Toward a comprehensive drug ontology: extraction of drug-indication relations from diverse information sources

Mark E Sharp

doi:10.1186/s13326-016-0110-0

Abstract

BackgroundDrug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, thus benefiting the industry and patients alike. Drug-disease relations, specifically drug-indication relations, are a prime candidate for representation in ontologies. There is a wealth of available drug-indication information, but structuring and integrating it is challenging.ResultsWe created a drug-indication database (DID) of data from 12 openly available, commercially available, and proprietary information sources, integrated by terminological normalization to UMLS and other authorities. Across sources, there are 29,964 unique raw drug/chemical names, 10,938 unique raw indication ”target” terms, and 192,008 unique raw drug-indication pairs. Drug/chemical name normalization to CAS numbers or UMLS concepts reduced the unique name count to 91 or 85% of the raw count, respectively, 84% if combined. Indication ”target” normalization to UMLS ”phenotypic-type” concepts reduced the unique term count to 57% of the raw count. The 12 sources of raw data varied widely in coverage (numbers of unique drug/chemical and indication concepts and relations) generally consistent with the idiosyncrasies of each source, but had strikingly little overlap, suggesting that we successfully achieved source/raw data diversity.ConclusionsThe DID is a database of structured drug-indication relations intended to facilitate building practical, comprehensive, integrated drug ontologies. The DID itself is not an ontology, but could be converted to one more easily than the contributing raw data. Our methodology could be adapted to the creation of other structured drug-disease databases such as for contraindications, precautions, warnings, and side effects.

Highlights

Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, benefiting the industry and patients alike
DailyMed DailyMed [25] is a free drug information resource provided by the U.S National Library of Medicine (NLM) that consists of digitized versions of drug labels as submitted to the U.S Food and Drug Administration (FDA)
The individual drug name mappings to Chemical Entities of Biological Interest (ChEBI), ChemIDplus, and Comparative Toxicogenomics Database (CTD) are encoded in drug-indication database (DID) columns I-AC

Summary

Introduction

Drug ontologies could help pharmaceutical researchers overcome information overload and speed the pace of drug discovery, benefiting the industry and patients alike. The basic goal is ontology-assisted inference of surprising and/or morelikely-to-succeed new drug candidate compounds for known uses, cutting costs and time to market. Drug ontology-assisted inference could be applied to finding new uses for known compounds (drug repurposing) [14], or “personalized” genome-dependent safety/efficacy profiling (pharmacogenomics) [15,16,17,18]. These ontologies include drug relations to chemically similar compounds, diseases (therapeutic classifications, indications, side effects), and biological pathways (mechanisms of action, molecular target proteins or their genes, secondary diseasegene and protein-protein interactions).

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Conclusion