Characterisation of Drug Interaction Related Signals Leading to EU Regulatory Action and a Methodological Review of Novel Drug Interaction Signal Detection Methods in the Post-Marketing Setting
Debabrata Roy1, 2, Lorna Hazell1, 2, Vicki Osborne1, 2 and Saad Shakir1, 2.
- Drug Safety Research Unit, Southampton, United Kingdom
- University of Portsmouth, Portsmouth, United Kingdom
A growing rise in polypharmacy is often attributed to an aging population and associated co-morbidities. In patients with complex therapeutic regimens, this increases the potential of harmful drug interactions (DI’s) which may result in unexpected adverse drug reactions. Current signal detection methods used to identify DI-related signals may not be sufficient. Novel methods utilizing big data sets, machine learning or algorithmic techniques may have a crucial role in DI-related signal detection.
The aim of this study is to assess current and novel methods used to identify DI-related safety signals in the post-marketing setting.
Current methods used to detect DI-related signals will be reviewed using publicly available information from the European Medicines Agency (EMA) on signals leading to post-marketing regulatory action between July 2012 and December 2018. A review of the published literature will also be conducted to identify novel methods used to detect DI-related signals which may have utility in the post-marketing setting. A systematic approach will be used to assess the performance of novel DI signal detection methods based on predefined criteria including, for example, sensitivity, precision and applicability.
Data collection, identification of valid data sources and methodological review are currently in progress. So far, one DI-related signal which led to post-marketing regulatory action has been identified from the search whereby dehydration was associated with an interaction between tolvaptan (vasopressin antagonist used to treat inappropriate antidiuretic hormone secretion) and diuretic use. This DI-related signal was identified in 2012 based on 16 spontaneous reports. The identification of this signal resulted in an update to product information, regarding a possible interaction between tolvaptan and diuretic use and the risk of renal dysfunction.
The final results of this study will identify whether the majority of DI-related signals in the post-marketing setting are identified from individual case safety and spontaneous reports. Novel methods, utilizing big data sets and advancements in machine learning and computational power, have the potential to identify previously unknown and unexpected DI-related signals.