Nanoparticles as Delivery Systems for Antigenic Saccharides: From Conjugation Chemistry to Vaccine Design
Bibliographic record
Abstract
Glycoconjugate vaccines have been effective in preventing numerous bacterial infectious diseases and have shown recent potential to treat cancers through active immunotherapy. Soluble polysaccharides elicit short-lasting immune responses and are usually covalently linked to immunogenic carrier proteins to enhance the antigen-specific immune response by stimulating T-cell-dependent mechanisms. Nonetheless, the conjugation of purified polysaccharides to carrier proteins complexifies vaccine production, and immunization with protein glycoconjugates can lead to the undesirable immunogenic interference of the carrier. Recently, the use of nanoparticles and nanoassemblies for the delivery of antigenic saccharides has gathered attention from the scientific community. Nanoparticles can be easily functionalized with a diversity of functionalities, including T-cell epitope, immunomodulator and synthetic saccharides, allowing for the modulation and polarization of the glycoantigen-specific immune response. Notably, the conjugation of glycan to nanoparticles protects the antigens from degradation and enhances their uptake by immune cells. Different types of nanoparticles, such as liposomes assembled from lipids, inorganic nanoparticles, virus-like particles and dendrimers, have been explored for glycovaccine design. The versatility of nanoparticles and their ability to induce robust immune responses make them attractive delivery platforms for antigenic saccharides. The present review aims at summarizing recent advancements in the use of nano-scaled systems for the delivery of synthetic glycoantigens. After briefly presenting the immunological mechanisms required to promote a robust immune response against antigenic saccharides, this review will offer an overview of the current trends in the nanoparticle-based delivery of glycoantigens.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".