Development of a scalable process for high‐yield lentiviral vector production by transient transfection of HEK293 suspension cultures
Bibliographic record
Abstract
BACKGROUND: Lentiviral vectors (LV) offer several advantages over other gene delivery vectors. Their potential for the integration and long-term expression of therapeutic genes renders them an interesting tool for gene and cell therapy interventions. However, large-scale LV production remains an important challenge for the translation of LV-based therapeutic strategies to the clinic. The development of robust processes for mass production of LV is needed. METHODS: A suspension-grown HEK293 cell line was exploited for the production of green fluorescent protein-expressing LV by transient polyethylenimine (PEI)-based transfection with LV-encoding plasmid constructs. Using third-generation packaging plasmids (Gag/Pol, Rev), a vesicular stomatitis virus G envelope and a self-inactivating transfer vector, we employed strategies to increase volumetric and specific productivity. Functional LV titers were determined using a flow cytometry-based gene transfer assay. RESULTS: A combination of the most promising conditions (increase in cell density, medium selection, reduction of PEI-DNA complexes per cell, addition of sodium butyrate) resulted in significantly increased LV titers of more than 150-fold compared to non-optimized small-scale conditions, reaching infectious titers of approximately 10(8) transducing units/ml. These conditions are readily scalable and were validated in 3-liter scale perfusion cultures. CONCLUSIONS: Our process produces LV in suspension cultures and is consequently easily scalable, industrially viable and generated more than 10(11) total functional LV particles in a single bioreactor run. This process will allow the production of LV by transient transfection in sufficiently large quantities for phase I clinical trials at the 10-20-liter bioreactor scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".