A Pharmacovigilance Signaling System Based on FDA Regulatory Action and Post-Marketing Adverse Event Reports

Abstract

Introduction

Many serious drug adverse events (AEs) only manifest well after regulatory approval. Therefore, the development of signaling methods to use with post-approval AE databases appears vital to comprehensively assess real-world drug safety. However, with millions of potential drug–AE pairs to analyze, the issue of focus is daunting.

Objective

Our objective was to develop a signaling platform that focuses on AEs with historically demonstrated regulatory interest and to analyze such AEs with a disproportional reporting method that offers broad signal detection and acceptable false-positive rates.

Methods

We analyzed over 1500 US FDA regulatory actions (safety communications and drug label changes) from 2008 to 2015 to construct a list of eligible signal AEs. The FDA Adverse Event Reporting System (FAERS) was used to evaluate disproportional reporting rates, constrained by minimum case counts and confidence interval limits, of these selected AEs for 109 training drugs. This step led to 45 AEs that appeared to have a low likelihood of being added to a label by FDA, so they were removed from the signal eligible list. We measured disproportional reporting for the final group of eligible AEs on a test group of 29 drugs that were not used in either the eligible list construction or the training steps.

Results

In a group of 29 test drugs, our model reduced the number of potential drug–AE signals from 41,834 to 97 and predicted 73 % of individual drug label changes. The model also predicted at least one AE–drug pair label change in 66 % of all the label changes for the test drugs.

Conclusions

By concentrating on AE types with already demonstrated interest to FDA, we constructed a signaling system that provided focus regarding drug–AE pairs and suitable accuracy with regard to the issuance of FDA labeling changes. We suggest that focus on historical regulatory actions may increase the utility of pharmacovigilance signaling systems.

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Acknowledgments

The authors are indebted to Colin B. Erdman and Dingguo Chen for their expert analytic assistance that enabled this study to be undertaken. We also thank Brian M. Overstreet for early conceptual input regarding these methods. MedDRA®, the Medical Dictionary for Regulatory Activities, terminology is the international medical terminology developed under the auspices of the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH).

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Correspondence to Keith B. Hoffman.

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Conflicts of interest

Keith B. Hoffman, Mo Dimbil, and Robert F. Kyle have all declared employment- and stock-related conflicts of interests in their declaration forms related to Advera Health Analytics, Inc. (AHA). Nicholas P. Tatonetti has declared a stock-related conflict of interest in his declaration forms related to AHA. Keith B. Hoffman, Mo Dimbil, Nicholas P. Tatonetti, and Robert F. Kyle have no other conflicts of interest that are directly relevant to the content of this manuscript.

Funding

This study, and the preparation of this manuscript, was funded solely in the form of salaries (KBH, MD, and RFK) paid by AHA. No specific funds were allocated for this study.

Author contributions

Keith B. Hoffman, Mo Dimbil, and Robert F. Kyle conceived of the study, analyzed and interpreted data, and approved the final submitted manuscript. Keith B. Hoffman drafted the final submitted manuscript. Nicholas P. Tatonetti made suggestions for data interpretation and approved the final submitted manuscript.

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Hoffman, K.B., Dimbil, M., Tatonetti, N.P. et al. A Pharmacovigilance Signaling System Based on FDA Regulatory Action and Post-Marketing Adverse Event Reports. Drug Saf 39, 561–575 (2016). https://doi.org/10.1007/s40264-016-0409-x

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Keywords

  • Progressive Multifocal Leukoencephalopathy
  • Reporting Odds Ratio
  • Label Change
  • Drug Pair
  • National Drug File Reference Terminology