Scaling up pluripotent stem cell-based therapies - considerations, current challenges and emerging technologies: perspectives from the ISCT Emerging Regenerative Medicine Working Group
Bibliographic record
Abstract
Approval of induced pluripotent stem cells (iPSCs) for the manufacture of cell therapies to support clinical trials is now becoming realized after more than 20 years of research and development. However, manufacturing these therapies at the scale required for patient treatment, as well as for key clinical trial enabling activities such as preclinical and stability studies, remains a challenge. In 2022 the International Society for Cell and Gene Therapy (ISCT) established a Working Group on Emerging Regenerative Medicine Technologies, an area in which iPSC-derived technologies are expected to play a key role. In this article, the Working Group provides an overview of the considerations and challenges facing stem cell therapy developers, including development-stage specific manufacturing processes, the decision on when to implement automation, the choice of technology and different requirements of expansion and differentiation aspects of the process, and the integration of automation for both manufacturing and analytics in an end-to-end manufacturing process. The role of scalable manufacturing technologies in the application of quality-by-design approaches to product development, and the use of design-of-experiment approaches for increased product characterization, is discussed. Finally, we provide an in-depth review of the different technologies that have been used for expansion and differentiation of iPSC-derived therapies to date, including compatibility with good manufacturing practice requirements and process analytical technologies. We hope that this overview will summarize the existing knowledge in the field and reduce the challenges that therapy developers face in translating their research into clinical and commercial scale manufacturing.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".