Development, optimization, and scale‐up of suspension Vero cell culture process for high titer production of oncolytic herpes simplex virus‐1
Bibliographic record
Abstract
Abstract Oncolytic viruses (OVs) have emerged as a novel cancer treatment modality, and four OVs have been approved for cancer immunotherapy. However, high‐yield and cost‐effective production processes remain to be developed for most OVs. Here suspension‐adapted Vero cell culture processes were developed for high titer production of an OV model, herpes simplex virus type 1 (HSV‐1). Our study showed the HSV‐1 productivity was significantly affected by multiplicity of infection, cell density, and nutritional supplies. Cell culture conditions were first optimized in shake flask experiments and then scaled up to 3 L bioreactors for virus production under batch and perfusion modes. A titer of 2.7 × 108 TCID50 mL−1 was obtained in 3 L batch culture infected at a cell density of 1.4 × 106 cells mL−1, and was further improved to 1.1 × 109 TCID50 mL−1 in perfusion culture infected at 4.6 × 106 cells mL−1. These titers are similar to or better than the previously reported best titer of 8.6 × 107 TCID50 mL−1 and 8.1 × 108 TCID50 mL−1 respectively obtained in labor‐intensive adherent Vero batch and perfusion cultures. HSV‐1 production in batch culture was successfully scaled up to 60 L pilot‐scale bioreactor to demonstrate the scalability. The work reported here is the first study demonstrating high titer production of HSV‐1 in suspension Vero cell culture under different bioreactor operating modes.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".