Induction of lung mucosal immunity by a next-generation inhaled aerosol COVID-19 vaccine: an open-label, multi-arm phase 1 clinical trial
Bibliographic record
Abstract
The current COVID-19 vaccines are suboptimal against the evolving SARS-CoV-2 variants, particularly in high-risk populations. A next-generation vaccine strategy capable of effective induction of respiratory mucosal immunity remains to be clinically developed. Here, we report an open-label, multi-arm phase 1 study (NCT05094609) to evaluate a multi-antigenic COVID-19 vaccine delivered once via inhaled aerosol to the lung of intramuscular mRNA-vaccinated humans without or with prior SARS-CoV-2 infection (uninfected vs infected). Escalating doses of a human adenoviral (HuAd)-vectored or chimpanzee Ad (ChAd)-vectored vaccine are evaluated in the uninfected cohort. A selected Ad vaccine is further evaluated in the infected cohort. The safety is assessed as a primary outcome. Ag-specific immune responses (secondary outcome) are assessed in peripheral blood and in respiratory tract via bronchoscopy at baseline and at timepoint(s) post-vaccination. Eighteen-65-year-old, healthy participants who have received at least 3 doses of mRNA COVID-19 vaccine are enrolled with those vaccinated with any Ad-vectored COVID-19 vaccine excluded. At baseline, there is minimally detectable mucosal immunity in the lung of uninfected or infected humans. While all tested doses (1 × 105 to 1 × 108 TCID50) of HuAd and ChAd vaccines are safe, ChAd vaccine markedly outperforms the HuAd counterpart in immunogenicity. Thus, an optimal aerosol dose of ChAd vaccine induces the tripartite respiratory mucosal immunity consisting of T cell, trained innate and antibody immunity. Our study thus presents a promising next-generation aerosol COVID-19 vaccine strategy for further clinical development. Vaccination provides protection from COVID-19, but optimization in design and route is an ever-ongoing process. Here the authors pursue an open-label, multi-arm phase I clinical trial to report the safety of a multi-valent, aerosol vaccine administered via inhalation, as well as superior mucosal immunity induced by ChAd over HuAd vectors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".