Production of adeno‐associated viral vector serotype 6 by triple transfection of suspension HEK293 cells at higher cell densities
Bibliographic record
Abstract
Abstract In recent years, the use of adeno‐associated viruses (AAVs) as vectors for gene and cell therapy has increased, leading to a rise in the amount of AAV vectors required during pre‐clinical and clinical trials. AAV serotype 6 (AAV6) has been found to be efficient in transducing different cell types and has been successfully used in gene and cell therapy protocols. However, the number of vectors required to effectively deliver the transgene to one single cell has been estimated at 106 viral genomes (VG), making large‐scale production of AAV6 necessary. Suspension cell‐based platforms are currently limited to low cell density productions due to the widely reported cell density effect (CDE), which results in diminished production at high cell densities and decreased cell‐specific productivity. This limitation hinders the potential of the suspension cell‐based production process to increase yields. In this study, we investigated the improvement of the production of AAV6 at higher cell densities by transiently transfecting HEK293SF cells. The results showed that when the plasmid DNA was provided on a cell basis, the production could be carried out at medium cell density (MCD, 4 × 106 cells mL−1) resulting in titers above 1010 VG mL−1. No detrimental effects on cell‐specific virus yield or cell‐specific functional titer were observed at MCD production. Furthermore, while medium supplementation alleviated the CDE in terms of VG/cell at high cell density (HCD, 10 × 106 cells mL−1) productions, the cell‐specific functional titer was not maintained, and further studies are necessary to understand the observed limitations for AAV production in HCD processes. The MCD production method reported here lays the foundation for large‐scale process operations, potentially solving the current vector shortage in AAV manufacturing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".