Development of virus-like particles with inbuilt immunostimulatory properties as vaccine candidates
Bibliographic record
Abstract
The development of virus-like particle (VLP) based vaccines for human papillomavirus, hepatitis B and hepatitis E viruses represented a breakthrough in vaccine development. However, for dengue and COVID-19, technical complications, such as an incomplete understanding of the requirements for protective immunity, but also limitations in processes to manufacture VLP vaccines for enveloped viruses to large scale, have hampered VLP vaccine development. Selecting the right adjuvant is also an important consideration to ensure that a VLP vaccine induces protective antibody and T cell responses. For diseases like COVID-19 and dengue fever caused by RNA viruses that exist as families of viral variants with the potential to escape vaccine-induced immunity, the development of more efficacious vaccines is also necessary. Here, we describe the development and characterisation of novel VLP vaccine candidates using SARS-CoV-2 and dengue virus (DENV), containing the major viral structural proteins, as protypes for a novel approach to produce VLP vaccines. The VLPs were characterised by Western immunoblot, enzyme immunoassay, electron and atomic force microscopy, and in vitro and in vivo immunogenicity studies. Microscopy techniques showed proteins self-assemble to form VLPs authentic to native viruses. The inclusion of the glycolipid adjuvant, α-galactosylceramide (α-GalCer) in the vaccine formulation led to high levels of natural killer T (NKT) cell stimulation in vitro, and strong antibody and memory CD8+ T cell responses in vivo, demonstrated with SARS-CoV-2, hepatitis C virus (HCV) and DEN VLPs. This study shows our unique vaccine formulation presents a promising, and much needed, new vaccine platform in the fight against infections caused by enveloped RNA viruses.
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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 | 0.001 |
| 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".