Targeting protein homeostasis with small molecules as a strategy for the development of pan-coronavirus antiviral therapies
Bibliographic record
Abstract
The COVID-19 pandemic has created a global health crisis, with challenges arising from the ongoing evolution of the SARS-CoV-2 virus, the emergence of new strains, and the long-term effects of COVID-19. Aiming to overcome the development of viral resistance, our study here focused on developing broad-spectrum pan-coronavirus antiviral therapies by targeting host protein quality control mechanisms essential for viral replication. Screening an in-house compound library led to the discovery of three candidate compounds targeting cellular proteostasis. The three compounds are (1) the nucleotide analog cordycepin, (2) a benzothiozole analog, and (3) an acyldepsipeptide analog initially developed as part of a campaign to target the mitochondrial ClpP protease. These compounds demonstrated dose-dependent efficacy against multiple coronaviruses, including SARS-CoV-2, effectively inhibiting viral replication in vitro as well as in lung organoids. Notably, the compounds also showed efficacy against SARS-CoV-2 delta and omicron strains. As part of this work, we developed a BSL2-level cell-integrated SARS-CoV-2 replicon, which could serve as a valuable tool for high-throughput screening and studying intracellular viral replication. Our study should aid in the advancement of antiviral drug development efforts.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".