Uncertainty and innovation: Understanding the role of cell-based manufacturing facilities in shaping regulatory and commercialization environments
Bibliographic record
Abstract
The purpose of this qualitative study is to elucidate stakeholder perceptions of, and institutional practices related to cell-based therapies and products (CTP) regulation and commercialization in Canada. The development of reproducible, safe and effective CTPs is predicated on regulatory and commercialization environments that enable innovation. Manufacturing processes constitute a critical step for CTP development in this regard. The road from CTP manufacturing to translation in the clinic, however, has yet to be paved. This study aims to fill an empirical gap in the literature by exploring how CTP manufacturing facilities navigate Canadian regulatory and commercialization environments, which together drive the translation of novel CTPs from bench to bedside. Using the multi-level model of practice-driven institutional change proposed by Smets et al., we demonstrate how CTP manufacturing practices are governed by established standards, yet meaningfully shape higher-order regulatory and commercial norms in CTP research and development. We identify four key themes that undergird such processes of innovation: 1) managing regulatory uncertainty, which stems from an inability to classify CTPs within existing regulatory categories for approval and commercialization purposes; 2) building a 'business case' whereby a CTP's market potential is determined in large part by proving its safety and effectiveness; 3) standardizing manufacturing procedures that mobilize CTPs from a research and development phase to a commercialization one; and 4) networking between researchers and regulators to develop responsible commercialization processes that reflect the uniqueness of CTPs as distinct from other biologics and medical devices.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.042 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".