Name
Abstract | ||
|---|---|---|
Background Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs. Methods We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. Drug Bank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in Drug Bank was computed to identify DDI candidates. Results The methodology was evaluated using as a gold standard the interactions retrieved from the initial Drug Bank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database. Conclusion The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1136/amiajnl-2012-000935 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Field | DocType | Volume |
Drug-drug interaction,Data mining,Similarity analysis,Adverse effect,Biological effect,Medicine,Drug,DrugBank,Drug administration | Journal | 19 |
Issue | ISSN | Citations |
6 | 1067-5027 | 29 |
PageRank | References | Authors |
1.15 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santiago Vilar | 1 | 106 | 9.12 |
| Rave Harpaz | 2 | 207 | 13.18 |
| Eugenio Uriarte | 3 | 68 | 7.62 |
| Lourdes Santana | 4 | 54 | 4.09 |
| Raul Rabadan | 5 | 80 | 8.83 |
| Carol Friedman | 6 | 1618 | 147.25 |