Harmonization of U.S., European Union, and Canadian First-in-Human Regulatory Requirements for Radiopharmaceuticals: Is This Possible?
Bibliographic record
Abstract
In recent years, several new radiotracers and radionuclide therapies have been developed. There is a renaissance in nuclear medicine and molecular imaging today, for example, in terms of the ability to image and treat neuroendocrine and prostate malignancies. In order to be able to bring a new drug product from bench to bedside and assist patients, while also ensuring patient safety, stringent regulations must be met. However, differences in regulatory requirements, often based on jurisdictional politics rather than scientific evidence, can hinder global co-operation, increase expense, and slow progress. In an effort to rise above these differences, nuclear medicine advocacy organizations, regulators, and international agencies have begun to identify commonalities in the regulations to achieve harmonization. Indeed, a more streamlined approach to radiopharmaceutical drug development across jurisdictions could be achieved through establishing harmonized requirements for pre-clinical studies and manufacturing standards. This paper provides an educational overview of the regulatory and submission requirements governing investigational radiopharmaceuticals for first-in-human radiopharmaceuticals across the European and North American continents. It is hoped that through ongoing collaboration, regulatory reform and harmonization can become a reality and speed access to the most up-to-date evidence-based patient care for all.
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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 | 0.121 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".