Manufacturing T cells in hollow fiber membrane bioreactors changes their programming and enhances their potency
Bibliographic record
Abstract
Engineered T cell therapies have revolutionized modern oncology, however processes for manufacturing T cell therapies vary and the impact of manufacturing processes On the cell product is poorly understood. Herein, we have used a commercially available hollow fiber membrane bioreactor (HFMBR) operated in a novel mode to demonstrate that T cells can be engineered with lentiviruses, grown to very high densities, and washed and harvested in a single, small volume bioreactor that is readily amenable to automation. Manufacturing within the HFMBR dramatically changed the programming of the T cells and yielded a product with greater therapeutic potency than T cells produced using the standard manual method. This change in programming was associated with increased resistance to cryopreservation, which is beneficial as T cell products are typically cryopreserved prior to administration to the patient. Transcriptional profiling of the T cells revealed a shift toward a glycolytic metabolism, which may protect cells from oxidative stress offering an explanation for the improved resistance to cryopreservation. This study reveals that the choice of bioreactor fundamentally impacts the engineered T cell product and must be carefully considered. Furthermore, these data challenge the premise that glycolytic metabolism is detrimental to T cell therapies.
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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 | 0.000 | 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) | 0.001 | 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 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".