Synthesizing regulatory guidance for demonstrating preclinical efficacy and translating promising cell therapies to early phase clinical trials: a scoping review
Bibliographic record
Abstract
BACKGROUND: Regulatory applications for cell therapy face more objections compared to conventional small molecule or biological drugs, leading to delays in market approval and clinical adoption. Increased regulatory objections frequently relate to issues regarding preclinical evidence, such as experimental design of animal studies, selection of animal models, endpoints, and determination of mechanism of action. Synthesis and clarification of the preclinical evidence necessary to demonstrate treatment efficacy and advance into early-phase clinical trials is needed to help researchers avoid regulatory objections. METHODS: We conducted a scoping review in which we searched repositories of the International Council for Harmonisation and all national member organizations (N = 38) for documents related to preclinical studies of cell therapies. Active guidance documents related to cell therapy were included, with no restrictions based on the year or language of publication. Data extraction was conducted in duplicate with conflicts resolved through consensus discussion. RESULTS: From 1215 identified documents, a total of 182 were included and analyzed, with 71% originating from ten major regulatory agencies. The most prevalent preclinical item addressed was the mechanism of action (n = 161, 88% of documents), underscoring its importance in bridging preclinical findings to clinical application. Most documents (n = 140, 77%) emphasized the importance of using clinically relevant preclinical models, though specific recommendations on models of disease were less common (n = 81, 45%). Selection of clinically relevant intervention parameters (n = 136, 75%) and outcome measures (n = 121, 66%) were also frequently recommended, but selection of relevant comparator groups appeared less frequently (n = 35, 19%). Furthermore, robust study design elements such as randomization and blinding were less frequently recommended, appearing in 31% of documents (n = 57). Comparison with clinical trial guidance revealed a significant gap in the rigor of study design recommendations for preclinical research. CONCLUSIONS: Regulatory guidance for preclinical efficacy studies often recommends a strong emphasis on the clinical relevance of animal models, intervention parameters, outcomes, and mechanism of action. Incorporating these recommendations into early preclinical studies should improve the acceptability of preclinical evidence for approval by the relevant national regulators and can be used as a guide to ensure that all evidence that regulators say they expect is efficiently assembled into new clinical trial applications.
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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 | 0.223 | 0.516 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.060 | 0.044 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".