The study of DPP4 confirmed that DEGs verified by our microarray may associated with FS

of the adverse effects caused by drugs in the patients are due to the administration of multiple medications. As an example of DDIs, some macrolides, such as erythromycin, inhibit the metabolism and the elimination of warfarin. This fact could cause an increased effect of warfarin with the consequent risk due to its anticoagulant properties. Another example is the combination of simvastatin and posaconazole, associated with a risk of myopathy and rhabdomyolysis due to increased statin plasma concentrations. Pharmacovigilance focuses on the collection, monitoring and evaluation of adverse events caused by drugs and other biological products in the pharmaceutical market. Pharmacovigilance agencies, such as the FDA U. S. Food and Drug Administration, are interested in the use of post-marketing data to analyze possible adverse drug effects and possible DDIs that PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19703425 cause higher impact in ADE development. However, improvements in the current approaches are still needed to help PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19706315 in the early detection of DDIs. Recently, a number of computational methods have been successfully applied to predict DDIs. Among them, cheminformatic methodologies, such as protein-structure-based and ligand-based methods, have been used in the detection of DDIs. Cheminformatics provides a useful approach through the use of 2D/3D QSAR , homology modeling and molecular docking. These methods can infer similarity between sets of drugs and study possible interactions with pharmacodynamics or 1022150-57-7 pharmacokinetic targets. In previous work, we have leveraged cheminformatics to construct general models of DDIs. On the other hand, scientific literature and pharmacovigilance databases are additional sources with important implications in DDI discovery. Percha et al. mined the scientific literature to detect DDIs through the extraction of gene-drug relationships. Mining electronic health records or the FDA’s Adverse Event Reporting System is an alternative for the discovery of DDIs. In fact, Tatonetti et al. recently provided an important source of DDI candidates, the TWOSIDES database, through mining FAERS. However, analysis of pharmacovigilance data is still very challenging and rampant confounding leads to high false positive rates. Alternatively, cheminformatic methods can be applied to rank the DDI candidates extracted from a pharmacovigilance study. These methods offer the possibility to study the final candidates from the point of view of the molecular structure, pharmacological action or adverse effects comparison. Similarity-based methods were useful to rank drug candidates extracted from pharmacovigilance data mining that produce some adverse events, such as rhabdomyolysis and pancreatitis. In this paper, we systematically apply six different similarity-based techniques to evaluate drug interaction hypotheses mined from pharmacovigilance data. The objective of the current study is to improve the detection of DDIs in the TWOSIDES database using methodologies we recently developed based on the application of similarity-based modeling. When applied to the TWOSIDES database a reference standard of DDIs that produce arrhythmia, we measured: 1) enrichment factor provided by TWOSIDES, and 2) performance when we rank the set of DDI candidates using proportional reporting ratio, p-values, and different similarity-based models. As is demonstrated by our results, the implementation of cheminformatic models in pharmacovigilance data is useful in DDI signal detection and decision making