Evidence map›Paper›PMID 38941956›Full record

ArticleEBioMedicine2024

Advancing drug development for atrial fibrillation by prioritising findings from human genetic association studies.

Kishore Kukendrarajah, Aliki-Eleni Farmaki, Pier D Lambiase, Richard Schilling, Chris Finan, Amand Floriaan Schmidt, Rui Providencia

Abstract read
In one paragraph

Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
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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

7 authors.

Kishore KukendrarajahInstitute of Health Informatics, University College London, 222 Euston Road, NW1 2DA, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom. Electronic address: kishore.kukendrarajah.17@ucl.ac.uk.
Aliki-Eleni FarmakiInstitute of Health Informatics, University College London, 222 Euston Road, NW1 2DA, United Kingdom.
Pier D LambiaseBarts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom; Institute of Cardiovascular Science, University College London, Gower Street, WC1E 6HX, United Kingdom.
Richard SchillingBarts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom.
Chris FinanInstitute of Cardiovascular Science, University College London, Gower Street, WC1E 6HX, United Kingdom; UCL British Heart Foundation Research Accelerator, United Kingdom; Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, the Netherlands.
Amand Floriaan SchmidtInstitute of Cardiovascular Science, University College London, Gower Street, WC1E 6HX, United Kingdom; UCL British Heart Foundation Research Accelerator, United Kingdom; Division Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centres, University of Amsterdam, the Netherlands.
Rui ProvidenciaInstitute of Health Informatics, University College London, 222 Euston Road, NW1 2DA, United Kingdom; Barts Heart Centre, St Bartholomew's Hospital, West Smithfield, EC1A 7BE, United Kingdom.

Funding

British Heart Foundation PG/18/50/33837British Heart Foundation PG/22/10989
6 · The paper itself

Abstract

backgroundDrug development for atrial fibrillation (AF) has failed to yield new approved compounds. We sought to identify and prioritise potential druggable targets with support from human genetics, by integrating the available evidence with bioinformatics sources relevant for AF drug development.

methodsGenetic hits for AF and related traits were identified through structured search of MEDLINE. Genes derived from each paper were cross-referenced with the OpenTargets platform for drug interactions. Confirmation/validation was demonstrated through structured searches and review of evidence on MEDLINE and ClinialTrials.gov for each drug and its association with AF.

findings613 unique drugs were identified, with 21 already included in AF Guidelines. Cardiovascular drugs from classes not currently used for AF (e.g. ranolazine and carperitide) and anti-inflammatory drugs (e.g. dexamethasone and mehylprednisolone) had evidence of potential benefit. Further targets were considered druggable but remain open for drug development.

interpretationOur systematic approach, combining evidence from different bioinformatics platforms, identified drug repurposing opportunities and druggable targets for AF.

fundingKK is supported by Barts Charity grant G-002089 and is mentored on the AFGen 2023-24 Fellowship funded by the AFGen NIH/NHLBI grant R01HL092577. RP is supported by the UCL BHF Research Accelerator AA/18/6/34223 and NIHR grant NIHR129463. AFS is supported by the BHF grants PG/18/5033837, PG/22/10989 and UCL BHF Accelerator AA/18/6/34223 as well as the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee EP/Z000211/1 and by the UKRI-NIHR grant MR/V033867/1 for the Multimorbidity Mechanism and Therapeutics Research Collaboration. AF is supported by UCL BHF Accelerator AA/18/6/34223. CF is supported by UCL BHF Accelerator AA/18/6/34223.

Indexed as

Atrial FibrillationDrug DevelopmentAnti-Arrhythmia AgentsComputational BiologyDrug DiscoveryDrug RepositioningGenetic Association StudiesGenetic Predisposition to DiseaseHumansAnti-Arrhythmia AgentsAtrial fibrillationBioinformaticsDrug developmentGWASSystematic review

Identifiers

PMID38941956
PMCPMC11260865

What OpenQuestion holds

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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.