Meeting report: Considerations for trial design and endpoints in licensing therapeutic HPV16/18 vaccines to prevent cervical cancer
Bibliographic record
Abstract
• Effective HPV therapeutic vaccines could reduce HPV-related cancer and associated deaths. • An expert convening was held in September 2023 to provide guidance on vaccine trial design. • Study design for vaccines targeting cervical precancers has regulatory precedent. • Licensure pathways for treating existing HPV infection is less clear. • Early engagement with regulators with supportive data is strongly encouraged. Cervical cancer is a major cause of morbidity and mortality globally with a disproportionate impact on women in low- and middle-income countries. In 2021, the World Health Organization (WHO) called for increased vaccination, screening, and treatment to eliminate cervical cancer. However, even with widespread rollout of human papillomavirus (HPV) prophylactic vaccines, millions of women who previously acquired HPV infections will remain at risk for progression to cancer for decades to come. The development and licensing of an affordable, accessible therapeutic HPV vaccine, designed to clear or control carcinogenic HPV and/or to induce regression precancer could significantly contribute to the elimination efforts, particularly benefiting those who missed out on the prophylactic vaccine. One barrier to development of such vaccines is clarity around the regulatory pathway for licensure. In Washington, D.C. on September 12–13, 2023, a meeting was convened to provide input and guidance on trial design with associated ethical and regulatory considerations. This report summarizes the discussion and conclusions from the meeting. Expert presentation topics included the current state of research, potential regulatory challenges, WHO preferred product characteristics, modeling results of impact of vaccine implementation, epidemiology and natural history of HPV infection, immune responses related to viral clearance and/or precancer regression including potential biomarkers, and ethical considerations. Panel discussions were held to explore specific trial design recommendations to support the licensure process for two vaccine indications: (1) treatment of prevalent HPV infection or (2) treatment of cervical precancers. Discussion covered inclusion/exclusion criteria, study endpoints, sample size and power, safety, study length, and additional data needed, which are reported here. Further research of HPV natural history is needed to address identified gaps in regulatory guidance, especially for therapeutic vaccines intended to treat existing HPV infections.
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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 | 0.458 | 0.531 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".