Repurposing Drugs and Synergistic Combinations as Potential Therapies for Inhibiting SARS-CoV-2 and Coronavirus Replication
Bibliographic record
Abstract
Drug repurposing can serve an important role in rapidly discovering medicament options for emerging microbial pandemics. In this study, a pragmatic approach is demonstrated for screening and testing drug combinations as potential broad-spectrum therapies against SARS-CoV-2 and other betacoronaviruses. Rapid cell-based phenotypic small molecule screens were executed using related common-cold-causing HCoV-OC43 betacoronavirus to identify replication inhibitors from a library of drugs approved by regulatory agencies for other indications. Given the best inhibitors, an expedient checkerboard strategy then served to identify synergistic drug combinations. These combinations were then validated using more challenging assays involving SARS-CoV-2 and variants. Promising drug combinations against multiple viral variants were discovered and involved Tilorone with Nelfinavir or Molnupiravir.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".