Rational plasmid design and bioprocess optimization to enhance recombinant adeno‐associated virus (AAV) productivity in mammalian cells
Bibliographic record
Abstract
Viral vectors used for gene and oncolytic therapy belong to the most promising biological products for future therapeutics. Clinical success of recombinant adeno-associated virus (rAAV) based therapies raises considerable demand for viral vectors, which cannot be met by current manufacturing strategies. Addressing existing bottlenecks, we improved a plasmid system termed rep/cap split packaging and designed a minimal plasmid encoding adenoviral helper function. Plasmid modifications led to a 12-fold increase in rAAV vector titers compared to the widely used pDG standard system. Evaluation of different production approaches revealed superiority of processes based on anchorage- and serum-dependent HEK293T cells, exhibiting about 15-fold higher specific and volumetric productivity compared to well-established suspension cells cultivated in serum-free medium. As for most other viral vectors, classical stirred-tank bioreactor production is thus still not capable of providing drug product of sufficient amount. We show that manufacturing strategies employing classical surface-providing culture systems can be successfully transferred to the new fully-controlled, single-use bioreactor system Integrity(TM) iCELLis(TM) . In summary, we demonstrate substantial bioprocess optimizations leading to more efficient and scalable production processes suggesting a promising way for flexible large-scale rAAV 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".