CC BY-NC-ND 4.0 · Appl Clin Inform 2017; 08(01): 291-305
DOI: 10.4338/ACI-2016-11-RA-0188
Research Article
Schattauer GmbH

Harnessing scientific literature reports for pharmacovigilance

Prototype software analytical tool development and usability testing
Alfred Sorbello
1   US Food and Drug Administration, Office of Translational Sciences, Silver Spring, MD, USA
,
Anna Ripple
2   Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
,
Joseph Tonning
1   US Food and Drug Administration, Office of Translational Sciences, Silver Spring, MD, USA
,
Monica Munoz
3   US Food and Drug Administration, Office of Surveillance and Epidemiology, Silver Spring, MD, USA
,
Rashedul Hasan
1   US Food and Drug Administration, Office of Translational Sciences, Silver Spring, MD, USA
,
Thomas Ly
1   US Food and Drug Administration, Office of Translational Sciences, Silver Spring, MD, USA
,
Henry Francis
1   US Food and Drug Administration, Office of Translational Sciences, Silver Spring, MD, USA
,
Olivier Bodenreider
2   Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
› Author Affiliations
Funding We wish to acknowledge financial support from the FDA/CDER/Office of Translational Sciences and the Intramural Research Program, NIH, National Library of Medicine.
Further Information

Publication History

Received: 02 November 2016

Accepted: 14 January 2017

Publication Date:
20 December 2017 (online)

Summary

Objectives: We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. Methods: A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use.

Results: All usability test participants cited the tool’s ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool’s automated literature search relative to a manual ‘all fields’ PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. Conclusions: Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

 
  • References

  • 1 Pontes H, Clément M, Rollason V. Safety signal detection: the relevance of literature review. Drug Saf 2014; 37 (Suppl. 07) 471-479. doi:10.1007/s40264-014–0180-9.
  • 2 Xu R, Wang Q. Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection. BMC Bioinformatics 2014; 15: 17. doi:10.1186/1471-2105–15-17.
  • 3 Shetty KD, Dalal SR. Using information mining of the medical literature to improve drug safety. J Am Med Inform Assoc 2011; 18 (Suppl. 05) 668-674. doi:10.1136/amiajnl-2011-000096.
  • 4 Sorbello A, Harpaz R, Szarfman A, Bodenreider O, Winnenburg R, Ripple A, Tonning J, Francis H. Detecting drug-adverse event safety signals through quantitative data mining of MEDLINE indexing terms: A pilot study.. 54th Interscience Conference on Antimicrobial Agents and Chemotherapy (ICAAC 2014); September 2014; Washington, DC. Poster A-055.
  • 5 Winnenburg R, Sorbello A, Bodenreider O. Exploring adverse drug events at the class level. J Biomed Semantics 2015; 6: 18. doi: 10.1186/s13326-015–0017-1.
  • 6 Duggirala HJ, Tonning JM, Smith E, Bright RA, Baker JD, Ball R, Bell C, Bright-Ponte S, Botsis T, Bouri K, Boyer M, Burkhart K, Condrey G, Chen J, Martin D, Oladipo T, O’Neill R, Palmer L, Paredes A, Rochester G, Sholtes D, Szarfman A, Wong H, Xu Z, Kass-Hout T. Use of data mining at the Food and Drug Administration. J Am Med Inform Assoc 2016; 23 (Suppl. 02) 428-434. doi: 10.1093/jamia/ocv063.
  • 7 Classen DC, Pestotnik SL, Evans RS, Lloyd JF, Burke JP. Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. JAMA 1997; 277 (Suppl. 04) 301-306.
  • 8 Bates DW, Spell N, Cullen DJ, Burdick E, Laird N, Petersen LA, Small S, Sweitzer B, Leape L. The costs of adverse drug events in hospitalized patients. Adverse Drug Events Prevention Study Group. JAMA 1997; 277 (Suppl. 04) 307-311.
  • 9 Web Link Help [Internet].. Bethesda (MD): National Center for Biotechnology Information (US); 2005 Creating a Web Link to PubMed. 2006 Oct 16 [Updated 2016 Jun 24]. Available from: https://www.ncbi. nlm.nih.gov/books/NBK3862.
  • 10 Winnenburg R, Sorbello A, Ripple A, Harpaz R, Tonning J, Szarfman A, Francis H, Bodenreider O. Leveraging MEDLINE indexing for pharmacovigilance – Inherent limitations and mitigation strategies. J Biomed Inform 2015; 57: 425-435. doi: 10.1016/j.jbi.2015.08.022.
  • 11 van Manen RP, Fram D, DuMouchel W. Signal detection methodologies to support effective safety management. Expert Opin Drug Saf 2007; 6 (Suppl. 04) 451-464.
  • 12 DuMouchel W, Pregibon D. Empirical Bayes Screening for Multi–Item Associations.. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA; August 2001. New York: ACM Press; 67-76.
  • 13 Huang L, Zalkikar J, Tiwari RC. A Likelihood Ratio Test Based Method for Signal Detection With Application to FDA’s Drug Safety Data. J Am Stat Assoc 2011; 106 (Suppl. 496) 1230-1241.
  • 14 Lindberg DA, Siegel ER, Rapp BA, Wallingford KT, Wilson SR. Use of MEDLINE by physicians for clinical problem solving. JAMA 1993; 269 (Suppl. 24) 3124-3129.
  • 15 Avillach P, Dufour JC, Diallo G, Salvo F, Joubert M, Thiessard F, Mougin F, Trifiro G, Fourrier-Reglat A, Pariente A, Fieschi M. Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project. J Am Med Inform Assoc 2013; 20 (Suppl. 03) 446-452. doi: 10.1136/amiajnl-2012-001083.
  • 16 Mork J, Jimeno Yepes A, Aronson A. The NLM Medical Text Indexer System for Indexing Biomedical Literature.. BioASQ 2013
  • 17 Rindflesch TC, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform 2003; 36 (Suppl. 06) 462-477.
  • 18 Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther 2012; 91 (Suppl. 06) 1010-1021. doi: 10.1038/clpt.2012.50.
  • 19 Ryan D, Schuemie M, Welebob E, Duke J, Vanemtine S, Hartzema A. Defining a reference set to support methodological research in drug safety. Drug Saf 2013; 36 (Suppl. 01) S33-S47.
  • 20 Harpaz R, Odgers D, Gaskin G, DuMouchel W, Winnenburg R, Bodenreider O, Ripple A, Szarfman A, Sorbello A, Horvitz E, White R, Shah N. A time-indexed reference standard of adverse drug reactions. Sci Data 2014; 1: 140043. doi:10.1038/sdata.2014.43.