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. 2024 Jul:105:105194.
doi: 10.1016/j.ebiom.2024.105194. Epub 2024 Jun 27.

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

Kishore Kukendrarajah  1 , Aliki-Eleni Farmaki  2 , Pier D Lambiase  3 , Richard Schilling  4 , Chris Finan  5 , Amand Floriaan Schmidt  6 , Rui Providencia  7
Affiliations

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

Kishore Kukendrarajah et al. EBioMedicine. 2024 Jul.

Abstract

Background: Drug 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.

Methods: Genetic 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.

Findings: 613 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.

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

Funding: KK 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.

Keywords: Atrial fibrillation; Bioinformatics; Drug development; GWAS; Systematic review.

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Conflict of interest statement

Declaration of interests AFS and CF have received unrestricted funding from New Amsterdam Pharma, for investigating the role of a specific drug not related to AF. CF has received funding from Pfizer investigating the role of specific drug targets not related to AF, he is not the principal investigator on this. PL has received grants from Boston Scientific and Abbott Medical as well as consulting fees from Boston Scientific. RJS has received grants, consulting fees and honoraria from Abbott medical, Biosense Webster, Boston Scientific and Medtronic.

Figures

Fig. 1
Fig. 1
Workflow diagram detailing sources of evidence including bioinformatics libraries, clinical trial registries and journal databases. Created with BioRender.com. Partly adapted from–https://genetics-docs.opentargets.org/our-approach/data-pipeline.
Fig. 2
Fig. 2
Dendrogram describing drug actions, targets and target classes for drugs which have a high prioritisation score.
Fig. 3
Fig. 3
Chord diagram describing the cross-over of indications for drugs with a high prioritisation score. Each sector represents proportion of indications within the specialty.
Fig. 4
Fig. 4
Panel a—Targets influencing electrophysiological functions in atrial myocytes and sino-atrial node. Panel b—Targets influencing mechanisms involved with contractility, calcium handling by sarcoplasmic reticulum and myofibrils and blood pressure/fluid status regulation. Panel c—Targets influencing some pathways involved with atrial fibrosis and inflammation. Key: Italics, drugged target not prioritised, Italics and Bold, drugged target prioritised; Green text, upregulation; Red text, downregulation; Green box, signalling protein; Purple box, Transcription factor/Nuclear receptor; Pink box, second messenger; Dotted line, possible mechanism in AF; Faded line, via intermediate signalling; Tapered line, production or secretion; DAG, Diacylglycerol; IP3, Inositol 1,4,5-trisphosphate; PLC, Phospholipase C; PIP2, Phosphatidylinositol 4; 5-bisphosphate; PKC, Protein kinase C; TGF-β(R), Transforming Growth Factor Beta (receptor); SMAD, Small Mothers Against Decapentaplegic; THRβ, Thyroid Hormone Receptor Beta; GR, Glucocorticoid Receptor; ER, Endoplasmic Reticulum; IL–Interleukin; TNF-α, Tumour Necrosis Factor Alpha; IFN-γ, Interferon Gamma; α-SMA, Alpha Smooth Muscle Actin; MAPK, Mitogen-Activated Protein Kinase; AT1/2R, Angiotensin 1/2 receptor; TLR, Toll-Like Receptor; TNFR, Tumour Necrosis Factor Receptor; MMP, Matrix Metalloproteinase; CAM, Cell Adhesion Molecule; TC, T cell; Mo–Monocyte; Mø, Macrophage; Nø, Neutrophil; MPO, Myeloperoxidase; PDE, Phosphodiesterase; (c)AMP, (cyclic) Adenosine Monophosphate; PKA, Protein Kinase A; NF-κB, Nuclear Factor Kappa-light-chain-enhancer of activated B cells; IκBα, nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha; SERCA, Sarcoplasmic Reticulum Calcium ATPase; RyR, Ryanodine Receptor; NCX, Sodium Calcium Exchanger. Created with BioRender.com.
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