Drug development for breast, colorectal, and non–small cell lung cancers from 1979 to 2014
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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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".