Clinical benefit of breakthrough cancer drugs approved by the United States Food and Drug Administration.
Bibliographic record
Abstract
6513 Background: The Breakthrough Therapy program was established in July 2012 to expedite drug development and approval by the FDA. We compared the characteristics of clinical trials leading to FDA approval as well as the magnitude of clinical benefit and value framework scores of breakthrough-designated and non-breakthrough-designated cancer drugs. Methods: We searched the Drugs@FDA website for cancer drug approvals from July 2012 and December 2017. For each indication, we applied the value frameworks and used thresholds of high clinical benefit developed by American Society of Clinical Oncology Value Framework version 2 (ASCO VF v2; scores ≥45), the ASCO Cancer Research Committee (OS gains ≥2.5 months PFS gains ≥3 months), the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale version 1.1 (ESMO-MCBS v1.1; grade of A or B for trials of curative intent and 4 or 5 for those of non-curative intent), and the National Comprehensive Cancer Network (NCCN) Evidence Blocks (scores of 4 and 5). Trial characteristics and value framework scores were compared using Chi squared or Mann Whitney U tests. Results: We identified 106 pivotal trials supporting the approval of 52 individual drugs for 96 indications. Of these indications, 38 (40%) received breakthrough designation. Compared with trials for non-breakthrough drugs (n = 62), trials for breakthrough drugs (n = 44) had smaller sample size (median 373 vs 612, P= .03), were less often randomized (57% vs 86%; P= .001) and more likely to be open-label (84% vs 53%, P= .001). Trials for breakthrough drugs were more likely to demonstrate high clinical benefit using ASCO VF (68% vs 31%, P= .002) and NCCN Evidence Blocks (86% vs 56%, P= .002). A similar proportion of trials supporting breakthrough and non-breakthrough drugs demonstrated high clinical benefit using the ASCO Cancer Research Committee (82% vs 68%, P= .25) and ESMO-MCBS (35% vs 33%; P= .87) frameworks. Conclusions: In patients with advanced solid tumors, cancer drugs approved under breakthrough therapy designation were more likely to demonstrate high clinical benefit as defined by the ASCO VF and NCCN value frameworks. A similar proportion of approved breakthrough and non-breakthrough therapy drugs met the high benefit thresholds using the ASCO Cancer Research Committee and ESMO-MCBS frameworks.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".