A clinically relevant large‐scale biomanufacturing workflow to produce natural killer cells and natural killer cell‐derived extracellular vesicles for cancer immunotherapy
Bibliographic record
Abstract
Abstract Natural killer cell‐derived extracellular vesicles (NK‐EVs) have shown promising potential as biotherapeutics for cancer due to their unique attributes as cytotoxic nanovesicles against cancer cells and immune‐modulatory activity towards immune cells. However, a biomanufacturing workflow is needed to produce clinical‐grade NK‐EVs for pre‐clinical and clinical applications. This study established a novel biomanufacturing workflow using a closed‐loop hollow‐fibre bioreactor to continuously produce NK‐EVs from the clinically relevant NK92‐MI cell line under serum‐free, Xeno‐free and feeder‐free conditions following GMP‐compliant conditions. The NK92 cells grown in the bioreactor for three continuous production lots resulted in large quantities of both NK cell and NK‐EV biotherapeutics at the end of each production lot (over 10 9 viable cells and 10 13 EVs), while retaining their cytotoxic payload (granzyme B and perforin), pro‐inflammatory cytokine (interferon‐gamma) content and cytotoxicity against the human leukemic cell line K562 with limited off‐target toxicity against healthy human fibroblast cells. This scalable biomanufacturing workflow has the potential to facilitate the clinical translation of adoptive NK cell‐based and NK‐EV‐based immunotherapies for cancer with GMP considerations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".