Using semantic predications to uncover drug-drug interactions in clinical data.
In this study we report on potential drug-drug interactions between drugs occurring in patient clinical data. Results are based on relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations (titles and abstracts) using SemRep. The core of our methodology is to construct two potential drug-drug interaction schemas, based on relationships extracted from SemMedDB. In the first schema, Drug1 and Drug2 interact through Drug1's effect on some gene, which in turn affects Drug2. In the second, Drug1 affects Gene1, while Drug2 affects Gene2. Gene1 and Gene2, together, then have an effect on some biological function. After checking each drug pair from the medication lists of each of 22 patients, we found 19 known and 62 unknown drug-drug interactions using both schemas. For example, our results suggest that the interaction of Lisinopril, an ACE inhibitor commonly prescribed for hypertension, and the antidepressant sertraline can potentially increase the likelihood and possibly the severity of psoriasis. We also assessed the relationships extracted by SemRep from a linguistic perspective and found that the precision of SemRep was 0.58 for 300 randomly selected sentences from MEDLINE. Our study demonstrates that the use of structured knowledge in the form of relationships from the biomedical literature can support the discovery of potential drug-drug interactions occurring in patient clinical data. Moreover, SemMedDB provides a good knowledge resource for expanding the range of drugs, genes, and biological functions considered as elements in various drug-drug interaction pathways.
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. Electronic address: zhan1386@umn.edu.
Rui
R
Zhang
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. Electronic address: cairellimj@mail.nih.gov.
Michael J
MJ
Cairelli
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. Electronic address: fiszmanm@mail.nih.gov.
Marcelo
M
Fiszman
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. Electronic address: grosemblat@mail.nih.gov.
Graciela
G
Rosemblat
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. Electronic address: kilicogluh@mail.nih.gov.
Halil
H
Kilicoglu
Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. Electronic address: trindflesch@mail.nih.gov.
Thomas C
TC
Rindflesch
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; College of Pharmacy, University of Minnesota, Minneapolis, MN, USA. Electronic address: pakh0002@umn.edu.
Serguei V
SV
Pakhomov
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Department of Surgery, University of Minnesota, Minneapolis, MN, USA. Electronic address: gmelton@umn.edu.
Genevieve B
GB
Melton
D000806:Angiotensin-Converting Enzyme Inhibitors / Q000008:administration & dosage / Q000009:adverse effects; D004347:Drug Interactions; D006801:Humans; D017706:Lisinopril / Q000008:administration & dosage / Q000009:adverse effects; D012660:Semantics; D017367:Selective Serotonin Reuptake Inhibitors / Q000008:administration & dosage / Q000009:adverse effects; D020280:Sertraline / Q000008:administration & dosage / Q000009:adverse effects
D000806:Angiotensin-Converting Enzyme Inhibitors; D017706:Lisinopril; D017367:Selective Serotonin Reuptake Inhibitors; D020280:Sertraline
Drug–drug interactions; MEDLINE; Natural language processing; SemMedDB; SemRep; Semantic predication