Combination of vemurafenib, pleconaril, and AG7404 attenuates enterovirus replication <i>in vitro</i> and <i>in vivo</i>
Bibliographic record
Abstract
Enteroviruses infect multiple human tissues and cause diseases including meningitis, the common cold, myocarditis, pancreatitis, hepatitis, poliomyelitis, sepsis, type 1 diabetes, hand, foot, and mouth disease. Despite this burden, no antiviral therapy has been approved to date. Progress has been limited by the structural and topical diversity of enteroviruses because many variants are intrinsically insensitive to candidate agents and sensitive strains develop resistance rapidly. Here, we report that the approved anticancer drug vemurafenib inhibited replication of some tested enteroviruses in cell cultures. Passage of echovirus EV1 and coxsackievirus CVB5 for six cycles in cell culture yielded vemurafenib-resistant virus variants harboring mainly missense mutations in the viral 3A and VP1 proteins, underscoring the need for combination therapy. We therefore evaluated cocktails, combining vemurafenib with the VP1 inhibitor pleconaril and the 3C protease inhibitor AG7404. In cell culture, the cocktails suppressed replication of all seven tested enteroviruses. The combination was also effective in human pancreatic, retinal, and brain organoids. In infected mice, the triple regimen reduced viral titers in the pancreas. These findings support multi-stage targeting of the enterovirus life cycle as a promising path toward broadly active therapeutic cocktails.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".