Production of lentiviral vectors by large‐scale transient transfection of suspension cultures and affinity chromatography purification
Bibliographic record
Abstract
The use of lentiviral vectors as gene delivery vehicles has become increasingly popular in recent years. The growing interest in these vectors has created a strong demand for large volumes of vector stocks, which entails the need for scaleable vector manufacturing procedures. In this work, we present a simple and robust process for the production of lentiviral vectors using scaleable production and purification methodologies. Lentivirus particles were produced by transient transfection of serum-free suspension-growing 293 EBNA-1 cells with four plasmids encoding the vector components using linear polyethylenimine (PEI) as transfection reagent. This process was successfully scaled-up from shake flasks to a 3-L bioreactor from which 10(10) IVP were recovered. In addition, an affinity chromatography protocol designed for purification of bioactive oncoretroviral vectors has been adapted in this work for the purification of VSV-G pseudotyped lentiviral vectors. Using heparin affinity chromatography, lentiviral particles were concentrated and purified directly from the clarified supernatants. During this step, a recovery of 53% of infective lentiviral particles was achieved while removing 94% of the impurities contained in the supernatant.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".