Assessing the potential clinical impact of reciprocal drug approval legislation on access to novel therapeutics in the USA: a cohort study
Bibliographic record
Abstract
OBJECTIVE: To quantify the potential effect of reciprocal approval legislation on access to clinically impactful therapeutics in the USA. DESIGN: A cohort study. SETTING: New therapeutics approved by the Food and Drug Administration (FDA), European Medicines Agency (EMA) and/or Health Canada between 2000 and 2010. MAIN OUTCOME MEASURES: Characteristics of new therapeutics approved by the EMA and/or Health Canada before the FDA, including mechanistic novelty, likely clinical impact, size of the affected population and FDA review outcome. RESULTS: From 2001 to 2010, 282 drugs were approved in the USA, Europe or Canada, including 172 (61%) first approved in the USA, 24 (9%) never approved in the USA, and 86 (30%) approved in the USA after Europe and/or Canada. Of the 110 new drugs approved in Europe and/or Canada before the USA, 37 (34%) had a novel mechanisms of action compared with drugs already approved by the FDA, but only 10 (9%) were for conditions lacking alternate available therapies in the USA at the time of ex-US approval-of which the majority (9/10; 90%) were indicated for rare diseases. 12 of the 37 agents with novel mechanisms of action approved first in Europe and/or Canada (32%) had their initial FDA submissions rejected for safety reasons-including 2 drugs that were ultimately withdrawn from the market in Europe due to safety concerns. CONCLUSIONS: If enacted, reciprocal approval legislation would most likely benefit only a small number of US patients receiving treatment for rare diseases, and the benefit may be somewhat mitigated by an increased exposure to harms.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".