Adverse drug reactions (ADRs) constitute a major cause of morbidity and mortality worldwide. Due to the relevance of ADRs for both public health and pharmaceutical industry, it is important to develop efficient ways to monitor ADRs in the population. In addition, it is also essential to comprehend why a drug produces and adverse effect. To unravel the molecular mechanisms of ADRs, it is necessary to consider the ADR in the context of current biomedical knowledge that might explain it. Nowadays there are plenty of information sources that can be exploited in order to accomplish this goal. Nevertheless, the fragmentation of information and, more importantly, the diverse knowledge domains that need to be traversed, pose challenges to the task of exploring the molecular mechanisms of ADRs. We present a novel computational framework to aid in the collection and exploration of evidences that support the causal inference of ADRs detected by mining clinical records. This framework was implemented as publicly available tools integrating state-of-the-art bioinformatics methods for the analysis of drugs, targets, biological processes and clinical events. The availability of such tools for in silico experiments will facilitate research on the mechanisms that underlie ADR, contributing to the development of safer drugs.
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Erik M. van Mulligen, Annie Fourrier-Reglat, David Gurwitz, Mariam Molokhia, Ainhoa Nieto, Gianluca Trifiro, Jan A. Kors, Laura I. Furlong. The EU-ADR Corpus: Annotated Drugs, Diseases, Targets, and their Relationships. J Biomed Inform. 2012 Apr 25. PubMed
Bauer-Mehren A, van Mullingen EM, Avillach P, Carrascosa MC, Garcia-Serna R, Piñero J, Singh B, Lopes P, Oliveira JL, Diallo G, Mestres J, Ahlberg Helgee E, Boyer S, Sanz F, Kors JA, Furlong LI. Automatic filtering and substantiation of drug safety signals. PLoS Comput Biol. 2012 Apr;8(4):e1002457. Epub 2012 Apr 5. PubMed