Analysis of the measurements used as potency tests for the 31 US FDA-approved cell therapy products
Bibliographic record
Abstract
Cell therapy product (CTP) developers face the significant challenge of developing appropriate potency tests for their CTPs. A review of the known potency tests used for the 31 United States Food and Drug Administration-approved CTPs (US FDA) can guide developers in designing effective potency tests for future CTPs. Data on these tests were primarily collected from publicly available regulatory documentation on the US FDA website (90%) as well as other sources (literature, company communications, etc.). Based on these data, an estimated 104 total potency tests have been used for the 31 CTPs. Of these, 33 are redacted (32%), leaving 71 non-redacted potency tests. On average, each CTP has 3.4 potency tests (standard deviation 2.0). The 71 non-redacted potency tests were categorized into 5 bins: "Viability and count" (37 tests, 52%), "Expression" (19 tests, 27%), "Bioassays" (7 tests, 7%), "Genetic modification" (6 tests, 9%) and "Histology" (2 tests, 3%). Measurements of gene or protein expression were used by 20 of the 31 CTPs (65%), and 19 CTPs (61%) used measurements of cell viability or cell count as a potency test. "Viability and count" and "Expression" are the two tests that have most often been used together for the same product, occurring for 16 CTPs (52%). It is unclear if bioassays are commonly used as potency tests since only 7 of 31 CTPs (23%) reported bioassays as potency tests. However, due to redactions, as many 24 (77%) CTPs could potentially have a bioassay as a potency test. Additionally, 26 of the 31 CTPs (84%) cite physicochemical assays (non-bioassays) as a potency test. This analysis of potency tests for approved CTPs provides valuable insights for developing potency tests for new CTPs.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".