The Importance of Adjuvant Formulation in the Development of a Tuberculosis Vaccine
Bibliographic record
Abstract
An effective protein-based vaccine for tuberculosis will require a safe and effective adjuvant. There are few adjuvants in approved human vaccines, including alum and the oil-in-water-based emulsions MF59 (Novartis Vaccines and Diagnostics), AS03 and AS04 (GlaxoSmithKline Biologics), AF03 (Sanofi), and liposomes (Crucell). When used with pure, defined proteins, both alum and emulsion adjuvants are effective at inducing primarily humoral responses. One of the newest adjuvants in approved products is AS04, which combines monophosphoryl lipid A, a TLR-4 agonist, with alum. In this study, we compared two adjuvants: a stable oil-in-water emulsion (SE) and a stable oil-in-water emulsion incorporating glucopyranosyl lipid adjuvant, a synthetic TLR-4 agonist (GLA-SE), each together with a recombinant protein, ID93. Both the emulsion SE and GLA-SE adjuvants induce potent cellular responses in combination with ID93 in mice. ID93/SE induced Th2-biased immune responses, whereas ID93/GLA-SE induced multifunctional CD4(+) Th1 cell responses (IFN-γ, TNF-α, and IL-2). The ID93/GLA-SE vaccine candidate induced significant protection in mice and guinea pigs, whereas no protection was observed with ID93/SE, as assessed by reductions in bacterial burden, survival, and pathology. These results highlight the importance of properly formulating subunit vaccines with effective adjuvants for use against tuberculosis.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".