Efficacy of a stable multivalent broadly protective subunit vaccine platform against SARS-CoV-2 variants of concern
Bibliographic record
Abstract
Abstract The emergence and ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for rapid vaccine development platforms that can be updated to counteract emerging variants of currently circulating and future emerging coronaviruses. Here we report the development of a “train engine and car” subunit vaccine platform that contains a SARS-CoV-2 Wuhan S1 protein linked to a series of flexible receptor binding domains (RBDs) derived from SARS-CoV-2 variants of concern (VOCs). We demonstrate that these linked subunit vaccines when combined with the SWE adjuvant are immunogenic in Syrian hamsters and subsequently provide protection from challenge with SARS-CoV-2 VOCs Omicron, Delta, and Beta. Importantly, the bivalent and trivalent vaccine candidates offered protection against some heterologous SARS-CoV-2 VOCs that were not included in the vaccine design, demonstrating the potential for broad protection against a range of different VOCs. Furthermore, these formulated vaccine candidates were stable at 2–8 °C for up to 12 months post-formulation, highlighting their utility in low-resource settings. Indeed, our vaccine platform will enable the development of safe and broadly protective vaccines against emerging betacoronaviruses that pose a significant health risk for humans and agricultural animals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".