Success rates for anti-cancer drug development efforts in pediatric oncology
Bibliographic record
Abstract
In adult cancers, 3.4-6.7% of interventions put into clinical testing ultimately advance to regulatory approval.Little is known about corresponding success rates in pediatric cancer drug development.The purpose of this thesis was to determine the proportion of interventions in pediatric anti-cancer trials that advance or "graduate" to later stages of development and/or clinical practice, and the proportion of participants enrolled in such trials who participate in graduating trials.We used ClinicalTrials.govto search and collect all anti-cancer drug trials recruiting patients below the age of 21 years and/or that were specifically indicated for pediatric malignancies.Key characteristics of trials were extracted, including cancer indication, drug type, and certain trial design features.Each trial was assessed for whether the intervention it tested graduated within 1-6 years of the trial launch to: a subsequent phase, a randomized trial, a Children's Oncology Group (COG)-supported trial, FDA approval, and/or FDA label changes of approved drugs regarding pediatrics.Secondarily, we used logistic regression analysis to determine whether certain trial features are predictive of graduation.We identified 410 pediatric anti-cancer trials launched between 1987 and 2013, enrolling 30,279 participants.Interventions tested in 69 trials (16.8%) graduated to a subsequent phase of clinical development and/or clinical practice, and 4,560 participants (15.1%) enrolled in these trials.We calculated that 19.9% (enrolling 22.2% participants) of interventions tested in phase 1 trials graduated to phase 2, and 1.6% (enrolling 2.8% participants) of interventions in phase 2 graduated to phase 3. FDA approval for a pediatric cancer as the primary or secondary indication was achieved for 2.9% of unapproved interventions in trials, and 5.1% of trials led to FDA label
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".