Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production
Bibliographic record
Abstract
Novel vaccination approaches based on rational design of B- and T-cell epitopes - epitope-based vaccines - are making progress in the clinical trial pipeline. The epitope-focused recombinant protein-based malaria vaccine (termed RTS,S) is a next-generation approach that successfully reached phase-III trials, and will potentially become the first commercial vaccine against a human parasitic disease. Progress made on methods such as recombinant DNA technology, advanced cell-culture techniques, immunoinformatics and rational design of immunogens are driving the development of these novel concepts. Synthetic recombinant proteins comprising both B- and T-cell epitopes can be efficiently produced through modern biotechnology and bioprocessing methods, and can enable the induction of large repertoires of immune specificities. In particular, the inclusion of appropriate CD4+ T-cell epitopes is increasingly considered a key vaccine component to elicit robust immune responses, as suggested by results coming from HIV-1 clinical trials. In silico strategies for vaccine design are under active development to address genetic variation in pathogens and several broadly protective "universal" influenza and HIV-1 vaccines are currently at different stages of clinical trials. Other methods focus on improving population coverage in target populations by rationally considering specificity and prevalence of the HLA proteins, though a proof-of-concept in humans has not been demonstrated yet. Overall, we expect immunoinformatics and bioprocessing methods to become a central part of the next-generation epitope-based vaccine development and production process.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".