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Record W4383724109 · doi:10.3390/biom13071095

The Metallodrug BOLD-100 Is a Potent Inhibitor of SARS-CoV-2 Replication and Has Broad-Acting Antiviral Activity

2023· article· en· W4383724109 on OpenAlexafffund
Daniel S. Labach, Hinissan P. Kohio, Edwin A. Tse, Ermela Paparisto, Nicole J. Friesen, Jim Pankovich, Mark Bazett, Stephen D. Barr

Bibliographic record

VenueBiomolecules · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchMitacs
KeywordsViral replicationCytopathic effectVirologyBiologyIn vitroVirusAntiviral drugChemistryGenetics

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has highlighted an urgent need to discover and test new drugs to treat patients. Metal-based drugs are known to interact with DNA and/or a variety of proteins such as enzymes and transcription factors, some of which have been shown to exhibit anticancer and antimicrobial effects. BOLD-100 (sodium trans-[tetrachlorobis(1H-indazole)ruthenate(III)]dihydrate) is a novel ruthenium-based drug currently being evaluated in a Phase 1b/2a clinical trial for the treatment of advanced gastrointestinal cancer. Given that metal-based drugs are known to exhibit antimicrobial activities, we asked if BOLD-100 exhibits antiviral activity towards SARS-CoV-2. We demonstrated that BOLD-100 potently inhibits SARS-CoV-2 replication and cytopathic effects in vitro. An RNA sequencing analysis showed that BOLD-100 inhibits virus-induced transcriptional changes in infected cells. In addition, we showed that the antiviral activity of BOLD-100 is not specific for SARS-CoV-2, but also inhibits the replication of the evolutionarily divergent viruses Human Immunodeficiency Virus type 1 and Human Adenovirus type 5. This study identifies BOLD-100 as a potentially novel broad-acting antiviral drug.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.094
GPT teacher head0.382
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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