Recent Advances in the Search for Antiviral Agents against Human Papillomaviruses
Bibliographic record
Abstract
Infection by human papillomavirus (HPV) is extremely common and associated with the development of benign warts or malignant lesions of the skin and mucosa. Infection by a high-risk (oncogenic) anogenital HPV type, most often through sexual contacts, is the starting point of virtually all cases of cervical cancers and the majority of anal cancers. The same viral types are also increasingly being linked with a subset of head-and-neck and non-melanoma skin cancers. Although prophylactic vaccines are now available to protect against the four types most commonly found in cervical and anal cancers (HPV16 and HPV18) and anogenital warts (HPV6 and HPV11), these neither protect against all genital HPVs nor are of therapeutic utility for already infected patients. Thus, the need for antiviral agents to treat HPV-associated diseases remains great, but none currently exist. This article reviews the recent progress made towards the development of antiviral agents to treat HPV infections, from target identification and validation to the discovery of lead compounds with therapeutic potential. Emphasis has been placed on novel low-molecular-weight compounds that antagonize HPV proteins or, alternatively, inhibit cellular proteins which have been usurped by papillomaviruses and are mediating their pathogenic effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".