Improving adeno‐associated vector yield in high density insect cell cultures
Bibliographic record
Abstract
BACKGROUND: Recombinant adeno-associated virus (rAAV) are the most promising vectors for gene therapy. However, large-scale rAAV production remains a challenge for the translation of rAAV-based therapeutic strategies to the clinic. The baculovirus expression vector system (BEVS) has been engineered to produce high rAAV titers in serum-free suspension cultures of insect cells. METHODS: The typical approach of rAAV production in BEVS has been based on a synchronous infection with three baculoviruses at high multiplicity of infection (MOI) [>3 plaque forming units (pfu)/cell]. An alternative approach is to co-infect at low MOI (0.1 pfu/cell). Both strategies (high and low MOI) were compared at a cell density of 1.0 x 10(6) cells/ml in shake-flask experiments. To increase the rAAV titer, a low MOI combined with an initial cell density at infection of 5.0 x 10(6) cells/ml, in fed-batch mode, was evaluated. Subsequently, the production strategy was validated in 3-l bioreactor runs. RESULTS: An increase of 210% in the rAAV titer (4.7 x 10(11) enhanced transduction units/l) was observed when using low MOI, an effect primarily caused by the increase in cell density. The fed-batch approach resulted in a seven-fold increase of rAAV yield. Controlled operations in bioreactor contributed to further increase the rAAV yield (2.8 x 10(14) vector genomes/l) by 25% in comparison to the shake flask results. CONCLUSIONS: This high yield production process using low MOIs and a feeding strategy successfully addresses several limitations of current rAAV production in insect cells and contributes to position the BEVS system as one of the most efficient for large-scale manufacturing of rAAV vectors.
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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 | 0.000 |
| 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 | 0.002 |
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".