Evidence-based support for phenotypic drug discovery in acute myeloid leukemia
Bibliographic record
Abstract
The discovery and development of effective drugs for cancer patients has seen limited success in the clinic from phase I trials onward. The high attrition rate of current drug development approaches requires careful evaluation to provide a better understanding of the factors that correlate with or predict positive clinical outcomes. Here, we examine pre-clinical drug development approaches and conduct a meta-analysis of 2918 clinical studies involving 466 unique drugs tested in clinical trials for acute myeloid leukemia (AML). Our goal was to determine whether there are key shared pre-clinical characteristics that ultimately relate to successful or unsuccessful drugs in patients. We provide an evidence-based recommendation for the use of phenotypic drug discovery rather than other methods during pre-clinical development. Although our analysis was limited to AML, similar analyses are likely to be informative for other tumor-specific drug discovery campaigns, informing and improving the foundational discovery screens and platforms for other cancers.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".