Probability of Success and Timelines for the Development of Vaccines for Emerging and Reemerged Viral Infectious Diseases
Bibliographic record
Abstract
BACKGROUND: Anticipated success rates and timelines for COVID-19 vaccine development vary. Recent experience with developing and testing viral vaccine candidates can inform expectations regarding the development of safe and effective vaccines. OBJECTIVE: To estimate timelines and probabilities of success for recent vaccine candidates. DESIGN: ClinicalTrials.gov was searched to identify trials testing viral vaccines that had not advanced to phase 2 before 2005, and the progress of each vaccine from phase 1 through to U.S. Food and Drug Administration (FDA) licensure was tracked. Trial characteristics were double-coded. (Registration: Open Science Framework [https://osf.io/dmuzx/]). SETTING: Trials launched between January 2005 and March 2020. PARTICIPANTS: Preventive viral vaccine candidates for 23 emerging or reemerged viral infectious diseases. MEASUREMENTS: The primary end point was the probability of vaccines advancing from launch of phase 2 to FDA licensure within 10 years. RESULTS: In total, 606 clinical trials forming 220 distinct development trajectories (267 343 enrolled participants) were identified. The probability of vaccines progressing from phase 2 to licensure within 10 years was 10.0% (95% CI, 2.6% to 16.9%), with most approvals representing H1N1 or H5N1 vaccines. The average timeline from phase 2 to approval was 4.4 years (range, 6.4 weeks to 13.9 years). The probabilities of advancing from phase 1 to 2, phase 2 to 3, and phase 3 to licensure within the total available follow-up time were 38.2% (CI, 30.7% to 45.0%), 38.3% (CI, 23.1% to 50.5%), and 61.1% (CI, 3.7% to 84.3%), respectively. LIMITATIONS: The study did not account for preclinical development and relied primarily on ClinicalTrials.gov and FDA resources. Success probabilities do not capture the varied reasons why vaccines fail to advance to regulatory approval. CONCLUSION: Success probabilities and timelines varied widely across different vaccine types and diseases. If a SARS-CoV-2 vaccine is licensed within 18 months of the start of the pandemic, it will mark an unprecedented achievement for noninfluenza viral vaccine development. PRIMARY FUNDING SOURCE: McGill Interdisciplinary Initiative in Infection and Immunity (MI4) Emergency COVID-19 Research Funding program.
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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 | 0.071 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".