Process evolution in cell and gene therapy from discovery to commercialization
Bibliographic record
Abstract
Abstract The field of cell and gene therapy (CGT) is maturing at a rapid rate. Manufacturing remains a major challenge and manufacturing failures and limitations are a notable source of CGT products not meeting specification or being held back from regulatory approvals. To ensure that products remain compliant and competitive throughout their lifecycle, CGT process development requires both ongoing advancement of fit‐for‐purpose technologies to allow for and support well‐designed development studies, and a methodology that enables these studies to occur effectively during focused product development campaigns. We describe a framework for ongoing innovation in CGT process development, manufacturing, and clinical testing, based on the quality by design (QbD) approach. We propose use of the concepts of process discovery, process characterization, and process development in an iterative way to refine the design space as a product matures. This strategy is enabled by the early implementation of broad and robust analytics, and a modular approach to accelerate optimization. With these strategies, CGT developers can prospectively plan for comparability studies and develop in a stage‐appropriate manner, to allow for ongoing innovation and improvements from discovery through to commercialization.
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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".