Aggregation and analysis of indication-symptom relationships for drugs approved in the USA.

Ananth Punyala, Rachana Lankapalli, Diane Hindman, Rebecca Racz

Abstract read

In one paragraph

Article in European journal of clinical pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–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

1 citing paper in PubMed.

Ontology characterization, enrichment analysis, and similarity calculation-based evaluation of disease-syndrome-formula associations by applying SoFDA.iMeta · 2023
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

4 authors.

Ananth PunyalaWeill Cornell Medical College, New York, NY, USA.

Rachana LankapalliDivision of Applied Regulatory Science, US Food and Drug Administration, Silver Spring, MD, USA.

Diane HindmanEmergency Department, Phoenix Children's Hospital, Phoenix, AZ, USA.

Rebecca RaczDivision of Applied Regulatory Science, US Food and Drug Administration, Silver Spring, MD, USA. Rebecca.Racz@fda.hhs.gov.ORCID http://orcid.org/0000-0002-5487-5692

Funding

Oak Ridge Institute for Science and Education N/A

6 · The paper itself

Abstract

purposeDrug indications and disease symptoms often confound adverse event reports in real-world datasets, including electronic health records and reports in the FDA Adverse Event Reporting System (FAERS). A thorough, standardized set of indications and symptoms is needed to identify these confounders in such datasets for drug research and safety assessment. The aim of this study is to create a comprehensive list of drug-indication associations and disease-symptom associations using multiple resources, including existing databases and natural language processing.

methodsDrug indications for drugs approved in the USA were extracted from two databases, RxNorm and Side Effect Resource (SIDER). Symptoms for these indications were extracted from MedlinePlus and using natural language processing from PubMed abstracts.

resultsA total of 1361 unique drugs, 1656 unique indications, and 2201 unique symptoms were extracted from a wide variety of MedDRA System Organ Classes. Text-mining precision was maximized at 0.65 by examining Term Frequency Inverse Document Frequency (TF-IDF) scores of the disease-symptom associations.

conclusionThe drug-indication associations and disease-symptom associations collected in this study may be useful in identifying confounders in other datasets, such as safety reports. With further refinement and additional drugs, indications, and symptoms, this dataset may become a quality resource for disease symptoms.

Indexed as

Adverse Drug Reaction Reporting SystemsConfounding Factors, EpidemiologicDatabases, FactualData MiningDrug ApprovalDrug-Related Side Effects and Adverse ReactionsHumansNatural Language ProcessingUnited StatesDatabasesIndicationsNatural language processingSymptomsText-mining

Identifiers

PMID32495081

DOI10.1007/s00228-020-02898-w

PMCPMC7419351

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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.