ReviewActa pharmacologica Sinica2025
Cardiovascular adverse effects of antiviral therapies for COVID-19: Evidence and plausible mechanisms.
Review in Acta pharmacologica Sinica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Thorough QT/QTc Clinical Study to Evaluate the Effect of Remdesivir on Cardiac Repolarization in Healthy Participants.Clinical and translational science · 2026Trial
- Drug repurposing against viral infections (2020-2025): clinical trials, computational strategies, and therapeutic interventions.Inflammopharmacology · 2026Review
- High incidence of post-discharge cardiovascular mortality after COVID-19 hospitalization: a data-linkage study in Southern Iran.BMC cardiovascular disorders · 2026Article
- Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches.Scientific reports · 2025Article
- Genetic and Epigenetic Intersections in COVID-19-Associated Cardiovascular Disease: Emerging Insights and Future Directions.Biomedicines · 2025Review
- Perioperative cardiovascular risk and preventions of patients with post-COVID-19 condition.Heliyon · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antiviral therapeutics have made a critical contribution in mitigating the symptoms and clinical outcomes of the coronavirus disease of 2019 (COVID-19), in which a single-stranded RNA viral pathogen, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causes multi-organ injuries. Several antivirals were widely prescribed to treat COVID-19, either through the emergency use authorization (EUA) by the governmental regulatory agencies (i.e., remdesivir, paxlovid, molnupiravir, and the SARS-CoV-2-targeted monoclonal antibodies - tixagevimab and cilgavimab), as well as the repurposed use of the existing antiviral or antimalarial drugs (e.g., hydroxychloroquine, chloroquine, and ivermectin). Despite their efficacy in ameliorating COVID-19 symptoms, some adverse side-effects of the antivirals were also reported during the COVID-19 pandemic. Our current review has aimed to gather and extrapolate the recently published information concerning cardiovascular adverse effects caused by each of the antivirals. We also provide further discussion on the potential cellular mechanisms underlying the cardiovascular adverse effects of the selected antiviral drugs, which should be carefully considered when evaluating risk factors in managing patients with COVID-19 or similar infectious diseases. It is foreseeable that future antiviral drug development assisted with the newest artificial intelligence platform may improve the accuracy to predict the structures of biomolecules of antivirals and therefore to mitigate their associated cardiovascular adversities.
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What OpenQuestion holds
Registered trials
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.