Success rates for US and Canadian anticancer drug development efforts in pediatric oncology
Bibliographic record
Abstract
BACKGROUND: Approximately 5% of adult cancer interventions put into clinical testing attain regulatory approval. Little is known about corresponding rates for pediatric cancer. METHODS: Our primary outcomes were the proportion of interventions graduating to the next trial phase, randomized trials, and/or clinical practice. We created a saturation sample of clinical trials by searching ClinicalTrials.gov for all pediatric anticancer trials in the United States or Canada. Trial characteristics were extracted automatically from ClinicalTrials.gov records, and cancer indication/drug class categorization, biomarker enrichment, and Food and Drug Administration (FDA) approval status at time of recruitment were double-extracted from each record. Regulatory approval status and labeling modifications for each intervention were determined by searching Drugs@FDA and the New Pediatric Labeling Information Database. RESULTS: Five hundred eighty-nine pediatric trials launched between 1987 and 2019 were captured. The overall probability of graduation was 17.0%; 18.9% of interventions graduated from phase 1 to phase 2 trials, and 1.6% of interventions graduated from phase 2 to phase 3 trials. The proportion of interventions advancing from phase 1 to FDA approval was 3.6%, and 1.9% of interventions tested in phase 1 advanced to a randomized phase 2 trial. Only biomarker enrichment was significantly predictive of graduation from phase 1 to phase 2 trials (p = .011). CONCLUSION: The proportion of interventions advancing from phase 1 testing to FDA approval was similar to estimates for adult oncology. Our findings highlight the challenges for current paradigms of pediatric anticancer drug development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".