ArticleScientific data2020
A high-content image-based drug screen of clinical compounds against cell transmission of adenovirus.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed, 18 citations in OpenAlex.
- Review
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- Replication-competent adenovirus reporters utilizing endogenous viral expression architecture.Journal of virology · 2025Article
- Stepwise virus assembly in the cell nucleus revealed by spatiotemporal click chemistry of DNA replication.Science advances · 2024Article
- Breaking the Chain: Protease Inhibitors as Game Changers in Respiratory Viruses Management.International journal of molecular sciences · 2024Review
- A versatile automated pipeline for quantifying virus infectivity by label-free light microscopy and artificial intelligence.Nature communications · 2024Article
- High-content, arrayed compound screens with rhinovirus, influenza A virus and herpes simplex virus infections.Scientific data · 2022Article
- Identification of broad anti-coronavirus chemical agents for repurposing against SARS-CoV-2 and variants of concern.Current research in virological science · 2022Article
- A fast open-source Fiji-macro to quantify virus infection and transfection on single-cell level by fluorescence microscopy.MethodsX · 2022Article
- Metabolic Modifications by Common Respiratory Viruses and Their Potential as New Antiviral Targets.Viruses · 2021Review
- Microscopy deep learning predicts virus infections and reveals mechanics of lytic-infected cells.iScience · 2021Article
- Emerging antiviral therapeutics for human adenovirus infection: Recent developments and novel strategies.Antiviral research · 2021Review
- The right tools for the job: the central role for next generation chemical probes and chemistry-based target deconvolution methods in phenotypic drug discovery.RSC medicinal chemistry · 2021Review
- Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case.Infection and drug resistance · 2021Review
- The FDA-Approved Drug Nelfinavir Inhibits Lytic Cell-Free but Not Cell-Associated Nonlytic Transmission of Human Adenovirus.Antimicrobial agents and chemotherapy · 2020Article
- A high-content image-based drug screen of clinical compounds against cell transmission of adenovirus.Scientific data · 2020Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Human adenoviruses (HAdVs) are fatal to immuno-suppressed individuals, but no effective anti-HAdV therapy is available. Here, we present a novel image-based high-throughput screening (HTS) platform, which scores the full viral replication cycle from virus entry to dissemination of progeny and second-round infections. We analysed 1,280 small molecular weight compounds of the Prestwick Chemical Library (PCL) for interference with HAdV-C2 infection in a quadruplicate, blinded format, and performed robust image analyses and hit filtering. We present the entire set of the screening data including all images, image analyses and data processing pipelines. The data are made available at the Image Data Resource (IDR, idr0081). Our screen identified Nelfinavir mesylate as an inhibitor of HAdV-C2 multi-round plaque formation, but not single round infection. Nelfinavir has been FDA-approved for anti-retroviral therapy in humans. Our results underscore the power of image-based full cycle infection assays in identifying viral inhibitors with clinical potential.
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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.