Tuberculosis vaccine candidate: Characterization of H4-IC31 formulation and H4 antigen conformation
Bibliographic record
Abstract
Tuberculosis (TB) is one of the leading causes of death worldwide, making the development of effective TB vaccines a global priority. A TB vaccine consisting of a recombinant fusion protein, H4, combined with a novel synthetic cationic adjuvant, IC31®, is currently being developed. The H4 fusion protein consists of two immunogenic mycobacterial antigens, Ag85 B and TB10.4, and the IC31® adjuvant is a mixture of KLK, a leucine-rich peptide (KLKL5KLK), and the oligodeoxynucleotide ODN1a, a TLR9 ligand. However, efficient and robust methods for assessing these formulated components are lacking. Here, we developed and optimized phase analysis light scattering (PALS), electrical sensing zone (ESZ), and Raman, FTIR, and CD spectroscopy methods to characterize the H4-IC31 vaccine formulation. PALS-measured conductivity and zeta potential values could differentiate between the similarly sized particles of IC31® adjuvant and the H4-IC31 vaccine candidate and could thereby serve as a control during vaccine formulation. In addition, zeta potential is indicative of the adjuvant to antigen ratio which is the key in the immunomodulatory response of the vaccine. ESZ was used as an orthogonal method to measure IC31® and H4-IC31 particle sizes. Raman, FTIR, and CD spectroscopy revealed structural changes in H4 protein and IC31® adjuvant, inducing an increase in both the β-sheet and random coil content as a result of adsorption. Furthermore, nanoDSF showed changes in the tertiary structure of H4 protein as a result of adjuvantation to IC31®. Our findings demonstrate the applicability of biophysical methods to characterize vaccine components in the final H4-IC31 drug product without the requirement for desorption.
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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 | 0.000 |
| 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".