Formulation Development of a COVID-19 Recombinant Spike Protein-Based Vaccine
Bibliographic record
Abstract
The purpose of this study was to develop a formulation for a recombinant prefusion spike protein vaccine against SARS-CoV-2. It was found that the spike protein was susceptible to aggregation due to mechanical stress. Therefore, formulation studies were initiated focused on screening pharmaceutical excipients capable of preventing this. The screening of a panel of potential stabilizing conditions found that Tween 20 could inhibit mechanically induced aggregation. A concentration-dependent study indicated that a higher concentration of Tween 20 (0.2% v/v) was required to prevent conformational changes in the trimer. The conformational changes induced by mechanical stress were characterized by size exclusion chromatography (SEC) and hydrogen–deuterium exchange mass spectrometry (HDX-MS), indicating the formation of an extended trimeric conformation that was also unable to bind to antibodies directed to the S2 domain. Long-term stability modeling, using advanced kinetic analysis, indicated that the formulation containing 0.2% (v/v) Tween 20 at a neutral pH was predicted to be stable for at least two years at 2 °C to 8 °C. Additional stabilizer screening conducted by thermal shift assay indicated that sucrose and glycerol were able to significantly increase the spike protein melting temperature (Tm) and improve the overall thermostability of the spike protein in a short-term stability study. Thus, while 0.2% (v/v) Tween 20 was sufficient to prevent aggregation and to maintain spike protein stability under refrigeration, the addition of sucrose further improved vaccine thermostability. Altogether, our study provides a systematic approach to the formulation of protein-based COVID-19 vaccine and highlights the impact of mechanical stress on the conformation of the spike protein and the significance of surfactants and stabilizers in maintaining the structural and functional integrity of the spike protein.
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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 | 0.001 | 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".