Initial and supplementary indication approval of new targeted cancer drugs by the FDA, EMA, Health Canada, and TGA
Bibliographic record
Abstract
BACKGROUND: Previous research focused on the clinical evidence supporting new cancer drugs' initial US Food and Drug Administration (FDA) approval. However, targeted drugs are increasingly approved for supplementary indications of unknown evidence and benefit. OBJECTIVES: To examine the clinical trial evidence supporting new targeted cancer drugs' initial and supplementary indication approval in the US, EU, Canada, and Australia. DATA AND METHODS: -tests. Multivariate logistic regressions compared characteristics of initial and supplementary indication approvals, reporting adjusted odds ratios (AOR) with 95% confidence intervals (CI). RESULTS: Out of 100 considered cancer indications, the FDA approved 96, the EMA 92, HC 86, and the TGA 83 (83%, p < 0.05). The FDA more frequently granted priority review, conditional approval, and orphan designations than other agencies. Initial approvals were more likely to receive conditional / accelerated approval (AOR: 2.69, 95%CI [1.07-6.77], p < 0.05), an orphan designation (AOR: 3.32, 95%CI [1.38-8.00], p < 0.01), be under priority review (AOR: 2.60, 95%CI [1.17-5.78], p < 0.05), and be monotherapies (AOR: 5.91, 95%CI [1.14-30.65], p < 0.05) than supplementary indications. Initial indications' pivotal trials tended to be shorter (AOR per month: 0.96, 95%CI [0.93-0.99], p < 0.05), of lower phase design (AOR per clinical phase: 0.28, 95%CI [0.09-0.85], p < 0.05), and enroll more patients (AOR per 100 patients: 1.19, 95%CI [1.01-1.39], p < 0.05). CONCLUSIONS: Targeted cancer drugs are increasingly approved for multiple indications of varying clinical benefit. Drugs are first approved as monotherapies in rare diseases with a high unmet need. Whilst expedited regulatory review incentivizes this prioritization, indication-specific safety, efficacy, and pricing policies are necessary to reflect each indication's differential clinical and economic value.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.006 | 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".