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2024 MHRA GPvP Symposium: Using AI in Pharmacovigilance

AI in pharmacovigilance

We recently attended the 2024 MHRA Good Pharmacovigilance Practice (GPvP) symposium which looked into GPvP inspection non-compliance, metrics and trends and regulatory updates. This included:

  • GPvP requirements and good practice in the UK
  • Latest trends in pharmacovigilance non-compliance identified during MHRA inspections
  • UK regulatory updates and changes
  • Regulator perspectives on hot topics in pharmacovigilance

What we found particularly interesting was the discussion surrounding some hot topics in pharmacovigilance, including the use of AI and machine learning (ML) technologies. As part of this discussion, those in attendance were asked a number of questions relating to the use of AI and ML – here’s what we learned.

According to a poll of those in attendance, 44% said their organisation is currently exploring or developing AI or ML technologies to carry out some pharmacovigilance tasks. Another poll revealed that 27% said these organisations are currently already using these technologies.

This is certainly a hot topic at the moment and there’s an increasing amount of research looking into the benefits and consequences of AI/ML in pharmacovigilance.

AI can and already does play a significant role in enhancing pharmacovigilance. In terms of how AI and ML technologies are currently being used (or developed), a separate poll found the following:

  • 43% use it in the collection and collation of adverse drug reactions, including Medical information
  • 32% use it in ICSR management and regulatory submission
  • 20% use it in signal management
  • 9% use it to aggregate Reports

It is worth noting that while a number of attendees claimed their organisations are at least exploring the use of AI and ML, almost half of those in attendance said their organisation is not currently considering these technologies at all.

At present, it appears that the key blockers to organisations using or developing AI/ML are cost, lack of expertise, lack of regulatory guidance and the fact that some organisations believe these technologies are not actually required for their business.

It’s also important to remember that human expertise remains essential. For instance, while AI/ML can crawl large datasets to automatically identify adverse events, Pharmacovigilance professionals would still need to assess these results and effectively communicate any findings.

What do you think? Is your organisation currently using or exploring the use of AI/ML in pharmacovigilance? Join the conversation on social media, we’d love to hear your thoughts!

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