Drug Safety Matters – Details, episodes & analysis

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Pharmacovigilance has traditionally focused on the patient at the receiving end of a medicine. But what happens to medicines once they leave the body? In this episode, Valentina Giunchi and Joe Mitchell unpack the emerging field of ecopharmacovigilance and explore how the One Health approach is reshaping the way we think about medicine safety across human, animal, and environmental systems. 

Tune in to find out:

• Where in the environment pharmaceutical residues are turning up, and why this should be on every pharmacovigilance scientist's radar
• How the dramatic collapse of India's vulture populations in the early 2000s speaks to the importance of the One Health approach 
• Practical solutions, from smarter prescribing and greener pharmaceuticals to take-back schemes for unused medicines, that can help curb pharmaceutical pollution.

Want to know more?

For a deep dive into ecopharmacovigilance, the following references provide foundational information on the topic in pharmacovigilance, most of them penned by Professor Giampaolo Velo, who coined the term:

• The revised Erice declaration for the inclusion of environmental issues in pharmacovigilance
• A book chapter on ecopharmacovigilance in the 2017 SpringerLink book “Pharmacovigilance”, featuring our very own Ralph Edwards and Marie Lindquist as editors
• A paper summarising the main concepts in ecopharmacovigilance and pharmaceutical pollution

A paper with Valentina Giunchi as a co-author on environmental sustainability as an essential component of rational medicine use.

A recent paper with Joe Mitchell as a co-author, using the One Health approach to investigate pharmacovigilance database reports on antimicrobial resistance. 

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For all its ease and speed, disproportionality analysis can be distorted by many biases, making it easy to misuse and misinterpret. Michele Fusaroli from Uppsala Monitoring Centre and Eugene van Puijenbroek from the Netherlands pharmacovigilance centre Lareb explain why we shouldn’t abuse this powerful but fragile tool.

Tune in to find out:

• Why we should never treat disproportionality signals as verdicts
• How poorly performed analyses affect scientists, regulators and patients
• How to avoid the most common sources of bias 

Want to know more?

• Michele and Eugene’s paper in Drug Safety is a concrete guide to charting and sidestepping the pitfalls of disproportionality analysis.
• In another Drug Safety paper, Michele and colleagues show how directed acyclic graphs (DAGs) can help map and address biases in disproportionality analysis.
• Michele also reviewed the method’s limitations in Uppsala Reports, where he argues that ‘pharmacovigilance must move past crude disproportionality’.
• Last year, Retraction Watch covered the spike in pharmacovigilance studies in the literature and why some journals decided to ban drug safety database papers.
• Previously on Drug Safety Matters, Michele and Daniele Sartori discussed the READUS-PV guidelines for reporting disproportionality analyses.
• In 2016, the IMI PROTECT project published recommendations to improve signal detection practices, especially for quantitative methods like disproportionality analysis.
• UMC’s guidebook on signal detection in small datasets offers step-by-step advice for qualitative methods and manual case review.

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There are many reasons why use of medical products during pregnancy requires special attention. First and foremost, we want to be sure that the medicine is as safe as possible for both the pregnant person and the unborn child. Unfortunately, the safety profiles of medicines used in pregnancy are often incomplete, which makes it difficult for patients and healthcare professionals to make informed decisions.

The Research section at Uppsala Monitoring Centre has a team that is currently focussing their efforts on pregnancy-related pharmacovigilance (PV). In this episode, data scientists Sara Vidlin and Levente Papai, and senior pharmacovigilance scientist Lovisa Sandberg from this team, discuss complexities and challenges of pregnancy-related PV, and new solutions for addressing those challenges. 

Tune in to find out

• Why is the world still behind when it comes to pregnancy-related PV?
• What are the challenges faced by PV assessors wanting to look at pregnancy cases?
• How can healthcare professionals, patients and carers help assessors overcome these challenges, when reporting pregnancy-related adverse drug events?
• How can the VigiBase pregnancy algorithm, and other algorithms, support the identification of pregnancy cases?

How to use the VigiBase pregnancy algorithm

• Users of VigiLyze and VigiBase Custom Searches can use the VigiBase pregnancy algorithm as a filter when performing searches. 
• In the qualitative view in VigiLyze, click on “Filter” -> “Patient” -> “Pregnancy” to apply the filter.

Want to know more?

• Read about the VigiBase pregnancy algorithm in this Uppsala Reports article and in this scientific paper, published in Drug Safety in 2025. 
• EURAP – an international prospective observational study of pregnancies with antiepileptic drugs. EURAP - International Registry of Antiepileptic Drugs and Pregnancy
• The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) - E2B(R3) Individual Case Safety Report (ICSR) Specification and Related Files
• The IMI-concePTION project
• Medical Dictionary for Regulatory Activities (MedDRA) support documentation
• Zaccaria C, Piccolo L, Gordillo-Marañón M, et al. Identification of Pregnancy Adverse Drug Reactions in Pharmacovigilance Reporting Systems: A Novel Algorithm Developed in EudraVigilance. Drug Safety. 2024
• Sakai T, Mori C, Koshiba H, et al. Pregnancy Loss Signal from Prostaglandin Eye Drop Use in Pregnancy: A Disproportionality Analysis Using Japanese and US Spontaneous Reporting Databases. Drugs Real World Outcomes. 2022

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Human and veterinary pharmacovigilance (PV) share many goals, challenges and approaches. But there are also significant differences, such as the numerous animal species that veterinary PV needs to take into account. In this two-part episode of Drug Safety Matters, James Mount, Veterinary Pharmacovigilance assessor at the Swedish Medical Products Agency, and EU elected chair of the Pharmacovigilance Working Party for veterinary medicinal products, joins the show to talk about veterinary PV practice and its differences and similarities to human PV. 

Tune in to find out

• How the types of ADRs reported for animals and humans differ
• How the many species and breeds included in veterinary PV affect the ADR coding 
• What the Veterinary Big Data Strategy is

... and much more!

Want to know more?

• The public portal of the European Union Veterinary Pharmacovigilance Database.
• One Health – an integrated approach to the well-being of people, animals and the environment.
• A survey of veterinary professionals in Sweden, about practices and attitudes in relation to ADR reporting.
• A review of adverse events in animals and children after secondary exposure to transdermal hormone-containing medicines. 
• The EMA Big Data strategy for veterinary medicines and Data quality framework for medicines regulation
• Small Animal Veterinary Surveillance Network (SAVSNET) - University of Liverpool
• VetCompass - Royal Veterinary College, RVC
• Veterinary good pharmacovigilance practices (VGVP) | European Medicines Agency (EMA)
• Veterinary Dictionary for Drug Regulatory Activities (VeDDRA) | European Medicines Agency (EMA)

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Human and veterinary pharmacovigilance (PV) share many goals, challenges and approaches. But there are also significant differences, such as the numerous species and breeds that veterinary PV needs to take into account. In this two-part episode of Drug Safety Matters, James Mount, Veterinary Pharmacovigilance assessor at the Swedish Medical Products Agency, and EU elected chair of the Pharmacovigilance Working Party for veterinary medicinal products, joins the show to talk about veterinary PV practice and its differences and similarities to human PV. 
 

Tune in to find out:

• What are the similarities and differences between veterinary and human pharmacovigilance?
• How is animal health connected to public health? 
• What types of adverse events are reported on the veterinary side compared with the human side? 
• What can be found in the EU veterinary pharmacovigilance database? 

Want to know more?

• The new veterinary medicines regulation (Regulation (EU) 2019/6) can be found here. 
• The public portal of the European Union Veterinary Pharmacovigilance Database.
• WHO's information page on One Health, an integrated approach to the well-being of people, animals and the environment.
• A survey of veterinary professionals in Sweden, about current practices and attitudes in relation to adverse events reporting and the accessibility of product safety information.
• A review of adverse events in animals and children after secondary exposure to transdermal hormone-containing medicinal products. 
• A study looking at suspected adverse drug reaction reporting in veterinary free‐text clinical narratives.
• The EMA Big Data strategy for veterinary medicines in the EU. 
• Data quality framework for medicines regulation | European Medicines Agency (EMA)
• Small Animal Veterinary Surveillance Network (SAVSNET) - University of Liverpool
• VetCompass - Royal Veterinary College, RVC
• Reflection paper on the use of artificial intelligence in the lifecycle of medicines | European Medicines Agency (EMA)
• The Swedish Medical Products Agency's online reporting form for suspected adverse drug reactions in animals (In Swedish).  

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While structured data elements such as patient identifier, medicine name and reaction, are fundamental for adverse event reporting, they may not capture all relevant details. This is where the narrative fields come in, allowing reporters to disclose important contextual information, such as the patient’s full clinical course. But how do PV assessors interact with these narratives in spontaneous reports? What needs and challenges do they experience? These and other questions were addressed in an exploratory interview study by UMC researchers Joana Félix and Alem Zekarias. 

Tune in to find out:

• What challenges are PV assessors faced with, when working with narratives? 
• How could automation of certain tasks help streamline narrative analyses in the future?
• How can reporters craft narratives that effectively document adverse events? 

Want to know more?

• Pharmacovigilance assessors’ experiences interacting with narrative fields in spontaneous reports: an exploratory interview study – poster presented at the 23rd ISoP Annual Meeting “Global Perspectives on Pharmacovigilance in the Digital Age and Advanced Therapeutics”, 1–5 October 2024 Montreal, Canada.
• Current Challenges in Pharmacovigilance: Pragmatic Approaches, by The Council for International Organisations of Medical Sciences (CIOMS). See page 133 on the role of narratives in good case management practices. 

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Medication-related-harm (MRH) is especially prevalent in older adults due to changing physiology as the body ages, increased frailty, and the incidence of polypharmacy in this patient group. Giovanni Furlan, Worldwide Safety Site Lead for Thessaloniki of Pfizer discusses what makes this patient group so vulnerable to adverse drug reactions, how poor representation and using age alone to define older adults exacerbates this problem, and suggests ways forward in monitoring drug safety in older patients.

 Tune in to find out:

• What makes older adults especially at risk of experiencing adverse drug reactions and medication errors
• Why frailty is far more useful than age in predicting adverse drug reaction risk
• How pharmacovigilance in older patients may be improved through pharmaceutical practice and better representation in clinical trials.

Want to know more?

This interview all started with Giovanni's Uppsala Reports article on how age is insufficient a measure of adverse event risk. Read it here.

For a summary of the key points discussed in this interview, read Giovanni’s paper on the status of drug safety in geriatric patients.

If our discussion of frailty piqued your interest, read this paper on the biology of frailty and how this impacts clinical pharmacology, this multi-centre cohort study that shows frailty is significantly correlated with MRH, and this commentary advocating for consideration of MRH as a geriatric syndrome, which needs to be managed as such.

As Giovanni mentioned in the interview, Harlan Krumholz was the first to describe post-hospital syndrome. Learn more about this syndrome by reading his paper.

For more on prescribing cascades, their prevention, detection, and reversal, read this paper by Brath and colleagues.

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Disproportionality analyses are a mainstay of pharmacovigilance research, but without clear guidelines, they often lead to confusion and misinterpretation. Enter the READUS-PV statement: the first-ever guide for reporting disproportionality analyses that are replicable, reliable, and reproducible.  

Tune in to find out: 

• The history of reporting guidelines in pharmacovigilance and why the READUS-PV guidelines were created 
• Why there has been a spike in the publication of disproportionality analyses in recent years and what this means for their reliability 
• What it means to publish “good” pharmacovigilance science  

Want to know more? 

• Read the READUS-PV guidelines, why they were created, and why they are important. 
• In 2021, Khouri and colleagues showed that current methods and models used for disproportionality analyses are unreliable, and Mouffak and colleagues found that there is a tendency to overstate results in published disproportionality analyses. 
• A book on data mining techniques in Pharmacovigilance by Poluzzi and colleagues delves deeper into this exponential increase in disproportionality analyses.  
• This paper elaborates on the Delphi technique, and how it is used to gather data from reviewers to achieve scientific consensus on a problem. 

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Duplicate reports are a big problem when it comes to signal detection, but with the help of machine learning and new ways of comparing reports, we may more effectively detect them. 

This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance news site, brought to you in audio format. Find the original article here.

After the read, we speak to author Jim Barrett, Senior Data Scientist at UMC, to learn more about the duplicate detection algorithm and UMC’s work to develop AI resources for pharmacovigilance.

Tune in to find out:

• How the new algorithm handles duplicates in VigiBase
• About different approaches for developing algorithms
• Why it can be challenging to evaluate the performance of an algorithm

Want to know more?

• Listen to the Drug Safety Matters interview with Michael Glaser about his Uppsala Reports article “Ensuring trust in AI/ML when used in pharmacovigilance” and check out the episode’s extensive list of links for more on AI in pharmacovigilance. 
• Artificial intelligence in pharmacovigilance – value proposition and the need for critical appraisal, a presentation by Niklas Norén, Head of Research at UMC, given at University of Verona in April 2024.  

Finally, don’t forget to ­subscribe to the monthly Uppsala Reports newsletter for free regular updates from the world of pharmacovigilance.

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Uppsala Monitoring Centre promotes safer use of medicines and vaccines for everyone everywhere. Follow us on Facebook, LinkedIn, X, and Bluesky.

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Ensuring trust in AI is vital to fully reap the benefits of the technology in pharmacovigilance. Yet, how do we do so while grappling with its ever-growing complexity?

This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance news site, brought to you in audio format. Find the original article here.

After the read, we speak to one of the authors of the article, Michael Glaser, to learn more about how AI and ML has been used in pharmacovigilance so far, and what needs to happen to ensure its continued use in the field.

Tune in to find out:

●      How AI and ML are being used today in pharmacovigilance processes

●     Why a mindset change is necessary to make full use of AI/ML in pharmacovigilance

●      How we may best move forward to implement AI/ML into healthcare.

 Want to know more?

 To know more about how AI and ML are being used in pharmacovigilance currently, read this scoping review.

To know more about future trends of the use of AI in Biopharma, read this Accenture survey.

• Despite there being major interest in ML and AI to do more than task automation, there are a number of barriers to its implementation in healthcare. Check out this future-focused paper on the use of AI/ML in pharmacovigilance that details how to utilise it to its fullest potential.
• A mindset shift is necessary in terms of how we think about data, in terms of sharing, how to generate data required to effectively train AI/ML models.
• A validation framework must be developed for AI-based pharmacovigilance systems. One suggestion is to do so using a risk-based approach.
• While there is much interest in using recently developed AI technologies such as chatGPT, preliminary studies like this one suggest that the technology has a ways to go to be useful in pharmacovigilance.
• The World Health Organization have published an extensive guideline on the ethics and governance of AI for health.

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