Exploiting the Achilles’ Heel of Viral RNA Processing to Develop Novel Antivirals
Bibliographic record
Abstract
Abstract Viruses continue to pose a significant health burden to the human population, and recent history has shown a concerning surge in viral threats. Treatment options for viral infections are limited, and viruses have proven adept at evolving resistance to many existing therapies, highlighting a significant vulnerability in our defenses. In response to this challenge, we explored the modulation of cellular RNA metabolic processes as an alternative paradigm to antiviral development. Many viruses depend on the host cell’s RNA splicing machinery, and small alterations to this host process results in catastrophic changes in viral protein production, ultimately inhibiting virus replication. Previously, the small molecule 5342191 was identified as a potent inhibitor of HIV-1 replication by altering viral RNA accumulation at doses that minimally affect host gene expression. In this report, we document 5342191 as a potent inhibitor of adenovirus, coronavirus, and influenza replication. In each case, 5342191-mediated reduction in virus replication was associated with altered viral RNA accumulation and loss of viral structural protein expression. Interestingly, while resistant viruses were rapidly isolated for compounds targeting either virus-encoded proteases or polymerases, we have not yet isolated 534219-resistant variants of coronavirus or influenza. As with HIV-1, 5342191’s inhibition of coronaviruses and influenza is mediated through the activation of specific cell signaling networks, including GPCR and/or MAPK signaling pathways that ultimately affect SR kinase expression. Together, these studies highlight the therapeutic potential of compounds that target cellular processes essential for the replication of multiple viruses. Not only do these compounds hold promise as broad-spectrum antivirals, but they also offer the potential of greater resilience in combating viral infections.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".