Using semantic predications to uncover drug–drug interactions in clinical data

• Published in: Journal of biomedical informatics 2014-06, Vol.49, p.134-147
• Main Authors: Zhang, Rui, Cairelli, Michael J., Fiszman, Marcelo, Rosemblat, Graciela, Kilicoglu, Halil, Rindflesch, Thomas C., Pakhomov, Serguei V., Melton, Genevieve B.
• Format: Article
• Language: English
• Subjects: Angiotensin-Converting Enzyme Inhibitors - administration & dosage; Angiotensin-Converting Enzyme Inhibitors - adverse effects; Biological; Drug Interactions; Drugs; Drug–drug interactions; Genes; Humans; Hypertension; Inhibitors; Lisinopril - administration & dosage; Lisinopril - adverse effects; MEDLINE; Natural language processing; Patients; Selective Serotonin Reuptake Inhibitors - administration & dosage; Selective Serotonin Reuptake Inhibitors - adverse effects; Semantic predication; Semantics; SemMedDB; SemRep; Sentences; Sertraline - administration & dosage; Sertraline - adverse effects
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2014.01.004

[Display omitted] •Discovery of drug–drug interactions in patient data using literature knowledge.•Structured knowledge extracted from MEDLINE and stored in SemMedDB.•Direct effects on a gene or indirect through a common physiological function.•Potential drug–drug interactions identified in medication lists from 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.