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Sous presse. Epreuves corrigées par l'auteur. Disponible en ligne depuis le mercredi 11 mars 2020
Doi : 10.1016/j.therap.2020.02.022
Received : 16 September 2019 ;  accepted : 21 February 2020
Uses of pharmacovigilance databases: An overview

Kévin Bihan a, , Bénédicte Lebrun-Vignes a, Christian Funck-Brentano b, Joe-Elie Salem b
a Regional pharmacovigilance center, department of pharmacology, Pitié-Salpêtrière hospital, 47/83, boulevard de l’Hôpital, 75013 Paris, France 
b INSERM, CIC Pitié-Salpêtrière (CIC-1901), Sorbonne université, faculty of medicine, AP-HP, Pitié-Salpêtrière hospital, department of pharmacology and clinical investigation center, UNICO-GRECO Cardio-oncology program, 75013 Paris, France 

Corresponding author.

Over the past decades, assessment of drug safety and of their benefits harms balance has been profoundly modified by the availability of large databases and computerized automated statistical approaches. Improvement of digital data storage capacity has been applied to pharmacovigilance reports. VigiBase, the international pharmacovigilance database, is now aggregating over 21 million individual case safety reports in 2020. Identification and investigation of drug safety signals – concerning notably rare and unknown adverse drug reactions – is one of the major tasks in pharmacovigilance that can be amplified by automated signal detection. Several quantitative statistical methods exist, each with its own strengths and limits. Integrating signal detection, pharmacovigilance databases can be used for a wide variety of retrospective observational studies illustrated here by concrete examples. Confirming these signals by orthogonal validation using pre-clinical platforms and prospective trials is helpful. Pharmacovigilance databases represent a considerable source of information. However, the quality of signal detection and of pharmacoepidemiology studies in the field of adverse drug reaction closely depends on the quality of the individual data recorded.

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Keywords : Pharmacovigilance, Database, Datamining, Adverse event, Pharmacology

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