Production of Lentiviral Vectors Using a HEK-293 Producer Cell Line and Advanced Perfusion Processing
Bibliographic record
Abstract
The field of lentiviral vector (LV) production continues to face challenges in large-scale manufacturing, specifically regarding producing enough vectors to meet the demand for treating patients as well as producing high and consistent quality of vectors for efficient dosing. Two areas of interest are the use of stable producer cell lines, which facilitates the scalability of LV production processes as well as making the process more reproducible and robust for clinical applications, and the search of a cell retention device scalable to industrial-size bioreactors. This manuscript investigates a stable producer cell line for producing LVs with GFP as the transgene at shake flask scale and demonstrates LV production at 3L bioreactor scale using the Tangential Flow Depth Filtration (TFDF) as a cell retention device in perfusion mode. Cumulative functional yields of 3.3 x 10 11 and 3.9 x 10 11 transducing units were achieved; the former over 6 days of LV production with 16.3 L of perfused media and the latter over 4 days with 16 L. In comparing to a previously published value that was achieved using the same stable producer cell line and the acoustic filter as the perfusion device at the same bioreactor scale, the TFDF perfusion run produced 1.5-fold higher cumulative functional yield. Given its scale-up potential, the TFDF is an excellent candidate to be further evaluated to determine optimized conditions that can ultimately support continuous manufacturing of LVs at large scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".