Preclinical efficacy in therapeutic area guidelines from the U.S. Food and Drug Administration and the European Medicines Agency: a cross‐sectional study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Therapeutic area guidelines (TAGs) published by the EMA and the FDA offer guidance in planning the launch of a trial in a certain indication. We assessed and compared the guidance on preclinical efficacy of all available TAGs from EMA and FDA. EXPERIMENTAL APPROACH: EMA and FDA websites and databases were searched for all TAGs. A mixed deductive and inductive approach was applied to analyse and cluster content for preclinical efficacy. KEY RESULTS: A total of 114 EMA and 120 FDA TAGs were identified, covering 126 indications. Our core finding is that 75% of EMA TAGs and 58% from the FDA TAGs do not offer any guidance on preclinical efficacy. TAGs varied widely on the extent, nature and detail of guidance. CONCLUSIONS AND IMPLICATIONS: Guidance on preclinical efficacy in a consistent, comprehensive and explicit way that still allows for justified deviations is an important but neglected aspect of transparency for drug development. This transparency would help sponsors in designing preclinical studies and in negotiating more efficiently with regulators.
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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 | 0.044 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".