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 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 | 0.000 | 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) | 0.000 | 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".