Incremental therapeutic value of FDA-designated “breakthrough” drugs based on ratings from four international organizations: a cross-sectional study
Bibliographic record
Abstract
Abstract Objectives To assess the additional therapeutic value of Food and Drug Administration (FDA) breakthrough-designated drugs using ratings from four international organizations and to address the implications of these evaluations for the program’s limitations and effectiveness. Methods We compiled a list of all breakthrough-designated new drugs from FDA reports from the start of the program in 2012 to the end of 2022. Therapeutic value was assessed using ratings from agencies in Canada, France, and Germany, as well as a nonprofit French drug bulletin. If a drug was rated by more than one organization, then the highest rating was used. Key findings The FDA approved 121 breakthrough therapies. Therapeutic evaluations were available for 96 drugs. Twenty-two (22.9%) offered major additional therapeutic benefits, 32 (33.3%) offered moderate benefits, and 42 (43.8%) minor benefits. Our findings align with prior studies suggesting a misalignment between FDA designations and actual therapeutic value. Conclusions Less than one in four drugs with a breakthrough designation that were evaluated by external organizations offered a major therapeutic advance. Given that the FDA has approved new molecular entities with limited therapeutic gain, these findings suggest a need for greater scrutiny of breakthrough designations. The FDA should periodically reevaluate approved breakthrough drugs based on new evidence that has emerged since their initial approval and withdraw the designation when drugs are shown to fall short of the early expectations on which initial breakthrough status was based.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".