Evidence map›Paper›PMID 38175395›Full record

ArticleDrug safety2024

Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS.

Michele Fusaroli, Valentina Giunchi, Vera Battini, Stefano Puligheddu, Charles Khouri, Carla Carnovale, Emanuel Raschi, Elisabetta Poluzzi

Abstract read
In one paragraph

Article in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Michele Fusaroli *Unit of Pharmacology, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy. michele.fusaroli2@unibo.it.ORCID http://orcid.org/0000-0002-0254-2212
Valentina Giunchi *Unit of Pharmacology, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Vera BattiniDepartment of Biomedical and Clinical Sciences, Pharmacovigilance and Clinical Research, International Centre for Pesticides and Health Risk Prevention, ASST Fatebenefratelli-Sacco, Università degli Studi di Milano, Milan, Italy.
Stefano PulighedduUnit of Pharmacology, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Charles KhouriPharmacovigilance Unit, Grenoble Alpes University Hospital, Grenoble, France.
Carla CarnovaleDepartment of Biomedical and Clinical Sciences, Pharmacovigilance and Clinical Research, International Centre for Pesticides and Health Risk Prevention, ASST Fatebenefratelli-Sacco, Università degli Studi di Milano, Milan, Italy.
Emanuel RaschiUnit of Pharmacology, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Elisabetta PoluzziUnit of Pharmacology, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study.

objectiveWe implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification.

methodsWe retrieved all drug names reported to the FAERS (2004-December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification.

resultsWe retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%).

conclusionThe DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results.

Indexed as

Adverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsHumansReproducibility of ResultsSoftwareUnited StatesUnited States Food and Drug Administration

Identifiers

PMID38175395
PMCPMC10874306

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.