Meeting report: Convening on the influenza human viral challenge model for universal influenza vaccines, Part 1: Value; challenge virus selection; regulatory, industry and ethical considerations; increasing standardization, access and capacity
Bibliographic record
Abstract
In response to global interest in the development of a universal influenza vaccine, the Bill & Melinda Gates Foundation, PATH, and the Global Funders Consortium for Universal Influenza Vaccine Development convened a meeting of experts (London, UK, May 2018) to assess the role of a standardized controlled human influenza virus infection model (CHIVIM) towards the development of novel influenza vaccine candidates. This report (in two parts) summarizes those discussions and offers consensus recommendations. This article (Part 1) covers challenge virus selection, regulatory and ethical considerations, and issues concerning standardization, access, and capacity. Part 2 covers specific methodologic considerations. Current methods for influenza vaccine development and licensure require large costly field trials. The CHIVIM requires fewer subjects and the controlled setting allows for better understanding of influenza transmission and host immunogenicity. The CHIVIM can be used to identify immune predictors of disease for at-risk populations and to measure efficacy of potential vaccines for further development. Limitations to the CHIVIM include lack of standardization, limited access to challenge viruses and assays, lack of consensus regarding role of the CHIVIM in vaccine development pathway, and concerns regarding risk to study participants and community. To address these issues, the panel of experts recommended that WHO and other key stakeholders provide guidance on standardization, challenge virus selection, and risk management. A common repository of well-characterized challenge viruses, harmonized protocols, and standardized assays should be made available to researchers. A network of research institutions performing CHIVIM trials should be created, and more study sites are needed to increase capacity. Experts agreed that a research network of institutions working with a standardized CHIVIM could contribute important data to support more rapid development and licensure of novel vaccines capable of providing long-lasting protection against seasonal and pandemic influenza strains.
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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 | 0.045 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".