Clinical trial risk in castration‐resistant prostate cancer: immunotherapies show promise
Bibliographic record
Abstract
OBJECTIVES: To determine the risk of failure during drug development in castration-resistant prostate cancer (CRPC) and to identify factors that could improve outcomes. METHODS: We investigated CRPC by analysing compounds in phase I to phase III clinical trials between 1998 and April 2011. Drug development failures were classified as medical or commercial and were compared with industry expectations. Compounds were excluded from analysis if their phase I occurred before 1998, if they targeted patients that were did not have hormone-refractory prostate cancer, or if they did not assess outcomes such as overall survival, time to disease progression, or prostate-specific antigen levels. RESULTS: Thorough searches of clinicaltrial.gov and other databases yielded 77 compounds that met the inclusion criteria. The cumulative pass rate for first-line compounds in CRPC was 3% and was far below aggregate industry expectations. In total, there were nearly equivalent numbers of commercial and medical failures. Biological products were found to have had greater relative success than small-molecule drugs and biotechnology firms had been slightly more successful than pharmaceutical firms in this disease indication. Phase III failures were high, despite equally high failures during phase II. CONCLUSIONS: Currently, one in 33 compounds that enters clinical testing will be awarded US Food and Drug Administration approval. This appears to be the highest risk indication investigated to date, based on clinical trial studies alone, with an average cost of $1.411bn to bring a new drug to market when adjusted for risk. Development of radical therapeutics such as immunotherapies may also be warranted instead of classic antineoplastic therapeutics. Given the high clinical trial risk, efforts may have to shift to biomarker and surrogate endpoints to manage future clinical trial risk in prostate cancer.
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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 | 0.063 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".