Ribavirin as an anti-cancer therapy: acute myeloid leukemia and beyond?
Bibliographic record
Abstract
Ribavirin was discovered nearly 40 years ago as a broad-spectrum antiviral drug. Recent data suggest that ribavirin may also be an effective cancer therapy. In this case, ribavirin targets an oncogene, the eukaryotic translation initiation factor eIF4E, elevated in approximately 30% of cancers including many leukemias and lymphomas. Specifically, ribavirin impedes eIF4E mediated oncogenic transformation by acting as an inhibitor of eIF4E. In a phase II clinical trial, ribavirin treatment led to substantial clinical benefit in patients with poor-prognosis acute myeloid leukemia (AML). Here molecular targeting of eIF4E correlated with clinical response. Ribavirin also targets a key enzyme in the guanosine biosynthetic pathway, inosine monophosphate dehydrogenase (IMPDH), and also modulates immunity. Parallels with known antiviral mechanisms could be informative; however, after 40 years, these are not entirely clear. The antiviral effects of ribavirin appear cell-type specific. This variation likely arises for many reasons, including cell specific variations in ribavirin metabolism as well as virus specific factors. Thus, it seems that the mechanisms for ribavirin action in cancer therapy may also vary in terms of the cancer/tissue under study. Here we review the anticancer activities of ribavirin and discuss the possible utility of incorporating ribavirin into diverse cancer therapeutic regimens.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".