Preclinical efficacy in therapeutic area guidelines from the U.S. Food and Drug Administration and the European Medicines Agency: a cross‐sectional study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it