The Metallodrug BOLD-100 Is a Potent Inhibitor of SARS-CoV-2 Replication and Has Broad-Acting Antiviral Activity
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".