Development of Long-Term Stability of Enveloped rVSV Viral Vector Expressing SARS-CoV-2 Antigen Using a DOE-Guided Approach
Bibliographic record
Abstract
Liquid formulations have been successfully used in many viral vector vaccines including influenza (Flu), hepatitis B, polio (IPV), Ebola, and COVID-19 vaccines. The main advantage of liquid formulations over lyophilized formulations is that they are cost-effective, as well as easier to manufacture and distribute. However, studies have shown that the liquid formulations of enveloped viral vector vaccines are not stable over extended periods of time. In this study, we explored the development of the liquid formulations of an enveloped recombinant Vesicular Stomatitis Virus (VSV) expressing the SARS-CoV-2 spike glycoprotein. To do so, we used a design of experiments (DOE) method, which allowed us to assess the stability dynamics of the viral vector in an effective manner. An initial stability study showed that trehalose, gelatin, and histidine were effective at maintaining functional viral titers during freeze-thaw stress and at different temperatures (-20, 4, 20, and 37 °C). These preliminary data helped to identify critical factors for the subsequent implementation of the DOE method that incorporated a stress condition at 37 °C. We used the DOE results to identify the optimal liquid formulations under the selected accelerated stress conditions, which then guided the identification of long-term storage conditions for further evaluation. In the long-term stability study, we identified several liquid formulations made of sugar (sucrose, trehalose, and sorbitol), gelatin, and a histidine buffer that resulted in the improved stability of rVSV-SARS-CoV-2 at 4 °C for six months. This study highlights an effective approach for the development of liquid formulations for viral vector vaccines, contributing significantly to the existing knowledge on enveloped viral vector thermostability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".