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A Critical Evaluation of Safety Signal Analysis Using Algorithmic Standardised MedDRA Queries

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Abstract

Introduction

Algorithmic Standardised MedDRA® Queries (aSMQs) are increasingly used to enhance the efficiency of safety signal detection. The manner that aSMQs affect capture of potential safety cases is unclear.

Objectives

Our objective was to characterise the performance of aSMQs with respect to their potential for double counting, the likelihood of events in aSMQ positive cases being clinically related, how frequently terms are used for algorithmically positive cases, and the face validity of positive cases based on the drug inducing events. We were also interested in what effect requiring symptoms to overlap temporally would have on performance.

Methods

We reviewed adverse event (AE) datasets of New Drug Applications and Biological License Applications and compiled a database including preferred terms and corresponding SMQs, SMQ term categories, AE start day, AE duration, drug name, and Anatomical Therapeutic Chemical class. Two reviewers independently determined if the algorithm was met and, if so, whether the broad terms overlapped temporally.

Results

A total of 107 marketing applications were reviewed, including 103,928 patients and 277,430 AEs. Use of algorithms condensed the number of AEs to between 5 and 8% and the incidence to about 1.5% relative to when the SMQs are used without the algorithm. Certain aSMQs exhibited a potential for overcounting. Requiring symptoms to temporally overlap helped to eliminate irrelevant cases.

Conclusions

Our findings demonstrate that algorithmic and temporal assessment increased specificity of case retrieval, though the reduction in the number of terms or incidence seemed excessive for certain aSMQs. Evaluating the day of AE onset and duration improve specificity through identification of outlying events. Identification of drug classes known to cause the aSMQ’s clinical condition provides face validity for this tool, yet detection of cases associated with novel classes may provide new understanding of these disorders. Improvements in some of the SMQ term lists may improve the performance of SMQs in general.

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Notes

  1. “MedDRA® the Medical Dictionary for Regulatory Activities terminology is the international medical terminology developed under the auspices of the ICH. MedDRA® trademark is owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) on behalf of ICH”.

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Acknowledgements

We thank Ms. Joean James and Ms. Gretchen Kindle for their administrative support.

Author information

Authors and Affiliations

  1. FDA Fellow in the Oak Ridge Institute for Science and Education (ORISE) Program, Silver Spring, MD, USA

    Carolyn Tieu

  2. Division of Neurology Products, Office of New Drugs, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA

    Carolyn Tieu & Christopher D. Breder

  3. Regulatory Science Program/Advanced Academic Programs, Johns Hopkins University, Rockville, USA

    Christopher D. Breder

  4. Center for Drug Safety and Effectiveness, Bloomberg School of Public Health, Johns Hopkins University, Washington, DC, USA

    Christopher D. Breder

  5. US Food and Drug Administration, 10903 New Hampshire Avenue, WO 22 RM 4218, Silver Spring, MD, 20903-1058, USA

    Christopher D. Breder

Authors
  1. Carolyn Tieu
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  2. Christopher D. Breder
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Corresponding author

Correspondence to Christopher D. Breder.

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Conflict of interest

Carolyn Tieu has no conflicts of interest that are directly relevant to the content of this study. Christopher Breder is a medical officer at the US Food and Drug Administration (FDA); he is also the FDA Topic Expert in the M1 Points to Consider Group.

Funding

Dr. Tieu’s fellowship was funded by an Oak Ridge Institute Science and Education Grant # RSR-702. The funding source had no role in the design and conduct of the study, analysis or interpretation of the data, and preparation or final approval of the manuscript prior to publication.

Disclaimer

The views expressed in this paper are the authors’ views and do not necessarily represent those of the FDA or the ICH.

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Tieu, C., Breder, C.D. A Critical Evaluation of Safety Signal Analysis Using Algorithmic Standardised MedDRA Queries. Drug Saf 41, 1375–1385 (2018). https://doi.org/10.1007/s40264-018-0706-7

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  • DOI: https://doi.org/10.1007/s40264-018-0706-7

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