Immunological study of COVID-19 vaccine candidate based on recombinant spike trimer protein from different SARS-CoV-2 variants of concern
Bibliographic record
Abstract
The emergency of new SARS-CoV-2 variants that feature increased immune escape marks an urgent demand for better vaccines that will provide broader immunogenicity. Here, we evaluated the immunogenic capacity of vaccine candidates based on the recombinant trimeric spike protein (S) of different SARS-CoV-2 variants of concern (VOC), including the ancestral Wuhan, Beta and Delta viruses. In particular, we assessed formulations containing either single or combined S protein variants. Our study shows that the formulation containing the single S protein from the ancestral Wuhan virus at a concentration of 2µg (SW2-Vac 2µg) displayed in the mouse model the highest IgG antibody levels against all the three (Wuhan, Beta, and Delta) SARS-CoV-2 S protein variants tested. In addition, this formulation induced significantly higher neutralizing antibody titers against the three viral variants when compared with authorized Gam-COVID-Vac-rAd26/rAd5 (Sputnik V) or ChAdOx1 (AstraZeneca) vaccines. SW2-Vac 2µg was also able to induce IFN-gamma and IL-17, memory CD4 populations and follicular T cells. Used as a booster dose for schedules performed with different authorized vaccines, SW2-Vac 2µg vaccine candidate also induced higher levels of total IgG and IgG isotypes against S protein from different SARS-CoV-2 variants in comparison with those observed with homologous 3-dose schedule of Sputnik V or AstraZeneca. Moreover, SW2-Vac 2µg booster induced broadly strong neutralizing antibody levels against the three tested SARS-CoV-2 variants. SW2-Vac 2µg booster also induced CD4+ central memory, CD4+ effector and CD8+ populations. Overall, the results demonstrate that SW2-Vac 2 µg is a promising formulation for the development of a next generation COVID-19 vaccine.
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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".