Scale-up of a low-temperature spray-drying process for a tuberculosis vaccine candidate using lab-scale equipment
Bibliographic record
Abstract
• Throughput was increased tenfold for low-temperature drying on a lab-scale dryer. • Optimization gave a visual output of process parameter choices and production rates. • Production rate of a tuberculosis vaccine candidate was increased tenfold. • Markers important for stability were preserved in the scaled-up vaccine powder. Laboratory-scale spray drying can be a useful tool in developing new dry powder formulations for the delivery of biologics such as therapeutic proteins or vaccines. Low-temperature drying is often used in these processes to prevent the exposure of biologics to harsh conditions that could potentially lead to degradation or instability of the final product. However, low-temperature drying on small-scale equipment can result in very low production rates that may not be practical for generating sufficient material for studies requiring larger sample quantities, such as key preclinical or toxicology studies. This study demonstrates a scale-up effort for a spray dried adjuvanted protein tuberculosis (TB) vaccine candidate using a custom lab-scale spray dryer. To achieve higher throughput without compromising the stability of the powder and biologic material, a process model for the spray dryer was used to determine optimal processing parameters and establish general vaccine powder manufacturing guidelines, such as minimizing exposure to high temperatures and relative humidity during drying. Maximizing dryer throughput and increasing overall feed concentration resulted in a tenfold increase in production rate using lab-scale equipment, such that 97.6 g of powder (the equivalent of about 5,000 human doses) could be produced using a lab-scale spray dryer in a single 6-hour spray drying run.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".