Adverse Drug Reaction Case Safety Practices in Large Biopharmaceutical Organizations from 2007 to 2017: An Industry Survey

  • Stella StergiopoulosEmail author
  • Mortiz Fehrle
  • Patrick Caubel
  • Louise Tan
  • Louise Jebson
Original Research Article



Drug safety remains a top global public health concern. An increase in the number of data sources available has increased the complexity of pharmacovigilance operations, so the US FDA has created draft guidance focusing on optimizing drug safety data for well-characterized medicines. However, to date, no data demonstrating changes in reports have been presented.


This study provided data assessing changes in individual case safety reports (ICSRs) and aggregate reports (ARs) for large biopharmaceutical companies from 2007 to 2017. This study also evaluated current trends on the use of advanced machine and deep learning in order to process all data captured for ICSRs as well as opinions from industry thought leaders on creating a sustainable case-processing operation.


Using data captured from Navitas Life Science’s annual pvnet® benchmark, we calculated workload indicators characterizing pharmacovigilance operations for large biopharmaceutical organizations. Workload indicators included the number of ICSRs by organization, the number of ARs, and the number and types of data sources used. We also conducted structured in-depth interviews with seven biopharmaceutical executives to discover the reasons for changes in workload indicators across time as well as current strategies for increasing efficiencies in drug safety reporting.


The median number of ICSRs increased from 84,960 cases in 2007 to over 200,000 cases in 2017; this increase was largely attributable to an increase in both nonserious cases and follow-up cases. Member companies reported using 12 ± 3 data sources for case identification. The number of ARs also increased from a median of 70 reports in 2007 to 258 reports in 2017. To address these increases, 61% of the biopharmaceutical organizations we surveyed planned to adopt machine learning for full ICSR processing; however, as of 2018, none of the organizations surveyed had mechanisms in place.


This study demonstrated that pharmacovigilance departments are currently burdened by ever-increasing case volumes. With increased guidance from regulatory agencies, as well as improvements in artificial intelligence and natural language processing, biopharmaceutical organizations must determine the most resource-efficient and sustainable methods to process the growing volume of cases.



The authors acknowledge Marie-Claire Wilson and Pete Boyd for their contributions to the study.

Compliance with Ethical Standards


No sources of funding were used to conduct this study or prepare this manuscript.

Conflict of interest

SS is a paid employee of Foundation Medicine, Inc. but was employed at the Tufts Center for the Study of Drug Development at the time the study was conducted. MF is a paid employee of Bayer AG. PC is a paid employee of Pfizer, Inc. LT and LJ are paid employees of Navitas Life Sciences. No companies contributed to or influenced the data analysis, study conduct, or writing of the manuscript. The manuscript reflects the authors’ personal opinions and contributions.

Ethical Approval

This article does not contain any studies with human or animal subjects performed by any of the authors.

Supplementary material

40290_2019_307_MOESM1_ESM.pdf (86 kb)
Supplement 1: Structured Interview Guide


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tufts Center for the Study of Drug DevelopmentTufts University School of MedicineBostonUSA
  2. 2.Bayer AGLeverkusenGermany
  3. 3.Pfizer IncNew YorkUSA
  4. 4.Pvnet®, Navitas Life Sciences GmbHFrankfurtGermany

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