Failure to launch commercially-approved mesenchymal stromal cell therapies: what's the path forward? Proceedings of the International Society for Cell & Gene Therapy (ISCT) Annual Meeting Roundtable held in May 2023, Palais des Congrès de Paris, Organized by the ISCT MSC Scientific Committee
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are promising cell therapy candidates, but their debated efficacy in clinical trials still limits successful adoption. Here, we discuss proceedings from a roundtable session titled "Failure to Launch Mesenchymal Stromal Cells 10 Years Later: What's on the Horizon?" held at the International Society for Cell & Gene Therapy 2023 Annual Meeting. Panelists discussed recent progress toward developing patient-stratification approaches for MSC treatments, highlighting the role of baseline levels of inflammation in mediating MSC treatment efficacy. In addition, MSC critical quality attributes (CQAs) are beginning to be elucidated and applied to investigational MSC products, including immunomodulatory functional assays and other potency markers that will help to ensure product consistency and quality. Lastly, next-generation MSC products, such as culture-priming strategies, were discussed as a promising strategy to augment MSC basal fitness and therapeutic potency. Key variables that will need to be considered alongside investigations of patient stratification approaches, CQAs and next-generation MSC products include the specific disease target being evaluated, route of administration of the cells and cell manufacturing parameters; these factors will have to be matched with postulated mechanisms of action towards treatment efficacy. Taken together, patient stratification metrics paired with the selection of therapeutically potent MSCs (using rigorous CQAs and/or engineered MSC products) represent a path forward to improve clinical successes and regulatory endorsements.
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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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".