The ‘Co-Delivery’ Approach to Liposomal Vaccines: Application to the Development of influenza-A and hepatitis-B Vaccine Candidates
Bibliographic record
Abstract
DNA vaccination with mammalian-expressible plasmid DNA encoding protein antigens is known to be an effective means to elicit cell-mediated immunity, sometimes in the absence of a significant antibody response. This may be contrasted with protein vaccination, which gives rise to antibody responses with little evidence of cell-mediated immunity. This has led to considerable interest in DNA vaccination as a means to elicit cell-mediated immune responses against conserved viral antigens or intracellular cancer antigens, for the purpose of therapeutic vaccination. However, almost all current vaccines are used prophylactically and work by producing antibodies rather than cell mediated immune responses. In the present study we have therefore explored the combination of DNA and protein forms of an antigen using two exemplary prophylactic vaccine antigens, namely inactivated influenza virion and hepatitis-B surface antigen. We studied the effects of various combinations of DNA and protein on the antibody response. Co-administration of soluble forms of DNA and protein representations of the same antigen gave rise to the same level of antibody response as if protein were administered alone. In contrast, we found that when these antigens are entrapped in the same liposomal compartment, that there was a strong synergistic effect on the immune response, which was much greater than when either antigen was administered alone, or in various other modes of combination (e.g. co-administration as free entities, also pooled liposomal formulations where the two materials were contained in separate liposomal vehicles in the same suspension). The synergistic effect of liposomally co-entrapped DNA and protein exceeded, markedly, the well known adjuvant effects of plasmid DNA and liposomes. We have termed this new approach to vaccination 'co-delivery' and suggest that it may derive from the simultaneous presentation of antigen via MHC class-I (DNA) and MHC class-II (protein) pathways to CD8+ and CD4+ cells at the same antigen presenting cell--a mode of presentation that would commonly occur with live viral pathogens. We conclude that co-delivery is a very effective means to generate protective antibody responses against viral pathogens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".