Manufacturing of recombinant adeno‐associated viruses using mammalian expression platforms
Bibliographic record
Abstract
Abstract Manufacturing practices for recombinant adeno‐associated viruses (AAV) have improved in the last decade through the development of new platforms in conjunction with better production and purification methods. In this review, we discuss the advantages and limitations of the most popular systems and methods employed with mammalian cell platforms. Methods and systems such as transient transfection, packaging and producer cells and adenovirus and herpes simplex virus are described. In terms of best production yields, they are comparable with about 104–105 vector genomes produced per cell but transient transfection of HEK293 cells is by far the most commonly used. For small‐scale productions, AAV can be directly purified from the producing cell lysate by ultracentrifugation on a CsCl or iodixanol‐step gradient whereas large‐scale purification requires a combination of multiple steps. Micro/macrofiltration (i.e. including tangential flow filtration and/or dead‐end filtration) and chromatography based‐methods are used for large‐scale purification. Purified AAV products must then be quantified and characterized to ensure quality. Recent purification methods and current analytical techniques are reviewed here. Finally, AAV technology is very promising, but manufacturing improvements are still required to meet the needs of affordable, safe and effective AAV vectors essential for licensing of gene therapy clinical protocols.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".