Achieving High-Yield Production of Functional AAV5 Gene Delivery Vectors via Fedbatch in an Insect Cell-One Baculovirus System
Bibliographic record
Abstract
Despite numerous advancements in production protocols, manufacturing AAV to meet exceptionally high demand (10 16 –10 17 viral genomes [VGs]) in late clinical stages and for eventual systemic delivery poses significant challenges. Here, we report an efficient, simple, scalable, robust AAV5 production process utilizing the most recent modification of the OneBac platform. An increase in volumetric yield of genomic particles by ∼6-fold and functional particles by ∼20-fold was achieved by operating a high-cell-density process in shake flasks and bioreactors that involves an Sf9-based rep/cap stable cell line grown at a density of about 10 million cells/mL infected with a single baculovirus. The overall volumetric yields of genomic (VG) and bioactive particles (enhanced transducing units [ETUs]) in representative fedbatch bioreactor runs ranged from 2.5 to 3.5 × 10 14 VG/L and from 1 to 2 × 10 11 ETU/L. Analytical ultracentrifugation analyses of affinity-purified AAV vector samples from side-by-side batch and fedbatch production runs showed vector preparations with a full and empty particle distribution of 20%–30% genomic and 70%–80% empty particles. Moreover, the stoichiometric analysis of capsid proteins from fedbatch production in shake flask and bioreactor run samples demonstrated the incorporation of higher VP1 subunits, resulting in better functionality.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".