Clinical trial risk in leukemia: Biomarkers and trial design
Bibliographic record
Abstract
This study analyzed the risk of clinical trial failure for leukemia drug development between January 1999 and January 2020. The specific leukemia subtypes of interest were acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myeloid leukemia (CML). Drug development was investigated using data obtained from https://www.clinicaltrials.gov and other publicly available databases. Drug compounds were excluded if they began phase I testing for the indication of interest before January 1999, if they were not industry sponsored, or if they treated secondary complications of the disease. Further analysis was conducted on biomarker usage, drug mechanisms of action, and line of treatment. Drugs were identified following our inclusion criteria for ALL (72), CLL (106), AML (159), and CML (47). The likelihood (cumulative pass rate), a drug would pass all phases of clinical testing and obtain Food and Drug Administration approval, was 18% (ALL), 10% (CLL), 7% (AML), and 12% (CML). Biomarker targeted therapies improved the success rates by three- and sevenfold, for ALL and AML, respectively. Enzyme inhibitors doubled the cumulative success rate for AML. First-line therapy and kinase inhibitors both independently doubled the cumulative success rate for CLL. Oncologists enrolling patients in clinical trials can increase success rates by up to sevenfold by prioritizing participation in trials involving biomarker usage, while consideration of factors such as drug mechanism of action and line of therapy can further double the clinical trial success rate.
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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 | 0.330 | 0.419 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".