Advances in Therapeutic Vaccines Against HPV: A Review of Human Clinical Trials
Bibliographic record
Abstract
Cervical cancer remains a major public health concern, particularly in low- and middle-income countries (LMICs) where access to preventive measures is limited. Persistent infection with high-risk human papillomavirus (HPV) types, mainly HPV16 and HPV18, is the key cause of cervical cancer. While prophylactic HPV vaccines effectively prevent new infections, they offer no therapeutic benefit for individuals with established lesions. This review evaluates the clinical evidence on therapeutic HPV vaccines, focusing on their ability to promote viral clearance. A bibliographic search was conducted in PubMed, selecting human studies reporting outcomes on HPV clearance. Seventeen clinical trials were identified, including DNA-based (VGX-3100, GX-188E), viral-vector (MVA E2, TG4001), peptide-based (Pepcan), and bacterial-vector (GLT 001) vaccines. Among them, DNA-based vaccines, particularly VGX-3100, showed the most consistent results, whereas several protein- or vector-based approaches demonstrated variable outcomes. Early therapeutic HPV vaccine trials faced setbacks due to limited efficacy, delivery approaches, and study design challenges, preventing progression to late-phase development. Recent DNA-based candidates, however, are advancing through phase II/III trials. While none have yet to be approved for commercial use, these vaccines elicit virus-specific T-cell responses and can induce regression of precancerous lesions, offering a promising addition to prophylactic vaccination and screening. Variability in study designs and endpoints underlines the need for standardized protocols and further phase III trials. Overall, therapeutic HPV vaccines represent a rapidly advancing field with the potential to complement prophylactic vaccination and screening, thereby strengthening global cervical cancer control efforts, particularly in LMICs.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".