Advances on Cyclin-dependent Kinases (CDKs) as Novel Targets for Antiviral Drugs
Bibliographic record
Abstract
Although targeting viral proteins has lead to many successful antiviral drugs, these antivirals have certain limitations. They rapidly select for resistance, tend to be active against only a few related viruses and the proteins of a pathogen must be characterized before such drugs can be developed. Consequently, a long period is required from the identification of a new pathogen to the development of relevant antivirals, a major concern for emerging diseases. Cellular proteins are now considered as potential targets for antivirals. Drugs that target cellular proteins required for several viral functions might not easily select for drug-resistance. They may also be active against a variety of unrelated viruses, which commonly require the same cellular proteins, and against viral strains resistant to conventional antiviral drugs. These antivirals could be promptly tested against emerging viruses because even distantly related viruses commonly require the same cellular proteins. Cellular cyclin-dependent kinases (CDKs) are required for replication of many viruses and specific pharmacological CDK inhibitors (PCIs) are proving to have surprisingly few negative side effects in clinical trials (against cancer). PCIs inhibit replication of wild-type and multi-drug resistant strains of HIV, HSV-1, HSV-2, HCMV, EBV and VZV. Two PCIs, roscovitine and flavopiridol, were recently proven active in a mouse model of HIV-induced nephropathy. Because the antiviral mechanisms of PCIs require no viral proteins, mutations in viral genes may not easily overcome inhibition by these drugs. In fact, no PCI-resistant viral mutant has been reported. PCIs are scheduled to enter clinical trials as antivirals in 2005.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".