Optimization of Culture Media and Feeding Strategy for High Titer Production of an Adenoviral Vector in HEK 293 Fed-Batch Culture
Bibliographic record
Abstract
Adenoviruses are efficient and safe vectors for delivering target antigens and adenovirus-based vaccines have been used against a wide variety of pathogens, including tuberculosis and COVID-19. Cost-effective and scalable biomanufacturing processes are critical for the commercialization of adenovirus-vectored vaccines. Adenoviral vectors are commonly produced through the infection of batch cultures at low cell density cultures, mostly because infections at high cell densities result in reduced cell-specific virus productivity and does not improve volumetric productivity. In this study, we have investigated the feasibility of improving the volumetric productivity by infecting fed-batch cultures at high cell densities. Four commercial and one in-house developed serum-free media were first tested for supporting growth of HEK 293 cells and production of adenovirus type 5 (Ad5) in batch culture. Two best media were then selected for development of fed-batch culture to improve cell growth and virus productivity. A maximum viable cell density up to 16 × 106 cells/mL was achieved in shake flask fed-batch cultures using the selected media and commercial or in-house developed feeds. The volumetric virus productivity was improved by up to six folds, reaching 3.0 × 1010 total viral particles/mL in the fed-batch culture cultivated with the media and feeds developed in house and infected at a cell density of 5 × 106 cells/mL. Additional rounds of optimization of media and feed were required to maintain the improved titer when the fed-batch culture was scaled up in a bench scale (3 L) bioreactor. Overall, the results suggested that fed-batch culture is a simple and feasible process to significantly improve the volumetric productivity of Ad5 through optimization and balance of nutrients in culture media and feeds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".