Large numbers of patients are needed to obtain additional approvals for new cancer drugs: A retrospective cohort study
Bibliographic record
Abstract
Patients endure risk and uncertainty when they participate in clinical trials. We previously estimated that 12,217 patient-participants are required to bring a new cancer drug to market. However, many development efforts are aimed at extending the label of already approved drugs. Herein, we estimate the number of patients required to extend the indication of an FDA approved cancer drug. We identified all anti-cancer drugs approved by the FDA 2012 to 2015. We searched clinicaltrials.gov to identify all drug development trajectories (i.e., a series of one or more clinical trials testing a unique drug-indication pairing) launched after FDA approval for each drug. We identified which trajectories produced the following milestones: secondary FDA approvals, secondary FDA approvals achieving substantial clinical benefit in ESMO-MCBS, and recommendations in NCCN clinical practice guidelines. Using the total enrollment, we estimated the number of patients needed to reach each milestone. Forty-two drugs were approved by the FDA between 2012 and 2015, leading to 451 post-approval trajectories enrolling 129,548 patients. Fourteen secondary FDA approvals were identified, of which 4 met the ESMO-MCBS definition of substantial clinical benefit. Fourteen NCCN off-label recommendations were obtained. A total of 9253, 32,387 and 4627 patients were needed to attain an FDA approval, an approval with substantial clinical benefit on ESMO-MCBS, and an NCCN guideline recommendation, respectively. The number of patients needed to obtain a first secondary FDA approval was 16,596. Large numbers of patients are needed to extend the label of prior FDA approved drugs. Label extension after approval entails lower marginal costs for developers. However, extra knowledge available to researchers about a drug's safety and pharmacology after FDA approval does not appear to translate into reduced patient numbers required for developing new cancer applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".