Impact of Precision Medicine on Efficiencies of Novel Drug Development in Cancer
Bibliographic record
Abstract
Precision medicine (PM) offers opportunities for reducing the costs, burdens, and time associated with drug development. We examined time, number of trials, indications tested, and patient burden needed to achieve first U.S. Food and Drug Administration license for all five novel anticancer PM drugs and all 10 novel non-PM drugs receiving U.S. Food and Drug Administration approval during 2010-2014. The 15 drug portfolios encompassed 242 trials: 87 for PM drugs and 155 for non-PM drugs. Embase and MEDLINE databases were searched for all prelicensure clinical trials, and data on time, patient numbers, indications tested, and total treatment-emergent grade 3-5 adverse events were measured from the first trial of each drug. We did not find patterns suggesting greater efficiencies in PM compared with non-PM. Gains in efficiency for PM drug development may be offset by challenges with recruitment.
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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 | 0.147 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".