Targeting Human Papillomavirus Genome Replication for Antiviral Drug Discovery
Bibliographic record
Abstract
Human papillomavirus (HPV) infections are a major human health problem; they are the cause of recurrent benign warts and of several cancers of the anogenital tract and head and neck region. Although there are two prophylactic HPV vaccines that could, if used universally, prevent as many as two-thirds of HPV-induced cancers, as well as several cytotoxic and immunomodulatory agents for localized treatment of infections, there are currently no HPV antiviral drugs in our arsenal of therapeutic agents. This review examines the status of past and ongoing research into the development of HPV antivirals, focused primarily upon approaches targeting the replication of the viral genome. The only HPV enzyme, E1, is a DNA helicase that interfaces with the cellular DNA replication machinery to replicate the HPV genome. To date, searches for small molecule inhibitors of E1 for use as antivirals have met with limited success. The lack of other viral enzymes has meant that the search for antivirals has shifted to a large degree to the modulation of protein-protein interactions. There has been some success in identifying small molecule inhibitors targeting interactions between HPV proteins but with activity against a small subset of viral types only. As noted in this review, it is thought that targeting E1 interactions with cellular replication proteins may provide inhibitors with broader activity against multiple HPV types. Herein, we outline the steps in HPV DNA replication and discuss those that appear to provide the most advantageous targets for the development of anti-HPV therapeutics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".