Contribution of Causality Assessment for an Automated Detection of Safety Signals: An Example Using the French Pharmacovigilance Database

Abstract

Introduction: Qualitative approaches based on drug causality assessment estimate the causal link between a drug and the occurrence of an adverse event from individual case safety reports. Quantitative approaches based on disproportionality analyses were developed subsequently to allow automated statistical signal detection from pharmacovigilance databases. This study assessed the potential value of causality assessment for automated safety signal detection.

Methods: All drug-serious adverse event pairs with a positive rechallenge and a semiology suggestive of drug causality were identified in the French pharmacovigilance database (BNPV) from 2011 to 2017. The results were compared with those obtained from automated disproportionality analyses of the BNPV/World Health Organization (WHO) VigiBase^®, complemented by the list of signals validated by the WHO-UMC (Uppsala Monitoring Centre). Summary of Product Characteristics (SmPCs), Martindale^®, Meyler's^® and MedLINE^® were used as other sources of information for the purpose of comparison.

Results: Of the 155 pairs of interest, 115 (74.2%) were also identified by another source of information. Since the individual case reporting in the BNPV, 23 (14.8%) of the adverse events (AEs) have been added to the SmPC, seven of which were not identified by disproportionality. Finally, 40 pairs were not identified by any other source of information, 13 of which were considered as potential new safety signals after analysis of case reports by pharmacovigilance experts. The signals identified by causality assessment involved antineoplastic and immunomodulatory drugs especially, in comparison with signals identified by WHO-UMC or by disproportionality within the BNPV.

Conclusion: The approach therefore appears useful as an additional tool for safety signal detection, especially for antineoplastic and immunomodulating agents.