingredients.
Article in Journal of biomedical semantics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research.Briefings in functional genomics · 2021Article
- Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison.JMIR medical informatics · 2020Article
- A 2018 workshop: vaccine and drug ontology studies (VDOS 2018).BMC bioinformatics · 2019Article
- Enhancing the drug ontology with semantically-rich representations of National Drug Codes and RxNorm unique concept identifiers.BMC bioinformatics · 2019Article
- Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective.Yearbook of medical informatics · 2019Article
- Therapeutic indications and other use-case-driven updates in the drug ontology: anti-malarials, anti-hypertensives, opioid analgesics, and a large term request.Journal of biomedical semantics · 2017Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
backgroundIn previous work, we built the Drug Ontology (DrOn) to support comparative effectiveness research use cases. Here, we have updated our representation of ingredients to include both active ingredients (and their strengths) and excipients. Our update had three primary lines of work: 1) analysing and extracting excipients, 2) analysing and extracting strength information for active ingredients, and 3) representing the binding of active ingredients to cytochrome P450 isoenzymes as substrates and inhibitors of those enzymes.
methodsTo properly differentiate between excipients and active ingredients, we conducted an ontological analysis of the roles that various ingredients, including excipients, have in drug products. We used the value specification model of the Ontology for Biomedical Investigations to represent strengths of active ingredients and then analyzed RxNorm to extract excipient and strength information and modeled them according to the results of our analysis. We also analyzed and defined dispositions of molecules used in aggregate as active ingredients to bind cytochrome P450 isoenzymes.
resultsOur analysis of excipients led to 17 new classes representing the various roles that excipients can bear. We then extracted excipients from RxNorm and added them to DrOn for branded drugs. We found excipients for 5,743 branded drugs, covering ~27% of the 21,191 branded drugs in DrOn. Our analysis of active ingredients resulted in another new class, active ingredient role. We also extracted strengths for all types of tablets, capsules, and caplets, resulting in strengths for 5,782 drug forms, covering ~41% of the 14,035 total drug forms and accounting for ~97 % of the 5,970 tablets, capsules, and caplets in DrOn. We represented binding-as-substrate and binding-as-inhibitor dispositions to two cytochrome P450 (CYP) isoenzymes (CYP2C19 and CYP2D6) and linked these dispositions to 65 compounds. It is now possible to query DrOn automatically for all drug products that contain active ingredients whose molecular grains inhibit or are metabolized by a particular CYP isoenzyme. DrOn is open source and is available at http://purl.obolibrary.org/obo/dron.owl.
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