Abstract
Pharmacovigilance plays a key role in the healthcare domain through the assessment, monitoring and discovery of interactions amongst drugs and their effects in the human organism. However, technological advances in this field have been slowing down over the last decade due to miscellaneous legal, ethical and methodological constraints. Pharmaceutical companies started to realize that collaborative and integrative approaches boost current drug research and development processes. Hence, new strategies are required to connect researchers, datasets, biomedical knowledge and analysis algorithms, allowing them to fully exploit the true value behind state-of-the-art pharmacovigilance efforts. This manuscript introduces a new platform directed towards pharmacovigilance knowledge providers. This system, based on a service-oriented architecture, adopts a plugin-based approach to solve fundamental pharmacovigilance software challenges. With the wealth of collected clinical and pharmaceutical data, it is now possible to connect knowledge providers’ analysis and exploration algorithms with real data. As a result, new strategies allow a faster identification of high-risk interactions between marketed drugs and adverse events, and enable the automated uncovering of scientific evidence behind them. With this architecture, the pharmacovigilance field has a new platform to coordinate large-scale drug evaluation efforts in a unique ecosystem, publicly available at http://bioinformatics.ua.pt/euadr/.
Highlights
Pharmacovigilance plays an essential role in the post-market analysis of newly developed drugs [1, 2]
With collaboration from partners within the EU-ADR project it was possible to implement a prototype of the proposed distributed platform
The involvement in a large-scale European project allowed for the implementation of real-world algorithms by multiple knowledge providers
Summary
Pharmacovigilance plays an essential role in the post-market analysis of newly developed drugs [1, 2]. Postmarket pharmacovigilance complements the traditional premarket austere drug approval process, where the European Medicines Agency (EMA) [3] and the US Food and Drug Administration (FDA) [4] establish guidelines for new medicine approval, requiring intense testing and trials [5]. Along with these recommendations, pharmaceutical companies must define thorough risk management plans for post-market drug stages [6, 7]. The majority of data is not publicly available for researchers, which, jointly with other barriers, severely limits signal detection [9, 10]
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