Challenges in conducting post-authorisation safety studies (PASS): A vaccine manufacturer's view

Publication date: 25 May 2017
Source:Vaccine, Volume 35, Issue 23
Author(s): Catherine Cohet, Dominique Rosillon, Corinne Willame, Francois Haguinet, Marie-Noëlle Marenne, Sandrine Fontaine, Hubert Buyse, Vincent Bauchau, Laurence Baril
Post-authorisation safety studies (PASS) of vaccines assess or quantify the risk of adverse events following immunisation that were not identified or could not be estimated pre-licensure. The aim of this perspective paper is to describe the authors' experience in the design and conduct of twelve PASS that contributed to the evaluation of the benefit-risk of vaccines in real-world settings. We describe challenges and learnings from selected PASS of rotavirus, malaria, influenza, human papillomavirus and measles-mumps-rubella-varicella vaccines that assessed or identified potential or theoretical risks, which may lead to changes to risk management plans and/or to label updates. Study settings include the use of large healthcare databases and de novo data collection. PASS methodology is influenced by the background incidence of the outcome of interest, vaccine uptake, availability and quality of data sources, identification of the at-risk population and of suitable comparators, availability of validated case definitions, and the frequent need for case ascertainment in large databases. Challenges include the requirement for valid exposure and outcome data, identification of, and access to, adequate data sources, and mitigating limitations including bias and confounding. Assessing feasibility is becoming a key step to confirm that study objectives can be met in a timely manner. PASS provide critical information for regulators, public health agencies, vaccine manufacturers and ultimately, individuals. Collaborative approaches and synergistic efforts between vaccine manufacturers and key stakeholders, such as regulatory and public health agencies, are needed to facilitate access to data, and to drive optimal study design and implementation, with the aim of generating robust evidence.



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