Drug development for breast, colorectal, and non–small cell lung cancers from 1979 to 2014
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Understanding the drug development pathway is critical for streamlining the development of effective cancer treatments. The objective of the current study was to delineate the drug development timeline and attrition rate of different drug classes for common cancer disease sites. METHODS: Drugs entering clinical trials for breast, colorectal, and non-small cell lung cancer were identified using a pharmaceutical business intelligence database. Data regarding drug characteristics, clinical trials, and approval dates were obtained from the database, clinical trial registries, PubMed, and regulatory Web sites. RESULTS: A total of 411 drugs met the inclusion criteria for breast cancer, 246 drugs met the inclusion criteria for colorectal cancer, and 315 drugs met the inclusion criteria for non-small cell lung cancer. Attrition rates were 83.9% for breast cancer, 87.0% for colorectal cancer, and 92.0% for non-small cell lung cancer drugs. In the case of non-small cell lung cancer, there was a trend toward higher attrition rates for targeted monoclonal antibodies compared with other agents. No tumor site-specific differences were noted with regard to cytotoxic chemotherapy, immunomodulatory, or small molecule kinase inhibitor drugs. Drugs classified as "others" in breast cancer had lower attrition rates, primarily due to the higher success of hormonal medications. Mean drug development times were 8.9 years for breast cancer, 6.7 years for colorectal cancer, and 6.6 years for non-small cell lung cancer. CONCLUSIONS: Overall oncologic drug attrition rates remain high, and drugs are more likely to fail in later-stage clinical trials. The refinement of early-phase trial design may permit the selection of drugs that are more likely to succeed in the phase 3 setting. Cancer 2017;123:4672-4679. © 2017 American Cancer Society.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it